├── HLI.js ├── LICENSE ├── README.md ├── TPI.js ├── climate.js ├── cloudMasking.js ├── example.js ├── fire.js ├── getNearestImage.js ├── radiometricCorrection.js ├── test.js └── utils.js /HLI.js: -------------------------------------------------------------------------------- 1 | var utils = require("users/aazuspan/geeTools:utils.js"); 2 | 3 | /** 4 | * Calculate McCune and Keon 2002 Heat Load Index (HLI) with corrected coefficients from McCune 2007. This 5 | * implementation follows the R spatialEco library with the addition of per-pixel latitude calculation. 6 | * @param {ee.Image} x An elevation image. 7 | * @param {object} optionalParamters A dictionary of parameters to use in place of default parameters. 8 | * @param {number} optionalParameters.forceLatitude A fixed latitude in degrees to use in HLI calculations. If missing, 9 | * latitudes will be calculated per-pixel. 10 | * @param {string} optionalParameters.forceHemisphere A fixed hemisphere to use in HLI calculations. One of "north" or 11 | * "south". If missing, the hemisphere will be calculated per-pixel based on latitude. 12 | * @return {ee.Image} The McCune and Keon 2002 Heat Load Index. 13 | */ 14 | exports.hli = function (x, optionalParameters) { 15 | var params = { 16 | forceLatitude: null, 17 | forceHemisphere: null, 18 | }; 19 | 20 | params = utils.updateParameters(params, optionalParameters); 21 | 22 | // If a latitude is forced, use the fixed latitude 23 | if (params.forceLatitude) { 24 | try { 25 | var lat = ee.Image.constant(ee.Number(params.forceLatitude)); 26 | } catch (err) { 27 | throw ( 28 | 'Invalid forceLatitude argument "' + 29 | params.forceLatitude + 30 | '". Argument must be a number or missing.' 31 | ); 32 | } 33 | } 34 | // Otherwise, use per-pixel latitudes 35 | else { 36 | lat = ee.Image.pixelLonLat().select("latitude"); 37 | } 38 | lat = utils.deg2rad(lat); 39 | 40 | // If a hemisphere is forced, set the aspect folding coefficient based on the hemisphere 41 | if (params.forceHemisphere) { 42 | var foldCoeffs = { north: 225, south: 315 }; 43 | try { 44 | var foldCoeff = ee.Image.constant( 45 | foldCoeffs[params.forceHemisphere.toLowerCase()] 46 | ); 47 | } catch (err) { 48 | throw ( 49 | 'Invalid forceHemisphere argument "' + 50 | params.forceHemisphere + 51 | '". Argument must be "north", "south", or missing.' 52 | ); 53 | } 54 | } 55 | // Otherwise, set the aspect folding coefficient based on latitude (N = 225, S = 315) 56 | else { 57 | foldCoeff = ee.Image(225).where(lat.lt(0), 315); 58 | } 59 | 60 | var slope = utils.deg2rad(ee.Terrain.slope(x)); 61 | // Folded aspect 62 | var aspect = utils.deg2rad( 63 | ee.Terrain.aspect(x).subtract(foldCoeff).abs().multiply(-1).add(180).abs() 64 | ); 65 | 66 | // Equation from McCune 2002 with corrected coefficients from McCune 2007 67 | var x1 = slope.cos().multiply(lat.cos()).multiply(1.582); 68 | var x2 = slope 69 | .sin() 70 | .multiply(lat.sin()) 71 | .multiply(aspect.cos()) 72 | .multiply(-1.5); 73 | var x3 = slope.sin().multiply(lat.sin()).multiply(-0.262); 74 | var x4 = slope.sin().multiply(aspect.sin()).multiply(0.607); 75 | 76 | var h = x1.add(x2).add(x3).add(x4).add(-1.467).exp().rename("HLI"); 77 | 78 | return h; 79 | }; 80 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # geeTools 2 | 3 | Remote sensing and spatial analysis tools for Google Earth Engine. 4 | 5 | ![Fire Perimeter example](https://i.imgur.com/yzDB6Ii.gif) 6 | 7 | ## Table of Contents 8 | 9 | - [Usage](https://github.com/aazuspan/geeTools#Usage) 10 | - [Examples](https://github.com/aazuspan/geeTools#Examples) 11 | - [Burn Severity](https://github.com/aazuspan/geeTools#Burn-Severity) 12 | - [Fire Perimeter Detection](https://github.com/aazuspan/geeTools#Fire-Perimeter-Detection) 13 | - [Cloud Probability Masking](https://github.com/aazuspan/geeTools#Cloud-Probability-Masking) 14 | - [Heat Load Index](https://github.com/aazuspan/geeTools#Heat-Load-Index) 15 | - [Slope Position](https://github.com/aazuspan/geeTools#Slope-Position) 16 | - [Dark Object Subtraction](https://github.com/aazuspan/geeTools#Dark-Object-Subtraction) 17 | - [References](https://github.com/aazuspan/geeTools#References) 18 | 19 | ## Usage 20 | 21 | To import a module, include the following code in your GEE script: 22 | 23 | ```javascript 24 | var foo = require("users/aazuspan/geeTools:{module name}"); 25 | foo.bar(); 26 | ``` 27 | 28 | For example: 29 | 30 | ```javascript 31 | var fire = require("users/aazuspan/geeTools:fire.js") 32 | fire.calculateBurnSeverity( ... ); 33 | ``` 34 | 35 | ## Examples 36 | 37 | ### Burn Severity 38 | 39 | Calculate pre- and post-fire NBR, dNBR (Key & Benson, 2005), RdNBR (Miller & Thode, 2007), and basal area mortality (Reilly et. al., 2017) using prefire and postfire imagery. 40 | 41 | ```javascript 42 | var fire = require("users/aazuspan/geeTools:fire.js"); 43 | 44 | // L8 imagery prior to the fire 45 | var prefire = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20170628"); 46 | // L8 imagery one year after the fire 47 | var postfire = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20180701"); 48 | 49 | // Specify relevant band labels for imagery 50 | var nir = "B5"; 51 | var swir = "B6"; 52 | 53 | // Calculate various burn severity metrics 54 | var severity = fire.calculateBurnSeverity(prefire, postfire, nir, swir); 55 | ``` 56 | 57 | ![Burn Severity example](https://i.imgur.com/wEaOgaQ.png) 58 | 59 | ### Fire Perimeter Detection 60 | 61 | Map active burning area or cumulative area burned at customizable time intervals over a time period, such as area burned every 6 hours over 10 days. This implementation uses GOES-16 and GOES-17 imagery, so fire dates are restricted based on availability of that data. 62 | 63 | ```javascript 64 | var fire = require("users/aazuspan/geeTools:fire.js"); 65 | 66 | // Set the date range 67 | var start = "2020-09-05"; 68 | var end = "2020-09-15"; 69 | 70 | // Apply a majority filter to smooth the GOES data 71 | var smooth = true; 72 | // Use the default kernel (or pass any ee.Kernel) 73 | var smoothKernel = null; 74 | // Generate cumulative area burned since start rather than instantaneous area burned. 75 | var cumulative = true; 76 | // Set the time interval in hours 77 | var timeDelta = 6; 78 | 79 | // Generate an image collection showing cumulative area burned since the start at each time interval 80 | var burnedAreaImg = fire.periodicFireBoundaries( 81 | start, 82 | end, 83 | aoi, 84 | smooth, 85 | smoothKernel, 86 | cumulative, 87 | timeDelta 88 | ); 89 | 90 | // Desired pixel size in meters 91 | var scale = 500; 92 | var maxPixels = 1e12; 93 | // Simplify the polygon perimeter to remove stairstep effect and reduce size 94 | var simplify = true; 95 | // Max error, in meters, for simplifcation. Higher error will increase the level of simplification 96 | var maxError = 500; 97 | 98 | // Convert the image collection into vector fire perimeters 99 | var burnedAreaPoly = fire.vectorizeBoundaryCollection( 100 | collection, 101 | scale, 102 | aoi, 103 | maxPixels, 104 | simplify, 105 | maxError 106 | ); 107 | ``` 108 | 109 | ![Fire perimeter example](https://i.imgur.com/HtAJPle.gif) 110 | 111 | ### Cloud Probability Masking 112 | 113 | Use cloud probability data to mask clouds in imagery. 