├── Part A - Applications
├── A1 - Human Applications
│ ├── A1.2 Urban Environments
│ │ ├── A12a-code.js
│ │ ├── A12b-code.js
│ │ ├── A12c-code.js
│ │ ├── A12d-code.js
│ │ ├── A12e-code.js
│ │ ├── A12f-code.js
│ │ ├── A12g-code.js
│ │ ├── A12h-code.js
│ │ └── README.md
│ ├── A1.3 Built Environments
│ │ ├── A13a-code.js
│ │ ├── A13b-code.js
│ │ ├── A13c-code.js
│ │ ├── A13d-code.js
│ │ ├── A13e-code.js
│ │ ├── A13f-code.js
│ │ ├── A32S1-Excercise-1.js
│ │ ├── A32S2-Excercise-2.js
│ │ └── README.md
│ ├── A1.4 Air pollution and population exposure
│ │ ├── A14a-code.js
│ │ ├── A14b-code.js
│ │ ├── A14c-code.js
│ │ └── README.md
│ ├── A1.5 Heat Islands
│ │ ├── A15a-code.js
│ │ ├── A15b-code.js
│ │ ├── A15c-code.js
│ │ ├── A15d-code.js
│ │ ├── A15e-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ ├── ASTER_bare_emiss.js
│ │ │ ├── Landsat_LST.js
│ │ │ ├── NCEP_TPW.js
│ │ │ ├── SMW_coefficients.js
│ │ │ ├── SMWalgorithm.js
│ │ │ ├── broadband_emiss.js
│ │ │ ├── compute_FVC.js
│ │ │ ├── compute_NDVI.js
│ │ │ ├── compute_emissivity.js
│ │ │ └── loudmask.js
│ ├── A1.6 Health Applications
│ │ ├── A16a-code.js
│ │ ├── A16b-code.js
│ │ ├── A16c-code.js
│ │ ├── A16d-code.js
│ │ ├── A16e-code.js
│ │ ├── A16f-code.js
│ │ ├── A16g-code.js
│ │ ├── A16h-code.js
│ │ └── README.md
│ ├── A1.7 Humanitarian Applications
│ │ ├── A17a-code.js
│ │ ├── A17b-code.js
│ │ ├── A17c-code.js
│ │ ├── A17d-code.js
│ │ └── README.md
│ ├── A1.8 Monitoring Gold Mining Activity using SAR
│ │ ├── A18a-code.js
│ │ ├── A18b-code.js
│ │ ├── A18c-code.js
│ │ ├── A18d-code.js
│ │ ├── A18e-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ ├── omnibusTest_v1.1.js
│ │ │ └── utilities_v1.1.js
│ ├── README.md
│ └── A1.1 Agricultural Environments
│ │ ├── A11a-code.js
│ │ ├── A11b-code.js
│ │ ├── A11c-code.js
│ │ ├── A11d-code.js
│ │ └── README.md
├── A2 - Aquatic and Hydrological Applications
│ ├── A2.1 Groundwater monitoring with GRACE
│ │ ├── A21a-code.js
│ │ ├── A21b-code.js
│ │ ├── A21c-code.js
│ │ ├── A21c2-code.js
│ │ ├── A21d-code.js
│ │ ├── A21d2-code.js
│ │ ├── A21e-code.js
│ │ ├── A21e2-code.js
│ │ ├── A21f-code.js
│ │ ├── A21f2-code.js
│ │ ├── A21s1-supplimental-code.js
│ │ └── README.md
│ ├── A2.2 Benthic Habitats
│ │ ├── A22a-code.js
│ │ ├── A22b-code.js
│ │ ├── A22c-code.js
│ │ ├── A22d-code.js
│ │ ├── A22e-code.js
│ │ └── README.md
│ ├── A2.3 Surface Water Mapping
│ │ ├── A23a-code.js
│ │ ├── A23b-code.js
│ │ ├── A23c-code.js
│ │ └── README.md
│ ├── A2.4 River morphology
│ │ ├── A24a-code.js
│ │ ├── A24b-code.js
│ │ ├── A24c-code.js
│ │ ├── A24d-code.js
│ │ ├── A24e-code.js
│ │ ├── A24f-code.js
│ │ └── README.md
│ ├── A2.5 Water Balance and Drought
│ │ ├── A25a-code.js
│ │ ├── A25b-code.js
│ │ ├── A25c-code.js
│ │ ├── A25d-code.js
│ │ ├── A25s1-annual.js
│ │ ├── A25s2-PET.js
│ │ ├── A25s3-monthly.js
│ │ ├── A25s4-per-class-balance.js
│ │ └── README.md
│ ├── A2.6 Defining Seasonality First Date of No Snow
│ │ ├── A26a-code.js
│ │ ├── A26b-code.js
│ │ └── README.md
│ └── README.md
├── A3 - Terrestial Applications
│ ├── A3.10 Conservation II - Assessing Agricultural Intensification Near Protected Areas
│ │ ├── A310a-code.js
│ │ ├── A310b-code.js
│ │ ├── A310c-code.js
│ │ └── README.md
│ ├── A3.2 Mangroves
│ │ ├── A32a-code.js
│ │ ├── A32b-code.js
│ │ ├── A32c-code.js
│ │ ├── A32s1-supplemental.js
│ │ └── README.md
│ ├── A3.3 Mangroves II - Change Mapping
│ │ ├── A33a-code.js
│ │ ├── A33b-code.js
│ │ ├── A33s1-supplemental.js
│ │ └── README.md
│ ├── A3.4 Forest Degradation and Deforestation
│ │ ├── A34a-code.js
│ │ ├── A34b-code.js
│ │ ├── A34c-code.js
│ │ ├── A34d-code.js
│ │ ├── A34e-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ └── palletes.js
│ ├── A3.5 Deforestation Viewed from Multiple Sensors
│ │ ├── A35a-code.js
│ │ ├── A35b-code.js
│ │ ├── A35c-code.js
│ │ ├── A35d-code.js
│ │ ├── A35e-code.js
│ │ ├── A35f-code.js
│ │ ├── A35g-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ ├── inputs.js
│ │ │ ├── slope_correction_lib.js
│ │ │ └── utilites.js
│ ├── A3.6 Working With GPS & Weather Data
│ │ ├── A36a-code.js
│ │ ├── A36b-code.js
│ │ └── README.md
│ ├── A3.7 Creating Presence and Absence Points
│ │ ├── A37a-code.js
│ │ ├── A37b-code.js
│ │ └── README.md
│ ├── A3.8 Detecting Land Cover Change in Rangelands
│ │ ├── A38a-code.js
│ │ ├── A38b-code.js
│ │ ├── A38c-code.js
│ │ ├── A38d-code.js
│ │ ├── A38e-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ └── calcResid.js
│ ├── A3.9 Conservation I - Assessing the spatial relationship between burned area and precipitation
│ │ ├── A39a-code.js
│ │ ├── A39b-code.js
│ │ ├── A39c-code.js
│ │ └── README.md
│ ├── README.md
│ └── A3.1 Active fire monitoring
│ │ ├── A31a-code.js
│ │ ├── A31b-code.js
│ │ ├── A31c-code.js
│ │ ├── A31d-code.js
│ │ └── README.md
└── README.md
├── Part F - Fundamentals
├── F1 - Programming and Remote Sensing Basics
│ ├── F1.1 Exploring Images
│ │ ├── F11a-code.js
│ │ ├── F11b-code.js
│ │ ├── F11c-code.js
│ │ ├── F11s1-example-4.js
│ │ ├── F11s2-example-5.js
│ │ └── README.md
│ ├── F1.2 Survey of Raster Datasets
│ │ ├── F12a-code.js
│ │ ├── F12b-code.js
│ │ ├── F12c-code.js
│ │ ├── F12d-code.js
│ │ ├── F12e-code.js
│ │ ├── F12f-code.js
│ │ ├── F12g-code.js
│ │ └── README.md
│ ├── F1.3 The Remote Sensing Vocabulary
│ │ ├── F13a-code.js
│ │ ├── F13b-code.js
│ │ ├── F13c-code.js
│ │ ├── F13d-code.js
│ │ ├── F13e-code.js
│ │ └── README.md
│ ├── README.md
│ └── F1.0 JavaScript and the Earth Engine API
│ │ ├── F10a-code.js
│ │ ├── F10b-code.js
│ │ └── README.md
├── F2 - Interpreting Images
│ ├── F2.1 Interpreting an Image Classification
│ │ ├── F21a-code.js
│ │ ├── F21b-code.js
│ │ ├── F21c-code.js
│ │ ├── F21s1-assignment-6.js
│ │ └── README.md
│ ├── F2.2 Accuracy Assessment Quantifying Classification Quality
│ │ ├── F22a-code.js
│ │ ├── F22b-code.js
│ │ ├── F22c-code.js
│ │ └── README.md
│ ├── README.md
│ └── F2.0 Image Manipulation Bands, Arithmetic, Thresholds, and Masks
│ │ ├── F20a-code.js
│ │ ├── F20b-code.js
│ │ └── README.md
├── F3 - Advanced Image Processing
│ ├── F3.1 Advanced Pixel-based Image Transformation
│ │ ├── F31a-code.js
│ │ ├── F31b-code.js
│ │ ├── F31c-code.js
│ │ └── README.md
│ ├── F3.2 Neighborhood-based Image Transformation
│ │ ├── F32a-code.js
│ │ ├── F32b-code.js
│ │ ├── F32c-code.js
│ │ ├── F32d-code.js
│ │ └── README.md
│ ├── F3.3 Object-based Image Analysis
│ │ ├── F33a-code.js
│ │ ├── F33b-code.js
│ │ ├── F33c-code.js
│ │ ├── F33d-code.js
│ │ ├── F33s1-additional-image.js
│ │ └── README.md
│ ├── README.md
│ └── F3.0 Interpreting an Image Regression
│ │ ├── F30a-code.js
│ │ ├── F30b-code.js
│ │ ├── F30c-code.js
│ │ └── README.md
├── F4 - Interpreting Image Series
│ ├── F4.1 Exploring Image Collections
│ │ ├── F41a-code.js
│ │ ├── F41b-code.js
│ │ ├── F41c-code.js
│ │ ├── F41d-code.js
│ │ └── README.md
│ ├── F4.2 Aggregating Images for Time Series
│ │ ├── F42a-code.js
│ │ ├── F42b-code.js
│ │ ├── F42c-code.js
│ │ ├── F42d-code.js
│ │ └── README.md
│ ├── F4.3 Clouds and Image Compositing
│ │ ├── F43a-code.js
│ │ ├── F43b-code.js
│ │ ├── F43c-code.js
│ │ ├── F43d-code.js
│ │ └── README.md
│ ├── F4.4 Change Detection
│ │ ├── F44a-code.js
│ │ ├── F44b-code.js
│ │ ├── README.md
│ │ └── vis
│ │ │ ├── incheon.js
│ │ │ ├── mining.js
│ │ │ ├── mtSHelen.js
│ │ │ └── paradise.js
│ ├── F4.5 Interpreting Annual Time Series with LandTrendr
│ │ ├── F45a-code.js
│ │ └── README.md
│ ├── F4.6 Fitting Functions to Time Series
│ │ ├── F46a-code.js
│ │ ├── F46b-code.js
│ │ ├── F46c-code.js
│ │ ├── F46d-code.js
│ │ ├── F46e-code.js
│ │ ├── F46s1-Synthesis.js
│ │ ├── F46s2-Synthesis.js
│ │ └── README.md
│ ├── F4.7 Interpreting Time Series with CCDC
│ │ ├── F47a-code.js
│ │ ├── F47b-code.js
│ │ ├── F47c-code.js
│ │ ├── F47d-code.js
│ │ ├── F47e-code.js
│ │ └── README.md
│ ├── F4.8 Data Fusion Merging Classification Streams
│ │ ├── F48a-code.js
│ │ ├── F48b-code.js
│ │ ├── F48c-code.js
│ │ ├── F48s1-suplemental.js
│ │ └── README.md
│ ├── F4.9 Exploring Lagged Effects in Time Series
│ │ ├── F49a-code.js
│ │ ├── F49b-code.js
│ │ ├── F49c-code.js
│ │ ├── F49d-code.js
│ │ └── README.md
│ ├── README.md
│ └── F4.0 Filter, Map, Reduce
│ │ ├── F40a-code.js
│ │ ├── F40b-code.js
│ │ ├── F40c-code.js
│ │ └── README.md
├── F5 - Vectors and Tables
│ ├── F5.1 RasterVector Conversions
│ │ ├── F51a-code.js
│ │ ├── F51b-code.js
│ │ ├── F51c-code.js
│ │ ├── F51d-code.js
│ │ ├── F51e-code.js
│ │ └── README.md
│ ├── F5.2 Zonal Statistics
│ │ ├── F52a-code.js
│ │ ├── F52b-code.js
│ │ ├── F52c-code.js
│ │ └── README.md
│ ├── F5.3 Advanced Vector Operations
│ │ ├── F53a-code.js
│ │ ├── F53b-code.js
│ │ └── README.md
│ ├── F5.4 GEEDiT Digitising from satellite imagery
│ │ ├── F54a-code.js
│ │ └── README.md
│ ├── README.md
│ └── F5.0 Exploring Vectors
│ │ ├── F50a-code.js
│ │ ├── F50b-code.js
│ │ ├── F50c-code.js
│ │ ├── F50d-code.js
│ │ ├── F50e-code.js
│ │ └── README.md
├── F6 - Advanced Topics
│ ├── F6.1 Collaborating in Earth Engine with Scripts and Assets
│ │ ├── F61a-code.js
│ │ └── README.md
│ ├── F6.2 Scaling up in Earth Engine
│ │ ├── F62a-code.js
│ │ ├── F62b-code.js
│ │ ├── F63c-code.js
│ │ ├── F63d-code.js
│ │ ├── F63e-code.js
│ │ ├── README.md
│ │ └── modules
│ │ │ └── s2cloudmask.js
│ ├── F6.3 Sharing Work in Earth Engine Basic UI and Apps
│ │ ├── F63a-code.js
│ │ ├── F63b-code.js
│ │ ├── F63c-code.js
│ │ ├── F63d-code.js
│ │ └── README.md
│ ├── F6.4 Combining R and Earth Engine
│ │ ├── F64a-code.js
│ │ ├── F64b-code.js
│ │ ├── F64c-code.js
│ │ ├── F64d-code.js
│ │ ├── F64e-code.js
│ │ ├── F64f-code.js
│ │ ├── F64g-code.js
│ │ └── README.md
│ ├── README.md
│ └── F6.0 Advanced Raster Visualization
│ │ ├── F60a-code.js
│ │ ├── F60b-code.js
│ │ ├── F60c-code.js
│ │ ├── F60d-code.js
│ │ ├── F60e-code.js
│ │ ├── F60f-code.js
│ │ ├── F60g-code.js
│ │ ├── F60h-code.js
│ │ ├── F60i-code.js
│ │ ├── F60j-code.js
│ │ ├── F60s1-supplemental.js
│ │ └── README.md
└── README.md
├── README.md
├── gee-tutorials.jpg
└── logo.png
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12a
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 | Map.centerObject(geometry);
7 |
8 | // Filter collection.
9 | var collection = L8
10 | .filterBounds(geometry)
11 | .filterDate('2010-01-01', '2020-12-31')
12 | .filter(ee.Filter.lte('CLOUD_COVER_LAND', 3));
13 |
14 | // Define GIF visualization arguments.
15 | var gifParams = {
16 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
17 | min: 0.07 * 65536,
18 | max: 0.3 * 65536,
19 | region: geometry,
20 | framesPerSecond: 15,
21 | format: 'gif'
22 | };
23 |
24 | // Render the GIF animation in the console.
25 | print(ui.Thumbnail(collection, gifParams));
26 |
27 | // -----------------------------------------------------------------------
28 | // CHECKPOINT
29 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12b
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // MODIS (Accra)
8 | // Center over Accra.
9 | Map.setCenter(-0.2264, 5.5801, 10);
10 |
11 | // Visualize the full classification.
12 | var MODIS_lc = MODIS.select('LC_Type1');
13 | var igbpLandCoverVis = {
14 | min: 1.0,
15 | max: 17.0,
16 | palette: ['05450a', '086a10', '54a708', '78d203', '009900',
17 | 'c6b044', 'dcd159', 'dade48', 'fbff13', 'b6ff05',
18 | '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c', '69fff8',
19 | 'f9ffa4', '1c0dff'
20 | ],
21 | };
22 | Map.addLayer(MODIS_lc, igbpLandCoverVis, 'IGBP Land Cover');
23 |
24 | // Visualize the urban extent in 2001 and 2019.
25 | // 2019
26 | var MODIS_2019 = MODIS_lc.filterDate(ee.Date('2019-01-01'));
27 |
28 | var M_urb_2019 = MODIS_2019.mosaic().eq(13);
29 | Map.addLayer(M_urb_2019.mask(M_urb_2019), {
30 | 'palette': 'FF0000'
31 | }, 'MODIS Urban 2019');
32 |
33 | var MODIS_2001 = MODIS_lc.filterDate(ee.Date('2001-01-01'));
34 | var M_urb_2001 = MODIS_2001.mosaic().eq(13);
35 | Map.addLayer(M_urb_2001.mask(M_urb_2001), {
36 | 'palette': 'a5a5a5'
37 | }, 'MODIS Urban 2001');
38 |
39 | // -----------------------------------------------------------------------
40 | // CHECKPOINT
41 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12c
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // CORINE (London)
8 | // Center over London
9 | Map.setCenter(-0.1795, 51.4931, 10);
10 |
11 | // Visualize the urban extent in 2000 and 2018.
12 | // 2018 (2017-2018)
13 | var CORINE_2018 = CORINE.select('landcover').filterDate(ee.Date(
14 | '2017-01-01'));
15 |
16 | var C_urb_2018 = CORINE_2018.mosaic().lte(133); //Select urban areas
17 | Map.addLayer(C_urb_2018.mask(C_urb_2018), {
18 | 'palette': 'FF0000'
19 | }, 'CORINE Urban 2018');
20 |
21 | // 2000 (1999-2001)
22 | var CORINE_2000 = CORINE.select('landcover').filterDate(ee.Date(
23 | '1999-01-01'));
24 | var C_urb_2000 = CORINE_2000.mosaic().lte(133); //Select urban areas
25 | Map.addLayer(C_urb_2000.mask(C_urb_2000), {
26 | 'palette': 'a5a5a5'
27 | }, 'CORINE Urban 2000');
28 |
29 | // -----------------------------------------------------------------------
30 | // CHECKPOINT
31 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12d
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // NLCD (Chicago)
8 | // Center over Chicago.
9 | Map.setCenter(-87.6324, 41.8799, 10);
10 |
11 | // Select the land cover band.
12 | var NLCD_lc = NLCD.select('landcover');
13 |
14 | // Filter NLCD collection to 2016.
15 | var NLCD_2016 = NLCD_lc.filter(ee.Filter.eq('system:index', '2016'))
16 | .first();
17 | Map.addLayer(NLCD_2016, {}, 'NLCD 2016');
18 |
19 | // Calculate the total area of the 'Developed high intensity' class (24) in Chicago.
20 | var Chicago = ee.FeatureCollection(
21 | 'projects/gee-book/assets/A1-2/Chicago');
22 |
23 | // Clip classification to Chicago
24 | var NLCD_2016_chi = NLCD_2016.clip(Chicago);
25 |
26 | // Set class 24 pixels to 1 and mask the rest.
27 | var NLCD_2016_chi_24 = NLCD_2016_chi.eq(24).selfMask();
28 | Map.addLayer(NLCD_2016_chi_24, {},
29 | 'Chicago developed high intensity');
30 |
31 | // Area calculation.
32 | var areaDev = NLCD_2016_chi_24.multiply(ee.Image.pixelArea())
33 | .reduceRegion({
34 | reducer: ee.Reducer.sum(),
35 | geometry: Chicago.geometry(),
36 | scale: 30
37 | })
38 | .get('landcover');
39 | print(areaDev);
40 |
41 | // -----------------------------------------------------------------------
42 | // CHECKPOINT
43 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12e-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12e
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Surface reflectance function from example:
8 | function maskL457sr(image) {
9 | var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111',
10 | 2)).eq(0);
11 | var saturationMask = image.select('QA_RADSAT').eq(0);
12 |
13 | // Apply the scaling factors to the appropriate bands.
14 | var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-
15 | 0.2);
16 | var thermalBand = image.select('ST_B6').multiply(0.00341802).add(
17 | 149.0);
18 |
19 | // Replace the original bands with the scaled ones and apply the masks.
20 | return image.addBands(opticalBands, null, true)
21 | .addBands(thermalBand, null, true)
22 | .updateMask(qaMask)
23 | .updateMask(saturationMask);
24 | }
25 |
26 | // Map the function over one year of data.
27 | var collection = L7.filterDate('2020-01-01', '2021-01-01').map(
28 | maskL457sr);
29 | var landsat7_2020 = collection.median();
30 |
31 | Map.addLayer(landsat7_2020, {
32 | bands: ['SR_B3', 'SR_B2', 'SR_B1'],
33 | min: 0,
34 | max: 0.3
35 | }, 'landsat 7, 2020');
36 |
37 | // -----------------------------------------------------------------------
38 | // CHECKPOINT
39 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.2 Urban Environments/A12f-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.2 Urban Environments
3 | // Checkpoint: A12f
4 | // Authors: Michelle Stuhlmacher and Ran Goldblatt
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 | Map.centerObject(bu, 13);
7 |
8 | // Surface reflectance function from example:
9 | function maskL457sr(image) {
10 | var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111',
11 | 2)).eq(0);
12 | var saturationMask = image.select('QA_RADSAT').eq(0);
13 |
14 | // Apply the scaling factors to the appropriate bands.
15 | var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-
16 | 0.2);
17 | var thermalBand = image.select('ST_B6').multiply(0.00341802).add(
18 | 149.0);
19 |
20 | // Replace the original bands with the scaled ones and apply the masks.
21 | return image.addBands(opticalBands, null, true)
22 | .addBands(thermalBand, null, true)
23 | .updateMask(qaMask)
24 | .updateMask(saturationMask);
25 | }
26 |
27 | // Map the function over one year of data.
28 | var collection = L7.filterDate('2020-01-01', '2021-01-01').map(
29 | maskL457sr);
30 | var landsat7_2020 = collection.median();
31 |
32 | Map.addLayer(landsat7_2020, {
33 | bands: ['SR_B3', 'SR_B2', 'SR_B1'],
34 | min: 0,
35 | max: 0.3
36 | }, 'landsat 7, 2020');
37 |
38 | // -----------------------------------------------------------------------
39 | // CHECKPOINT
40 | // -----------------------------------------------------------------------
41 |
42 | var lc = nbu.merge(bu);
43 |
44 | // -----------------------------------------------------------------------
45 | // CHECKPOINT
46 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.3 Built Environments/A13d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.3 Built Environments
3 | // Checkpoint: A13d
4 | // Author: Erin Trochim
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import roads data.
8 | var grip4_africa = ee.FeatureCollection(
9 | 'projects/sat-io/open-datasets/GRIP4/Africa'),
10 | grip4_europe = ee.FeatureCollection(
11 | 'projects/sat-io/open-datasets/GRIP4/Europe'),
12 | grip4_north_america = ee.FeatureCollection(
13 | 'projects/sat-io/open-datasets/GRIP4/North-America');
14 |
15 | // Add a function to add line length in km.
16 | var addLength = function(feature) {
17 | return feature.set({
18 | lengthKm: feature.length().divide(1000)
19 | }); // km;
20 | };
21 |
22 | // Calculate line lengths for all roads in Africa.
23 | var grip4_africaLength = grip4_africa.map(addLength);
24 |
25 | // Convert the roads to raster.
26 | var empty = ee.Image().float();
27 |
28 | var grip4_africaRaster = empty.paint({
29 | featureCollection: grip4_africaLength,
30 | color: 'lengthKm'
31 | });
32 |
33 | // Import simplified countries.
34 | var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
35 |
36 | // Filter to Africa.
37 | var Africa = countries.filter(ee.Filter.eq('wld_rgn', 'Africa'));
38 |
39 | // Import global power transmission lines.
40 | var transmission = ee.FeatureCollection(
41 | 'projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines'
42 | );
43 |
44 | // Filter transmission lines to Africa.