114 | 115 | ```javascript 116 | var cloudMasking = require("users/aazuspan/geeTools:cloudMasking.js"); 117 | 118 | // Load a Sentinel-2 image (1C or 2A) 119 | var s2 = ee.Image("COPERNICUS/S2/20190113T190741_20190113T190736_T10TEK"); 120 | // Load the corresponding cloud probability image 121 | var prob = ee.Image( 122 | "COPERNICUS/S2_CLOUD_PROBABILITY/20190113T190741_20190113T190736_T10TEK" 123 | ); 124 | 125 | // Mask clouds in the original image 126 | var cloudMasked = cloudMasking.probabilityCloudMask(s2, prob); 127 | ``` 128 | 129 | ![Cloud masking example](https://i.imgur.com/P4oyNTH.png) 130 | 131 | ### Heat Load Index 132 | 133 | Calculate Heat Load Index (HLI) from elevation (McCune, 2007). 134 | 135 | ```javascript 136 | var hli = require("users/aazuspan/geeTools:HLI.js"); 137 | 138 | // Load elevation data 139 | var srtm = ee.Image("CGIAR/SRTM90_V4"); 140 | 141 | // Generate HLI data 142 | var h = hli.hli(srtm); 143 | ``` 144 | 145 | ![Heat Load Index example](https://i.imgur.com/nIe6Jcb.png) 146 | 147 | ### Slope Position 148 | 149 | Calculate TPI and slope position from elevation (Weiss, 2001). 150 | 151 | ```javascript 152 | var tpi = require("users/aazuspan/geeTools:TPI.js"); 153 | 154 | // Load elevation data 155 | var srtm = ee.Image("CGIAR/SRTM90_V4"); 156 | 157 | // Define an area of interest 158 | var aoi = ee.Geometry.Polygon( 159 | [ 160 | [ 161 | [-123.92382561385939, 42.39507820959633], 162 | [-123.92382561385939, 41.57642883612384], 163 | [-122.83343254745314, 41.57642883612384], 164 | [-122.83343254745314, 42.39507820959633], 165 | ], 166 | ], 167 | null, 168 | false 169 | ); 170 | 171 | // Calculate slope in degrees 172 | var slope = ee.Terrain.slope(srtm); 173 | 174 | // Set TPI window parameters. These have a significant impact on output results. 175 | var radius = 300; 176 | var shape = "square"; 177 | var units = "meters"; 178 | 179 | // Calculate a TPI image 180 | var tpi300 = tpi.tpi(srtm, radius, shape, units); 181 | 182 | // Use the default "flat" definition of 5 degrees 183 | var flat = null; 184 | // Set the output pixel size in meters 185 | var scale = 100; 186 | 187 | // Reclassify TPI to discrete slope positions 188 | var slopePosition300 = tpi.slopePosition(tpi300, slope, flat, aoi, scale); 189 | ``` 190 | 191 | ![Slope Position example](https://i.imgur.com/v7ZqBfR.png) 192 | 193 | ### Dark Object Subtraction 194 | 195 | Use dark object subtraction (DOS) to perform radiometric normalization and atmospheric correction. 196 | 197 | ```javascript 198 | var radCor = require("users/aazuspan/geeTools:radiometricCorrection.js"); 199 | 200 | // Identify a reference dark object, such as deep water 201 | var darkObject = ee.Geometry.Polygon( 202 | [ 203 | [ 204 | [-124.74266276966597, 42.12268590007055], 205 | [-124.74266276966597, 41.93396768286303], 206 | [-124.52705608021284, 41.93396768286303], 207 | [-124.52705608021284, 42.12268590007055], 208 | ], 209 | ], 210 | null, 211 | false 212 | ); 213 | 214 | // Load any image 215 | var img = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20170628"); 216 | 217 | // Pixel size in meters 218 | var scale = 30; 219 | var maxPixels = 1e13; 220 | 221 | // Use Dark Object Subtraction to correct for atmospheric distortion 222 | var imgDOS = radCor.darkObjectSubtraction(img, darkObject, scale, maxPixels); 223 | ``` 224 | 225 | ![Dark Object Subtraction example](https://i.imgur.com/lVY156s.png) 226 | 227 | ## References 228 | 229 | - Key, C. H., & Benson, N. C. (2005). Landscape assessment: Remote sensing ofseverity, the Normalized Burn Ratio. In D. C. Lutes (Ed.), FIREMON: Fireeffects monitoring and inventory system. General Technical Report, RMRS-GTR-164-CD:LA1-LA51. (pp.) Ogden, UT: USDA Forest Service, Rocky Mountain Research Station. 230 | - McCune, B. (2007). Improved estimates of incident radiation and heat load using non- parametric regression against topographic variables. Journal of Vegetation Science, 18(5), 751–754. doi:10.1111/j.1654-1103.2007.tb02590.x 231 | - Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109(1), 66–80. doi:10.1016/j.rse.2006.12.006 232 | - Reilly, M. J., Dunn, C. J., Meigs, G. W., Spies, T. A., Kennedy, R. E., Bailey, J. D., & Briggs, K. (2017). Contemporary patterns of fire extent and severity in forests of the Pacific Northwest, USA (1985-2010). Ecosphere, 8(3), e01695. doi:10.1002/ecs2.1695 233 | - Weiss, A.D., 2001. Topographic position and landforms analysis. Poster Presentation, 234 | ESRI Users Conference, San Diego, CA. 235 | 236 | --- 237 | 238 | [Top](https://github.com/aazuspan/geeTools#geeTools) 239 | -------------------------------------------------------------------------------- /TPI.js: -------------------------------------------------------------------------------- 1 | var utils = require("users/aazuspan/geeTools:utils.js"); 2 | 3 | // Calculate topographic position index based on a DEM image, following Weiss 2001. 4 | // Radius, window_shape, and units define the TPI kernel, and are passed to ee.Image.focal_mean 5 | exports.tpi = function (dem, optionalParameters) { 6 | // Default parameters 7 | var params = { 8 | radius: 300, 9 | windowShape: "circle", 10 | units: "meters", 11 | }; 12 | 13 | params = utils.updateParameters(params, optionalParameters); 14 | 15 | dem = dem.double(); 16 | var r = dem 17 | .subtract(dem.focal_mean(params.radius, params.windowShape, params.units)) 18 | .add(0.5) 19 | .int() 20 | .rename("TPI"); 21 | return r; 22 | }; 23 | 24 | // Reclassify a continuous TPI image into slope positions, following Weiss 2001 25 | exports.slopePosition = function (tpi, slope, region, optionalParameters) { 26 | var params = { 27 | flatDegrees: 5, 28 | scale: null, 29 | maxPixels: 1e12, 30 | }; 31 | 32 | params = utils.updateParameters(params, optionalParameters); 33 | 34 | // Calculate the TPI standard deviation 35 | var sd = tpi 36 | .reduceRegion({ 37 | reducer: ee.Reducer.stdDev(), 38 | geometry: region, 39 | scale: params.scale, 40 | maxPixels: params.maxPixels, 41 | }) 42 | .getNumber(tpi.bandNames().get(0)); 43 | 44 | // Reclassify TPI to slope position 45 | var tpiReclass = ee 46 | .Image(0) 47 | // Ridge 48 | .where(tpi.gt(sd), 1) 49 | // Upper slope 50 | .where(tpi.gt(sd.multiply(0.5)).and(tpi.lte(sd)), 2) 51 | // Middle slope 52 | .where( 53 | tpi 54 | .gt(sd.multiply(-0.5)) 55 | .and(tpi.lt(sd.multiply(0.5)).and(slope.gt(params.flatDegrees))), 56 | 3 57 | ) 58 | // Flat slope 59 | .where( 60 | tpi 61 | .gte(sd.multiply(-0.5)) 62 | .and(tpi.lte(sd.multiply(0.5)).and(slope.lte(params.flatDegrees))), 63 | 4 64 | ) 65 | // Lower slope 66 | .where(tpi.gte(sd.multiply(-1)).and(tpi.lt(sd.multiply(-0.5))), 5) 67 | // Valley 68 | .where(tpi.lt(sd.multiply(-1)), 6) 69 | .rename("slopePosition"); 70 | 71 | return tpiReclass; 72 | }; 73 | -------------------------------------------------------------------------------- /climate.