45 | var transmissionAfrica = transmission.filterBounds(Africa);
46 |
47 | // Calculate line lengths for all transmission lines in Africa.
48 | var transmissionAfricaLength = transmissionAfrica.map(addLength);
49 |
50 | // Convert the transmission lines to raster.
51 | var transmissionAfricaRaster = empty.paint({
52 | featureCollection: transmissionAfricaLength,
53 | color: 'lengthKm'
54 | });
55 |
56 | // -----------------------------------------------------------------------
57 | // CHECKPOINT
58 | // -----------------------------------------------------------------------
59 |
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.3 Built Environments/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 | 
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 | 
26 |
27 |
28 |
29 | # Part A1: Human Applications
30 |
31 | This Part covers some of the many Human Applications of Earth Engine. It includes demonstrations of how Earth Engine can be used in agricultural and urban settings, including for sensing the built environment and the effects it has on air composition and temperature. This Part also covers the complex topics of human health, illicit deforestation activity, and humanitarian actions.
32 |
33 |
34 | # Chapter A1.3: Built Environments
35 |
36 | ## Author
37 | Erin Trochim
38 |
39 |
40 |
41 | ## Overview
42 | The built environment consists of the human-made physical parts of the environment, including homes, buildings, streets, open spaces, and infrastructure. This chapter will focus on analyzing global infrastructure datasets.
43 |
44 |
45 | ## Learning Outcomes
46 | - Quantifying road characteristics.
47 | - Comparing road and transmission line distributions.
48 | - Contrasting changes in impervious surfaces with flooding.
49 | - Understanding vector-based versus raster-based approaches.
50 |
51 | ## Link for details:
52 | https://google-earth-engine.com/Human-Applications/Built-Environments/
53 |
54 | # Invitation for collaborators:
55 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
56 |
57 | https://google-earth-engine.com/contact/
58 |
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.5 Heat Islands/A15a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.5 Heat Islands
3 | // Checkpoint: A15a
4 | // Author: TC Chakraborty
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load feature collection of New Haven's census tracts from user assets.
8 | var regionInt = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A1-5/TC_NewHaven');
10 |
11 | // Get dissolved feature collection using an error margin of 50 meters.
12 | var regionInt = regionInt.union(50);
13 |
14 | // Set map center and zoom level (Zoom level varies from 1 to 20).
15 | Map.setCenter(-72.9, 41.3, 12);
16 |
17 | // Add layer to map.
18 | Map.addLayer(regionInt, {}, 'New Haven boundary');
19 |
20 | // Load MODIS image collection from the Earth Engine data catalog.
21 | var modisLst = ee.ImageCollection('MODIS/006/MYD11A2');
22 |
23 | // Select the band of interest (in this case: Daytime LST).
24 | var landSurfTemperature = modisLst.select('LST_Day_1km');
25 |
26 | // Create a summer filter.
27 | var sumFilter = ee.Filter.dayOfYear(152, 243);
28 |
29 | // Filter the date range of interest using a date filter.
30 | var lstDateInt = landSurfTemperature
31 | .filterDate('2014-01-01', '2019-01-01').filter(sumFilter);
32 |
33 | // Take pixel-wise mean of all the images in the collection.
34 | var lstMean = lstDateInt.mean();
35 |
36 | // Multiply each pixel by scaling factor to get the LST values.
37 | var lstFinal = lstMean.multiply(0.02);
38 |
39 | // Generate a water mask.
40 | var water = ee.Image('JRC/GSW1_0/GlobalSurfaceWater').select(
41 | 'occurrence');
42 | var notWater = water.mask().not();
43 |
44 | // Clip data to region of interest, convert to degree Celsius, and mask water pixels.
45 | var lstNewHaven = lstFinal.clip(regionInt).subtract(273.15)
46 | .updateMask(notWater);
47 |
48 | // Add layer to map.
49 | Map.addLayer(lstNewHaven, {
50 | palette: ['blue', 'white', 'red'],
51 | min: 25,
52 | max: 38
53 | },
54 | 'LST_MODIS');
55 |
56 | // -----------------------------------------------------------------------
57 | // CHECKPOINT
58 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.5 Heat Islands/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part A1: Human Applications
30 |
31 | This Part covers some of the many Human Applications of Earth Engine. It includes demonstrations of how Earth Engine can be used in agricultural and urban settings, including for sensing the built environment and the effects it has on air composition and temperature. This Part also covers the complex topics of human health, illicit deforestation activity, and humanitarian actions.
32 |
33 | # Chapter A1.5: Heat Islands
34 |
35 | ## Author
36 | TC Chakraborty
37 |
38 |
39 | ## Overview
40 | In this chapter, you will learn about urban heat islands and how they can be calculated from satellite measurements of thermal radiation from the Earth’s surface.
41 |
42 | ## Learning Outcomes
43 | - Understanding how to derive land surface temperature.
44 | - Understanding how to generate urban and rural references.
45 | - Knowing how to calculate the surface urban heat island intensity.
46 |
47 | ## Link for details:
48 | https://google-earth-engine.com/Human-Applications/Heat-Islands/
49 |
50 | # Invitation for collaborators:
51 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
52 |
53 | https://google-earth-engine.com/contact/
54 |
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.5 Heat Islands/modules/compute_FVC.js:
--------------------------------------------------------------------------------
1 | /*
2 | Author: Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia)
3 |
4 | This code is free and open.
5 | By using this code and any data derived with it,
6 | you agree to cite the following reference
7 | in any publications derived from them:
8 | Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020.
9 | Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series.
10 | Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471
11 |
12 | this function computes the Fraction of Vegetation Cover from NDVI
13 | the compute_NDVI.js function must be called before this one
14 |
15 | to call this function use:
16 |
17 | var FVCfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_FVC.js')
18 | var ImagewithFVC = FVCfun.addBand(landsat)(image)
19 | or
20 | var collectionwithFVC = ImageCollection.map(FVCfun.addBand(landsat))
21 |
22 | USES:
23 | - SMW_coefficients.js
24 |
25 | INPUTS:
26 | - landsat:
27 | the Landsat satellite id
28 | valid inputs: 'L4', 'L5', 'L7' and 'L8'
29 | - image:
30 | image for which to calculate the FVC
31 | OUTPUTS:
32 | -
33 | the input image with 1 new band:
34 | 'FVC': fraction of vegetation cover
35 | */
36 | exports.addBand = function(landsat){
37 | var wrap = function(image){
38 |
39 | var ndvi = image.select('NDVI')
40 |
41 | // Compute FVC
42 | var fvc = image.expression('((ndvi-ndvi_bg)/(ndvi_vg - ndvi_bg))**2',
43 | {'ndvi':ndvi,'ndvi_bg':0.2,'ndvi_vg':0.86});
44 | fvc = fvc.where(fvc.lt(0.0),0.0);
45 | fvc = fvc.where(fvc.gt(1.0),1.0);
46 |
47 | return image.addBands(fvc.rename('FVC'));
48 | }
49 | return wrap
50 | };
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.5 Heat Islands/modules/compute_NDVI.js:
--------------------------------------------------------------------------------
1 | /*
2 | Author: Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia)
3 |
4 | this function computes NDVI values for Landsat
5 |
6 |
7 | to call this function use:
8 |
9 | var NDVIfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_NDVI.js')
10 | var ImagewithNDVI = NDVIfun.addBand(landsat)(image)
11 | or
12 | var collectionwithNDVI = ImageCollection.map(NDVIfun.addBand(landsat))
13 |
14 | INPUTS:
15 | - landsat:
16 | the Landsat satellite id
17 | valid inputs: 'L4', 'L5', 'L7' and 'L8'
18 | - image:
19 | image for which to calculate the NDVI
20 | OUTPUTS:
21 | -
22 | the input image with 1 new band:
23 | 'NDVI': normalized difference vegetation index
24 | */
25 |
26 | exports.addBand = function(landsat){
27 | var wrap = function(image){
28 |
29 | // choose bands
30 | var nir = ee.String(ee.Algorithms.If(landsat==='L8','B5','B4'))
31 | var red = ee.String(ee.Algorithms.If(landsat==='L8','B4','B3'))
32 |
33 | // compute NDVI
34 | return image.addBands(image.expression('(nir-red)/(nir+red)',{
35 | 'nir':image.select(nir).multiply(0.0001),
36 | 'red':image.select(red).multiply(0.0001)
37 | }).rename('NDVI'))
38 | }
39 | return wrap
40 | };
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.5 Heat Islands/modules/loudmask.js:
--------------------------------------------------------------------------------
1 | /*
2 | Author: Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia)
3 |
4 | this function mask clouds and cloud shadow using the Quality band
5 |
6 | to call this function use:
7 |
8 | var cloudmask = require('users/sofiaermida/landsat_smw_lst:modules/cloudmask.js')
9 | var TOAImageMasked = cloudmask.toa(image)
10 | var SRImageMasked = cloudmask.sr(image)
11 | or
12 | var TOAcollectionMasked = ImageCollection.map(cloudmask.toa)
13 | var SRcollectionMasked = ImageCollection.map(cloudmask.sr)
14 |
15 |
16 | INPUTS:
17 | - image:
18 | image for which clouds are masked
19 | OUTPUTS:
20 | -
21 | the input image with updated mask
22 | */
23 |
24 |
25 | // cloudmask for TOA data
26 | exports.toa = function(image) {
27 | var qa = image.select('BQA');
28 | var mask = qa.bitwiseAnd(1 << 4).eq(0);
29 | return image.updateMask(mask);
30 | };
31 |
32 | // cloudmask for SR data
33 | exports.sr = function(image) {
34 | var qa = image.select('pixel_qa');
35 | var mask = qa.bitwiseAnd(1 << 3)
36 | .or(qa.bitwiseAnd(1 << 5))
37 | return image.updateMask(mask.not());
38 | };
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.6 Health Applications/A16a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.6 Health Applications
3 | // Checkpoint: A16a
4 | // Author: Dawn Nekorchuk
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Section 1: Data Import
8 | var woredas = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A1-6/amhara_woreda_20170207');
10 | // Create region outer boundary to filter products on.
11 | var amhara = woredas.geometry().bounds();
12 | var gpm = ee.ImageCollection('NASA/GPM_L3/IMERG_V06');
13 | var LSTTerra8 = ee.ImageCollection('MODIS/061/MOD11A2')
14 | // Due to MCST outage, only use dates after this for this script.
15 | .filterDate('2001-06-26', Date.now());
16 | var brdfReflect = ee.ImageCollection('MODIS/006/MCD43A4');
17 | var brdfQa = ee.ImageCollection('MODIS/006/MCD43A2');
18 |
19 | // Visualize woredas with black borders and no fill.
20 | // Create an empty image into which to paint the features, cast to byte.
21 | var empty = ee.Image().byte();
22 | // Paint all the polygon edges with the same number and width.
23 | var outline = empty.paint({
24 | featureCollection: woredas,
25 | color: 1,
26 | width: 1
27 | });
28 | // Add woreda boundaries to the map.
29 | Map.setCenter(38, 11.5, 7);
30 | Map.addLayer(outline, {
31 | palette: '000000'
32 | }, 'Woredas');
33 |
34 | // -----------------------------------------------------------------------
35 | // CHECKPOINT
36 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.8 Monitoring Gold Mining Activity using SAR/A18a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.8 Monitoring Gold Mining Activity using SAR
3 | // Checkpoint: A18a
4 | // Authors: Lucio Villa, Sidney Novoa, Milagros Becerra,
5 | // Andréa Puzzi Nicolau, Karen Dyson, Karis Tenneson, John Dilger
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | //////////////////////////////////////////////////////
9 | /// Section One
10 | //////////////////////////////////////////////////////
11 |
12 | // Define the area of study.
13 | var aoi = ee.FeatureCollection('projects/gee-book/assets/A1-8/mdd');
14 |
15 | // Center the map at the aoi.
16 | Map.centerObject(aoi, 9);
17 |
18 | // Create an empty image.
19 | var empty = ee.Image().byte();
20 |
21 | // Convert the area of study to an EE image object
22 | // so we can visualize only the boundary.
23 | var aoiOutline = empty.paint({
24 | featureCollection: aoi,
25 | color: 1,
26 | width: 2
27 | });
28 |
29 | // Select the satellite basemap view.
30 | Map.setOptions('SATELLITE');
31 |
32 | // Add the area of study boundary to the map.
33 | Map.addLayer(aoiOutline, {
34 | palette: 'red'
35 | }, 'Area of Study');
36 |
37 | // Function to mask the SAR images acquired with an incidence angle
38 | // lower or equal to 31 and greater or equal to 45 degrees.
39 | function maskAngle(image) {
40 | var angleMask = image.select('angle');
41 | return image.updateMask(angleMask.gte(31).and(angleMask.lte(45)));
42 | }
43 |
44 | // Function to get the SAR Collection.
45 | function getCollection(dates, roi, orbitPass0) {
46 | var sarCollFloat = ee.ImageCollection('COPERNICUS/S1_GRD_FLOAT')
47 | .filterBounds(roi)
48 | .filterDate(dates[0], dates[1])
49 | .filter(ee.Filter.eq('orbitProperties_pass', orbitPass0));
50 | return sarCollFloat.map(maskAngle).select(['VV', 'VH']);
51 | }
52 |
53 | // Define variables: the period of time and the orbitpass.
54 | var listOfDates = ['2021-08-01', '2021-08-12'];
55 | var orbitPass = 'DESCENDING';
56 |
57 | // Apply the function to get the SAR mosaic.
58 | var sarImageColl = getCollection(listOfDates, aoi, orbitPass)
59 | .mosaic()
60 | .clip(aoi);
61 | print('SAR Image Mosaic', sarImageColl);
62 |
63 | // Apply logarithmic scale.
64 | var sarImageScaled = sarImageColl.log10().multiply(10.0);
65 |
66 | // Visualize results.
67 | var sarVis = {
68 | bands: ['VV', 'VH', 'VV'],
69 | min: [-18, -23, 3],
70 | max: [-4, -11, 15]
71 | };
72 | Map.addLayer(sarImageScaled, sarVis, 'Sentinel-1 / SAR Mosaic');
73 |
74 | // -----------------------------------------------------------------------
75 | // CHECKPOINT
76 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/A1.8 Monitoring Gold Mining Activity using SAR/A18b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A1.8 Monitoring Gold Mining Activity using SAR
3 | // Checkpoint: A18b
4 | // Authors: Lucio Villa, Sidney Novoa, Milagros Becerra,
5 | // Andréa Puzzi Nicolau, Karen Dyson, Karis Tenneson, John Dilger
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | //////////////////////////////////////////////////////
9 | /// Section Two
10 | //////////////////////////////////////////////////////
11 |
12 | // Define the area of study.
13 | var aoi = ee.FeatureCollection('projects/gee-book/assets/A1-8/mdd');
14 |
15 | // Center the map at the aoi.
16 | Map.centerObject(aoi, 9);
17 |
18 | // Create an empty image.
19 | var empty = ee.Image().byte();
20 |
21 | // Convert the area of study to an EE image object
22 | // so we can visualize only the boundary.
23 | var aoiOutline = empty.paint({
24 | featureCollection: aoi,
25 | color: 1,
26 | width: 2
27 | });
28 |
29 | // Select the satellite basemap view.
30 | Map.setOptions('SATELLITE');
31 |
32 | // Add the area of study boundary to the map.
33 | Map.addLayer(aoiOutline, {
34 | palette: 'red'
35 | }, 'Area of Study');
36 |
37 | // Function to mask the SAR images acquired with an incidence angle
38 | // lower equal than 31 and greater equal than 45 degrees.
39 | function maskAngle(image) {
40 | var angleMask = image.select('angle');
41 | return image.updateMask(angleMask.gte(31).and(angleMask.lte(45)));
42 | }
43 |
44 | // Function to get the SAR Collection.
45 | function getCollection(dates, roi, orbitPass0) {
46 | var sarCollFloat = ee.ImageCollection('COPERNICUS/S1_GRD_FLOAT')
47 | .filterBounds(roi)
48 | .filterDate(dates[0], dates[1])
49 | .filter(ee.Filter.eq('orbitProperties_pass', orbitPass0));
50 | return sarCollFloat.map(maskAngle).select(['VV', 'VH']);
51 | }
52 |
53 | // Define variables: the period of time and the orbitpass.
54 | var listOfDates = ['2021-01-01', '2022-01-01'];
55 | var orbitPass = 'DESCENDING';
56 |
57 | // Apply the function to get the SAR Collection.
58 | var sarImageColl = getCollection(listOfDates, aoi, orbitPass);
59 | print('SAR Image Collection', sarImageColl);
60 |
61 | // -----------------------------------------------------------------------
62 | // CHECKPOINT
63 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A1 - Human Applications/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 | 
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 | 
26 |
27 |
28 |
29 | # Part A1: Human Applications
30 |
31 | This Part covers some of the many Human Applications of Earth Engine. It includes demonstrations of how Earth Engine can be used in agricultural and urban settings, including for sensing the built environment and the effects it has on air composition and temperature. This Part also covers the complex topics of human health, illicit deforestation activity, and humanitarian actions.
32 |
33 | https://google-earth-engine.com/Human-Applications/Agricultural-Environments/
34 |
35 | # Invitation for collaborators:
36 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
37 |
38 | https://google-earth-engine.com/contact/
39 |
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.1 Groundwater monitoring with GRACE/A21a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A2.1 Groundwater Monitoring with GRACE
3 | // Checkpoint: A21a
4 | // Authors: A.J. Purdy, J.S. Famiglietti
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import Basins.
8 | var basins = ee.FeatureCollection('USGS/WBD/2017/HUC04');
9 |
10 | // Extract the 3 HUC 04 basins for the Central Valley.
11 | var codes = ['1802', '1803', '1804'];
12 | var basin = basins.filter(ee.Filter.inList('huc4', codes));
13 |
14 | // Add the basin to the map to show the extent of our analysis.
15 | Map.centerObject(basin, 6);
16 | Map.addLayer(basin, {
17 | color: 'green'
18 | }, 'Central Valley Basins', true, 0.5);
19 |
20 | var landcover = ee.ImageCollection('USDA/NASS/CDL')
21 | .filter(ee.Filter.date('2019-01-01', '2019-12-31'))
22 | .select('cultivated');
23 |
24 | Map.addLayer(landcover.first().clip(basin), {}, 'Cropland', true,
25 | 0.5);
26 |
27 | // This table was generated using the index from the CDEC website
28 | var res = ee.FeatureCollection(
29 | 'projects/gee-book/assets/A2-1/ca_reservoirs_index');
30 | // Filter reservoir locations by the Central Valley geometry
31 | var res_cv = res.filterBounds(basin);
32 | Map.addLayer(res_cv, {
33 | 'color': 'blue'
34 | }, 'Reservoirs');
35 |
36 | // -----------------------------------------------------------------------
37 | // CHECKPOINT
38 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.1 Groundwater monitoring with GRACE/A21s1-supplimental-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A2.1 Groundwater Monitoring with GRACE
3 | // Section: Section 3 (A21s1 - Supplemental)
4 | // Authors: A.J. Purdy, J.S. Famiglietti
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // The basins feature is being used to subset GLDAS geographically
8 | // The first 7 lines are set for California.
9 | // A user will need to adjust the basin to reflect another region
10 | var basins = ee.FeatureCollection('USGS/WBD/2017/HUC04');
11 | // Extract the 3 HUC 04 basins for the Central Valley.
12 | var codes = ['1802', '1803', '1804'];
13 | var basin = basins.filter(ee.Filter.inList('huc4', codes));
14 |
15 | // Set start / end year.
16 | var yrStart = 2003;
17 | var yrEnd = 2016;
18 | var years = ee.List.sequence(yrStart, yrEnd);
19 |
20 | // The varBand variable is set to evaluated Snow Water Equivalent.
21 | // Need to adjust to export Soil Moisture (SM_inst)
22 | var varBand = 'SWE_inst';
23 |
24 | var waterstorage = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H')
25 | .select(varBand)
26 | .filterDate({
27 | start: ee.Date.fromYMD(yrStart, 1, 1),
28 | end: ee.Date.fromYMD(yrEnd, 12, 1)
29 | });
30 | var waterstorage_mean = waterstorage.select(varBand).mean();
31 | print(waterstorage_mean);
32 |
33 | var y = 2003;
34 | var date = ee.Date.fromYMD(y, 1, 1);
35 |
36 | var waterstorageIC = ee.Image(ee.ImageCollection(
37 | 'NASA/GLDAS/V021/NOAH/G025/T3H')
38 | .select(varBand)
39 | .filter(ee.Filter.calendarRange(y, y, 'year'))
40 | .mean());
41 | print(waterstorageIC);
42 |
43 | var waterstorage_out = ee.Image(waterstorageIC.subtract(
44 | waterstorage_mean)
45 | .set('year', y)
46 | .set('system:time_start', date));
47 | print(waterstorage_out);
48 |
49 | // Change the assetId & description below to reflect the variable being exported.
50 | // These should be changed to reflect SM, SWE, Can etc.
51 |
52 | Export.image.toAsset({
53 | image: waterstorage_out,
54 | description: 'swe2003',
55 | assetId: 'swe2003',
56 | region: basin,
57 | scale: 10000,
58 | maxPixels: 1e13
59 | });
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.2 Benthic Habitats/A22a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A2.2 Benthic Habitats
3 | // Checkpoint: A22a
4 | // Authors: Dimitris Poursanidis, Aurélie C. Shapiro, Spyros Christofilakos
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Section 1
8 | // Import and display satellite image.
9 | var planet = ee.Image('projects/gee-book/assets/A2-2/20200505_N2000')
10 | .divide(10000);
11 |
12 | Map.centerObject(planet, 12);
13 | var visParams = {
14 | bands: ['b3', 'b2', 'b1'],
15 | min: 0.17,
16 | max: 0.68,
17 | gamma: 0.8
18 | };
19 | Map.addLayer({
20 | eeObject: planet,
21 | visParams: visParams,
22 | name: 'planet initial',
23 | shown: true
24 | });
25 |
26 | // ------------------------------------------------------------------------------
27 | // CHECKPOINT
28 | // ------------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.2 Benthic Habitats/A22b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A2.2 Benthic Habitats
3 | // Checkpoint: A22b
4 | // Authors: Dimitris Poursanidis, Aurélie C. Shapiro, Spyros Christofilakos
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Section 1
8 | // Import and display satellite image.
9 | var planet = ee.Image('projects/gee-book/assets/A2-2/20200505_N2000')
10 | .divide(10000);
11 |
12 | Map.centerObject(planet, 12);
13 | var visParams = {
14 | bands: ['b3', 'b2', 'b1'],
15 | min: 0.17,
16 | max: 0.68,
17 | gamma: 0.8
18 | };
19 | Map.addLayer({
20 | eeObject: planet,
21 | visParams: visParams,
22 | name: 'planet initial',
23 | shown: true
24 | });
25 |
26 | // ------------------------------------------------------------------------------
27 | // CHECKPOINT
28 | // ------------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.4 River morphology/A24a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A2.4 River Morphology
3 | // Checkpoint: A24a
4 | // Authors: Xiao Yang, Theodore Langhorst, Tamlin M. Pavelsky
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 | // THIS SCRIPT IS DESIGNED AS A TUTORIAL TO SHOWCASE USING GOOGLE EARTH ENGINE TO ANALYSE
7 | // RIVER PLANVIEW GEOMETRIES AND MORPHOLOGICAL DYNAMICS. THE ANALYSIS IS BUILT ON EXISTING
8 | // MONTHLY WATER CLASSIFICATIONS DATASETS AVAILABLE IN GOOGLE EARTH ENGINE. BY SHOWING
9 | // PREPROCESSING STEPS LIKE HOW TO IDENTIFY RIVERS FROM OTHER TYPES OF WATER BODIES, AND HOW
10 | // TO USE MULTI TEMPORAL WATER LAYERS TO EXTRACT DYNAMICAL CHANGES IN RIVER MORPHOLOGY, IT PROVIDES
11 | // A GUIDE TO EXTRACT INFORMATIONS ON RIVERS USING GOOGLE EARTH ENGINE.