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Calculate relative humidity percentage following Bolton 1980. 3 | * @see {@link https://archive.eol.ucar.edu/projects/ceop/dm/documents/refdata_report/eqns.html} 4 | * @param {ee.Image | ee.Number} q - Specific humidity, unitless. 5 | * @param {ee.Image | ee.Number} p - Pressure in Pa. 6 | * @param {ee.Image | ee.Number} t - Temperature in C. 7 | * @returns {ee.Image | ee.Number} Relative humidity, values between 0 and 100. 8 | */ 9 | exports.relativeHumidity = function (q, p, t) { 10 | var es = t.multiply(17.67).divide(t.add(243.5)).exp().multiply(6.112); 11 | var e = q.multiply(p).divide(q.multiply(0.378).add(0.622)); 12 | 13 | var RH = e.divide(es).clamp(0, 100); 14 | 15 | if (RH instanceof ee.Image) { 16 | RH = RH.rename("RH"); 17 | } 18 | 19 | return RH; 20 | }; 21 | 22 | 23 | /** Calculate vapor pressure deficit 24 | * @param {ee.Image | ee.Number} t - Air temperature. 25 | * @param {ee.Image | ee.Number} rh - Relative humidity. 26 | * @returns {ee.Image | ee.Number} Vapor pressure deficit. 27 | */ 28 | exports.vaporPressureDeficit = function(t, rh) { 29 | var vpd = t.multiply(17.27).divide(t.add(237.3)).exp() 30 | .multiply(0.6108) 31 | .multiply(rh.divide(-100).add(1)); 32 | 33 | if (vpd instanceof ee.Image) { 34 | vpd = vpd.rename("VPD"); 35 | } 36 | 37 | return vpd; 38 | } 39 | 40 | /** 41 | * Calculate wind velocity from vector components. 42 | * @param {ee.Image | ee.Number} u - The U component of wind. 43 | * @param {ee.Image | ee.Number} v - The V component of wind. 44 | * @returns {ee.Image | ee.Number} The wind velocity. 45 | */ 46 | exports.windVelocity = function(u, v) { 47 | var wind = u.pow(2).add(v.pow(2)).sqrt(); 48 | 49 | if (wind instanceof ee.Image) { 50 | wind = wind.rename("WIND"); 51 | } 52 | 53 | return wind; 54 | } 55 | 56 | 57 | /** 58 | * Calculate hot-dry-windy index, Srock et al., 2018 59 | * @see {@link https://www.mdpi.com/2073-4433/9/7/279} 60 | * @param {ee.Image | ee.Number} vpd - Vapor-pressure deficit 61 | * @param {ee.Image | ee.Number} wind - Max wind speed. 62 | * @return {ee.Image | ee.Number} Hot-dry-windy index. 63 | */ 64 | exports.hotDryWindyIndex = function(vpd, wind) { 65 | var hdwi = vpd.multiply(wind); 66 | 67 | if (hdwi instanceof ee.Image) { 68 | hdwi = hdwi.rename("HDWI"); 69 | } 70 | 71 | return hdwi; 72 | } -------------------------------------------------------------------------------- /cloudMasking.js: -------------------------------------------------------------------------------- 1 | // Generate a binary cloud mask from a cloud probability image 2 | exports.generateCloudMask = function (probability, probabilityThreshold) { 3 | return probability.gt(probabilityThreshold); 4 | }; 5 | 6 | // Generate a binary shadow mask from a cloud mask of an image by looking for dark pixels near clouds. 7 | exports.generateShadowMask = function ( 8 | img, 9 | cloudMask, 10 | shadowNIR, 11 | shadowDist, 12 | solarAzimuth, 13 | scale 14 | ) { 15 | // Identify potential shadows 16 | var darkMask = img.select("B8").lt(shadowNIR); 17 | 18 | // If no azimuth is provided, try to pull it from the image properties 19 | solarAzimuth = solarAzimuth 20 | ? solarAzimuth 21 | : ee.Number(img.get("MEAN_SOLAR_AZIMUTH_ANGLE")); 22 | 23 | var shadowAz = ee.Number(90).subtract(solarAzimuth); 24 | 25 | // Get the potential location of shadows based on cloud location 26 | var shadowProj = cloudMask 27 | .directionalDistanceTransform(shadowAz, shadowDist * 10) 28 | .reproject({ crs: img.select(0).projection(), scale: scale }) 29 | .select("distance") 30 | .mask(); 31 | 32 | return shadowProj.multiply(darkMask); 33 | }; 34 | 35 | // Apply morphological closing to remove small groups of pixels in a binary mask 36 | exports.cleanMask = function (mask, bufferDist, scale) { 37 | var cleaned = mask.focal_max((2 * bufferDist) / scale).focal_min(2); 38 | 39 | return cleaned; 40 | }; 41 | 42 | // Mask clouds and shadows in Sentinel-2 imagery using a cloud probability image. 43 | exports.probabilityCloudMask = function ( 44 | img, 45 | probability, 46 | probabilityThreshold, 47 | bufferDist, 48 | scale, 49 | maskShadow, 50 | shadowNIR, 51 | shadowDist 52 | ) { 53 | probabilityThreshold = probabilityThreshold ? probabilityThreshold : 30; 54 | bufferDist = bufferDist ? bufferDist : 15; 55 | scale = scale ? scale : img.select(0).projection().nominalScale(); 56 | maskShadow = maskShadow ? maskShadow : false; 57 | shadowNIR = shadowNIR ? shadowNIR : 1000; 58 | shadowDist = shadowDist ? shadowDist : 10; 59 | 60 | var cloudMask = exports.generateCloudMask(probability, probabilityThreshold); 61 | 62 | var shadowMask = exports.generateShadowMask( 63 | img, 64 | cloudMask, 65 | shadowNIR, 66 | shadowDist, 67 | scale 68 | ); 69 | 70 | var cloudAndShadowMask = cloudMask.add(shadowMask).gt(0); 71 | 72 | if (maskShadow) { 73 | var mask = cloudAndShadowMask.eq(0); 74 | } else { 75 | mask = cloudMask.eq(0); 76 | } 77 | 78 | mask = exports.cleanMask(mask, bufferDist, scale); 79 | 80 | return img.mask(mask); 81 | }; 82 | 83 | // A fast implementation of cloud masking that uses a simple binary cloud mask, such as the 84 | // QA60 band in Sentinel-2 imagery to mask clouds. 