12 |
13 | // ==========================================================
14 |
15 | var getUTMProj = function(lon, lat) {
16 | // given longitude and latitude (in degree decimals) return EPSG string for the
17 | // corresponding UTM projection
18 | // see https://apollomapping.com/blog/gtm-finding-a-utm-zone-number-easily and
19 | // https://sis.apache.org/faq.html
20 | var utmCode = ee.Number(lon).add(180).divide(6).ceil().int();
21 | var output = ee.Algorithms.If(ee.Number(lat).gte(0),
22 | ee.String('EPSG:326').cat(utmCode.format('%02d')),
23 | ee.String('EPSG:327').cat(utmCode.format('%02d')));
24 | return (output);
25 | };
26 |
27 | var coords = aoi.centroid(30).coordinates();
28 | var lon = coords.get(0);
29 | var lat = coords.get(1);
30 | var crs = getUTMProj(lon, lat);
31 | var scale = ee.Number(30);
32 |
33 | var rpj = function(image) {
34 | return image.reproject({
35 | crs: crs,
36 | scale: scale
37 | });
38 | };
39 |
40 | // ------------------------------------------------------------------------------
41 | // CHECKPOINT
42 | // ------------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.4 River morphology/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | # Part A2: Aquatic and Hydrological Applications
31 |
32 | Earth Engine’s global scope and long time series allow analysts to understand the water cycle in new and unique ways. These include surface water in the form of floods and river characteristics, long-term issues of water balance, and the detection of subsurface ground water.
33 |
34 | # Chapter A2.4 River Morphology
35 | ## Authors
36 | Xiao Yang, Theodore Langhorst, Tamlin M. Pavelsky
37 |
38 |
39 |
40 | ## Overview
41 | The purpose of this chapter is to showcase Earth Engine’s application in fluvial hydrology and geomorphology. Specifically, we show examples demonstrating how to use Earth Engine to extract a river’s centerline and width, and how to calculate the bank erosion rate. At the end of this chapter, you will be able to distinguish rivers from other water bodies, perform basic morphological analyses, and detect changes in river form over time.
42 |
43 |
44 | ## Learning Outcomes
45 | - Working with Landsat surface water products.
46 | - Calculating river centerline location and width.
47 | - Quantifying river bank erosion.
48 |
49 | ## Link for details:
50 | https://google-earth-engine.com/Aquatic-and-Hydrological-Applications/River-morphology/
51 |
52 |
53 |
54 | # Invitation for collaborators:
55 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
56 |
57 | https://google-earth-engine.com/contact/
58 |
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/A2.6 Defining Seasonality First Date of No Snow/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | # Part A2: Aquatic and Hydrological Applications
31 |
32 | Earth Engine’s global scope and long time series allow analysts to understand the water cycle in new and unique ways. These include surface water in the form of floods and river characteristics, long-term issues of water balance, and the detection of subsurface ground water.
33 |
34 | # Chapter A2.6: Defining Seasonality: First Date of No Snow
35 |
36 | ## Authors
37 | Amanda Armstrong, Morgan Tassone, Justin Braaten
38 |
39 |
40 |
41 | ## Overview
42 | The purpose of this chapter is to demonstrate how to produce annual maps representing the first day within a year on which a given pixel reaches 0% snow cover. It also provides suggestions for summarizing and visualizing the results over time and space.
43 |
44 |
45 | ## Learning Outcomes
46 | - Generating and using a date band in image compositing.
47 | - Applying temporal filtering to an ImageCollection.
48 | - Identifying patterns of seasonal snowmelt.
49 |
50 | ## Link for details:
51 | https://google-earth-engine.com/Aquatic-and-Hydrological-Applications/Defining-Seasonality-First-Date-of-No-Snow/
52 |
53 |
54 |
55 | # Invitation for collaborators:
56 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
57 |
58 | https://google-earth-engine.com/contact/
59 |
--------------------------------------------------------------------------------
/Part A - Applications/A2 - Aquatic and Hydrological Applications/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | # Part A2: Aquatic and Hydrological Applications
31 |
32 | Earth Engine’s global scope and long time series allow analysts to understand the water cycle in new and unique ways. These include surface water in the form of floods and river characteristics, long-term issues of water balance, and the detection of subsurface ground water.
33 |
34 | https://google-earth-engine.com/Aquatic-and-Hydrological-Applications/Groundwater-monitoring-with-GRACE/
35 |
36 | # Invitation for collaborators:
37 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
38 |
39 | https://google-earth-engine.com/contact/
40 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.10 Conservation II - Assessing Agricultural Intensification Near Protected Areas/A310a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: Chapter A3.10 Conservation II - Assessing Agricultural
3 | // Intensification Near Protected Areas
4 | // Checkpoint: A310a
5 | // Authors: Pradeep Koulgi, MD Madhusudan
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | // 1. Parameters to function calls
9 |
10 | // 1.1. Annual dry season max NDVI calculation
11 | var modis_veg = ee.ImageCollection('MODIS/006/MOD13Q1');
12 | var ndviBandName = 'NDVI';
13 | var ndviValuesScaling = 0.0001;
14 | var modisVegScale = 250; // meters
15 | var maxNDVIBandname = 'max_dryseason_ndvi';
16 | var yearTimestampBandname = 'year';
17 | var years = ee.List.sequence(2000, 2021, 1);
18 | var drySeasonStart_doy = 1;
19 | var drySeasonEnd_doy = 90;
20 |
21 | // 1.2. Boundaries of Protected Areas of interest
22 | var paBoundaries = ee.FeatureCollection(
23 | 'projects/gee-book/assets/A3-10/IndiaMainlandPAs');
24 | var boundaryBufferWidth = 5000; // meters
25 | var bufferingMaxError = 30; // meters
26 | // Choose PAs in only the western states
27 | var western_states = [
28 | 'Rajasthan', 'Gujarat', 'Madhya Pradesh',
29 | 'Maharashtra', 'Goa', 'Karnataka', 'Kerala'
30 | ];
31 | var western_pas = paBoundaries
32 | .filter(ee.Filter.inList('STATE', western_states));
33 |
34 | // 1.3. Regression analysis
35 | var regressionReducer = ee.Reducer.sensSlope();
36 | var regressionX = yearTimestampBandname;
37 | var regressionY = maxNDVIBandname;
38 |
39 | // 1.4. Surface water layer to mask water pixels from assessment
40 | // Selects pixels where water has ever been detected between 1984 and 2021
41 | var surfaceWaterExtent = ee.Image('JRC/GSW1_3/GlobalSurfaceWater')
42 | .select('max_extent');
43 |
44 | // 1.5. Average annual precipitation layer
45 | var rainfall = ee.Image('WORLDCLIM/V1/BIO').select('bio12');
46 |
47 | // 1.6. Visualization parameters
48 | var regressionResultVisParams = {
49 | min: -3,
50 | max: 3,
51 | palette: ['ff8202', 'ffffff', '356e02']
52 | };
53 | var regressionSummaryChartingOptions = {
54 | title: 'Yearly change in dry-season vegetation greenness ' +
55 | 'in PA buffers in relation to average annual rainfall',
56 | hAxis: {
57 | title: 'Annual Precipitation'
58 | },
59 | vAxis: {
60 | title: 'Median % yearly change in vegetation greenness ' +
61 | 'in 5 km buffer'
62 | },
63 | series: {
64 | 0: {
65 | visibleInLegend: false
66 | }
67 | },
68 | };
69 |
70 | // -----------------------------------------------------------------------
71 | // CHECKPOINT
72 | // -----------------------------------------------------------------------
73 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.2 Mangroves/A32a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.2 Mangroves
3 | // Checkpoint: A32a
4 | // Author: Aurélie Shapiro
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Create an ee.Geometry.
8 | var aoi = ee.Geometry.Polygon([
9 | [
10 | [88.3, 22.61],
11 | [90, 22.61],
12 | [90, 21.47],
13 | [88.3, 21.47]
14 | ]
15 | ]);
16 |
17 | // Locate a coordinate in the aoi with land and water.
18 | var point = ee.Geometry.Point([89.2595, 21.7317]);
19 |
20 | // Position the map.
21 | Map.centerObject(point, 13);
22 | Map.addLayer(aoi, {}, 'AOI');
23 |
24 | // Sentinel-1 wet season data.
25 | var wetS1 = ee.Image(
26 | 'projects/gee-book/assets/A3-2/wet_season_tscan_2020');
27 | // Sentinel-1 dry season data.
28 | var dryS1 = ee.Image(
29 | 'projects/gee-book/assets/A3-2/dry_season_tscan_2020');
30 | // Sentinel-2 mosaic.
31 | var S2 = ee.Image('projects/gee-book/assets/A3-2/Sundarbans_S2_2020');
32 |
33 | //Visualize the input data.
34 | var s1VisParams = {
35 | bands: ['VV_min', 'VH_min', 'VVVH_ratio_min'],
36 | min: -36,
37 | max: 3
38 | };
39 | var s2VisParams = {
40 | bands: ['swir1', 'nir', 'red'],
41 | min: 82,
42 | max: 3236
43 | };
44 |
45 | Map.addLayer(dryS1, s1VisParams, 'S1 dry', false);
46 | Map.addLayer(wetS1, s1VisParams, 'S1 wet', false);
47 | Map.addLayer(S2, s2VisParams, 'S2 2020');
48 |
49 | var NDVI = S2.normalizedDifference(['nir', 'red']).rename(['NDVI']);
50 |
51 | var ratio_swir1_nir = S2.expression(
52 | 'swir1/(nir+0.1)', {
53 | 'swir1': S2.select('swir1'),
54 | 'nir': S2.select('nir')
55 | })
56 | .rename('ratio_swir1_nir_wet');
57 |
58 | var data_stack = S2.addBands(NDVI).addBands(ratio_swir1_nir).addBands(
59 | dryS1).addBands(wetS1).addBands(S2);
60 |
61 | print(data_stack);
62 | // -----------------------------------------------------------------------
63 | // CHECKPOINT
64 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.3 Mangroves II - Change Mapping/A33a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.3 Mangroves II - Change Mapping
3 | // Checkpoint: A33a
4 | // Authors: Celio de Sousa, David Lagomasino, and Lola Fatoyinbo
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // STEP 1 - ADD THE MAPS
8 | var areaOfstudy = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A3-3/Border5km');
10 | var mangrove2000 = ee.Image(
11 | 'projects/gee-book/assets/A3-3/MangroveGuinea2000_v2');
12 | var mangrove2020 = ee.Image(
13 | 'projects/gee-book/assets/A3-3/MangroveGuinea2020_v2');
14 |
15 | Map.setCenter(-13.6007, 9.6295, 10);
16 | // Sets the map center to Conakry, Guinea
17 | Map.addLayer(areaOfstudy, {}, 'Area of Study');
18 | Map.addLayer(mangrove2000, {
19 | palette: '#16a596'
20 | }, 'Mangrove Extent 2000');
21 | Map.addLayer(mangrove2020, {
22 | palette: '#9ad3bc'
23 | }, 'Mangrove Extent 2020');
24 |
25 | // STEP 2 - MAP TO MAP CHANGE
26 |
27 | var mang2020 = mangrove2020.unmask(0);
28 | var mang2000 = mangrove2000.unmask(0);
29 | var change = mang2020.subtract(mang2000)
30 | .clip(areaOfstudy);
31 |
32 | var paletteCHANGE = [
33 | 'red', // Loss/conversion
34 | 'white', // No Change
35 | 'green', // Gain/Expansion
36 | ];
37 |
38 | Map.addLayer(change, {
39 | min: -1,
40 | max: 1,
41 | palette: paletteCHANGE
42 | }, 'Changes 2000-2020');
43 |
44 | // Calculate the area of each pixel
45 | var gain = change.eq(1);
46 | var loss = change.eq(-1);
47 |
48 | var gainArea = gain.multiply(ee.Image.pixelArea().divide(1000000));
49 | var lossArea = loss.multiply(ee.Image.pixelArea().divide(1000000));
50 |
51 | // Sum all the areas
52 | var statsgain = gainArea.reduceRegion({
53 | reducer: ee.Reducer.sum(),
54 | scale: 30,
55 | maxPixels: 1e14
56 | });
57 |
58 | var statsloss = lossArea.reduceRegion({
59 | reducer: ee.Reducer.sum(),
60 | scale: 30,
61 | maxPixels: 1e14
62 | });
63 |
64 | print(statsgain.get('classification'),
65 | 'km² of new mangroves in 2020');
66 | print(statsloss.get('classification'),
67 | 'of mangrove was lost in 2020');
68 |
69 | Map.addLayer(gain.selfMask(), {
70 | palette: 'green'
71 | }, 'Gains');
72 | Map.addLayer(loss.selfMask(), {
73 | palette: 'red'
74 | }, 'Loss');
75 |
76 | // -----------------------------------------------------------------------
77 | // CHECKPOINT
78 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.4 Forest Degradation and Deforestation/A34c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.4 Forest Degradation and Deforestation
3 | // Checkpoint: A34c
4 | // Author: Carlos Souza Jr., Karis Tenneson, John Dilger,
5 | // Crystal Wespestad, Eric Bullock
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | var api = require('users/bullocke/coded:CODED/api');
9 | var utils = require('projects/GLANCE:ccdcUtilities/api');
10 |
11 | // ------------------------------------------------------------------------
12 | // CHECKPOINT
13 | // ------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.4 Forest Degradation and Deforestation/A34d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.4 Forest Degradation and Deforestation
3 | // Checkpoint: A34d
4 | // Author: Carlos Souza Jr., Karis Tenneson, John Dilger,
5 | // Crystal Wespestad, Eric Bullock
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | var api = require('users/bullocke/coded:CODED/api');
9 | var utils = require('projects/GLANCE:ccdcUtilities/api');
10 |
11 | // ------------------------------------------------------------------------
12 | // CHECKPOINT
13 | // ------------------------------------------------------------------------
14 |
15 | // We will use the geometry of the image from the previous section as
16 | // the study area.
17 | var studyArea = ee.Image(
18 | 'LANDSAT/LT05/C02/T1_L2/LT05_226068_19840411')
19 | .geometry();
20 |
21 | // Get cloud masked (Fmask) Landsat imagery.
22 | var landsat = utils.Inputs.getLandsat()
23 | .filterBounds(studyArea)
24 | .filterDate('1984-01-01', '2021-01-01');
25 |
26 | // Make a forest mask
27 | var gfwImage = ee.Image('UMD/hansen/global_forest_change_2019_v1_7');
28 |
29 | // Get areas of forest cover above the threshold
30 | var treeCover = 40;
31 | var forestMask = gfwImage.select('treecover2000')
32 | .gte(treeCover)
33 | .rename('landcover');
34 |
35 | var samples = ee.FeatureCollection(
36 | 'projects/gee-book/assets/A3-4/sample_with_pred_hansen_2010');
37 |
38 | var minObservations = 4;
39 | var chiSquareProbability = 0.97;
40 | var training = samples;
41 | var forestValue = 1;
42 | var startYear = 1990;
43 | var endYear = 2020;
44 | var classBands = ['NDFI', 'GV', 'Shade', 'NPV', 'Soil'];
45 | var prepTraining = false;
46 |
47 | //---------------- CODED parameters
48 | var codedParams = {
49 | minObservations: minObservations,
50 | chiSquareProbability: chiSquareProbability,
51 | training: training,
52 | studyArea: studyArea,
53 | forestValue: forestValue,
54 | forestMask: forestMask,
55 | classBands: classBands,
56 | collection: landsat,
57 | startYear: startYear,
58 | endYear: endYear,
59 | prepTraining: prepTraining
60 | };
61 |
62 | // -------------- Run CODED
63 | var results = api.ChangeDetection.coded(codedParams);
64 | print(results);
65 |
66 | // ------------------------------------------------------------------------
67 | // CHECKPOINT
68 | // ------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.4 Forest Degradation and Deforestation/modules/palletes.js:
--------------------------------------------------------------------------------
1 | var ndfiColors =
2 | 'FFFFFF,FFFCFF,FFF9FF,FFF7FF,FFF4FF,FFF2FF,FFEFFF,FFECFF,FFEAFF,FFE7FF,'+
3 | 'FFE5FF,FFE2FF,FFE0FF,FFDDFF,FFDAFF,FFD8FF,FFD5FF,FFD3FF,FFD0FF,FFCEFF,'+
4 | 'FFCBFF,FFC8FF,FFC6FF,FFC3FF,FFC1FF,FFBEFF,FFBCFF,FFB9FF,FFB6FF,FFB4FF,'+
5 | 'FFB1FF,FFAFFF,FFACFF,FFAAFF,FFA7FF,FFA4FF,FFA2FF,FF9FFF,FF9DFF,FF9AFF,'+
6 | 'FF97FF,FF95FF,FF92FF,FF90FF,FF8DFF,FF8BFF,FF88FF,FF85FF,FF83FF,FF80FF,'+
7 | 'FF7EFF,FF7BFF,FF79FF,FF76FF,FF73FF,FF71FF,FF6EFF,FF6CFF,FF69FF,FF67FF,'+
8 | 'FF64FF,FF61FF,FF5FFF,FF5CFF,FF5AFF,FF57FF,FF55FF,FF52FF,FF4FFF,FF4DFF,'+
9 | 'FF4AFF,FF48FF,FF45FF,FF42FF,FF40FF,FF3DFF,FF3BFF,FF38FF,FF36FF,FF33FF,'+
10 | 'FF30FF,FF2EFF,FF2BFF,FF29FF,FF26FF,FF24FF,FF21FF,FF1EFF,FF1CFF,FF19FF,'+
11 | 'FF17FF,FF14FF,FF12FF,FF0FFF,FF0CFF,FF0AFF,FF07FF,FF05FF,FF02FF,FF00FF,'+
12 | 'FF00FF,FF0AF4,FF15E9,FF1FDF,FF2AD4,FF35C9,FF3FBF,FF4AB4,FF55AA,FF5F9F,'+
13 | 'FF6A94,FF748A,FF7F7F,FF8A74,FF946A,FF9F5F,FFAA55,FFB44A,FFBF3F,FFC935,'+
14 | 'FFD42A,FFDF1F,FFE915,FFF40A,FFFF00,FFFF00,FFFB00,FFF700,FFF300,FFF000,'+
15 | 'FFEC00,FFE800,FFE400,FFE100,FFDD00,FFD900,FFD500,FFD200,FFCE00,FFCA00,'+
16 | 'FFC600,FFC300,FFBF00,FFBB00,FFB700,FFB400,FFB000,FFAC00,FFA800,FFA500,'+
17 | 'FFA500,F7A400,F0A300,E8A200,E1A200,D9A100,D2A000,CA9F00,C39F00,BB9E00,'+
18 | 'B49D00,AC9C00,A59C00,9D9B00,969A00,8E9900,879900,7F9800,789700,709700,'+
19 | '699600,619500,5A9400,529400,4B9300,439200,349100,2D9000,258F00,1E8E00,'+
20 | '168E00,0F8D00,078C00,008C00,008C00,008700,008300,007F00,007A00,007600,'+
21 | '007200,006E00,006900,006500,006100,005C00,005800,005400,005000,004C00';
22 | exports = {
23 | ndfiColors : ndfiColors
24 | };
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.5 Deforestation Viewed from Multiple Sensors/A35a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.5 Deforestation Viewed from Multiple Sensors
3 | // Checkpoint: A35a
4 | // Author: Xiaojing Tang
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Fusion Near Real-time (GUI)
8 | // Near real-time monitoring of forest disturbance by fusion of
9 | // multi-sensor data. @author Xiaojing Tang (xjtang@bu.edu).
10 |
11 | // To run this interface, please access it by entering the link
12 | // below in a separate browser window.
13 |
14 | // https://gee-book.earthengine.app/view/fnrt
15 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.5 Deforestation Viewed from Multiple Sensors/A35b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.5 Deforestation Viewed from Multiple Sensors
3 | // Checkpoint: A35b
4 | // Author: Xiaojing Tang
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var testArea = ee.Geometry.Polygon(
8 | [
9 | [
10 | [-66.73156878460787, -8.662236005089952],
11 | [-66.73156878460787, -8.916025640576244],
12 | [-66.44867083538912, -8.916025640576244],
13 | [-66.44867083538912, -8.662236005089952]
14 | ]
15 | ]);
16 |
17 | Map.centerObject(testArea);
18 |
19 | // Start and end of the training and monitoring period.
20 | var trainPeriod = ee.Dictionary({
21 | 'start': '2017-01-01',
22 | 'end': '2020-01-01'
23 | });
24 | var monitorPeriod = ee.Dictionary({
25 | 'start': '2020-01-01',
26 | 'end': '2021-01-01'
27 | });
28 |
29 | // Near-real-time monitoring parameters.
30 | var nrtParam = {
31 | z: 2,
32 | m: 5,
33 | n: 4
34 | };
35 |
36 | // Sensor specific parameters.
37 | var lstParam = {
38 | band: 'NDFI',
39 | minRMSE: 0.05,
40 | strikeOnly: false
41 | };
42 | var s2Param = {
43 | band: 'NDFI',
44 | minRMSE: 0.05,
45 | strikeOnly: false
46 | };
47 | var s1Param = {
48 | band: 'VV',
49 | minRMSE: 0.01,
50 | strikeOnly: true
51 | };
52 |
53 | // ------------------------------------------------------------------------
54 | // CHECKPOINT
55 | // ------------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.6 Working With GPS & Weather Data/A36a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.6 Working With GPS and Weather Data
3 | // Checkpoint: A36a
4 | // Authors: Peder Engelstad, Daniel Carver, Nicholas E. Young
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the data and add it to the map and print.
8 | var cougarF53 = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A3-6/cougarF53');
10 |
11 | Map.centerObject(cougarF53, 10);
12 |
13 | Map.addLayer(cougarF53, {}, 'cougar presence data');
14 |
15 | print(cougarF53, 'cougar data');
16 |
17 | // Call in image collection and filter.
18 | var Daymet = ee.ImageCollection('NASA/ORNL/DAYMET_V4')
19 | .filterDate('2014-02-11', '2014-11-02')
20 | .filterBounds(geometry)
21 | .map(function(image) {
22 | return image.clip(geometry);
23 | });
24 |
25 | print(Daymet, 'Daymet');
26 |
27 |
28 | // Convert to a multiband image.
29 | var DaymetImage = Daymet.toBands();
30 |
31 | print(DaymetImage, 'DaymetImage');
32 |
33 | // Call the sample regions function.
34 | var samples = DaymetImage.sampleRegions({
35 | collection: cougarF53,
36 | properties: ['id'],
37 | scale: 1000
38 | });
39 |
40 | print(samples, 'samples');
41 |
42 | // -----------------------------------------------------------------------
43 | // CHECKPOINT
44 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.6 Working With GPS & Weather Data/A36b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.6 Working With GPS and Weather Data
3 | // Checkpoint: A36b
4 | // Authors: Peder Engelstad, Daniel Carver, Nicholas E. Young
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the data and add it to the map and print.
8 | var cougarF53 = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A3-6/cougarF53');
10 |
11 | Map.centerObject(cougarF53, 10);
12 |
13 | Map.addLayer(cougarF53, {}, 'cougar presence data');
14 |
15 | print(cougarF53, 'cougar data');
16 |
17 | // Call in image collection and filter.
18 | var Daymet = ee.ImageCollection('NASA/ORNL/DAYMET_V4')
19 | .filterDate('2014-02-11', '2014-11-02')
20 | .filterBounds(geometry)
21 | .map(function(image) {
22 | return image.clip(geometry);
23 | });
24 |
25 | print(Daymet, 'Daymet');
26 |
27 | // Convert to a multiband image.
28 | var DaymetImage = Daymet.toBands();
29 |
30 | print(DaymetImage, 'DaymetImage');
31 |
32 | // Call the sample regions function.
33 | var samples = DaymetImage.sampleRegions({
34 | collection: cougarF53,
35 | properties: ['id'],
36 | scale: 1000
37 | });
38 |
39 | print(samples, 'samples');
40 |
41 | // -----------------------------------------------------------------------
42 | // CHECKPOINT
43 | // -----------------------------------------------------------------------
44 |
45 | // Export value added data to your Google Drive.
46 | Export.table.toDrive({
47 | collection: samples,
48 | description: 'cougarDaymetToDriveExample',
49 | fileFormat: 'csv'
50 | });
51 |
52 | // Apply a median reducer to the dataset.
53 | var daymet1 = Daymet
54 | .median()
55 | .clip(geometry);
56 |
57 | print(daymet1);
58 |
59 | // Export the image to drive.
60 | Export.image.toDrive({
61 | image: daymet1,
62 | description: 'MedianValueForStudyArea',
63 | scale: 1000,
64 | region: geometry,
65 | maxPixels: 1e9
66 | });
67 |
68 | // -----------------------------------------------------------------------
69 | // CHECKPOINT
70 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.8 Detecting Land Cover Change in Rangelands/A38a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.8 Detecting Land Cover Change in Rangelands
3 | // Checkpoint: A38a
4 | // Authors: Ginger Allington, Natalie Kreitzer
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load the shapefile asset for the AOI as a Feature Collection
8 | var aoi = ee.FeatureCollection(
9 | 'projects/gee-book/assets/A3-8/GEE_Ch_AOI');
10 | Map.centerObject(aoi, 11);
11 | Map.addLayer(aoi, {}, 'Subset of Naiman Banner');
12 |
13 | // Filter the MODIS Collection
14 | var MODIS_LC = ee.ImageCollection('MODIS/006/MCD12Q1').select(
15 | 'LC_Type1');
16 |
17 | // Function to clip an image from the collection and set the year
18 | var clipCol = function(img) {
19 | var date = ee.String(img.get('system:index'));
20 | date = date.slice(0, 4);
21 | return img.select('LC_Type1').clip(aoi) // .clip(aoi)
22 | .set('year', date);
23 | };
24 |
25 | // Generate images for diff years you want to compare
26 | var modis01 = MODIS_LC.filterDate('2001-01-01', '2002-01-01').map(
27 | clipCol);
28 | var modis09 = MODIS_LC.filterDate('2009-01-01', '2010-01-01').map(
29 | clipCol);
30 | var modis16 = MODIS_LC.filterDate('2016-01-01', '2017-01-01').map(
31 | clipCol);
32 | // Create an Image for each of the years
33 | var modis01 = modis01.first();
34 | var modis09 = modis09.first();
35 | var modis16 = modis16.first();
36 |
37 | Map.addLayer(modis01.randomVisualizer(), {}, 'modis 2001', false);
38 | Map.addLayer(modis09.randomVisualizer(), {}, 'modis 2009', false);
39 | Map.addLayer(modis16.randomVisualizer(), {}, 'modis 2016', false);
40 |
41 | // Add and clip the WorldCover data
42 | var wCov = ee.ImageCollection('ESA/WorldCover/v100').first();
43 | var landcover20 = wCov.clip(aoi);
44 | Map.addLayer(landcover20, {}, 'Landcover 2020');
45 |
46 | // -----------------------------------------------------------------------
47 | // CHECKPOINT
48 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part A3: Terrestrial Applications
30 |
31 | Earth’s terrestrial surface is analyzed regularly by satellites, in search of both change and stability. These are of great interest to a wide cross-section of Earth Engine users, and projects across large areas illustrate both the challenges and opportunities for life on Earth. Chapters in this Part illustrate the use of Earth Engine for disturbance, understanding long-term changes of rangelands, and creating optimum study sites.
32 |
33 | https://google-earth-engine.com/Terrestrial-Applications-part-1/Active-fire-monitoring/
34 |
35 | # Invitation for collaborators:
36 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
37 |
38 | https://google-earth-engine.com/contact/
39 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.1 Active fire monitoring/A31b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.1 Active fire monitoring
3 | // Checkpoint: A31b
4 | // Authors: Morgan A. Crowley* and Tianjia Liu* (*shared first-authorship)
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Bobcat Fire in the Earth Engine app FIRMS Active Fires (
8 |
9 | // https://globalfires.earthengine.app/view/firms
10 |
11 | // ).
12 |
13 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.1 Active fire monitoring/A31c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.1 Active fire monitoring
3 | // Checkpoint: A31c
4 | // Authors: Morgan A. Crowley* and Tianjia Liu* (*shared first-authorship)
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Your app should now look like this (click the Submit button):
8 |
9 | // https://bit.ly/FIRMS_Bobcat.
10 |
--------------------------------------------------------------------------------
/Part A - Applications/A3 - Terrestial Applications/A3.1 Active fire monitoring/A31d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: A3.1 Active fire monitoring
3 | // Checkpoint: A31d
4 | // Authors: Morgan A. Crowley* and Tianjia Liu* (*shared first-authorship)
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 |
8 | // the Earth Engine app U.S. Fire Dashboard (
9 |
10 | // https://globalfires.earthengine.app/view/us-fire-dashboard
11 |
12 | // ).
--------------------------------------------------------------------------------
/Part A - Applications/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 | 
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 | 
26 |
27 |
28 |
29 |
30 | # Invitation for collaborators:
31 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
32 |
33 | https://google-earth-engine.com/contact/
34 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.1 Exploring Images/F11a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.1 Exploring images
3 | // Checkpoint: F11a
4 | // Author: Jeff Howarth
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load an image from its Earth Engine ID.
8 | var first_image = ee.Image(
9 | 'LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606');
10 |
11 | // Inspect the image object in the Console.
12 | print(first_image);
13 |
14 | // Display band 1 of the image as the first map layer.
15 | Map.addLayer(
16 | first_image, // dataset to display
17 | {
18 | bands: ['SR_B1'], // band to display
19 | min: 8000, // display range
20 | max: 17000
21 | },
22 | 'Layer 1' // name to show in Layer Manager
23 | );
24 |
25 | // Display band 2 as the second map layer.
26 | Map.addLayer(
27 | first_image,
28 | {
29 | bands: ['SR_B2'],
30 | min: 8000,
31 | max: 17000
32 | },
33 | 'Layer 2',
34 | 0, // shown
35 | 1 // opacity
36 | );
37 |
38 | // Display band 3 as the third map layer.
39 | Map.addLayer(
40 | first_image,
41 | {
42 | bands: ['SR_B3'],
43 | min: 8000,
44 | max: 17000
45 | },
46 | 'Layer 3',
47 | 1, // shown
48 | 0 // opacity
49 | );
50 |
51 | // -----------------------------------------------------------------------
52 | // CHECKPOINT
53 | // -----------------------------------------------------------------------
54 |
55 |
56 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.1 Exploring Images/F11b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.1 Exploring images
3 | // Checkpoint: F11b
4 | // Author: Jeff Howarth
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load an image from its Earth Engine ID.
8 | var first_image = ee.Image(
9 | 'LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606');
10 |
11 | // Inspect the image object in the Console.
12 | print(first_image);
13 |
14 | // Display band 1 of the image as the first map layer.
15 | Map.addLayer(
16 | first_image, // dataset to display
17 | {
18 | bands: ['SR_B1'], // band to display
19 | min: 8000, // display range
20 | max: 17000
21 | },
22 | 'Layer 1' // name to show in Layer Manager
23 | );
24 |
25 | // Display band 2 as the second map layer.
26 | Map.addLayer(
27 | first_image,
28 | {
29 | bands: ['SR_B2'],
30 | min: 8000,
31 | max: 17000
32 | },
33 | 'Layer 2',
34 | 0, // shown
35 | 1 // opacity
36 | );
37 |
38 | // Display band 3 as the third map layer.
39 | Map.addLayer(
40 | first_image,
41 | {
42 | bands: ['SR_B3'],
43 | min: 8000,
44 | max: 17000
45 | },
46 | 'Layer 3',
47 | 1, // shown
48 | 0 // opacity
49 | );
50 |
51 | // -----------------------------------------------------------------------
52 | // CHECKPOINT
53 | // -----------------------------------------------------------------------
54 |
55 | // Add a natural color layer by using the first three sensor bands for RGB.
56 | Map.addLayer(
57 | first_image,
58 | {
59 | bands: ['SR_B3', 'SR_B2', 'SR_B1'],
60 | min: 8000,
61 | max: 17000
62 | },
63 | 'Natural Color');
64 |
65 | // Add a NIR false-color layer using NIR, red, green sensor bands for RGB.
66 | Map.addLayer(
67 | first_image,
68 | {
69 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
70 | min: 8000,
71 | max: 17000
72 | },
73 | 'False Color');
74 |
75 | // Add a SWIR false-color layer using SWIR, NIR, green sensor bands for RGB.
76 | Map.addLayer(
77 | first_image,
78 | {
79 | bands: ['SR_B5', 'SR_B4', 'SR_B2'],
80 | min: 8000,
81 | max: 17000
82 | },
83 | 'Short wave false color');
84 |
85 | // -----------------------------------------------------------------------
86 | // CHECKPOINT
87 | // -----------------------------------------------------------------------
88 |
89 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.1 Exploring Images/F11s1-example-4.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.1 Exploring images
3 | // Section: Practice problem (Assignment 4)
4 | // Author: Jeff Howarth
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load the practice image from the Earth Engine ID.
8 | var practice_image = ee.Image(
9 | 'LANDSAT/LT05/C02/T1_L2/LT05_022039_20050907');
10 |
11 | // Print image metadata to the Console.
12 | print(practice_image);
13 |
14 | // Center the Map on the image.
15 | Map.centerObject(practice_image, 9);
16 |
17 | // Add a natural color composite to the Map.
18 | Map.addLayer(
19 | practice_image,
20 | {
21 | bands: ['SR_B3', 'SR_B2', 'SR_B1'],
22 | min: 8000,
23 | max: 17000
24 | },
25 | 'Natural color'
26 | );
27 |
28 | // Add an NIR false color composite to the Map.
29 | Map.addLayer(
30 | practice_image,
31 | {
32 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
33 | min: 8000,
34 | max: 17000
35 | },
36 | 'NIR false color'
37 | );
38 |
39 | // Add a SWIR false color composite to the Map.
40 | Map.addLayer(
41 | practice_image,
42 | {
43 | bands: ['SR_B5', 'SR_B4', 'SR_B2'],
44 | min: 8000,
45 | max: 17000
46 | },
47 | 'SWIR false color'
48 | );
49 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.1 Exploring Images/F11s2-example-5.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.1 Exploring images
3 | // Section: Practice problem (Assignment 5)
4 | // Author: Jeff Howarth
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var image = ee.Image('LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606');
8 |
9 | Map.addLayer(
10 | image,
11 | {
12 | bands: ['SR_B1'],
13 | min: 8000,
14 | max: 17000
15 | },
16 | 'Layer 1'
17 | );
18 |
19 | Map.addLayer(
20 | image.select('SR_B1'),
21 | {
22 | min: 8000,
23 | max: 17000
24 | },
25 | 'Layer 2'
26 | );
27 |
28 |
29 |
30 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12a
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // View an Image Collection
9 | /////
10 |
11 | // Import the Landsat 8 Raw Collection.
12 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1');
13 |
14 | // Print the size of the Landsat 8 dataset.
15 | print('The size of the Landsat 8 image collection is:', landsat8
16 | .size());
17 |
18 | // Try to print the image collection.
19 | // WARNING! Running the print code immediately below produces an error because
20 | // the Console can not print more than 5000 elements.
21 | print(landsat8);
22 |
23 | // Add the Landsat 8 dataset to the Map as a mosaic. The collection is
24 | // already chronologically sorted, so the most recent pixel is displayed.
25 | Map.addLayer(landsat8,
26 | {
27 | bands: ['B4', 'B3', 'B2'],
28 | min: 5000,
29 | max: 15000
30 | },
31 | 'Landsat 8 Image Collection');
32 |
33 | // -----------------------------------------------------------------------
34 | // CHECKPOINT
35 | // -----------------------------------------------------------------------
36 |
37 |
38 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12c
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Collections of single images - Landsat 8 Surface Reflectance
9 | /////
10 |
11 | // Create and Earth Engine Point object over San Francisco.
12 | var pointSF = ee.Geometry.Point([-122.44, 37.76]);
13 |
14 | // Import the Landsat 8 Surface Reflectance collection.
15 | var landsat8SR = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2');
16 |
17 | // Filter the collection and select the first image.
18 | var landsat8SRimage = landsat8SR.filterDate('2014-03-18',
19 | '2014-03-19')
20 | .filterBounds(pointSF)
21 | .first();
22 |
23 | print('Landsat 8 Surface Reflectance image', landsat8SRimage);
24 |
25 | // Center map to the first image.
26 | Map.centerObject(landsat8SRimage, 8);
27 |
28 | // Add first image to the map.
29 | Map.addLayer(landsat8SRimage,
30 | {
31 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
32 | min: 7000,
33 | max: 13000
34 | },
35 | 'Landsat 8 SR');
36 |
37 | // -----------------------------------------------------------------------
38 | // CHECKPOINT
39 | // -----------------------------------------------------------------------
40 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12d
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Collections of single images - Landsat 8 Surface Reflectance
9 | /////
10 |
11 | // Create and Earth Engine Point object over San Francisco.
12 | var pointSF = ee.Geometry.Point([-122.44, 37.76]);
13 |
14 | // Import the Landsat 8 Surface Reflectance collection.
15 | var landsat8SR = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2');
16 |
17 | // Filter the collection and select the first image.
18 | var landsat8SRimage = landsat8SR.filterDate('2014-03-18',
19 | '2014-03-19')
20 | .filterBounds(pointSF)
21 | .first();
22 |
23 |
24 | print('Landsat 8 Surface Reflectance image', landsat8SRimage);
25 |
26 | // Center map to the first image.
27 | Map.centerObject(landsat8SRimage, 8);
28 |
29 | // Add first image to the map.
30 | Map.addLayer(landsat8SRimage,
31 | {
32 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
33 | min: 7000,
34 | max: 13000
35 | },
36 | 'Landsat 8 SR');
37 |
38 | // -----------------------------------------------------------------------
39 | // CHECKPOINT
40 | // -----------------------------------------------------------------------
41 |
42 | /////
43 | // Pre-made composites
44 | /////
45 |
46 | // Import a MODIS dataset of daily BRDF-corrected reflectance.
47 | var modisDaily = ee.ImageCollection('MODIS/006/MCD43A4');
48 |
49 | // Filter the dataset to a recent date.
50 | var modisDailyRecent = modisDaily.filterDate('2021-11-01');
51 |
52 | // Add the dataset to the map.
53 | var modisVis = {
54 | bands: [
55 | 'Nadir_Reflectance_Band1',
56 | 'Nadir_Reflectance_Band4',
57 | 'Nadir_Reflectance_Band3'
58 | ],
59 | min: 0,
60 | max: 4000
61 | };
62 | Map.addLayer(modisDailyRecent, modisVis, 'MODIS Daily Composite');
63 |
64 |
65 | // Import the MODIS monthly burned areas dataset.
66 | var modisMonthly = ee.ImageCollection('MODIS/006/MCD64A1');
67 |
68 | // Filter the dataset to a recent month during fire season.
69 | var modisMonthlyRecent = modisMonthly.filterDate('2021-08-01');
70 |
71 | // Add the dataset to the map.
72 | Map.addLayer(modisMonthlyRecent, {}, 'MODIS Monthly Burn');
73 |
74 | // -----------------------------------------------------------------------
75 | // CHECKPOINT
76 | // -----------------------------------------------------------------------
77 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12e-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12e
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Other satellite products
9 | /////
10 |
11 | // Import a Sentinel-5 methane dataset.
12 | var methane = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4');
13 |
14 | // Filter the methane dataset.
15 | var methane2018 = methane.select(
16 | 'CH4_column_volume_mixing_ratio_dry_air')
17 | .filterDate('2018-11-28', '2018-11-29')
18 | .first();
19 |
20 | // Make a visualization for the methane data.
21 | var methaneVis = {
22 | palette: ['black', 'blue', 'purple', 'cyan', 'green',
23 | 'yellow', 'red'
24 | ],
25 | min: 1770,
26 | max: 1920
27 | };
28 |
29 | // Center the Map.
30 | Map.centerObject(methane2018, 3);
31 |
32 | // Add the methane dataset to the map.
33 | Map.addLayer(methane2018, methaneVis, 'Methane');
34 |
35 | // Import the ERA5 Monthly dataset
36 | var era5Monthly = ee.ImageCollection('ECMWF/ERA5/MONTHLY');
37 |
38 | // Filter the dataset
39 | var era5MonthlyTemp = era5Monthly.select('mean_2m_air_temperature')
40 | .filterDate('2018-01-01', '2019-01-31')
41 | .first();
42 |
43 | // Add the ERA dataset to the map.
44 | Map.addLayer(era5MonthlyTemp,
45 | {
46 | palette: ['yellow', 'red'],
47 | min: 260,
48 | max: 320
49 | },
50 | 'ERA5 Max Monthly Temp');
51 |
52 | // -----------------------------------------------------------------------
53 | // CHECKPOINT
54 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12f-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12f
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Pre-classified Land Use Land Cover
9 | /////
10 |
11 | // Import the ESA WorldCover dataset.
12 | var worldCover = ee.ImageCollection('ESA/WorldCover/v100').first();
13 |
14 | // Center the Map.
15 | Map.centerObject(worldCover, 3);
16 |
17 | // Add the worldCover layer to the map.
18 | Map.addLayer(worldCover, {
19 | bands: ['Map']
20 | }, 'WorldCover');
21 |
22 | // Import the Hansen Global Forest Change dataset.
23 | var globalForest = ee.Image(
24 | 'UMD/hansen/global_forest_change_2020_v1_8');
25 |
26 | // Create a visualization for tree cover in 2000.
27 | var treeCoverViz = {
28 | bands: ['treecover2000'],
29 | min: 0,
30 | max: 100,
31 | palette: ['black', 'green']
32 | };
33 |
34 | // Add the 2000 tree cover image to the map.
35 | Map.addLayer(globalForest, treeCoverViz, 'Hansen 2000 Tree Cover');
36 |
37 | // Create a visualization for the year of tree loss over the past 20 years.
38 | var treeLossYearViz = {
39 | bands: ['lossyear'],
40 | min: 0,
41 | max: 20,
42 | palette: ['yellow', 'red']
43 | };
44 |
45 | // Add the 2000-2020 tree cover loss image to the map.
46 | Map.addLayer(globalForest, treeLossYearViz, '2000-2020 Year of Loss');
47 |
48 | // -----------------------------------------------------------------------
49 | // CHECKPOINT
50 | // -----------------------------------------------------------------------
51 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.2 Survey of Raster Datasets/F12g-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.2 Survey of Raster Datasets
3 | // Checkpoint: F12g
4 | // Authors: Andréa, Karen, Nick Clinton, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Other datasets
9 | /////
10 |
11 | // Import and filter a gridded population dataset.
12 | var griddedPopulation = ee.ImageCollection(
13 | 'CIESIN/GPWv411/GPW_Population_Count')
14 | .first();
15 |
16 | // Predefined palette.
17 | var populationPalette = [
18 | 'ffffe7',
19 | '86a192',
20 | '509791',
21 | '307296',
22 | '2c4484',
23 | '000066'
24 | ];
25 |
26 | // Center the Map.
27 | Map.centerObject(griddedPopulation, 3);
28 |
29 | // Add the population data to the map.
30 | Map.addLayer(griddedPopulation,
31 | {
32 | min: 0,
33 | max: 1200,
34 | 'palette': populationPalette
35 | },
36 | 'Gridded Population');
37 |
38 | // Import the NASA DEM Dataset.
39 | var nasaDEM = ee.Image('NASA/NASADEM_HGT/001');
40 |
41 | // Add the elevation layer to the map.
42 | Map.addLayer(nasaDEM, {
43 | bands: ['elevation'],
44 | min: 0,
45 | max: 3000
46 | }, 'NASA DEM');
47 |
48 | // -----------------------------------------------------------------------
49 | // CHECKPOINT
50 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F1: Programming and Remote Sensing Basics
30 |
31 |
32 | In order to use Earth Engine well, you will need to develop basic skills in remote sensing and programming. The language of this book is JavaScript, and you will begin by learning how to manipulate variables using it. With that base, you’ll learn about viewing individual satellite images, viewing collections of images in Earth Engine, and how common remote sensing terms are referenced and used in Earth Engine.
33 |
34 | https://google-earth-engine.com/Programming-and-Remote-Sensing-Basics/JavaScript-and-the-Earth-Engine-API/
35 |
36 |
37 |
38 |
39 | # Invitation for collaborators:
40 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
41 |
42 | https://google-earth-engine.com/contact/
43 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.0 JavaScript and the Earth Engine API/F10a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.0 Exploring images
3 | // Checkpoint: F10a
4 | // Author: Ujaval Gandhi
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | print('Hello World');
8 |
9 | var city = 'San Francisco';
10 | print(city);
11 |
12 | var population = 873965;
13 | print(population);
14 |
15 | var cities = ['San Francisco', 'Los Angeles', 'New York', 'Atlanta'];
16 | print(cities);
17 |
18 | var cityData = {
19 | 'city': 'San Francisco',
20 | 'coordinates': [-122.4194, 37.7749],
21 | 'population': 873965
22 | };
23 | print(cityData);
24 |
25 | var greet = function(name) {
26 | return 'Hello ' + name;
27 | };
28 | print(greet('World'));
29 | print(greet('Readers'));
30 |
31 | // This is a comment!
32 |
33 | // -----------------------------------------------------------------------
34 | // CHECKPOINT
35 | // -----------------------------------------------------------------------
36 |
37 |
38 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F1 - Programming and Remote Sensing Basics/F1.0 JavaScript and the Earth Engine API/F10b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F1.0 Exploring images
3 | // Checkpoint: F10b
4 | // Author: Ujaval Gandhi
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var a = 1;
8 | var b = 2;
9 |
10 | var result = ee.Number(a).add(b);
11 | print(result);
12 |
13 | var yearList = ee.List.sequence(1980, 2020, 5);
14 | print(yearList);
15 |
16 | // -----------------------------------------------------------------------
17 | // CHECKPOINT
18 | // -----------------------------------------------------------------------
19 |
20 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F2 - Interpreting Images/F2.1 Interpreting an Image Classification/F21a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F2.1 Interpreting an Image: Classification
3 | // Checkpoint: F21a
4 | // Author: Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Create an Earth Engine Point object over Milan.
8 | var pt = ee.Geometry.Point([9.453, 45.424]);
9 |
10 | // Filter the Landsat 8 collection and select the least cloudy image.
11 | var landsat = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
12 | .filterBounds(pt)
13 | .filterDate('2019-01-01', '2020-01-01')
14 | .sort('CLOUD_COVER')
15 | .first();
16 |
17 | // Center the map on that image.
18 | Map.centerObject(landsat, 8);
19 |
20 | // Add Landsat image to the map.
21 | var visParams = {
22 | bands: ['SR_B4', 'SR_B3', 'SR_B2'],
23 | min: 7000,
24 | max: 12000
25 | };
26 | Map.addLayer(landsat, visParams, 'Landsat 8 image');
27 |
28 | // -----------------------------------------------------------------------
29 | // CHECKPOINT
30 | // -----------------------------------------------------------------------
31 |
32 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F2 - Interpreting Images/F2.2 Accuracy Assessment Quantifying Classification Quality/F22a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F2.2 Accuracy Assessment: Quantifying Classification Quality
3 | // Checkpoint: F22a
4 | // Authors: Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the reference dataset.
8 | var data = ee.FeatureCollection(
9 | 'projects/gee-book/assets/F2-2/milan_data');
10 |
11 | // Define the prediction bands.
12 | var predictionBands = [
13 | 'SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7',
14 | 'ST_B10',
15 | 'ndvi', 'ndwi'
16 | ];
17 |
18 | // Split the dataset into training and testing sets.
19 | var trainingTesting = data.randomColumn();
20 | var trainingSet = trainingTesting
21 | .filter(ee.Filter.lessThan('random', 0.8));
22 | var testingSet = trainingTesting
23 | .filter(ee.Filter.greaterThanOrEquals('random', 0.8));
24 |
25 | // Train the Random Forest Classifier with the trainingSet.
26 | var RFclassifier = ee.Classifier.smileRandomForest(50).train({
27 | features: trainingSet,
28 | classProperty: 'class',
29 | inputProperties: predictionBands
30 | });
31 |
32 | // Now, to test the classification (verify model's accuracy),
33 | // we classify the testingSet and get a confusion matrix.
34 | var confusionMatrix = testingSet.classify(RFclassifier)
35 | .errorMatrix({
36 | actual: 'class',
37 | predicted: 'classification'
38 | });
39 |
40 | // Print the results.