85 | exports.simpleCloudMask = function (img, maskBand) { 86 | maskBand = maskBand ? maskBand : "QA60"; 87 | 88 | if (!img.bandNames().contains(maskBand).getInfo()) { 89 | throw 'Image does not contain a band called "' + maskBand + '".'; 90 | } 91 | 92 | var cloudMask = img.select(maskBand).eq(0); 93 | 94 | return img.mask(cloudMask); 95 | }; 96 | -------------------------------------------------------------------------------- /example.js: -------------------------------------------------------------------------------- 1 | /* 2 | Example: Calculating burn severity metrics for the 2017 Oak Fire 3 | */ 4 | var fire = require("users/aazuspan/geeTools:fire.js"); 5 | 6 | // L8 imagery prior to the fire 7 | var prefire = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20170628"); 8 | // L8 imagery one year after the fire 9 | var postfire = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20180701"); 10 | 11 | var severity = fire.calculateBurnSeverity(prefire, postfire, "B5", "B6"); 12 | 13 | Map.addLayer( 14 | severity, 15 | { min: -250, max: 600, bands: ["preNBR", "postNBR", "postNBR"] }, 16 | "severity", 17 | false 18 | ); 19 | 20 | /* 21 | Example: Calculating HLI from SRTM data. 22 | */ 23 | var hli = require("users/aazuspan/geeTools:HLI.js"); 24 | var srtm = ee.Image("CGIAR/SRTM90_V4"); 25 | var h = hli.hli(srtm); 26 | 27 | Map.addLayer(h, { min: 0.5, max: 1 }, "HLI", false); 28 | 29 | /* 30 | Example: Calculating TPI and slope position 31 | */ 32 | var tpi = require("users/aazuspan/geeTools:TPI.js"); 33 | 34 | var aoi = ee.Geometry.Polygon( 35 | [ 36 | [ 37 | [-123.92382561385939, 42.39507820959633], 38 | [-123.92382561385939, 41.57642883612384], 39 | [-122.83343254745314, 41.57642883612384], 40 | [-122.83343254745314, 42.39507820959633], 41 | ], 42 | ], 43 | null, 44 | false 45 | ); 46 | 47 | // Calculate slope in degrees 48 | var slope = ee.Terrain.slope(srtm); 49 | 50 | // Calculate a TPI image using a 300m kernel 51 | var tpi300 = tpi.tpi(srtm, 300, "square", "meters"); 52 | 53 | // Reclassify TPI to discrete slope positions 54 | var slopePosition300 = tpi.slopePosition(tpi300, slope, null, aoi, 100, 1e12); 55 | 56 | Map.addLayer(slopePosition300, { min: 1, max: 6 }, "Slope Position", false); 57 | 58 | /* 59 | Example: Applying radiometric correction 60 | */ 61 | 62 | var radCor = require("users/aazuspan/geeTools:radiometricCorrection.js"); 63 | 64 | // Identify a reference dark object, such as deep water 65 | var darkObject = ee.Geometry.Polygon( 66 | [ 67 | [ 68 | [-124.74266276966597, 42.12268590007055], 69 | [-124.74266276966597, 41.93396768286303], 70 | [-124.52705608021284, 41.93396768286303], 71 | [-124.52705608021284, 42.12268590007055], 72 | ], 73 | ], 74 | null, 75 | false 76 | ); 77 | 78 | // Use Dark Object Subtraction to correct for atmospheric distortion 79 | var prefireDOS = radCor.darkObjectSubtraction(prefire, darkObject, 30, 1e13); 80 | var postfireDOS = radCor.darkObjectSubtraction(postfire, darkObject, 30, 1e13); 81 | 82 | Map.addLayer( 83 | prefireDOS, 84 | { min: 0, max: 0.4, bands: ["B5", "B4", "B3"] }, 85 | "Prefire DOS", 86 | false 87 | ); 88 | Map.addLayer( 89 | postfireDOS, 90 | { min: 0, max: 0.4, bands: ["B5", "B4", "B3"] }, 91 | "Postfire DOS", 92 | false 93 | ); 94 | 95 | // Identify pseudo-invariant features between prefire and postfire images; in 96 | // this case, ocean and a building. 97 | var PIF = ee.Geometry.MultiPolygon([ 98 | [ 99 | [ 100 | [-124.73859734350975, 41.90979183965181], 101 | [-124.73688072974022, 41.88551473447749], 102 | [-124.71834130102928, 41.88628152101927], 103 | [-124.7216028671914, 41.91426293134146], 104 | ], 105 | ], 106 | [ 107 | [ 108 | [-122.87238706850988, 42.428688503225544], 109 | [-122.87242998385412, 42.4279599378424], 110 | [-122.8702842166422, 42.4279124224146], 111 | [-122.87024130129797, 42.42865682664584], 112 | ], 113 | ], 114 | ]); 115 | 116 | // Use pseudo-invariant features to match the histogram of postfire imagery to 117 | // prefire imagery. All bands within each image must have the same projection, 118 | // so a subset of bands are selected. 119 | var postfireMatch = radCor.linearHistogramMatch( 120 | postfire.select(["B5", "B4", "B3", "B2"]), 121 | prefire.select(["B5", "B4", "B3", "B2"]), 122 | PIF 123 | ); 124 | 125 | Map.addLayer( 126 | postfireMatch, 127 | { min: 0, max: 0.4, bands: ["B5", "B4", "B3"] }, 128 | "Postfire Matched", 129 | false 130 | ); 131 | 132 | /* 133 | Example: Cloud masking Sentinel-2 imagery 134 | */ 135 | 136 | var cloudMasking = require("users/aazuspan/geeTools:cloudMasking.js"); 137 | 138 | // Load a Sentinel-2 image (1C or 2A) 139 | var s2 = ee.Image("COPERNICUS/S2/20190113T190741_20190113T190736_T10TEK"); 140 | // Load the corresponding cloud probability image 141 | var prob = ee.Image( 142 | "COPERNICUS/S2_CLOUD_PROBABILITY/20190113T190741_20190113T190736_T10TEK" 143 | ); 144 | 145 | var cloudMasked = cloudMasking.probabilityCloudMask(s2, prob); 146 | 147 | Map.addLayer(s2, { bands: ["B4", "B3", "B2"], min: 0, max: 2000 }, "S2", false); 148 | Map.addLayer( 149 | cloudMasked, 150 | { bands: ["B4", "B3", "B2"], min: 0, max: 2000 }, 151 | "S2 masked", 152 | false 153 | ); 154 | 155 | /* 156 | Example: Fire perimeter detection 157 | */ 158 | 159 | // Select an area near the 2020 Lionshead and Beachie Creek fires 160 | var aoi = ee.Geometry.Point([-122.12284375769744, 44.729350689066244]).buffer( 161 | 50000 162 | ); 163 | 164 | // Select a date range to generate daily fire perimeters for 165 | var start = "2020-09-06"; 166 | var end = "2020-09-09"; 167 | 168 | // Use GOES imagery to generate a daily fire area ImageCollection 169 | var collection = fire.periodicFireBoundaries(start, end, aoi, true); 170 | // Convert the ImageCollection to a FeatureCollection of daily fire perimeters 171 | var perimeters = fire.vectorizeBoundaryCollection( 172 | collection, 173 | 250, 174 | aoi, 175 | 1e12, 176 | true, 177 | 500 178 | ); 179 | 180 | Map.addLayer(perimeters, {}, "Fire perimeters", false); 181 | -------------------------------------------------------------------------------- /fire.js: -------------------------------------------------------------------------------- 1 | var utils = require("users/aazuspan/geeTools:utils.js"); 2 | 3 | /** 4 | * Calculate various burn severity metrics between pre- and post-fire imagery. 5 | * @param {ee.Image} pre A multispectral prefire image. 6 | * @param {ee.Image} post A multispectral postfire image. 7 | * @param {string} NIR The name of the NIR band in both images. 8 | * @param {string} SWIR The name of the SWIR band in both images. 9 | * @return {ee.Image} A multiband image containing pre- and post-fire NBR, 10 | * dNBR, RdNBR, estimated basal area mortality, and refugia status as bands. 11 | */ 12 | exports.calculateBurnSeverity = function (pre, post, NIR, SWIR) { 13 | // Normalized burn ratio 14 | var preNBR = pre 15 | .normalizedDifference([NIR, SWIR]) 16 | .multiply(1000) 17 | .rename("preNBR"); 18 | var postNBR = post 19 | .normalizedDifference([NIR, SWIR]) 20 | .multiply(1000) 21 | .rename("postNBR"); 22 | 23 | // Delta normalized burn ratio 24 | var dNBR = preNBR.subtract(postNBR).rename("dNBR"); 25 | 26 | // Relativized dNBR, Miller & Thode 2007 27 | var RdNBR = dNBR.divide(preNBR.divide(1000).abs().sqrt()).rename("RdNBR"); 28 | 29 | // Basal area mortality regression equation, Reilly et. al. 2017 30 | var basalMortality = RdNBR.multiply(1135360) 31 | .add(-119487011) 32 | .sqrt() 33 | .multiply(0.00003938) 34 | .add(-0.22845617) 35 | .rename("percentMortality"); 36 | 37 | // Refugia are areas with < 10% basal area mortality, Meigs & Krawchuck 2018 38 | var refugia = ee.Image(1).where(basalMortality.gt(0.1), 0).rename("refugia"); 39 | 40 | var severityMetrics = preNBR 41 | .addBands(postNBR) 42 | .addBands(dNBR) 43 | .addBands(RdNBR) 44 | .addBands(basalMortality) 45 | .addBands(refugia); 46 | 47 | return severityMetrics; 48 | }; 49 | 50 | /** 51 | * Generate a single image mask of active fire between a start and end time using GOES16 and GOES17 data. 52 | * @param {ee.Date} start The starting time. 53 | * @param {ee.Date} end The ending time. 54 | * @param {ee.Geometry} region The area to search for fire perimeters. 