41 | print('Confusion matrix:', confusionMatrix);
42 | print('Overall Accuracy:', confusionMatrix.accuracy());
43 | print('Producers Accuracy:', confusionMatrix.producersAccuracy());
44 | print('Consumers Accuracy:', confusionMatrix.consumersAccuracy());
45 | print('Kappa:', confusionMatrix.kappa());
46 |
47 | // -----------------------------------------------------------------------
48 | // CHECKPOINT
49 | // -----------------------------------------------------------------------
50 |
51 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F2 - Interpreting Images/F2.2 Accuracy Assessment Quantifying Classification Quality/F22c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F2.2 Accuracy Assessment: Quantifying Classification Quality
3 | // Checkpoint: F22c
4 | // Authors: Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the reference dataset.
8 | var data = ee.FeatureCollection(
9 | 'projects/gee-book/assets/F2-2/milan_data');
10 |
11 | // Define the prediction bands.
12 | var predictionBands = [
13 | 'SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7',
14 | 'ST_B10',
15 | 'ndvi', 'ndwi'
16 | ];
17 |
18 | // Split the dataset into training and testing sets.
19 | var trainingTesting = data.randomColumn();
20 | var trainingSet = trainingTesting
21 | .filter(ee.Filter.lessThan('random', 0.8));
22 | var testingSet = trainingTesting
23 | .filter(ee.Filter.greaterThanOrEquals('random', 0.8));
24 |
25 | // Spatial join.
26 | var distFilter = ee.Filter.withinDistance({
27 | distance: 1000,
28 | leftField: '.geo',
29 | rightField: '.geo',
30 | maxError: 10
31 | });
32 |
33 | var join = ee.Join.inverted();
34 |
35 | var trainingSet = join.apply(trainingSet, testingSet, distFilter);
36 |
37 | // Train the Random Forest Classifier with the trainingSet.
38 | var RFclassifier = ee.Classifier.smileRandomForest(50).train({
39 | features: trainingSet,
40 | classProperty: 'class',
41 | inputProperties: predictionBands
42 | });
43 |
44 | // Now, to test the classification (verify model's accuracy),
45 | // we classify the testingSet and get a confusion matrix.
46 | var confusionMatrix = testingSet.classify(RFclassifier)
47 | .errorMatrix({
48 | actual: 'class',
49 | predicted: 'classification'
50 | });
51 |
52 | // Print the results.
53 | print('Confusion matrix:', confusionMatrix);
54 | print('Overall Accuracy:', confusionMatrix.accuracy());
55 | print('Producers Accuracy:', confusionMatrix.producersAccuracy());
56 | print('Consumers Accuracy:', confusionMatrix.consumersAccuracy());
57 | print('Kappa:', confusionMatrix.kappa());
58 |
59 | // -----------------------------------------------------------------------
60 | // CHECKPOINT
61 | // -----------------------------------------------------------------------
62 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F2 - Interpreting Images/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F2: Interpreting Images
30 |
31 |
32 | Now that you know how images are viewed and what kinds of images exist in Earth Engine, how do we manipulate them? To gain the skills of interpreting images, you’ll work with bands, combining values to form indices and masking unwanted pixels. Then, you’ll learn some of the techniques available in Earth Engine for classifying images and interpreting the results.
33 |
34 | https://google-earth-engine.com/Interpreting-Images/Image-Manipulation-Bands-Arithmetic-Thresholds-and-Masks/
35 |
36 |
37 |
38 | # Invitation for collaborators:
39 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
40 |
41 | https://google-earth-engine.com/contact/
42 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F2 - Interpreting Images/F2.0 Image Manipulation Bands, Arithmetic, Thresholds, and Masks/F20a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F2.0 Image Manipulation: Bands, Arithmetic, Thresholds, and Masks
3 | // Checkpoint: F20a
4 | // Authors: Karen Dyson, Andrea Puzzi Nicolau, David Saah, and Nicholas Clinton
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /////
8 | // Band Arithmetic
9 | /////
10 |
11 | // Calculate NDVI using Sentinel 2
12 |
13 | // Import and filter imagery by location and date.
14 | var sfoPoint = ee.Geometry.Point(-122.3774, 37.6194);
15 | var sfoImage = ee.ImageCollection('COPERNICUS/S2')
16 | .filterBounds(sfoPoint)
17 | .filterDate('2020-02-01', '2020-04-01')
18 | .first();
19 |
20 | // Display the image as a false color composite.
21 | Map.centerObject(sfoImage, 11);
22 | Map.addLayer(sfoImage, {
23 | bands: ['B8', 'B4', 'B3'],
24 | min: 0,
25 | max: 2000
26 | }, 'False color');
27 |
28 | // Extract the near infrared and red bands.
29 | var nir = sfoImage.select('B8');
30 | var red = sfoImage.select('B4');
31 |
32 | // Calculate the numerator and the denominator using subtraction and addition respectively.
33 | var numerator = nir.subtract(red);
34 | var denominator = nir.add(red);
35 |
36 | // Now calculate NDVI.
37 | var ndvi = numerator.divide(denominator);
38 |
39 | // Add the layer to our map with a palette.
40 | var vegPalette = ['red', 'white', 'green'];
41 | Map.addLayer(ndvi, {
42 | min: -1,
43 | max: 1,
44 | palette: vegPalette
45 | }, 'NDVI Manual');
46 |
47 | // Now use the built-in normalizedDifference function to achieve the same outcome.
48 | var ndviND = sfoImage.normalizedDifference(['B8', 'B4']);
49 | Map.addLayer(ndviND, {
50 | min: -1,
51 | max: 1,
52 | palette: vegPalette
53 | }, 'NDVI normalizedDiff');
54 |
55 | // Use normalizedDifference to calculate NDWI
56 | var ndwi = sfoImage.normalizedDifference(['B8', 'B11']);
57 | var waterPalette = ['white', 'blue'];
58 | Map.addLayer(ndwi, {
59 | min: -0.5,
60 | max: 1,
61 | palette: waterPalette
62 | }, 'NDWI');
63 |
64 | // -----------------------------------------------------------------------
65 | // CHECKPOINT
66 | // -----------------------------------------------------------------------
67 |
68 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F3 - Advanced Image Processing/F3.1 Advanced Pixel-based Image Transformation/F31a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F3.1 Advanced Pixel-Based Image Transformations
3 | // Checkpoint: F31a
4 | // Authors: Karen, Andrea, Nick, and David
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import and filter imagery by location and date.
8 | var sfoPoint = ee.Geometry.Point(-122.3774, 37.6194);
9 |
10 | var sfoImage = ee.ImageCollection('COPERNICUS/S2')
11 | .filterBounds(sfoPoint)
12 | .filterDate('2020-02-01', '2020-04-01')
13 | .first();
14 | Map.centerObject(sfoImage, 11);
15 |
16 | // Calculate EVI using Sentinel 2
17 |
18 | // Extract the bands and divide by 10,000 to account for scaling done.
19 | var nirScaled = sfoImage.select('B8').divide(10000);
20 | var redScaled = sfoImage.select('B4').divide(10000);
21 | var blueScaled = sfoImage.select('B2').divide(10000);
22 |
23 | // Calculate the numerator, note that order goes from left to right.
24 | var numeratorEVI = (nirScaled.subtract(redScaled)).multiply(2.5);
25 |
26 | // Calculate the denominator.
27 | var denomClause1 = redScaled.multiply(6);
28 | var denomClause2 = blueScaled.multiply(7.5);
29 | var denominatorEVI = nirScaled.add(denomClause1)
30 | .subtract(denomClause2).add(1);
31 |
32 | // Calculate EVI and name it.
33 | var EVI = numeratorEVI.divide(denominatorEVI).rename('EVI');
34 |
35 | // And now map EVI using our vegetation palette.
36 | var vegPalette = ['red', 'white', 'green'];
37 | var visParams = {min: -1, max: 1, palette: vegPalette};
38 | Map.addLayer(EVI, visParams, 'EVI');
39 |
40 | // Calculate EVI.
41 | var eviExpression = sfoImage.expression({
42 | expression: '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',
43 | map: { // Map between variables in the expression and images.
44 | 'NIR': sfoImage.select('B8').divide(10000),
45 | 'RED': sfoImage.select('B4').divide(10000),
46 | 'BLUE': sfoImage.select('B2').divide(10000)
47 | }
48 | });
49 |
50 | // And now map EVI using our vegetation palette.
51 | Map.addLayer(eviExpression, visParams, 'EVI Expression');
52 |
53 | // -----------------------------------------------------------------------
54 | // CHECKPOINT
55 | // -----------------------------------------------------------------------
56 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F3 - Advanced Image Processing/F3.1 Advanced Pixel-based Image Transformation/F31b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F3.1 Advanced Pixel-Based Image Transformations
3 | // Checkpoint: F31b
4 | // Authors: Karen, Andrea, Nick, and David
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Examine the true-color Landsat 8 images for the 2013 Rim Fire.
8 | var burnImage = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
9 | .filterBounds(ee.Geometry.Point(-120.083, 37.850))
10 | .filterDate('2013-09-15', '2013-09-27')
11 | .sort('CLOUD_COVER')
12 | .first();
13 |
14 | Map.centerObject(ee.Geometry.Point(-120.083, 37.850), 11);
15 |
16 | var rgbParams = {
17 | bands: ['B4', 'B3', 'B2'],
18 | min: 0,
19 | max: 0.3
20 | };
21 | Map.addLayer(burnImage, rgbParams, 'True-Color Burn Image');
22 |
23 | // Calculate BAI.
24 | var bai = burnImage.expression(
25 | '1.0 / ((0.1 - RED)**2 + (0.06 - NIR)**2)', {
26 | 'NIR': burnImage.select('B5'),
27 | 'RED': burnImage.select('B4'),
28 | });
29 |
30 | // Display the BAI image.
31 | var burnPalette = ['green', 'blue', 'yellow', 'red'];
32 | Map.addLayer(bai, {
33 | min: 0,
34 | max: 400,
35 | palette: burnPalette
36 | }, 'BAI');
37 |
38 | // -----------------------------------------------------------------------
39 | // CHECKPOINT
40 | // -----------------------------------------------------------------------
--------------------------------------------------------------------------------
/Part F - Fundamentals/F3 - Advanced Image Processing/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F3: Advanced Image Processing
30 |
31 | Once you understand the basics of processing images in Earth Engine, this Part will present some of the more advanced processing tools available for treating individual images. These include creating regressions among image bands, transforming images with pixel-based and neighborhood-based techniques, and grouping individual pixels into objects that can then be classified.
32 |
33 | https://google-earth-engine.com/Advanced-Image-Processing/Interpreting-an-Image-Regression/
34 |
35 |
36 | # Invitation for collaborators:
37 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
38 |
39 | https://google-earth-engine.com/contact/
40 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.1 Exploring Image Collections/F41a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.1 Exploring Image Collections
3 | // Checkpoint: F41a
4 | // Author: Gennadii Donchyts
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Define a region of interest as a point in Lisbon, Portugal.
8 | var lisbonPoint = ee.Geometry.Point(-9.179473, 38.763948);
9 |
10 | // Center the map at that point.
11 | Map.centerObject(lisbonPoint, 16);
12 |
13 | // filter the large ImageCollection to be just images from 2020
14 | // around Lisbon. From each image, select true-color bands to draw
15 | var filteredIC = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
16 | .filterDate('2020-01-01', '2021-01-01')
17 | .filterBounds(lisbonPoint)
18 | .select(['B6', 'B5', 'B4']);
19 |
20 | // Add the filtered ImageCollection so that we can inspect values
21 | // via the Inspector tool
22 | Map.addLayer(filteredIC, {}, 'TOA image collection');
23 |
24 | // Construct a chart using values queried from image collection.
25 | var chart = ui.Chart.image.series({
26 | imageCollection: filteredIC,
27 | region: lisbonPoint,
28 | reducer: ee.Reducer.first(),
29 | scale: 10
30 | });
31 |
32 | // Show the chart in the Console.
33 | print(chart);
34 |
35 | // -----------------------------------------------------------------------
36 | // CHECKPOINT
37 | // -----------------------------------------------------------------------
38 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.1 Exploring Image Collections/F41b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.1 Exploring Image Collections
3 | // Checkpoint: F41b
4 | // Author: Gennadii Donchyts
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Define a region of interest as a point in Lisbon, Portugal.
8 | var lisbonPoint = ee.Geometry.Point(-9.179473, 38.763948);
9 |
10 | // Center the map at that point.
11 | Map.centerObject(lisbonPoint, 16);
12 |
13 | // filter the large ImageCollection to be just images from 2020
14 | // around Lisbon. From each image, select true-color bands to draw
15 | var filteredIC = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
16 | .filterDate('2020-01-01', '2021-01-01')
17 | .filterBounds(lisbonPoint)
18 | .select(['B6', 'B5', 'B4']);
19 |
20 | // Add the filtered ImageCollection so that we can inspect values
21 | // via the Inspector tool
22 | Map.addLayer(filteredIC, {}, 'TOA image collection');
23 |
24 | // Construct a chart using values queried from image collection.
25 | var chart = ui.Chart.image.series({
26 | imageCollection: filteredIC,
27 | region: lisbonPoint,
28 | reducer: ee.Reducer.first(),
29 | scale: 10
30 | });
31 |
32 | // Show the chart in the Console.
33 | print(chart);
34 |
35 | // -----------------------------------------------------------------------
36 | // CHECKPOINT
37 | // -----------------------------------------------------------------------
38 |
39 | // compute and show the number of observations in an image collection
40 | var count = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
41 | .filterDate('2020-01-01', '2021-01-01')
42 | .select(['B6'])
43 | .count();
44 |
45 | // add white background and switch to HYBRID basemap
46 | Map.addLayer(ee.Image(1), {
47 | palette: ['white']
48 | }, 'white', true, 0.5);
49 | Map.setOptions('HYBRID');
50 |
51 | // show image count
52 | Map.addLayer(count, {
53 | min: 0,
54 | max: 50,
55 | palette: ['d7191c', 'fdae61', 'ffffbf', 'a6d96a',
56 | '1a9641']
57 | }, 'landsat 8 image count (2020)');
58 |
59 | // Center the map at that point.
60 | Map.centerObject(lisbonPoint, 5);
61 |
62 | // -----------------------------------------------------------------------
63 | // CHECKPOINT
64 | // -----------------------------------------------------------------------
65 |
66 |
67 |
68 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.2 Aggregating Images for Time Series/F42a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.2 Aggregating Images for Time Series
3 | // Checkpoint: F42a
4 | // Author: Ujaval Gandhi
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD');
8 | var startDate = '2019-01-01';
9 | var endDate = '2020-01-01';
10 | var yearFiltered = chirps.filter(ee.Filter.date(startDate, endDate));
11 |
12 | print(yearFiltered, 'Date-filtered CHIRPS images');
13 |
14 |
15 | // -----------------------------------------------------------------------
16 | // CHECKPOINT
17 | // -----------------------------------------------------------------------
18 |
19 |
20 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.2 Aggregating Images for Time Series/F42b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.2 Aggregating Images for Time Series
3 | // Checkpoint: F42b
4 | // Author: Ujaval Gandhi
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD');
8 | var year = 2019;
9 | var startDate = ee.Date.fromYMD(year, 1, 1);
10 |
11 | var endDate = startDate.advance(1, 'year');
12 |
13 | var yearFiltered = chirps
14 | .filter(ee.Filter.date(startDate, endDate));
15 | print(yearFiltered, 'Date-filtered CHIRPS images');
16 |
17 | print(startDate, 'Start date');
18 | print(endDate, 'End date');
19 |
20 | print('Start date as timestamp', startDate.millis());
21 | print('End date as timestamp', endDate.millis());
22 |
23 | // -----------------------------------------------------------------------
24 | // CHECKPOINT
25 | // -----------------------------------------------------------------------
26 |
27 |
28 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.2 Aggregating Images for Time Series/F42c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.2 Aggregating Images for Time Series
3 | // Checkpoint: F42c
4 | // Author: Ujaval Gandhi
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD');
8 | var year = 2019;
9 | var startDate = ee.Date.fromYMD(year, 1, 1);
10 |
11 | var endDate = startDate.advance(1, 'year');
12 |
13 | var yearFiltered = chirps
14 | .filter(ee.Filter.date(startDate, endDate));
15 | print(yearFiltered, 'Date-filtered CHIRPS images');
16 |
17 | print(startDate, 'Start date');
18 | print(endDate, 'End date');
19 |
20 | print('Start date as timestamp', startDate.millis());
21 | print('End date as timestamp', endDate.millis());
22 |
23 | // -----------------------------------------------------------------------
24 | // CHECKPOINT
25 | // -----------------------------------------------------------------------
26 |
27 | // Aggregate this time series to compute monthly images.
28 | // Create a list of months
29 | var months = ee.List.sequence(1, 12);
30 |
31 | // Write a function that takes a month number
32 | // and returns a monthly image.
33 | var createMonthlyImage = function(beginningMonth) {
34 | var startDate = ee.Date.fromYMD(year, beginningMonth, 1);
35 | var endDate = startDate.advance(1, 'month');
36 | var monthFiltered = yearFiltered
37 | .filter(ee.Filter.date(startDate, endDate));
38 |
39 | // Calculate total precipitation.
40 | var total = monthFiltered.reduce(ee.Reducer.sum());
41 | return total.set({
42 | 'system:time_start': startDate.millis(),
43 | 'system:time_end': endDate.millis(),
44 | 'year': year,
45 | 'month': beginningMonth
46 | });
47 | };
48 |
49 | // map() the function on the list of months
50 | // This creates a list with images for each month in the list
51 | var monthlyImages = months.map(createMonthlyImage);
52 |
53 | // Create an ee.ImageCollection.
54 | var monthlyCollection = ee.ImageCollection.fromImages(monthlyImages);
55 | print(monthlyCollection);
56 |
57 | // -----------------------------------------------------------------------
58 | // CHECKPOINT
59 | // -----------------------------------------------------------------------
60 |
61 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.3 Clouds and Image Compositing/F43c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.3 Clouds and Image Compositing
3 | // Checkpoint: F43c
4 | // Authors: Txomin Hermosilla, Saverio Francini, Andréa P. Nicolau,
5 | // Michael A. Wulder, Joanne C. White, Nicholas C. Coops,
6 | // Gherardo Chirici
7 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 |
9 | // Access the GEE-BAP interface at the following link,
10 | // by copy-pasting the line below into your browser.
11 |
12 | // https://code.earthengine.google.com/?accept_repo=users/sfrancini/bap
13 |
14 | // This will create a repository on your Earth Engine account.
15 | // The repository will appear in the Reader section of the Scripts panel.
16 |
17 | // To access the user interface, open the “ui” script and click Run.
18 |
19 | // You can find more information about GEE-BAP at:
20 | // https://github.com/saveriofrancini/bap
21 |
22 | // -----------------------------------------------------------------------
23 | // CHECKPOINT
24 | // -----------------------------------------------------------------------
25 |
26 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.3 Clouds and Image Compositing/F43d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.3 Clouds and Image Compositing
3 | // Checkpoint: F43d
4 | // Authors: Txomin Hermosilla, Saverio Francini, Andréa P. Nicolau,
5 | // Michael A. Wulder, Joanne C. White, Nicholas C. Coops,
6 | // Gherardo Chirici
7 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 |
9 | // Define required parameters.
10 | var targetDay = '06-01';
11 | var daysRange = 75;
12 | var cloudsTh = 70;
13 | var SLCoffPenalty = 0.7;
14 | var opacityScoreMin = 0.2;
15 | var opacityScoreMax = 0.3;
16 | var cloudDistMax = 1500;
17 | var despikeTh = 0.65;
18 | var despikeNbands = 3;
19 | var startYear = 2015;
20 | var endYear = 2017;
21 |
22 | // Define study area.
23 | var worldCountries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
24 | var colombia = worldCountries.filter(ee.Filter.eq('country_na',
25 | 'Colombia'));
26 |
27 | // Load the bap library.
28 | var library = require('users/sfrancini/bap:library');
29 |
30 | // Calculate BAP.
31 | var BAPCS = library.BAP(null, targetDay, daysRange, cloudsTh,
32 | SLCoffPenalty, opacityScoreMin, opacityScoreMax, cloudDistMax);
33 |
34 | // Despike the collection.
35 | BAPCS = library.despikeCollection(despikeTh, despikeNbands, BAPCS,
36 | 1984, 2021, true);
37 |
38 | // Infill datagaps.
39 | BAPCS = library.infill(BAPCS, 1984, 2021, false, true);
40 |
41 | // Visualize the image.
42 | Map.centerObject(colombia, 5);
43 | library.ShowCollection(BAPCS, startYear, endYear, colombia, false,
44 | null);
45 | library.AddSLider(startYear, endYear);
46 |
47 | // -----------------------------------------------------------------------
48 | // CHECKPOINT
49 | // -----------------------------------------------------------------------
50 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/F44a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.4 Change Detection
3 | // Checkpoint: F44a
4 | // Author: Karis Tenneson, John Dilger, Crystal Wespestad, Brian Zutta,
5 | // Andréa P Nicolau, Karen Dyson
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
9 | .select(
10 | ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'],
11 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']
12 | );
13 |
14 | var point = ee.Geometry.Point([-123.64, 42.96]);
15 | Map.centerObject(point, 11);
16 |
17 | var preImage = landsat8
18 | .filterBounds(point)
19 | .filterDate('2013-06-01', '2013-06-30')
20 | .sort('CLOUD_COVER', true)
21 | .first();
22 |
23 | var postImage = landsat8
24 | .filterBounds(point)
25 | .filterDate('2020-06-01', '2020-06-30')
26 | .sort('CLOUD_COVER', true)
27 | .first();
28 |
29 | var visParam = {
30 | 'bands': ['swir2', 'nir', 'red'],
31 | 'min': 7750,
32 | 'max': 22200
33 | };
34 | Map.addLayer(preImage, visParam, 'pre');
35 | Map.addLayer(postImage, visParam, 'post');
36 |
37 | // Calculate NBR.
38 | var nbrPre = preImage.normalizedDifference(['nir', 'swir2'])
39 | .rename('nbr_pre');
40 | var nbrPost = postImage.normalizedDifference(['nir', 'swir2'])
41 | .rename('nbr_post');
42 |
43 | // -----------------------------------------------------------------------
44 | // CHECKPOINT
45 | // -----------------------------------------------------------------------
46 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/F44b-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.4 Change Detection
3 | // Checkpoint: F44b
4 | // Author: Karis Tenneson, John Dilger, Crystal Wespestad, Brian Zutta,
5 | // Andréa P Nicolau, Karen Dyson
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
9 | .select(
10 | ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'],
11 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']
12 | );
13 |
14 | var point = ee.Geometry.Point([-123.64, 42.96]);
15 | Map.centerObject(point, 11);
16 |
17 | var preImage = landsat8
18 | .filterBounds(point)
19 | .filterDate('2013-06-01', '2013-06-30')
20 | .sort('CLOUD_COVER', true)
21 | .first();
22 |
23 | var postImage = landsat8
24 | .filterBounds(point)
25 | .filterDate('2020-06-01', '2020-06-30')
26 | .sort('CLOUD_COVER', true)
27 | .first();
28 |
29 | var visParam = {
30 | 'bands': ['swir2', 'nir', 'red'],
31 | 'min': 7750,
32 | 'max': 22200
33 | };
34 | Map.addLayer(preImage, visParam, 'pre');
35 | Map.addLayer(postImage, visParam, 'post');
36 |
37 | // Calculate NBR.
38 | var nbrPre = preImage.normalizedDifference(['nir', 'swir2'])
39 | .rename('nbr_pre');
40 | var nbrPost = postImage.normalizedDifference(['nir', 'swir2'])
41 | .rename('nbr_post');
42 |
43 | // -----------------------------------------------------------------------
44 | // CHECKPOINT
45 | // -----------------------------------------------------------------------
46 |
47 | // 2-date change.