55 | * @param {boolean} smooth If true, a majority filter will be used to smooth the low-resolution pixels. 56 | * @param {ee.Kernel} smoothKernel If smoothing, the kernel used to perform the majority filter. If null, a 2000 meter 57 | * normalized circular kernel will be used. 58 | * @return {ee.Image} A binary mask where 1 is active fire within the time period. 59 | */ 60 | exports.periodFireBoundary = function ( 61 | start, 62 | end, 63 | region, 64 | smooth, 65 | smoothKernel 66 | ) { 67 | smoothKernel = smoothKernel 68 | ? smoothKernel 69 | : ee.Kernel.circle(2000, "meters", true); 70 | 71 | start = ee.Date(start); 72 | end = ee.Date(end); 73 | 74 | var goes16 = ee.ImageCollection("NOAA/GOES/16/FDCF"); 75 | var goes17 = ee.ImageCollection("NOAA/GOES/17/FDCF"); 76 | 77 | // Generate boundaries from GOES16 and GOES17 separately 78 | var boundaries = ee.List([goes16, goes17]).map(function (collection) { 79 | var filtered = ee 80 | .ImageCollection(collection) 81 | .filterDate(start, end) 82 | .filterBounds(region); 83 | 84 | // Remap mask to binary fire by selecting good quality fire pixels 85 | var fireQuality = filtered.select("DQF"); 86 | // Take the minimum DQF value where the minimum (0) represents good quality fire signal. This will cause false 87 | // positives for each data source, but these will be removed when data sources are combined. Other reducers have 88 | // unacceptably high false negative rates (eg. max) and/or are very sensitive to the timeDelta (eg. median or mode). 89 | var fireMask = fireQuality.reduce(ee.Reducer.min()).eq(0); 90 | 91 | return fireMask; 92 | }); 93 | // Combine GOES16 and GOES17 into one image. Take the min to require 94 | // agreement between the data sources and minimize false positives. 95 | var combined = ee.ImageCollection(boundaries).reduce(ee.Reducer.min()); 96 | 97 | if (smooth === true) { 98 | combined = combined.reduceNeighborhood({ 99 | reducer: ee.Reducer.mode(), 100 | kernel: smoothKernel, 101 | }); 102 | } 103 | 104 | // Mask and store the date as a property 105 | combined = combined.selfMask().rename("fire_mask"); 106 | 107 | combined = setImageMetadata(combined, start, end); 108 | 109 | return combined; 110 | }; 111 | 112 | /** 113 | * Add a binary mask to the last image in a list of binary masks. Used for iterating over an image collection. 114 | * @param {ee.Image} next A binary mask to accumulate with past masks. 115 | * @param {ee.List of ee.Image} list A list of past masks. 116 | * @return {ee.List of ee.Image} The input list with the next accumulated mask added. 117 | */ 118 | var accumulateMask = function (next, list) { 119 | // Select the last image of the current collection 120 | var previous = ee.Image(ee.List(list).get(-1)).unmask(); 121 | next = next.unmask(); 122 | 123 | // Add the previous presence to the current presence 124 | var accumulated = next.add(previous).gt(0); 125 | accumulated = accumulated.selfMask(); 126 | 127 | // Because images are accumulated over the time series, they will all have the 128 | // same start date. 129 | var start = ee.Image(ee.List(list).get(0)).get("start_date"); 130 | var end = next.get("end_date"); 131 | 132 | accumulated = setImageMetadata(accumulated, start, end); 133 | 134 | return ee.List(list).add(accumulated); 135 | }; 136 | 137 | /** 138 | * Set the title and date metadata for a fire mask image. Image titles use the 139 | * end date so that accumulated images have unique titles. 140 | * @param {ee.Image} img A binary fire mask. 141 | * @param {ee.Date} startDate The starting date for the fire mask. 142 | * @return {ee.Image} The binary fire mask with an ID and dates set. 143 | */ 144 | var setImageMetadata = function (img, startDate, endDate) { 145 | var dateString = ee.Date(endDate).format("yyyy_MM_dd_HH:mm:ss"); 146 | var imgName = ee.String("FireMask/").cat(dateString); 147 | 148 | return img.set( 149 | "system:id", 150 | imgName, 151 | "start_date", 152 | startDate, 153 | "end_date", 154 | endDate 155 | ); 156 | }; 157 | 158 | /** 159 | * Generate a collection of image masks of active fire at a regular interval between a start and end time using GOES16 160 | * and GOES17 data. Masks can represent either instantaneous fire area within each interval or cumulative fire area 161 | * between the start time and the current interval. 162 | * @param {ee.Date} start The starting time. 163 | * @param {ee.Date} end The ending time. 164 | * @param {ee.Geometry} region The area to search for fire perimeters. 165 | * @param {boolean} smooth If true, a majority filter will be used to smooth the low-resolution pixels. 166 | * @param {ee.Kernel} smoothKernel If smoothing, the kernel used to perform the majority filter. If null, a 2000 meter 167 | * normalized circular kernel will be used. 168 | * @param {boolean} cumulative If true, each mask in the collection will represent cumulative area burned since the 169 | * start time. If false, each mask in the collection will represent only the area burned within that time period. 170 | * @param {number} timeDelta The length of each interval, in hours, to generate fire boundaries for. 171 | * @return {ee.ImageCollection} A collection of binary masks where 1 is active fire or cumulative area burned within 172 | * each time period. 173 | */ 174 | exports.periodicFireBoundaries = function ( 175 | start, 176 | end, 177 | region, 178 | optionalParameters 179 | ) { 180 | // Default parameters 181 | var params = { 182 | smooth: false, 183 | smoothKernel: ee.Kernel.circle(2000, "meters", true), 184 | timeDelta: 24, 185 | cumulative: false, 186 | }; 187 | 188 | // Swap default parameters for user-defined parameters 189 | params = utils.updateParameters(params, optionalParameters); 190 | 191 | // Convert time delta in hours to milliseconds 192 | var msDelta = params.timeDelta * 3.6e6; 193 | 194 | // Millisecond epoch time of each day in the time series 195 | var periodList = ee.List.sequence( 196 | ee.Date(start).millis(), 197 | ee.Date(end).millis(), 198 | msDelta 199 | ); 200 | 201 | var periodCollection = ee.ImageCollection.fromImages( 202 | periodList.map(function (time) { 203 | var start = time; 204 | var end = ee.Date(time).advance(params.timeDelta, "hours"); 205 | 206 | return exports.periodFireBoundary( 207 | start, 208 | end, 209 | region, 210 | params.smooth, 211 | params.smoothKernel, 212 | params.timeDelta 213 | ); 214 | }) 215 | ); 216 | 217 | if (params.cumulative === true) { 218 | // Create a placeholder element 219 | var first = ee.List([ 220 | ee 221 | .Image(0) 222 | .rename("fire_mask") 223 | .int() 224 | // Store the start date since it will be used for all accumulated images. 