48 | var diff = nbrPost.subtract(nbrPre).rename('change');
49 |
50 | var palette = [
51 | '011959', '0E365E', '1D5561', '3E6C55', '687B3E',
52 | '9B882E', 'D59448', 'F9A380', 'FDB7BD', 'FACCFA'
53 | ];
54 | var visParams = {
55 | palette: palette,
56 | min: -0.2,
57 | max: 0.2
58 | };
59 | Map.addLayer(diff, visParams, 'change');
60 |
61 | // Classify change
62 | var thresholdGain = 0.10;
63 | var thresholdLoss = -0.10;
64 |
65 | var diffClassified = ee.Image(0);
66 |
67 | diffClassified = diffClassified.where(diff.lte(thresholdLoss), 2);
68 | diffClassified = diffClassified.where(diff.gte(thresholdGain), 1);
69 |
70 | var changeVis = {
71 | palette: 'fcffc8,2659eb,fa1373',
72 | min: 0,
73 | max: 2
74 | };
75 |
76 | Map.addLayer(diffClassified.selfMask(),
77 | changeVis,
78 | 'change classified by threshold');
79 |
80 | // -----------------------------------------------------------------------
81 | // CHECKPOINT
82 | // -----------------------------------------------------------------------
83 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/vis/incheon.js:
--------------------------------------------------------------------------------
1 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
2 | .select(
3 | ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'],
4 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);
5 | var landsat3 = ee.ImageCollection('LANDSAT/LM03/C01/T2')
6 | .select(['B4', 'B5', 'B6', 'B7'], ['green', 'red', 'nir1', 'nir2']);
7 |
8 | var preImage = landsat3
9 | .filterBounds(point)
10 | .filterDate('1981-01-01', '1981-12-30')
11 | .sort('CLOUD_COVER', true)
12 | .first();
13 | var postImage = landsat8
14 | .filterBounds(point)
15 | .filterDate('2020-01-01', '2020-12-30')
16 | .sort('CLOUD_COVER', true)
17 | .first();
18 |
19 | Map.centerObject(point, 10);
20 | Map.addLayer(preImage, preIncheon, 'pre');
21 | Map.addLayer(postImage, postIncheon, 'post');
22 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/vis/mining.js:
--------------------------------------------------------------------------------
1 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
2 | .select(
3 | ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'],
4 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);
5 | var landsat5 = ee.ImageCollection('LANDSAT/LT05/C01/T2_SR')
6 | .select(
7 | ['B1', 'B2', 'B3', 'B4', 'B5', 'B7'],
8 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);
9 |
10 | var preImage = landsat5
11 | .filterBounds(point)
12 | .filterDate('1985-01-01', '2002-12-30')
13 | .sort('CLOUD_COVER', true)
14 | .first();
15 | var postImage = landsat8
16 | .filterBounds(point)
17 | .filterDate('2020-01-01', '2020-12-30')
18 | .sort('CLOUD_COVER', true)
19 | .first();
20 |
21 | Map.centerObject(point, 10);
22 | Map.addLayer(preImage, miningPreVis, 'pre');
23 | Map.addLayer(postImage, mininPostVis, 'post');
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/vis/mtSHelen.js:
--------------------------------------------------------------------------------
1 | var landsat2 = ee.ImageCollection('LANDSAT/LM02/C02/T2')
2 | .select(['B4', 'B5', 'B6', 'B7'],
3 | ['green', 'red', 'nir1', 'nir2']);
4 |
5 | var preImage = landsat2
6 | .filterBounds(point)
7 | .filterDate('1979-08-01', '1979-10-30')
8 | .sort('CLOUD_COVER', true)
9 | .first();
10 | var postImage = landsat2
11 | .filterBounds(point)
12 | .filterDate('1980-04-01', '1981-10-30')
13 | .sort('CLOUD_COVER', true)
14 | .first();
15 |
16 | var visParam = {
17 | 'opacity': 1,
18 | 'bands': ['nir1', 'red', 'green'],
19 | 'min': 0,
20 | 'max': 128,
21 | 'gamma':1
22 | };
23 |
24 | Map.centerObject(point, 10);
25 | Map.addLayer(preImage, visParam, 'pre');
26 | Map.addLayer(postImage, visParam, 'post');
27 |
28 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.4 Change Detection/vis/paradise.js:
--------------------------------------------------------------------------------
1 | var landsat8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
2 | .select(
3 | ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'],
4 | ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);
5 |
6 | var preImage = landsat8
7 | .filterBounds(point)
8 | .filterDate('2018-10-01', '2018-10-30')
9 | .sort('CLOUD_COVER', true)
10 | .first();
11 | var postImage = landsat8
12 | .filterBounds(point)
13 | .filterDate('2018-12-01', '2019-04-30')
14 | .sort('CLOUD_COVER', true)
15 | .first();
16 |
17 | Map.centerObject(point, 10);
18 | Map.addLayer(preImage, imageVisParam, 'pre');
19 | Map.addLayer(postImage, imageVisParam, 'post');
20 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.5 Interpreting Annual Time Series with LandTrendr/F45a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.5 Interpreting Annual Time Series with LandTrendr
3 | // Checkpoint: F45a
4 | // Authors: Robert Kennedy, Justin Braaten, Peter Clary
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // material is in the section for this chapter.
8 | // Or you can dig into the script-based application of these same concepts by exploring the more
9 | // expansive set of manuals on the https://emapr.github.io/LT-GEE/ site.
10 |
11 |
12 | // LandTrendr Github manual. A link to this material is available in the book’s repository in the section for this chapter. . , available at https://emapr.github.io/LT-GEE/. Th
13 |
14 | // (Find the script at users/emaprlab/public/LT-data-download/LT-Data-Visualization-Download-App_v1.0.)
15 |
16 | // Detailed instructions and script links for application of the LandTrendr algorithms are covered in substantial depth in the LandTrendr Github manual. We have provided functions that simplify the use of LandTrendr, with sample scripts that can be adapted to a variety of situations.
17 | // A link to this material is available in the book’s repository in the section for this chapter. . , available at https://emapr.github.io/LT-GEE/.
18 |
19 | // -----------------------------------------------------------------------
20 | // CHECKPOINT
21 | // -----------------------------------------------------------------------
22 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.7 Interpreting Time Series with CCDC/F47a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.7 Interpreting Time Series with CCDC
3 | // Checkpoint: F47a
4 | // Authors: Paulo Arévalo, Pontus Olofsson
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Access the CCDC interface at the following link,
8 | // by copy-pasting the line below into your browser.
9 |
10 |
11 | // https://parevalo-bu.users.earthengine.app/view/advanced-tstools
12 |
13 | // This will open an app that you can use to explore time series and familiarize
14 | // yourself with CCDC capabilities
15 |
16 | // -----------------------------------------------------------------------
17 | // CHECKPOINT
18 | // -----------------------------------------------------------------------
19 |
20 |
21 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.7 Interpreting Time Series with CCDC/F47b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.7 Interpreting Time Series with CCDC
3 | // Checkpoint: F47b
4 | // Authors: Paulo Arévalo, Pontus Olofsson
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var utils = require(
8 | 'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api');
9 |
10 | var studyRegion = ee.Geometry.Rectangle([
11 | [-63.9533, -10.1315],
12 | [-64.9118, -10.6813]
13 | ]);
14 |
15 | // Define start, end dates and Landsat bands to use.
16 | var startDate = '2000-01-01';
17 | var endDate = '2020-01-01';
18 | var bands = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'];
19 |
20 | // Retrieve all clear, Landsat 4, 5, 7 and 8 observations (Collection 2, Tier 1).
21 | var filteredLandsat = utils.Inputs.getLandsat({
22 | collection: 2
23 | })
24 | .filterBounds(studyRegion)
25 | .filterDate(startDate, endDate)
26 | .select(bands);
27 |
28 | print(filteredLandsat.first());
29 |
30 | // Set CCD params to use.
31 | var ccdParams = {
32 | breakpointBands: ['GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
33 | tmaskBands: ['GREEN', 'SWIR2'],
34 | minObservations: 6,
35 | chiSquareProbability: 0.99,
36 | minNumOfYearsScaler: 1.33,
37 | dateFormat: 1,
38 | lambda: 0.002,
39 | maxIterations: 10000,
40 | collection: filteredLandsat
41 | };
42 |
43 | // Run CCD.
44 | var ccdResults = ee.Algorithms.TemporalSegmentation.Ccdc(ccdParams);
45 | print(ccdResults);
46 |
47 | var exportResults = false;
48 | if (exportResults) {
49 | // Create a metadata dictionary with the parameters and arguments used.
50 | var metadata = ccdParams;
51 | metadata['breakpointBands'] = metadata['breakpointBands'].toString();
52 | metadata['tmaskBands'] = metadata['tmaskBands'].toString();
53 | metadata['startDate'] = startDate;
54 | metadata['endDate'] = endDate;
55 | metadata['bands'] = bands.toString();
56 |
57 | // Export results, assigning the metadata as image properties.
58 | //
59 | Export.image.toAsset({
60 | image: ccdResults.set(metadata),
61 | region: studyRegion,
62 | pyramidingPolicy: {
63 | ".default": 'sample'
64 | },
65 | scale: 30
66 | });
67 | }
68 |
69 | // -----------------------------------------------------------------------
70 | // CHECKPOINT
71 | // -----------------------------------------------------------------------
72 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.7 Interpreting Time Series with CCDC/F47c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.7 Interpreting Time Series with CCDC
3 | // Checkpoint: F47c
4 | // Authors: Paulo Arévalo, Pontus Olofsson
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var palettes = require('users/gena/packages:palettes');
8 |
9 | var resultsPath =
10 | 'projects/gee-book/assets/F4-7/Rondonia_example_small';
11 | var ccdResults = ee.Image(resultsPath);
12 | Map.centerObject(ccdResults, 10);
13 | print(ccdResults);
14 |
15 | // Select time of break and change probability array images.
16 | var change = ccdResults.select('tBreak');
17 | var changeProb = ccdResults.select('changeProb');
18 |
19 | // Set the time range we want to use and get as mask of
20 | // places that meet the condition.
21 | var start = 2000;
22 | var end = 2021;
23 | var mask = change.gt(start).and(change.lte(end)).and(changeProb.eq(
24 | 1));
25 | Map.addLayer(changeProb, {}, 'change prob');
26 |
27 | // Obtain the number of breaks for the time range.
28 | var numBreaks = mask.arrayReduce(ee.Reducer.sum(), [0]);
29 | Map.addLayer(numBreaks, {
30 | min: 0,
31 | max: 5
32 | }, 'Number of breaks');
33 |
34 | // Obtain the first change in that time period.
35 | var dates = change.arrayMask(mask).arrayPad([1]);
36 | var firstChange = dates
37 | .arraySlice(0, 0, 1)
38 | .arrayFlatten([
39 | ['firstChange']
40 | ])
41 | .selfMask();
42 |
43 | var timeVisParams = {
44 | palette: palettes.colorbrewer.YlOrRd[9],
45 | min: start,
46 | max: end
47 | };
48 | Map.addLayer(firstChange, timeVisParams, 'First change');
49 |
50 | // Obtain the last change in that time period.
51 | var lastChange = dates
52 | .arraySlice(0, -1)
53 | .arrayFlatten([
54 | ['lastChange']
55 | ])
56 | .selfMask();
57 | Map.addLayer(lastChange, timeVisParams, 'Last change');
58 |
59 | // Get masked magnitudes.
60 | var magnitudes = ccdResults
61 | .select('SWIR1_magnitude')
62 | .arrayMask(mask)
63 | .arrayPad([1]);
64 |
65 | // Get index of max abs magnitude of change.
66 | var maxIndex = magnitudes
67 | .abs()
68 | .arrayArgmax()
69 | .arrayFlatten([
70 | ['index']
71 | ]);
72 |
73 | // Select max magnitude and its timing
74 | var selectedMag = magnitudes.arrayGet(maxIndex);
75 | var selectedTbreak = dates.arrayGet(maxIndex).selfMask();
76 |
77 | var magVisParams = {
78 | palette: palettes.matplotlib.viridis[7],
79 | min: -0.15,
80 | max: 0.15
81 | };
82 | Map.addLayer(selectedMag, magVisParams, 'Max mag');
83 | Map.addLayer(selectedTbreak, timeVisParams, 'Time of max mag');
84 |
85 | // -----------------------------------------------------------------------
86 | // CHECKPOINT
87 | // -----------------------------------------------------------------------
88 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.7 Interpreting Time Series with CCDC/F47d-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.7 Interpreting Time Series with CCDC
3 | // Checkpoint: F47d
4 | // Authors: Paulo Arévalo, Pontus Olofsson
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var palettes = require('users/gena/packages:palettes');
8 |
9 | var resultsPath =
10 | 'projects/gee-book/assets/F4-7/Rondonia_example_small';
11 | var ccdResults = ee.Image(resultsPath);
12 | Map.centerObject(ccdResults, 10);
13 | print(ccdResults);
14 |
15 | // Display segment start and end times.
16 | var start = ccdResults.select('tStart');
17 | var end = ccdResults.select('tEnd');
18 | Map.addLayer(start, {
19 | min: 1999,
20 | max: 2001
21 | }, 'Segment start');
22 | Map.addLayer(end, {
23 | min: 2010,
24 | max: 2020
25 | }, 'Segment end');
26 |
27 | // Find the segment that intersects a given date.
28 | var targetDate = 2005.5;
29 | var selectSegment = start.lte(targetDate).and(end.gt(targetDate));
30 | Map.addLayer(selectSegment, {}, 'Identified segment');
31 |
32 | // Get all coefs in the SWIR1 band.
33 | var SWIR1Coefs = ccdResults.select('SWIR1_coefs');
34 | Map.addLayer(SWIR1Coefs, {}, 'SWIR1 coefs');
35 |
36 | // Select only those for the segment that we identified previously.
37 | var sliceStart = selectSegment.arrayArgmax().arrayFlatten([
38 | ['index']
39 | ]);
40 | var sliceEnd = sliceStart.add(1);
41 | var selectedCoefs = SWIR1Coefs.arraySlice(0, sliceStart, sliceEnd);
42 | Map.addLayer(selectedCoefs, {}, 'Selected SWIR1 coefs');
43 |
44 | // Retrieve only the intercept coefficient.
45 | var intercept = selectedCoefs.arraySlice(1, 0, 1).arrayProject([1]);
46 | var intVisParams = {
47 | palette: palettes.matplotlib.viridis[7],
48 | min: -6,
49 | max: 6
50 | };
51 | Map.addLayer(intercept.arrayFlatten([
52 | ['INTP']
53 | ]), intVisParams, 'INTP_SWIR1');
54 |
55 | // -----------------------------------------------------------------------
56 | // CHECKPOINT
57 | // -----------------------------------------------------------------------
58 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.7 Interpreting Time Series with CCDC/F47e-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.7 Interpreting Time Series with CCDC
3 | // Checkpoint: F47e
4 | // Authors: Paulo Arévalo, Pontus Olofsson
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load the required libraries.
8 | var palettes = require('users/gena/packages:palettes');
9 | var utils = require(
10 | 'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api');
11 |
12 | // Load the results.
13 | var resultsPath =
14 | 'projects/gee-book/assets/F4-7/Rondonia_example_small';
15 | var ccdResults = ee.Image(resultsPath);
16 | Map.centerObject(ccdResults, 10);
17 |
18 | // Convert a date into fractional years.
19 | var inputDate = '2005-09-25';
20 | var dateParams = {
21 | inputFormat: 3,
22 | inputDate: inputDate,
23 | outputFormat: 1
24 | };
25 | var formattedDate = utils.Dates.convertDate(dateParams);
26 |
27 | // Band names originally used as inputs to the CCD algorithm.
28 | var BANDS = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'];
29 |
30 | // Names for the time segments to retrieve.
31 | var SEGS = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9',
32 | 'S10'
33 | ];
34 |
35 | // Transform CCD results into a multiband image.
36 | var ccdImage = utils.CCDC.buildCcdImage(ccdResults, SEGS.length,
37 | BANDS);
38 | print(ccdImage);
39 |
40 | // Define bands to select.
41 | var SELECT_BANDS = ['RED', 'GREEN', 'BLUE', 'NIR'];
42 |
43 | // Define coefficients to select.
44 | // This list contains all possible coefficients, and the RMSE
45 | var SELECT_COEFS = ['INTP', 'SLP', 'RMSE'];
46 |
47 | // Obtain coefficients.
48 | var coefs = utils.CCDC.getMultiCoefs(
49 | ccdImage, formattedDate, SELECT_BANDS, SELECT_COEFS, true,
50 | SEGS, 'after');
51 | print(coefs);
52 |
53 | // Show a single coefficient.
54 | var slpVisParams = {
55 | palette: palettes.matplotlib.viridis[7],
56 | min: -0.0005,
57 | max: 0.005
58 | };
59 | Map.addLayer(coefs.select('RED_SLP'), slpVisParams,
60 | 'RED SLOPE 2005-09-25');
61 |
62 | var rmseVisParams = {
63 | palette: palettes.matplotlib.viridis[7],
64 | min: 0,
65 | max: 0.1
66 | };
67 | Map.addLayer(coefs.select('NIR_RMSE'), rmseVisParams,
68 | 'NIR RMSE 2005-09-25');
69 |
70 | // Show an RGB with three coefficients.
71 | var rgbVisParams = {
72 | bands: ['RED_INTP', 'GREEN_INTP', 'BLUE_INTP'],
73 | min: 0,
74 | max: 0.1
75 | };
76 | Map.addLayer(coefs, rgbVisParams, 'RGB 2005-09-25');
77 |
78 | // -----------------------------------------------------------------------
79 | // CHECKPOINT
80 | // -----------------------------------------------------------------------
81 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.8 Data Fusion Merging Classification Streams/F48a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.8 Data Fusion: Merging Classification Streams
3 | // Checkpoint: F48a
4 | // Authors: Jeff Cardille, Rylan Boothman, Mary Villamor, Elijah Perez,
5 | // Eidan Willis, Flavie Pelletier
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | var events = ee.ImageCollection(
9 | 'projects/gee-book/assets/F4-8/cleanEvents');
10 | print(events, 'List of Events');
11 | print('Number of events:', events.size());
12 |
13 | print(ui.Thumbnail(events, {
14 | min: 0,
15 | max: 3,
16 | palette: ['black', 'green', 'blue', 'yellow'],
17 | framesPerSecond: 1,
18 | dimensions: 1000
19 | }));
20 |
21 | // -----------------------------------------------------------------------
22 | // CHECKPOINT
23 | // -----------------------------------------------------------------------
24 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.8 Data Fusion Merging Classification Streams/F48b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.8 Data Fusion: Merging Classification Streams
3 | // Checkpoint: F48b
4 | // Authors: Jeff Cardille, Rylan Boothman, Mary Villamor, Elijah Perez,
5 | // Eidan Willis, Flavie Pelletier
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | // Access the BULC interface at the following link,
9 | // by copy-pasting the line below into your browser.
10 | // Run the script that loads.
11 |
12 | // https://code.earthengine.google.com/?scriptPath=users%2Falemlakes%2Fr-2909-BULC-Releases%3AInterfaces%2FBULC-Interface%2FCurrent%2FBULC-Interface-Current
13 |
14 |
15 | // -----------------------------------------------------------------------
16 | // CHECKPOINT
17 | // -----------------------------------------------------------------------
18 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.8 Data Fusion Merging Classification Streams/F48c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.8 Data Fusion: Merging Classification Streams
3 | // Checkpoint: F48c
4 | // Authors: Jeff Cardille, Rylan Boothman, Mary Villamor, Elijah Perez,
5 | // Eidan Willis, Flavie Pelletier
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | // Access the BULC-D interface at the following link,
9 | // by copy-pasting the line below into your browser.
10 | // Run the script that loads.
11 |
12 | // https://code.earthengine.google.com/?scriptPath=users/alemlakes/r-2909-BULC-Releases%3AInterfaces%2FBULC-D-Interface%2FCurrent%2FBULC-D-Interface-Current
13 |
14 |
15 | // -----------------------------------------------------------------------
16 | // CHECKPOINT
17 | // -----------------------------------------------------------------------
18 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.8 Data Fusion Merging Classification Streams/F48s1-suplemental.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.8 Data Fusion: Merging Classification Streams
3 | // Checkpoint: F48s1
4 | // Authors: Jeff Cardille, Rylan Boothman, Mary Villamor, Elijah Perez,
5 | // Eidan Willis, Flavie Pelletier
6 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 |
8 | // You can learn more about the operation of BULC at
9 | // the following link: enter it into your browser.
10 |
11 | // https://docs.google.com/spreadsheets/d/1Xo7UllSWFade4_lh9sLc2mqeYwHxJ-JEJGV7mInAILU/edit#gid=0
12 |
13 | // -----------------------------------------------------------------------
14 | // CHECKPOINT
15 | // -----------------------------------------------------------------------
16 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/README.md:
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1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F4: Interpreting Image Series
30 |
31 | One of the paradigm-changing features of Earth Engine is the ability to access decades of imagery without the previous limitation of needing to download all the data to a local disk for processing. Because remote-sensing data files can be enormous, this used to limit many projects to viewing two or three images from different periods. With Earth Engine, users can access tens or hundreds of thousands of images to understand the status of places across decades.
32 |
33 | https://google-earth-engine.com/Interpreting-Image-Series/Filter-Map-Reduce/
34 |
35 | # Invitation for collaborators:
36 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
37 |
38 | https://google-earth-engine.com/contact/
39 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F4 - Interpreting Image Series/F4.0 Filter, Map, Reduce/F40a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F4.0 Filter, Map, Reduce
3 | // Checkpoint: F40a
4 | // Author: Jeff Cardille
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var imgCol = ee.ImageCollection('LANDSAT/LT05/C02/T1_L2');
8 | // How many Tier 1 Landsat 5 images have ever been collected?
9 | print("All images ever: ", imgCol.size()); // A very large number
10 |
11 | // How many images were collected in the 2000s?
12 | var startDate = '2000-01-01';
13 | var endDate = '2010-01-01';
14 |
15 | var imgColfilteredByDate = imgCol.filterDate(startDate, endDate);
16 | print("All images 2000-2010: ", imgColfilteredByDate.size());
17 | // A smaller (but still large) number
18 |
19 | var ShanghaiImage = ee.Image(
20 | 'LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606');
21 | Map.centerObject(ShanghaiImage, 9);
22 |
23 | var imgColfilteredByDateHere = imgColfilteredByDate.filterBounds(Map
24 | .getCenter());
25 | print("All images here, 2000-2010: ", imgColfilteredByDateHere
26 | .size()); // A smaller number
27 |
28 | var L5FilteredLowCloudImages = imgColfilteredByDateHere
29 | .filterMetadata('CLOUD_COVER', 'less_than', 50);
30 | print("Less than 50% clouds in this area, 2000-2010",
31 | L5FilteredLowCloudImages.size()); // A smaller number
32 |
33 | var chainedFilteredSet = imgCol.filterDate(startDate, endDate)
34 | .filterBounds(Map.getCenter())
35 | .filterMetadata('CLOUD_COVER', 'less_than', 50);
36 | print('Chained: Less than 50% clouds in this area, 2000-2010',
37 | chainedFilteredSet.size());
38 |
39 | var efficientFilteredSet = imgCol.filterBounds(Map.getCenter())
40 | .filterDate(startDate, endDate)
41 | .filterMetadata('CLOUD_COVER', 'less_than', 50);
42 | print('Efficient filtering: Less than 50% clouds in this area, 2000-2010',
43 | efficientFilteredSet.size());
44 |
45 | // -----------------------------------------------------------------------
46 | // CHECKPOINT
47 | // -----------------------------------------------------------------------
48 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.1 RasterVector Conversions/F51c-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F5.1 Raster/Vector Conversions
3 | // Checkpoint: F51c
4 | // Authors: Keiko Nomura, Samuel Bowers
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | //-------------//
8 | // Section 1.4 //
9 | //-------------//
10 |
11 | // Load required datasets.
12 | var gfc = ee.Image('UMD/hansen/global_forest_change_2020_v1_8');
13 | var wdpa = ee.FeatureCollection('WCMC/WDPA/current/polygons');
14 |
15 | // Display deforestation.
16 | var deforestation = gfc.select('lossyear');
17 |
18 | Map.addLayer(deforestation, {
19 | min: 1,
20 | max: 20,
21 | palette: ['yellow', 'orange', 'red']
22 | }, 'Deforestation raster');
23 |
24 | // Select protected areas in the Colombian Amazon.
25 | var amazonianProtectedAreas = [
26 | 'Cordillera de los Picachos', 'La Paya', 'Nukak',
27 | 'Serrania de Chiribiquete',
28 | 'Sierra de la Macarena', 'Tinigua'
29 | ];
30 |
31 | var wdpaSubset = wdpa.filter(ee.Filter.inList('NAME',
32 | amazonianProtectedAreas));
33 |
34 | // Display protected areas as an outline.