225 | .set("start_date", ee.Date(start)), 226 | ]); 227 | 228 | // Iteratively add all previous boundaries to each boundary to get cumulative area burned for each time period 229 | var cumulativeBoundary = ee.List( 230 | periodCollection.iterate(accumulateMask, first) 231 | ); 232 | // Remove the first placeholder element 233 | cumulativeBoundary = ee.ImageCollection(cumulativeBoundary.slice(1)); 234 | periodCollection = cumulativeBoundary; 235 | } 236 | 237 | var dateString = ee.Date(end).format("yyyy_MM_dd_HH:mm:ss"); 238 | var collectionName = ee.String("FireMaskCollection/").cat(dateString); 239 | 240 | periodCollection = periodCollection.set( 241 | "system:id", 242 | collectionName, 243 | "start_date", 244 | start, 245 | "end_date", 246 | end, 247 | "cumulative", 248 | params.cumulative, 249 | "smoothed", 250 | params.smooth, 251 | "region", 252 | region, 253 | "time_delta", 254 | params.timeDelta 255 | ); 256 | 257 | return periodCollection; 258 | }; 259 | 260 | /** 261 | * Convert a binary mask image into a Feature Collection. The date field from the image will be transferred to the 262 | * features. 263 | * @param {ee.Image} img A binary mask to convert into polygons. 264 | * @param {number} scale The desired pixel size, in meters, of the input image. 265 | * @param {ee.Geometry} region The area to containing the image to generate polygons from. 266 | * @param {number, default 1e13} maxPixels The maximum number of pixels to sample when converting the image to vector. 267 | * @param {boolean, default false} simplify If true, ee.Geometry.simplify() will be run on the vectorized boundary to 268 | * remove stairstepping. 269 | * @param {number} maxError If simplifying, the maximum error introduced by simplification, in meters. Higher values 270 | * will lead to greater simplification. 271 | * @return {ee.FeatureCollection} A collection of polygons representing the binary mask. 272 | */ 273 | exports.vectorizeBoundary = function ( 274 | img, 275 | scale, 276 | region, 277 | maxPixels, 278 | simplify, 279 | maxError 280 | ) { 281 | maxPixels = maxPixels ? maxPixels : 1e13; 282 | 283 | var poly = img.reduceToVectors({ 284 | scale: scale, 285 | maxPixels: maxPixels, 286 | geometry: region, 287 | }); 288 | 289 | if (simplify === true) { 290 | poly = ee.FeatureCollection(poly).map(function (feature) { 291 | return ee.Feature(feature).simplify(ee.Number(maxError)); 292 | }); 293 | } 294 | // Convert the FeatureCollection to a Feature 295 | poly = ee.Feature(poly.geometry()); 296 | poly = poly.set({ start_date: img.get("start_date"), end_date: img.get("end_date") }); 297 | return poly; 298 | }; 299 | 300 | /** 301 | * Convert a collection of binary mask images into a Feature Collection. The date field from the image will be 302 | * transferred from each image to each corresponding feature. 303 | * @param {ee.ImageCollection} collection A collection of binary masks to convert into polygons. 304 | * @param {number} scale The desired pixel size, in meters, of the input image. 305 | * @param {ee.Geometry} region The area to containing the images to generate polygons from. 306 | * @param {number, default 1e13} maxPixels The maximum number of pixels to sample when converting the images to vectors. 307 | * @param {boolean, default false} simplify If true, ee.Geometry.simplify() will be run on the vectorized boundaries to 308 | * remove stairstepping. 309 | * @param {number} maxError If simplifying, the maximum error introduced by simplification, in meters. Higher values 310 | * will lead to greater simplification. 311 | * @return {ee.FeatureCollection} A collection of polygons representing the binary masks. 312 | */ 313 | exports.vectorizeBoundaryCollection = function ( 314 | collection, 315 | scale, 316 | region, 317 | maxPixels, 318 | simplify, 319 | maxError 320 | ) { 321 | maxPixels = maxPixels ? maxPixels : 1e13; 322 | 323 | var collectionPoly = collection.map(function (img) { 324 | return exports.vectorizeBoundary( 325 | img, 326 | scale, 327 | region, 328 | maxPixels, 329 | simplify, 330 | maxError 331 | ); 332 | }); 333 | return ee.FeatureCollection(collectionPoly); 334 | }; 335 | -------------------------------------------------------------------------------- /getNearestImage.js: -------------------------------------------------------------------------------- 1 | // Chooose the nearest image from a collection to a given date. 2 | exports.getNearestImage = function (imageCollection, targetDate) { 3 | var beforeImage = getNearestBeforeImage(imageCollection, targetDate); 4 | var afterImage = getNearestAfterImage(imageCollection, targetDate); 5 | 6 | // Creating a new collection and sorting allows for async operations that are MUCH faster than comparing time deltas synchronously 7 | var nearImages = ee.ImageCollection([beforeImage, afterImage]); 8 | nearImages = nearImages.sort("DATE_ACQUIRED"); 9 | return nearImages.first(); 10 | }; 11 | 12 | // Choose the nearest image from a collection after a given date 13 | function getNearestAfterImage(imageCollection, targetDate) { 14 | var filteredCollection = imageCollection 15 | .filterDate(targetDate, targetDate.advance(999, "year")) 16 | .sort("DATE_ACQUIRED"); 17 | return filteredCollection.first(); 18 | } 19 | 20 | // Choose the nearest image from a collection before a given date 21 | function getNearestBeforeImage(imageCollection, targetDate) { 22 | var filteredCollection = imageCollection 23 | .filterDate(targetDate.advance(-999, "year"), targetDate) 24 | .sort("DATE_ACQUIRED", false); 25 | return filteredCollection.first(); 26 | } 27 | -------------------------------------------------------------------------------- /radiometricCorrection.js: -------------------------------------------------------------------------------- 1 | var utils = require("users/aazuspan/geeTools:utils.js"); 2 | 3 | /** 4 | * Apply band-wise radiometric correction to remote sensing imagery using the 5 | * dark object subtraction (DOS) method. 6 | * @param {ee.Image} img The image to apply correction to. 7 | * @param {ee.Feature | ee.FeatureCollection} obj The location or extent of 8 | * the dark object within the image. 9 | * @param {ee.Number} scale The scale to calculate dark object statistics at. 10 | * @param {ee.Number} maxPixels Maximum number of pixels used to calculate 11 | * statistics. 12 | * @return {ee.Image} The radiometrically corrected image. 13 | */ 14 | exports.darkObjectSubtraction = function (img, obj, scale, maxPixels) { 15 | var offset = exports.reduceImage( 16 | img, 17 | ee.Reducer.mean(), 18 | obj, 19 | scale, 20 | maxPixels 21 | ); 22 | 23 | var imgAdj = img.subtract(offset); 24 | 25 | return imgAdj; 26 | }; 27 | 28 | /** 29 | * Rescale the mean and standard deviation of a target image to match a 30 | * reference image. This can be used to implement pseudo-invariant feature 31 | * (PIF) normalization if an appropriate geometry is passed. 32 | * @param {ee.Image} targetImage An image to rescale. 33 | * @param {ee.Image} referenceImage An image to rescale towards. 34 | * @param {ee.Geometry} geometry The region to generate image statistics over. 35 | * @param {ee.Number} scale The scale to generate image statistics at. 36 | * @param {ee.Number} maxPixels Maximum number of pixels used to calculate 37 | * statistics. 38 | * @return {ee.Image} A rescaled version of targetImage. 39 | */ 40 | exports.linearHistogramMatch = function ( 41 | targetImage, 42 | referenceImage, 43 | geometry, 44 | scale, 45 | maxPixels 46 | ) { 47 | var offsetTarget = exports.reduceImage( 48 | targetImage, 49 | ee.Reducer.mean(), 50 | geometry, 51 | scale, 52 | maxPixels 53 | ); 54 | var offset = exports.reduceImage( 55 | referenceImage, 56 | ee.Reducer.mean(), 57 | geometry, 58 | scale, 59 | maxPixels 60 | ); 61 | var rescale = exports 62 | .reduceImage( 63 | referenceImage, 64 | ee.Reducer.stdDev(), 65 | geometry, 66 | scale, 67 | maxPixels 68 | ) 69 | .divide( 70 | exports.reduceImage( 71 | targetImage, 72 | ee.Reducer.stdDev(), 73 | geometry, 74 | scale, 75 | maxPixels 76 | ) 77 | ); 78 | 79 | var rescaledTarget = targetImage 80 | .subtract(offsetTarget) 81 | .multiply(rescale) 82 | .add(offset); 83 | return rescaledTarget; 84 | }; 85 | 86 | /** 87 | * Create a constant image where each band represents the reduced value of the 88 | * corresponding band of the input image. 89 | * @param {ee.Image} img The input image to calculate reduced values for. 90 | * @param {ee.Reducer} reducer The reducer to apply to the image, such as 91 | * ee.Reducer.min() 92 | * @param {ee.Geometry} geometry The region to generate image statistics over. 93 | * Defaults to the geometry of the input image. 94 | * @param {ee.Number} scale The scale to generate image statistics at. Defaults 95 | * to the nominal scale of the input image. 96 | * @param {ee.Number} maxPixels Maximum number of pixels used to calculate 97 | * statistics. 98 | * @return {ee.Image} An image with the same number of bands as the input 99 | * image, where each band is a constant value of the reduced value of the 100 | * corresponding band of the input image. 101 | */ 102 | exports.reduceImage = function (img, reducer, geometry, scale, maxPixels) { 103 | if (utils.isMissing(geometry)) { 104 | geometry = img.geometry(); 105 | } 106 | 107 | if (utils.isMissing(scale)) { 108 | scale = img.projection().nominalScale(); 109 | } 110 | 111 | if (utils.isMissing(maxPixels)) { 112 | maxPixels = 1e12; 113 | } 114 | 115 | // Calculate the reduced image value(s) 116 | var imgReducedVal = img.reduceRegion({ 117 | reducer: reducer, 118 | geometry: geometry, 119 | scale: scale, 120 | maxPixels: maxPixels, 121 | }); 122 | 123 | var imgReduced = imgReducedVal.toImage(img.bandNames()); 124 | return imgReduced; 125 | }; 126 | -------------------------------------------------------------------------------- /test.js: -------------------------------------------------------------------------------- 1 | var should = require("users/aazuspan/should:test"); 2 | var climateTools = require("users/aazuspan/geeTools:climate.js"); 3 | 4 | 5 | should.equal(climateTools.hotDryWindyIndex(ee.Number(10), ee.Number(10)), 100, "Calculate HDWI"); 6 | should.equal(climateTools.windVelocity(ee.Number(-3), ee.Number(4)), 5, "Calculate wind velocity"); 7 | 8 | // should-test needs an almost equal test. Until then, we'll just round the result to the nearest int. 9 | should.equal(climateTools.vaporPressureDeficit(ee.Number(27), ee.Number(18)).round(), 3, "Calculate VPD"); 10 | should.equal(climateTools.relativeHumidity(ee.Number(0.0127), ee.Number(90000), ee.Number(25)).round(), 58, "Calculate RH"); -------------------------------------------------------------------------------- /utils.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Convert degrees to radians. 3 | * @param {ee.Number or ee.Image} deg An angle in degrees 4 | * @return {ee.Number or ee.Image} The angle in radians 5 | */ 6 | exports.deg2rad = function (deg) { 7 | var coeff = 180 / Math.PI; 8 | 9 | return deg.divide(coeff); 10 | }; 11 | 12 | /** 13 | * Check if an object has a value. Helpful for finding missing arguments. 14 | * @param {object} x Any object 15 | * @return {boolean} True if the object is missing, false if it is not. 16 | */ 17 | exports.isMissing = function (x) { 18 | if (x === undefined || x === null) { 19 | return true; 20 | } 21 | return false; 22 | }; 23 | 24 | /** 25 | * Check if an argument matches a list of choices. Return the argument if so, 26 | * throw an error if not. 27 | * @param {Object} arg An argument or arguments to test against choices. 28 | * @param {array} choices An array of choices that arg is allowed to take. 29 | * @param {bool, default false} allowSeveral If true, all args will be checked 30 | * against choices and each must be valid. If false, arg must be a single 31 | * object that is in choices. 32 | * @return {Object} If arg was in choices, arg will be returned. If not, an 33 | * error will be thrown. 34 | */ 35 | exports.matchArg = function (arg, choices, allowSeveral) { 36 | allowSeveral = allowSeveral | false; 37 | 38 | if (!Array.isArray(arg)) { 39 | if (exports.itemInList(arg, choices)) { 40 | return arg; 41 | } 42 | } else if (allowSeveral === 1) { 43 | if ( 44 | arg.every(function (item) { 45 | return exports.itemInList(item, choices); 46 | }) 47 | ) { 48 | return arg; 49 | } 50 | } 51 | 52 | throw 'Argument "' + arg + '" must be in "' + choices + '"'; 53 | }; 54 | 55 | /** 56 | * Check if an item is in a list. 57 | * @param {Object} item 58 | * @param {array} list 59 | * @return {bool} True if item is in list. False if not. 60 | */ 61 | exports.itemInList = function (item, list) { 62 | if (list.indexOf(item) >= 0) { 63 | return true; 64 | } 65 | return false; 66 | }; 67 | 68 | /** 69 | * Swap given parameters into a dictionary of default parameters. Given parameters can be null or false and will still 70 | * be swapped. Given parameters not included in the default set will be added, but I'm not sure why you'd want to do 71 | * that. 72 | * @param {Object} def A dictionary of default parameters. 