35 | var protectedAreasOutline = ee.Image().byte().paint({
36 | featureCollection: wdpaSubset,
37 | color: 1,
38 | width: 1
39 | });
40 |
41 | Map.addLayer(protectedAreasOutline, {
42 | palette: 'white'
43 | }, 'Amazonian protected areas');
44 |
45 | // Set up map display.
46 | Map.centerObject(wdpaSubset);
47 | Map.setOptions('SATELLITE');
48 |
49 | var scale = deforestation.projection().nominalScale();
50 |
51 | // Use 'reduceRegions' to sum together pixel areas in each protected area.
52 | wdpaSubset = deforestation.gte(1)
53 | .multiply(ee.Image.pixelArea().divide(10000)).reduceRegions({
54 | collection: wdpaSubset,
55 | reducer: ee.Reducer.sum().setOutputs([
56 | 'deforestation_area']),
57 | scale: scale
58 | });
59 |
60 | print(wdpaSubset); // Note the new 'deforestation_area' property.
61 |
62 | // Normalize by area.
63 | wdpaSubset = wdpaSubset.map(
64 | function(feat) {
65 | return feat.set('deforestation_rate',
66 | ee.Number(feat.get('deforestation_area'))
67 | .divide(feat.area().divide(10000)) // m2 to ha
68 | .divide(20) // number of years
69 | .multiply(100)); // to percentage points
70 | });
71 |
72 | // Print to identify rates of change per protected area.
73 | // Which has the fastest rate of loss?
74 | print(wdpaSubset.reduceColumns({
75 | reducer: ee.Reducer.toList().repeat(2),
76 | selectors: ['NAME', 'deforestation_rate']
77 | }));
78 |
79 | // -----------------------------------------------------------------------
80 | // CHECKPOINT
81 | // -----------------------------------------------------------------------
82 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.1 RasterVector Conversions/F51d-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F5.1 Raster/Vector Conversions
3 | // Checkpoint: F51d
4 | // Authors: Keiko Nomura, Samuel Bowers
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | //-------------//
8 | // Section 2.1 //
9 | //-------------//
10 |
11 | // Load required datasets.
12 | var gfc = ee.Image('UMD/hansen/global_forest_change_2020_v1_8');
13 | var wdpa = ee.FeatureCollection('WCMC/WDPA/current/polygons');
14 |
15 | // Get deforestation.
16 | var deforestation = gfc.select('lossyear');
17 |
18 | // Generate a new property called 'protected' to apply to the output mask.
19 | var wdpa = wdpa.map(function(feat) {
20 | return feat.set('protected', 1);
21 | });
22 |
23 | // Rasterize using the new property.
24 | // unmask() sets areas outside protected area polygons to 0.
25 | var wdpaMask = wdpa.reduceToImage(['protected'], ee.Reducer.first())
26 | .unmask();
27 |
28 | // Center on Colombia.
29 | Map.setCenter(-75, 3, 6);
30 |
31 | // Display on map.
32 | Map.addLayer(wdpaMask, {
33 | min: 0,
34 | max: 1
35 | }, 'Protected areas (mask)');
36 |
37 | // Set the deforestation layer to 0 where outside a protected area.
38 | var deforestationProtected = deforestation.where(wdpaMask.eq(0), 0);
39 |
40 | // Update mask to hide where deforestation layer = 0
41 | var deforestationProtected = deforestationProtected
42 | .updateMask(deforestationProtected.gt(0));
43 |
44 | // Display deforestation in protected areas
45 | Map.addLayer(deforestationProtected, {
46 | min: 1,
47 | max: 20,
48 | palette: ['yellow', 'orange', 'red']
49 | }, 'Deforestation protected');
50 |
51 | // Produce an image with unique ID of protected areas.
52 | var wdpaId = wdpa.reduceToImage(['WDPAID'], ee.Reducer.first());
53 |
54 | Map.addLayer(wdpaId, {
55 | min: 1,
56 | max: 100000
57 | }, 'Protected area ID');
58 |
59 | // -----------------------------------------------------------------------
60 | // CHECKPOINT
61 | // -----------------------------------------------------------------------
62 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.1 RasterVector Conversions/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F5: Vectors and Tables
30 |
31 |
32 | In addition to raster data processing, Earth Engine supports a rich set of vector processing tools. This Part introduces you to the vector framework in Earth Engine, shows you how to create and to import your vector data, and how to combine vector and raster data for analyses.
33 |
34 | # Chapter F5.1: Raster/Vector Conversions
35 | ## Authors
36 | Keiko Nomura, Samuel Bowers
37 |
38 |
39 |
40 | ## Overview
41 | The purpose of this chapter is to review methods of converting between raster and vector data formats, and to understand the circumstances in which this is useful. By way of example, this chapter focuses on topographic elevation and forest cover change in Colombia, but note that these are generic methods that can be applied in a wide variety of situations.
42 |
43 |
44 | ## Learning Outcomes
45 | - Understanding raster and vector data in Earth Engine and their differing properties.
46 | - Knowing how and why to convert from raster to vector.
47 | - Knowing how and why to convert from vector to raster.
48 | - Write a function and map it over a FeatureCollection.
49 |
50 | ## Link for details:
51 | https://google-earth-engine.com/Vectors-and-Tables/Raster-Vector-Conversions/
52 |
53 | # Invitation for collaborators:
54 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
55 |
56 | https://google-earth-engine.com/contact/
57 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.2 Zonal Statistics/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F5: Vectors and Tables
30 |
31 |
32 | In addition to raster data processing, Earth Engine supports a rich set of vector processing tools. This Part introduces you to the vector framework in Earth Engine, shows you how to create and to import your vector data, and how to combine vector and raster data for analyses.
33 |
34 | # Chapter F5.2: Zonal Statistics
35 | ## Authors
36 | Sara Winsemius and Justin Braaten
37 |
38 |
39 |
40 | ## Overview
41 | The purpose of this chapter is to extract values from rasters for intersecting points or polygons. We will lay out the process and a function to calculate zonal statistics, which includes optional parameters to modify the function, and then apply the process to three examples using different raster datasets and combinations of parameters.
42 |
43 |
44 | ## Learning Outcomes
45 | - Buffering points as square or circular regions.
46 | - Writing and applying functions with optional parameters.
47 | - Learning what zonal statistics are and how to use reducers.
48 | - Exporting computation results to a table.
49 | - Copying properties from one image to another.
50 |
51 | ## Link for details:
52 | https://google-earth-engine.com/Vectors-and-Tables/Zonal-Statistics/
53 |
54 |
55 | # Invitation for collaborators:
56 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
57 |
58 | https://google-earth-engine.com/contact/
59 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.3 Advanced Vector Operations/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F5: Vectors and Tables
30 |
31 |
32 | In addition to raster data processing, Earth Engine supports a rich set of vector processing tools. This Part introduces you to the vector framework in Earth Engine, shows you how to create and to import your vector data, and how to combine vector and raster data for analyses.
33 |
34 | # Chapter F5.3: Advanced Vector Operations
35 | ## Author
36 | Ujaval Gandhi
37 |
38 |
39 |
40 | # Overview
41 | This chapter covers advanced techniques for visualizing and analyzing vector data in Earth Engine. There are many ways to visualize feature collections, and you will learn how to pick the appropriate method to create visualizations, such as a choropleth map. We will also cover geoprocessing techniques involving multiple vector layers, such as selecting features in one layer by their proximity to features in another layer and performing spatial joins.
42 |
43 |
44 | # Learning Outcomes
45 | - Visualizing any vector dataset and creating a thematic map.
46 | - Understanding joins in Earth Engine.
47 | - Carrying out geoprocessing tasks with vector layers in Earth Engine.
48 |
49 | ## Link for details:
50 | https://google-earth-engine.com/Vectors-and-Tables/Advanced-Vector-Operations/
51 |
52 |
53 | # Invitation for collaborators:
54 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
55 |
56 | https://google-earth-engine.com/contact/
57 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.4 GEEDiT Digitising from satellite imagery/F54a-code.js:
--------------------------------------------------------------------------------
1 | // To access the latest version of GEEDiT (and legacy versions), use the following link:
2 | // https://liverpoolgee.wordpress.com/geedit-geedit-reviewer/
3 |
4 | // This tutorial was built using GEEDiT v2.02, and should also be valid for any later versions of version 2 that become available in the future. On this page you will see that there are actually two different GEEDiTs for each version. The first accesses Landsat Tier 1 imagery, and the second Landsat Tier 2. The former represent the highest quality imagery with respect to both spectral properties and geolocation accuracy, whereas the latter contains imagery that does not meet Tier 1 criteria. For some regions (e.g., Antarctica and some smaller islands) Tier 2 does provide a greater volume of imagery compared to Tier 1 that may be usable for particular research questions. However, in most cases, you will find that Tier 1 imagery is available.
5 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 | # Part F5: Vectors and Tables
30 |
31 |
32 | In addition to raster data processing, Earth Engine supports a rich set of vector processing tools. This Part introduces you to the vector framework in Earth Engine, shows you how to create and to import your vector data, and how to combine vector and raster data for analyses.
33 |
34 | https://google-earth-engine.com/Vectors-and-Tables/Exploring-Vectors/
35 |
36 | # Invitation for collaborators:
37 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
38 |
39 | https://google-earth-engine.com/contact/
40 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.0 Exploring Vectors/F50a-code.js:
--------------------------------------------------------------------------------
1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F5.0 Exploring Vectors
3 | // Checkpoint: F50a
4 | // Authors: AJ Purdy, Ellen Brock, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // This script contains the Imports, but no code (yet).
8 |
9 | // -----------------------------------------------------------------------
10 | // CHECKPOINT
11 | // -----------------------------------------------------------------------
12 |
13 |
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/Part F - Fundamentals/F5 - Vectors and Tables/F5.0 Exploring Vectors/F50b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F5.0 Exploring Vectors
3 | // Checkpoint: F50b
4 | // Authors: AJ Purdy, Ellen Brock, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the Census Tiger Boundaries from GEE.
8 | var tiger = ee.FeatureCollection('TIGER/2010/Blocks');
9 |
10 | // Add the new feature collection to the map, but do not display.
11 | Map.addLayer(tiger, {
12 | 'color': 'black'
13 | }, 'Tiger', false);
14 |
15 | // Assign the feature collection to the variable sfNeighborhoods.
16 | var sfNeighborhoods = ee.FeatureCollection(
17 | 'path/to/your/asset/assetname');
18 |
19 | // Note: if you are unable to load the feature collection, you
20 | // can access the data by uncommenting out the following two lines:
21 | // var tablePath = 'projects/gee-book/assets/F5-0/SFneighborhoods';
22 | // var sfNeighborhoods = ee.FeatureCollection(tablePath);
23 |
24 | // Print the size of the feature collection.
25 | // (Answers the question: how many features?)
26 | print(sfNeighborhoods.size());
27 | Map.addLayer(sfNeighborhoods, {
28 | 'color': 'blue'
29 | }, 'sfNeighborhoods');
30 |
31 | // -----------------------------------------------------------------------
32 | // CHECKPOINT
33 | // -----------------------------------------------------------------------
34 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F5 - Vectors and Tables/F5.0 Exploring Vectors/F50c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F5.0 Exploring Vectors
3 | // Checkpoint: F50c
4 | // Authors: AJ Purdy, Ellen Brock, David Saah
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Import the Census Tiger Boundaries from GEE.
8 | var tiger = ee.FeatureCollection('TIGER/2010/Blocks');
9 |
10 | // Add the new feature collection to the map, but do not display.
11 | Map.addLayer(tiger, {
12 | 'color': 'black'
13 | }, 'Tiger', false);
14 |
15 | // Assign the feature collection to the variable sfNeighborhoods.
16 | var sfNeighborhoods = ee.FeatureCollection(
17 | 'path/to/your/asset/assetname');
18 |
19 | // Note: if you are unable to load the feature collection, you
20 | // can access the data by uncommenting out the following two lines:
21 | // var tablePath = 'projects/gee-book/assets/F5-0/SFneighborhoods';
22 | // var sfNeighborhoods = ee.FeatureCollection(tablePath);
23 |
24 |
25 |
26 | // Print the size of the feature collection.
27 | // (Answers the question: how many features?)
28 | print(sfNeighborhoods.size());
29 | Map.addLayer(sfNeighborhoods, {
30 | 'color': 'blue'
31 | }, 'sfNeighborhoods');
32 |
33 | // -----------------------------------------------------------------------
34 | // CHECKPOINT
35 | // -----------------------------------------------------------------------
36 |
37 | // Filter sfNeighborhoods by USF.
38 | var usfNeighborhood = sfNeighborhoods.filterBounds(usf_point);
39 |
40 | // Filter the Census blocks by the boundary of the neighborhood layer.
41 | var usfTiger = tiger.filterBounds(usfNeighborhood);
42 | Map.addLayer(usfTiger, {}, 'usf_Tiger');
43 |
44 | print(usfTiger);
45 |
46 | // Filter for census blocks by housing units
47 | var housing10_l250 = usfTiger
48 | .filter(ee.Filter.lt('housing10', 250));
49 |
50 | var housing10_g50_l250 = housing10_l250.filter(ee.Filter.gt(
51 | 'housing10', 50));
52 |
53 | Map.addLayer(housing10_g50_l250, {
54 | 'color': 'Magenta'
55 | }, 'housing');
56 |
57 | var housing_area = housing10_g50_l250.geometry().area();
58 | print('housing_area:', housing_area);
59 |
60 | var housing10_mean = usfTiger.reduceColumns({
61 | reducer: ee.Reducer.mean(),
62 | selectors: ['housing10']
63 | });
64 |
65 | print('housing10_mean', housing10_mean);
66 |
67 | // -----------------------------------------------------------------------
68 | // CHECKPOINT
69 | // -----------------------------------------------------------------------
70 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F6 - Advanced Topics/F6.1 Collaborating in Earth Engine with Scripts and Assets/F61a-code.js:
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https://raw.githubusercontent.com/krishnakafle/gee-tutorials/d63ff3a676334821fb76c7964a3aebd9faa2d39f/Part F - Fundamentals/F6 - Advanced Topics/F6.1 Collaborating in Earth Engine with Scripts and Assets/F61a-code.js
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/Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling up in Earth Engine/F63c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.2 Scaling Up in Earth Engine
3 | // Checkpoint: F62c
4 | // Authors: Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Set the Region of Interest:Seattle, Washington, United States
8 | var roi = ee.Geometry.Point([-122.33524518034544, 47.61356183942883]);
9 |
10 | // Dates over which to create a median composite.
11 | var start = ee.Date('2019-03-01');
12 | var end = ee.Date('2019-09-01');
13 |
14 | // Specify module with cloud mask functions.
15 | var s2mask_tools = require(
16 | 'projects/gee-edu/book:Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling Up/modules/s2cloudmask.js'
17 | );
18 |
19 |
20 | // Specify S2 collections and filter.
21 |
22 | // Sentinel-2 surface reflectance data for the composite.
23 | var s2Sr = ee.ImageCollection('COPERNICUS/S2_SR')
24 | .filterDate(start, end)
25 | .filterBounds(roi)
26 | .select(['B2', 'B3', 'B4', 'B5']);
27 |
28 | // Sentinel-2 Level 1C data (top-of-atmosphere).
29 | // Bands B7, B8, B8A and B10 needed for CDI and the cloud mask function.
30 | var s2 = ee.ImageCollection('COPERNICUS/S2')
31 | .filterBounds(roi)
32 | .filterDate(start, end)
33 | .select(['B7', 'B8', 'B8A', 'B10']);
34 |
35 | // Cloud probability dataset - used in cloud mask function
36 | var s2c = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
37 | .filterDate(start, end)
38 | .filterBounds(roi);
39 |
40 | // Apply the cloud mask.
41 |
42 | // Join the cloud probability dataset to surface reflectance.
43 | var withCloudProbability = s2mask_tools.indexJoin(s2Sr, s2c,
44 | 'cloud_probability');
45 |
46 | // Join the L1C data to get the bands needed for CDI.
47 | var withS2L1C = s2mask_tools.indexJoin(withCloudProbability, s2,
48 | 'l1c');
49 |
50 | // Map the cloud masking function over the joined collection.
51 | // Cast output to ImageCollection
52 | var masked = ee.ImageCollection(withS2L1C.map(s2mask_tools
53 | .maskImage));
54 |
55 | // Take the median, specifying a tileScale to avoid memory errors.
56 | var median = masked.reduce(ee.Reducer.median(), 8);
57 |
58 | // Display the results.
59 | Map.centerObject(roi, 12);
60 | Map.addLayer(roi);
61 |
62 | var viz = {
63 | bands: ['B4_median', 'B3_median', 'B2_median'],
64 | min: 0,
65 | max: 3000
66 | };
67 | Map.addLayer(median, viz, 'median');
68 |
69 | Map.centerObject(roi, 9);
70 | Map.addLayer(roi);
71 | Map.addLayer(median, viz, 'median');
72 |
73 | // -----------------------------------------------------------------------
74 | // CHECKPOINT
75 | // -----------------------------------------------------------------------
76 |
77 |
78 |
79 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling up in Earth Engine/F63e-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.2 Scaling Up in Earth Engine
3 | // Checkpoint: F62e
4 | // Authors: Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // load image collection and mosaic into single image
8 | var assetCollection = 'projects/gee-book/assets/F6-2/s2_composite_WA';
9 | var composite = ee.ImageCollection(assetCollection).mosaic();
10 |
11 | // Display the results
12 | var geometry = ee.Geometry.Point([-120.5873563817392,
13 | 47.39035206888694
14 | ]);
15 | Map.centerObject(geometry, 6);
16 | var vizParams = {
17 | bands: ['B4_median', 'B3_median', 'B2_median'],
18 | min: 0,
19 | max: 3000
20 | };
21 | Map.addLayer(composite, vizParams, 'median');
22 |
23 | // -----------------------------------------------------------------------
24 | // CHECKPOINT
25 | // -----------------------------------------------------------------------
26 |
27 |
28 |
29 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.2 Scaling up in Earth Engine/modules/s2cloudmask.js:
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1 | // Functions for implementing a Sentinel II cloud mask.
2 |
3 | // Join two collections on their 'system:index' property.
4 | // The propertyName parameter is the name of the property
5 | // that references the joined image.
6 | function indexJoin(collectionA, collectionB, propertyName) {
7 | var joined = ee.ImageCollection(ee.Join.saveFirst(propertyName).apply({
8 | primary: collectionA,
9 | secondary: collectionB,
10 | condition: ee.Filter.equals({
11 | leftField: 'system:index',
12 | rightField: 'system:index'})
13 | }));
14 | // Merge the bands of the joined image.
15 | return joined.map(function(image) {
16 | return image.addBands(ee.Image(image.get(propertyName)));
17 | });
18 | }
19 |
20 | // Aggressively mask clouds and shadows.
21 | function maskImage(image) {
22 | // Compute the cloud displacement index from the L1C bands.
23 | var cdi = ee.Algorithms.Sentinel2.CDI(image);
24 | var s2c = image.select('probability');
25 | var cirrus = image.select('B10').multiply(0.0001);
26 |
27 | // Assume low-to-mid atmospheric clouds to be pixels where probability
28 | // is greater than 65%, and CDI is less than -0.5. For higher atmosphere
29 | // cirrus clouds, assume the cirrus band is greater than 0.01.
30 | // The final cloud mask is one or both of these conditions.
31 | var isCloud = s2c.gt(65).and(cdi.lt(-0.5)).or(cirrus.gt(0.01));
32 |
33 | // Reproject is required to perform spatial operations at 20m scale.
34 | // 20m scale is for speed, and assumes clouds don't require 10m precision.
35 | isCloud = isCloud.focal_min(3).focal_max(16);
36 | isCloud = isCloud.reproject({crs: cdi.projection(), scale: 20});
37 |
38 | // Project shadows from clouds we found in the last step. This assumes we're working in
39 | // a UTM projection.
40 | var shadowAzimuth = ee.Number(90)
41 | .subtract(ee.Number(image.get('MEAN_SOLAR_AZIMUTH_ANGLE')));
42 |
43 | // With the following reproject, the shadows are projected 5km.
44 | isCloud = isCloud.directionalDistanceTransform(shadowAzimuth, 50);
45 | isCloud = isCloud.reproject({crs: cdi.projection(), scale: 100});
46 |
47 | isCloud = isCloud.select('distance').mask();
48 | return image.select('B2', 'B3', 'B4').updateMask(isCloud.not());
49 | }
50 |
51 | exports.maskImage = maskImage;
52 | exports.indexJoin = indexJoin;
53 |
54 | // LGTM (nclinton)
--------------------------------------------------------------------------------
/Part F - Fundamentals/F6 - Advanced Topics/F6.3 Sharing Work in Earth Engine Basic UI and Apps/F63b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.3 Sharing Work in Earth Engine: Basic UI and Apps
3 | // Checkpoint: F63b
4 | // Author: Qiusheng Wu
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 |
8 | // Follow the steps below to set up a conda environment and install geemap. More information about the geemap installation can be found at https://geemap.org/installation.
9 |
10 | // First, you need to download and install Miniconda or Anaconda on your computer. After installation, open the Anaconda Prompt. If you are using Windows, go to Start Menu > Anaconda3 > Anaconda Prompt. If you are using macOS or Linux, open the Terminal. Enter the following commands into the Anaconda Prompt or Terminal and follow the on-screen instructions to create a conda environment and install geemap.
11 |
12 | // conda create -n gee python=3.9
13 | // conda activate gee
14 | // conda install mamba -c conda-forge
15 | // mamba install geemap voila -c conda-forge
16 |
17 |
18 | // Note that mamba is a reimplementation of the conda package manager in C++. It enables the parallel downloading of repository data and package files using multithreading and usually resolves dependencies much faster than conda. Once geemap has been installed successfully, enter the following commands into the Terminal and press Enter to launch a Jupyter notebook in your browser.
19 |
20 | // conda activate gee
21 | // jupyter notebook
22 |
23 |
24 |
25 |
26 | // -----------------------------------------------------------------------
27 | // CHECKPOINT
28 | // -----------------------------------------------------------------------
29 |
30 |
31 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.3 Sharing Work in Earth Engine Basic UI and Apps/F63c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.3 Sharing Work in Earth Engine: Basic UI and Apps
3 | // Checkpoint: F63c
4 | // Author: Qiusheng Wu
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 |
8 | // Enter the link below into your browser to see how your code should look at this point
9 |
10 | // https://geemap.org/notebooks/nlcd_app
11 |
12 |
13 |
14 |
15 | // -----------------------------------------------------------------------
16 | // CHECKPOINT
17 | // -----------------------------------------------------------------------
18 |
19 |
20 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.3 Sharing Work in Earth Engine Basic UI and Apps/F63d-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.3 Sharing Work in Earth Engine: Basic UI and Apps
3 | // Checkpoint: F63d
4 | // Author: Qiusheng Wu
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 |
8 | // Enter the link below into your browser to see how your code should look at this point
9 |
10 | // https://github.com/giswqs/earthengine-apps
11 |
12 |
13 | // Note from Ellen below: "Congratulations! You have successfully deployed the Earth Engine App on Heroku. "
14 | // Does not work although I followed the instructions :). https://docs.google.com/document/d/197jvxxajqEfCHbuw_2EcxHAv83Pjka36/edit?usp=sharing&ouid=101065852418423274055&rtpof=true&sd=true
15 | // Author will have to test. I can test it if desired. Others are also welcome to test this.
16 | // Was tested and closed by author in beginning of March. Feel bad that is happening right now.
17 | // @jeffcardille@gmail.com
18 | // Show less
19 | // Hi, I am also tagging Quisheng @qwu18@utk.edu here. Perhaps this got missed due to the editing comments. Hi Qiusheng, I ran into an issue here. Can you please check once more please? Thanks.