73 | * @param {Object} given A dictionary of given parameters to substitue for defaults. 74 | * @return {Object} The default dictionary with values from the given dictionary. 75 | */ 76 | exports.updateParameters = function (def, given) { 77 | if (given) { 78 | for (var prop in given) { 79 | def[prop] = given[prop]; 80 | } 81 | } 82 | return def; 83 | }; 84 | 85 | /** 86 | * Combine a list of images into a single multi-band image. This is a convenience function over repeatedly calling 87 | * addBands for each image you want to combine. 88 | * @param {ee.List} imgList A list of images to combine. Images can be single or multiband. 89 | * @param {Object} [optionalParameters] A dictionary of optional parameters to override defaults. 90 | * @param {boolean, default true} [optionalParameters.prefix] If true, all band names will be prefixed with the list 91 | * index of the image it came from. This allows combining images with identical band names. If false, original band 92 | * names will be kept. If there are duplicate band names, an error will be thrown. 93 | * @param {ee.Dictionary, default null} [optionalParameters.props] Properties to store in the combined image. If null, 94 | * properties will be taken from the first image in imgList and the result will be identical to using addBands. 95 | * @return {ee.Image} An image with the bands of all images in imgList 96 | */ 97 | exports.combineImages = function (imgList, optionalParameters) { 98 | var first = ee.Image(ee.List(imgList).get(0)); 99 | 100 | // Default parameters 101 | var params = { 102 | prefix: true, 103 | props: first.toDictionary(first.propertyNames()), 104 | }; 105 | 106 | params = exports.updateParameters(params, optionalParameters); 107 | 108 | // Convert the list to a collection and collapse the collection into a multiband image. 109 | // Rename bands to match original band names. 110 | var combined = ee.ImageCollection 111 | // Convert the image list to a collection 112 | .fromImages(imgList) 113 | // Convert the collection to a multiband image 114 | .toBands() 115 | // Store properties 116 | .set(params.props); 117 | 118 | if (params.prefix === false) { 119 | // Grab a 1D list of original band names 120 | var bandNames = ee 121 | .List( 122 | imgList.map(function (img) { 123 | return img.bandNames(); 124 | }) 125 | ) 126 | .flatten(); 127 | combined = combined.rename(bandNames); 128 | } 129 | 130 | return combined; 131 | }; 132 | 133 | /** 134 | * Perform band-wise normalization on an image to convert values to range 0 - 1. 135 | * @param {ee.Image} img An image. 136 | * @param {object} [optionalParameters] A dictionary of optional parameters to override defaults. 137 | * @param {number} [optionalParameters.scale] The scale, in image units, to calculate image statistics at. 138 | * @param {ee.Geometry} [optionalParameters.region] The area to calculate image statistics over. 139 | * @param {number, default 1e13} [optionalParameters.maxPixels] The maximum number of pixels to sample when calculating 140 | * image statistics. 141 | * @return {ee.Image} The input image with all bands rescaled between 0 and 1. 142 | */ 143 | exports.normalizeImage = function (img, optionalParameters) { 144 | var params = { 145 | region: null, 146 | scale: null, 147 | maxPixels: 1e13, 148 | }; 149 | 150 | params = exports.updateParameters(params, optionalParameters); 151 | 152 | var min = img 153 | .reduceRegion({ 154 | reducer: ee.Reducer.min(), 155 | geometry: params.region, 156 | scale: params.scale, 157 | maxPixels: params.maxPixels, 158 | }) 159 | .toImage(img.bandNames()); 160 | 161 | var max = img 162 | .reduceRegion({ 163 | reducer: ee.Reducer.max(), 164 | geometry: params.region, 165 | scale: params.scale, 166 | maxPixels: params.maxPixels, 167 | }) 168 | .toImage(img.bandNames()); 169 | 170 | return img.subtract(min).divide(max.subtract(min)); 171 | }; 172 | 173 | /** 174 | * Perform band-wise cyclic encoding with a sine or cosine transformation and rescale to range 0 - 1. 175 | * @param {ee.Image} img An image with cyclic data. 176 | * @param {object} [optionalParameters] A dictionary of optional parameters to override defaults. 177 | * @param {string} [optionalParameters.transform] The encoding transformation to apply, either "sin" or "cos". Default 178 | * transformation is "sin". 179 | * @param {number} [optionalParameters.scale] The scale, in image units, to calculate image statistics at. 180 | * @param {ee.Geometry} [optionalParameters.region] The area to calculate image statistics over. 181 | * @param {number, default 1e13} [optionalParameters.maxPixels] The maximum number of pixels to sample when calculating 182 | * image statistics. 183 | * @return {ee.Image} The input image with all bands sine or cosine transformed and rescaled between 0 and 1. 184 | */ 185 | exports.cyclicEncode = function (img, optionalParameters) { 186 | var params = { 187 | transform: "sin", 188 | region: null, 189 | scale: null, 190 | maxPixels: 1e13, 191 | }; 192 | 193 | params = exports.updateParameters(params, optionalParameters); 194 | 195 | var max = img 196 | .reduceRegion({ 197 | reducer: ee.Reducer.max(), 198 | geometry: params.region, 199 | scale: params.scale, 200 | maxPixels: params.maxPixels, 201 | }) 202 | .toImage(img.bandNames()); 203 | 204 | var rescale = img.multiply(Math.PI).multiply(2).divide(max); 205 | 206 | var transform = ee.String(params.transform).toLowerCase().getInfo(); 207 | 208 | switch (transform) { 209 | case "cos": 210 | return rescale.cos(); 211 | case "sin": 212 | return rescale.sin(); 213 | default: 214 | throw 'Bad transform: "' + transform + '" not in [sin, cos].'; 215 | } 216 | }; 217 | 218 | /** 219 | * Perform band-wise stretching of an input image using a list of stretch values. For example, stretching an RGB image 220 | * with a stretch list [1, 2, 1] would double values of the green channel 221 | * @param {ee.Image} img A multiband image to stretch. 222 | * @param {list} stretch A list of stretch factors to apply band-wise to the image. The number of stretch values must 223 | * be equal to the number of image bands. 224 | * @returns An image with band values stretched. 225 | */ 226 | exports.bandStretch = function (img, stretch) { 227 | if (ee.List(stretch).size().neq(img.bandNames().size()).getInfo()) { 228 | throw "The number of stretch values must match the number of image bands."; 229 | } 230 | 231 | var stretchBands = ee 232 | .ImageCollection( 233 | stretch.map(function (val) { 234 | return ee.Image.constant(val); 235 | }) 236 | ) 237 | .toBands(); 238 | 239 | return img.multiply(stretchBands); 240 | }; 241 | --------------------------------------------------------------------------------