20 |
21 |
22 | // -----------------------------------------------------------------------
23 | // CHECKPOINT
24 | // -----------------------------------------------------------------------
25 |
26 |
27 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64b
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Enter the link below into your browser to see how your code should look at this point
8 |
9 | // https://github.com/csaybar/rgee_examples/blob/main/script01.R
10 |
11 |
12 |
13 |
14 | // -----------------------------------------------------------------------
15 | // CHECKPOINT
16 | // -----------------------------------------------------------------------
17 |
18 |
19 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64c
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Enter the link below into your browser to see how your code should look at this point
8 |
9 | // https://github.com/csaybar/rgee_examples/blob/main/script02.R
10 |
11 |
12 |
13 |
14 | // -----------------------------------------------------------------------
15 | // CHECKPOINT
16 | // -----------------------------------------------------------------------
17 |
18 |
19 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64d-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64d
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Enter the link below into your browser to see how your code should look at this point
8 |
9 | // https://github.com/csaybar/rgee_examples/blob/main/script03.R
10 |
11 |
12 |
13 |
14 | // -----------------------------------------------------------------------
15 | // CHECKPOINT
16 | // -----------------------------------------------------------------------
17 |
18 |
19 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64e-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64e
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Enter the link below into your browser to see how your code should look at this point
8 |
9 | // https://github.com/csaybar/rgee_examples/blob/main/script04.R
10 |
11 |
12 |
13 |
14 | // -----------------------------------------------------------------------
15 | // CHECKPOINT
16 | // -----------------------------------------------------------------------
17 |
18 |
19 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64f-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64f
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var LandsatLST = require(
8 | 'users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js');
9 |
10 | var geometry = ee.Geometry.Rectangle([-8.91, 40.0, -8.3, 40.4]);
11 | var satellite = 'L8';
12 | var date_start = '2018-05-15';
13 | var date_end = '2018-05-31';
14 | var use_ndvi = true;
15 |
16 | var LandsatColl = LandsatLST.collection(satellite, date_start,
17 | date_end, geometry, use_ndvi);
18 |
19 | var exImage = LandsatColl.first();
20 |
21 | var cmap = ['blue', 'cyan', 'green', 'yellow', 'red'];
22 |
23 | Map.centerObject(geometry);
24 |
25 | Map.addLayer(exImage.select('LST'), {
26 | min: 290,
27 | max: 320,
28 | palette: cmap
29 | }, 'LST')
30 |
31 |
32 |
33 |
34 | // -----------------------------------------------------------------------
35 | // CHECKPOINT
36 | // -----------------------------------------------------------------------
37 |
38 |
39 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.4 Combining R and Earth Engine/F64g-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.4 Combining R and Earth Engine
3 | // Checkpoint: F64g
4 | // Authors: Cesar Aybar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Enter the link below into your browser to see how your code should look at this point
8 |
9 | // https://github.com/csaybar/rgee_examples/blob/main/script05.R
10 |
11 |
12 |
13 |
14 | // -----------------------------------------------------------------------
15 | // CHECKPOINT
16 | // -----------------------------------------------------------------------
17 |
18 |
19 |
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/Part F - Fundamentals/F6 - Advanced Topics/README.md:
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1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | # Part F6: Advanced Topics
31 |
32 | Although you now know the most basic fundamentals of Earth Engine, there is still much more that can be done. The Part presents some advanced topics that can help expand your skill set for doing larger and more complex projects. These include tools for sharing code among users, scaling up with efficient project design, creating apps for non-expert users, and combining R with other information processing platforms.
33 |
34 | https://google-earth-engine.com/Advanced-Topics/Advanced-Raster-Visualization/
35 |
36 |
37 | # Invitation for collaborators:
38 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
39 |
40 | https://google-earth-engine.com/contact/
41 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60a-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60a
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Load the ERA5 reanalysis monthly means.
8 | var era5 = ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY');
9 |
10 | // Load the palettes package.
11 | var palettes = require('users/gena/packages:palettes');
12 |
13 | // Select temperature near ground.
14 | era5 = era5.select('temperature_2m');
15 |
16 | // Choose a diverging colormap for anomalies.
17 | var balancePalette = palettes.cmocean.Balance[7];
18 | var threeColorPalette = ['blue', 'white', 'red'];
19 |
20 | // Show the palette in the Inspector window.
21 | palettes.showPalette('temperature anomaly', balancePalette);
22 | palettes.showPalette('temperature anomaly', threeColorPalette);
23 |
24 | // Select 2 time windows of 10 years.
25 | var era5_1980 = era5.filterDate('1981-01-01', '1991-01-01').mean();
26 | var era5_2010 = era5.filterDate('2011-01-01', '2020-01-01').mean();
27 |
28 | // Compute the temperature change.
29 | var era5_diff = era5_2010.subtract(era5_1980);
30 |
31 | // Show it on the map.
32 | Map.addLayer(era5_diff, {
33 | palette: threeColorPalette,
34 | min: -2,
35 | max: 2
36 | }, 'Blue White Red palette');
37 |
38 | Map.addLayer(era5_diff, {
39 | palette: balancePalette,
40 | min: -2,
41 | max: 2
42 | }, 'Balance palette');
43 |
44 | // -----------------------------------------------------------------------
45 | // CHECKPOINT
46 | // -----------------------------------------------------------------------
47 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60b-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60b
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // An image of the Thwaites glacier.
8 | var imageId =
9 | 'COPERNICUS/S1_GRD/S1B_EW_GRDM_1SSH_20211216T041925_20211216T042029_030045_03965B_AF0A';
10 |
11 | // Look it up and select the HH band.
12 | var img = ee.Image(imageId).select('HH');
13 |
14 | // Use the palette library.
15 | var palettes = require('users/gena/packages:palettes');
16 |
17 | // Access the ice palette.
18 | var icePalette = palettes.cmocean.Ice[7];
19 |
20 | // Show it in the console.
21 | palettes.showPalette('Ice', icePalette);
22 |
23 | // Use it to visualize the radar data.
24 | Map.addLayer(img, {
25 | palette: icePalette,
26 | min: -15,
27 | max: 1
28 | }, 'Sentinel-1 radar');
29 |
30 | // Zoom to the grounding line of the Thwaites Glacier.
31 | Map.centerObject(ee.Geometry.Point([-105.45882094907664, -
32 | 74.90419580705336
33 | ]), 8);
34 |
35 | // -----------------------------------------------------------------------
36 | // CHECKPOINT
37 | // -----------------------------------------------------------------------
38 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60c-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60c
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Advanced remapping using NLCD.
8 | // Import NLCD.
9 | var nlcd = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL');
10 |
11 | // Use Filter to select the 2016 dataset.
12 | var nlcd2016 = nlcd.filter(ee.Filter.eq('system:index', '2016'))
13 | .first();
14 |
15 | // Select the land cover band.
16 | var landcover = nlcd2016.select('landcover');
17 |
18 | // Map the NLCD land cover.
19 | Map.addLayer(landcover, null, 'NLCD Landcover');
20 |
21 |
22 | // Now suppose we want to change the color palette.
23 | var newPalette = ['466b9f', 'd1def8', 'dec5c5',
24 | 'ab0000', 'ab0000', 'ab0000',
25 | 'b3ac9f', '68ab5f', '1c5f2c',
26 | 'b5c58f', 'af963c', 'ccb879',
27 | 'dfdfc2', 'd1d182', 'a3cc51',
28 | '82ba9e', 'dcd939', 'ab6c28',
29 | 'b8d9eb', '6c9fb8'
30 | ];
31 |
32 | // Try mapping with the new color palette.
33 | Map.addLayer(landcover, {
34 | palette: newPalette
35 | }, 'NLCD New Palette');
36 |
37 | // Extract the class values and save them as a list.
38 | var values = ee.List(landcover.get('landcover_class_values'));
39 |
40 | // Print the class values to console.
41 | print('raw class values', values);
42 |
43 | // Determine the maximum index value
44 | var maxIndex = values.size().subtract(1);
45 |
46 | // Create a new index for the remap
47 | var indexes = ee.List.sequence(0, maxIndex);
48 |
49 | // Print the updated class values to console.
50 | print('updated class values', indexes);
51 |
52 | // Remap NLCD and display it in the map.
53 | var colorized = landcover.remap(values, indexes)
54 | .visualize({
55 | min: 0,
56 | max: maxIndex,
57 | palette: newPalette
58 | });
59 | Map.addLayer(colorized, {}, 'NLCD Remapped Colors');
60 |
61 | // -----------------------------------------------------------------------
62 | // CHECKPOINT
63 | // -----------------------------------------------------------------------
64 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60d-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60d
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /**
8 | * Draws a string as a raster image at a given point.
9 | *
10 | * @param {string} str - string to draw
11 | * @param {ee.Geometry} point - location the the string will be drawn
12 | * @param {{string, Object}} options - optional properties used to style text
13 | *
14 | * The options dictionary may include one or more of the following:
15 | * fontSize - 16|18|24|32 - the size of the font (default: 16)
16 | * fontType - Arial|Consolas - the type of the font (default: Arial)
17 | * alignX - left|center|right (default: left)
18 | * alignY - top|center|bottom (default: top)
19 | * textColor - text color string (default: ffffff - white)
20 | * textOpacity - 0-1, opacity of the text (default: 0.9)
21 | * textWidth - width of the text (default: 1)
22 | * outlineColor - text outline color string (default: 000000 - black)
23 | * outlineOpacity - 0-1, opacity of the text outline (default: 0.4)
24 | * outlineWidth - width of the text outlines (default: 0)
25 | */
26 |
27 | // Include the text package.
28 | var text = require('users/gena/packages:text');
29 |
30 | // Configure map (change center and map type).
31 | Map.setCenter(0, 0, 10);
32 | Map.setOptions('HYBRID');
33 |
34 | // Draw text string and add to map.
35 | var pt = Map.getCenter();
36 | var scale = Map.getScale();
37 | var image = text.draw('Hello World!', pt, scale);
38 | Map.addLayer(image);
39 |
40 | // -----------------------------------------------------------------------
41 | // CHECKPOINT
42 | // -----------------------------------------------------------------------
43 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60e-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60e
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | /**
8 | * Draws a string as a raster image at a given point.
9 | *
10 | * @param {string} str - string to draw
11 | * @param {ee.Geometry} point - location the the string will be drawn
12 | * @param {{string, Object}} options - optional properties used to style text
13 | *
14 | * The options dictionary may include one or more of the following:
15 | * fontSize - 16|18|24|32 - the size of the font (default: 16)
16 | * fontType - Arial|Consolas - the type of the font (default: Arial)
17 | * alignX - left|center|right (default: left)
18 | * alignY - top|center|bottom (default: top)
19 | * textColor - text color string (default: ffffff - white)
20 | * textOpacity - 0-1, opacity of the text (default: 0.9)
21 | * textWidth - width of the text (default: 1)
22 | * outlineColor - text outline color string (default: 000000 - black)
23 | * outlineOpacity - 0-1, opacity of the text outline (default: 0.4)
24 | * outlineWidth - width of the text outlines (default: 0)
25 | */
26 |
27 | // Include the text package.
28 | var text = require('users/gena/packages:text');
29 |
30 | // Configure map (change center and map type).
31 | Map.setCenter(0, 0, 10);
32 | Map.setOptions('HYBRID');
33 |
34 | // Draw text string and add to map.
35 | var pt = Map.getCenter();
36 | var scale = Map.getScale();
37 | var image = text.draw('Hello World!', pt, scale);
38 |
39 | // -----------------------------------------------------------------------
40 | // CHECKPOINT
41 | // -----------------------------------------------------------------------
42 |
43 | var image = text.draw('Hello World!', pt, scale, {
44 | fontSize: 32,
45 | fontType: 'Consolas',
46 | textColor: 'black',
47 | outlineColor: 'white',
48 | outlineWidth: 1,
49 | outlineOpacity: 0.8
50 | });
51 |
52 | // Add the text image to the map.
53 | Map.addLayer(image);
54 |
55 | // -----------------------------------------------------------------------
56 | // CHECKPOINT
57 | // -----------------------------------------------------------------------
58 |
--------------------------------------------------------------------------------
/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60f-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60f
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var text = require('users/gena/packages:text');
8 |
9 | var geometry = ee.Geometry.Polygon(
10 | [
11 | [
12 | [-109.248, 43.3913],
13 | [-109.248, 33.2689],
14 | [-86.5283, 33.2689],
15 | [-86.5283, 43.3913]
16 | ]
17 | ], null, false);
18 |
19 | Map.centerObject(geometry, 6);
20 |
21 | function annotate(image) {
22 | // Annotates an image by adding outline border and cloudiness
23 | // Cloudiness is shown as a text string rendered at the image center.
24 |
25 | // Add an edge around the image.
26 | var edge = ee.FeatureCollection([image])
27 | .style({
28 | color: 'cccc00cc',
29 | fillColor: '00000000'
30 | });
31 |
32 | // Draw cloudiness as text.
33 | var props = {
34 | textColor: '0000aa',
35 | outlineColor: 'ffffff',
36 | outlineWidth: 2,
37 | outlineOpacity: 0.6,
38 | fontSize: 24,
39 | fontType: 'Consolas'
40 | };
41 | var center = image.geometry().centroid(1);
42 | var str = ee.Number(image.get('CLOUD_COVER')).format('%.2f');
43 | var scale = Map.getScale();
44 | var textCloudiness = text.draw(str, center, scale, props);
45 |
46 | // Shift left 25 pixels.
47 | textCloudiness = textCloudiness
48 | .translate(-scale * 25, 0, 'meters', 'EPSG:3857');
49 |
50 | // Merge results.
51 | return ee.ImageCollection([edge, textCloudiness]).mosaic();
52 | }
53 |
54 | // Select images.
55 | var images = ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')
56 | .select([5, 4, 2])
57 | .filterBounds(geometry)
58 | .filterDate('2018-01-01', '2018-01-7');
59 |
60 | // dim background.
61 | Map.addLayer(ee.Image(1), {
62 | palette: ['black']
63 | }, 'black', true, 0.5);
64 |
65 | // Show images.
66 | Map.addLayer(images, {
67 | min: 0.05,
68 | max: 1,
69 | gamma: 1.4
70 | }, 'images');
71 |
72 | // Show annotations.
73 | var labels = images.map(annotate);
74 | var labelsLayer = ui.Map.Layer(labels, {}, 'annotations');
75 | Map.layers().add(labelsLayer);
76 |
77 | // re-render (rescale) annotations when map zoom changes.
78 | Map.onChangeZoom(function(zoom) {
79 | labelsLayer.setEeObject(images.map(annotate));
80 | });
81 |
82 | // -----------------------------------------------------------------------
83 | // CHECKPOINT
84 | // -----------------------------------------------------------------------
85 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60g-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60g
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Include packages.
8 | var palettes = require('users/gena/packages:palettes');
9 | var text = require('users/gena/packages:text');
10 |
11 | var point = /* color: #98ff00 */ ee.Geometry.Point([-
12 | 106.15944300895228, -74.58262940096245
13 | ]);
14 |
15 | var rect = /* color: #d63000 */
16 | ee.Geometry.Polygon(
17 | [
18 | [
19 | [-106.19789515738981, -74.56509549360152],
20 | [-106.19789515738981, -74.78071448733921],
21 | [-104.98115931754606, -74.78071448733921],
22 | [-104.98115931754606, -74.56509549360152]
23 | ]
24 | ], null, false);
25 |
26 | // Lookup the ice palette.
27 | var palette = palettes.cmocean.Ice[7];
28 |
29 | // Show it in the console.
30 | palettes.showPalette('Ice', palette);
31 |
32 | // Center map on geometry.
33 | Map.centerObject(point, 9);
34 |
35 | // Select S1 images for the Thwaites glacier.
36 | var images = ee.ImageCollection('COPERNICUS/S1_GRD')
37 | .filterBounds(rect)
38 | .filterDate('2021-01-01', '2021-03-01')
39 | .select('HH')
40 | // Make sure we include only images which fully contain the region geometry.
41 | .filter(ee.Filter.isContained({
42 | leftValue: rect,
43 | rightField: '.geo'
44 | }))
45 | .sort('system:time_start');
46 |
47 | // Print number of images.
48 | print(images.size());
49 |
50 | // -----------------------------------------------------------------------
51 | // CHECKPOINT
52 | // -----------------------------------------------------------------------
53 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60h-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60h
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | // Include packages.
8 | var palettes = require('users/gena/packages:palettes');
9 | var text = require('users/gena/packages:text');
10 |
11 | var point = /* color: #98ff00 */ ee.Geometry.Point([-
12 | 106.15944300895228, -74.58262940096245
13 | ]);
14 |
15 | var rect = /* color: #d63000 */
16 | ee.Geometry.Polygon(
17 | [
18 | [
19 | [-106.19789515738981, -74.56509549360152],
20 | [-106.19789515738981, -74.78071448733921],
21 | [-104.98115931754606, -74.78071448733921],
22 | [-104.98115931754606, -74.56509549360152]
23 | ]
24 | ], null, false);
25 |
26 | // Lookup the ice palette.
27 | var palette = palettes.cmocean.Ice[7];
28 |
29 | // Show it in the console.
30 | palettes.showPalette('Ice', palette);
31 |
32 | // Center map on geometry.
33 | Map.centerObject(point, 9);
34 |
35 | // Select S1 images for the Thwaites glacier.
36 | var images = ee.ImageCollection('COPERNICUS/S1_GRD')
37 | .filterBounds(rect)
38 | .filterDate('2021-01-01', '2021-03-01')
39 | .select('HH')
40 | // Make sure we include only images which fully contain the region geometry.
41 | .filter(ee.Filter.isContained({
42 | leftValue: rect,
43 | rightField: '.geo'
44 | }))
45 | .sort('system:time_start');
46 |
47 | // Print number of images.
48 | print(images.size());
49 |
50 | // -----------------------------------------------------------------------
51 | // CHECKPOINT
52 | // -----------------------------------------------------------------------
53 |
54 | // Render images.
55 | var vis = {
56 | palette: palette,
57 | min: -15,
58 | max: 1
59 | };
60 |
61 | var scale = Map.getScale();
62 | var textProperties = {
63 | outlineColor: '000000',
64 | outlineWidth: 3,
65 | outlineOpacity: 0.6
66 | };
67 |
68 | var imagesRgb = images.map(function(i) {
69 | // Use the date as the label.
70 | var label = i.date().format('YYYY-MM-dd');
71 | var labelImage = text.draw(label, point, scale,
72 | textProperties);
73 |
74 | return i.visualize(vis)
75 | .blend(labelImage) // Blend label image on top.
76 | .set({
77 | label: label
78 | }); // Keep the text property.
79 | });
80 | Map.addLayer(imagesRgb.first());
81 | Map.addLayer(rect, {color:'blue'}, 'rect', 1, 0.5);
82 |
83 | // -----------------------------------------------------------------------
84 | // CHECKPOINT
85 | // -----------------------------------------------------------------------
86 |
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60j-code.js:
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1 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 | // Chapter: F6.0 Advanced Raster Visualization
3 | // Checkpoint: F60j
4 | // Authors: Gennadii Donchyts, Fedor Baart
5 | // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
6 |
7 | var dem = ee.Image('AHN/AHN2_05M_RUW');
8 |
9 | // Change map style to HYBRID and center map on the Netherlands
10 | Map.setOptions('HYBRID');
11 | Map.setCenter(4.4082, 52.1775, 18);
12 |
13 | // Visualize DEM using black-white color palette
14 | var palette = ['black', 'white'];
15 | var demRGB = dem.visualize({
16 | min: -5,
17 | max: 5,
18 | palette: palette
19 | });
20 | Map.addLayer(demRGB, {}, 'DEM');
21 |
22 | var utils = require('users/gena/packages:utils');
23 |
24 | var weight =
25 | 0.4; // Weight of Hillshade vs RGB (0 - flat, 1 - hillshaded).
26 | var exaggeration = 5; // Vertical exaggeration.
27 | var azimuth = 315; // Sun azimuth.
28 | var zenith = 20; // Sun elevation.
29 | var brightness = -0.05; // 0 - default.
30 | var contrast = 0.05; // 0 - default.
31 | var saturation = 0.8; // 1 - default.
32 | var castShadows = false;
33 |
34 | var rgb = utils.hillshadeRGB(
35 | demRGB, dem, weight, exaggeration, azimuth, zenith,
36 | contrast, brightness, saturation, castShadows);
37 |
38 | Map.addLayer(rgb, {}, 'DEM (no shadows)');
39 |
40 | var castShadows = true;
41 |
42 | var rgb = utils.hillshadeRGB(
43 | demRGB, dem, weight, exaggeration, azimuth, zenith,
44 | contrast, brightness, saturation, castShadows);
45 |
46 | Map.addLayer(rgb, {}, 'DEM (with shadows)');
47 |
48 | var palettes = require('users/gena/packages:palettes');
49 | var palette = palettes.crameri.oleron[50];
50 |
51 | var demRGB = dem.visualize({min: -5, max: 5, palette: palette});
52 |
53 | var castShadows = true;
54 |
55 | var rgb = utils.hillshadeRGB(
56 | demRGB, dem, weight, exaggeration, azimuth, zenith,
57 | contrast, brightness, saturation, castShadows);
58 |
59 | Map.addLayer(rgb, {}, 'DEM colormap');
60 |
61 | // -----------------------------------------------------------------------
62 | // CHECKPOINT
63 | // -----------------------------------------------------------------------
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/Part F - Fundamentals/F6 - Advanced Topics/F6.0 Advanced Raster Visualization/F60s1-supplemental.js:
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1 | var text = require('users/gena/packages:text');
2 | var gallery = require('users/gena/packages:gallery');
3 |
4 | Map.centerObject(geometryGallery, 12);
5 |
6 | var images = ee.ImageCollection('COPERNICUS/S2')
7 | .filterDate('2020-01-01', '2022-01-01')
8 | .filterBounds(geometryLabel);
9 |
10 | var imagesMonthly = ee.List.sequence(0, 11).map(function(month) {
11 | month = ee.Number(month);
12 | return images.filter(ee.Filter.calendarRange(month, month.add(1), 'month'))
13 | .select(['B12', 'B8', 'B4'])
14 | .reduce(ee.Reducer.percentile([15]))
15 | .set({
16 | label: ee.Date.fromYMD(2000, month.add(1), 1).format('MMM')
17 | });
18 | });
19 |
20 | imagesMonthly = ee.ImageCollection(imagesMonthly);
21 |
22 | // Render monthly images + label.
23 | var imagesRGB = imagesMonthly.map(function(i) {
24 | var label = text.draw(i.get('label'), geometryLabel, Map.getScale(), {
25 | fontSize: 24,
26 | textColor: 'ffffff',
27 | outlineColor: '000000',
28 | outlineWidth: 3,
29 | outlineOpacity: 0.6
30 | });
31 | return i.visualize({min: 300, max: 3500}).blend(label);
32 | });
33 |
34 | // Generate a single filmstrip image (rows x columns).
35 | var rows = 3;
36 | var columns = 4;
37 | var imageFilmstrip = gallery
38 | .draw(imagesRGB, geometryGallery.bounds(), rows, columns);
39 |
40 | Map.addLayer(imageFilmstrip);
41 |
42 | // LGTM (nclinton)
43 |
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/Part F - Fundamentals/README.md:
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1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 |
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | # Invitation for collaborators:
31 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
32 |
33 | https://google-earth-engine.com/contact/
34 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # GOOGLE EARTH ENGINE TUTORIALS
2 |
3 | We have included the detail explaination of the cocepts in the blog link below.. Please do visit the blog for details and give feedback if you have any.
4 | https://google-earth-engine.com/
5 |
6 |
7 | 
8 |
9 |
10 |
11 | ## Background
12 |
13 |
14 | This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy aceess to the normal users via web page. The entire content of the oage are the hardwork from the authors mentioned in the page itself. We don't own it. We simply made the materials available in very easy and effective way for the users. In a manner of saying we are looking for the value addition of the existing tutorials available for the users.
15 |
16 |
17 |
18 | The entire lab work and explanation includes work from undergraduates, master’s students, PhD students, postdocs, assistant professors, associate professors, and independent consultants.
19 |
20 |
21 |
22 | ### HTML page output for blog is as follows:
23 |
24 |
25 | 
26 |
27 |
28 |
29 |
30 | # Invitation for collaborators:
31 | Please feel free to contribute to the document. Let's try bring the spatial data science contents together so that it would be easier for every one to learn and share the knowledge. You can also provide your valuable suggestion from our contact page.
32 |
33 | https://google-earth-engine.com/contact/
34 |
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https://raw.githubusercontent.com/krishnakafle/gee-tutorials/d63ff3a676334821fb76c7964a3aebd9faa2d39f/gee-tutorials.jpg
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/logo.png:
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https://raw.githubusercontent.com/krishnakafle/gee-tutorials/d63ff3a676334821fb76c7964a3aebd9faa2d39f/logo.png
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