├── .gitattributes ├── .github └── workflows │ └── dispatch.yml ├── .gitignore ├── LICENSE ├── README.md ├── images └── force-udf.png ├── python ├── __init__.py ├── ard │ ├── __init__.py │ ├── exposed_soil │ │ ├── exp_soil.py │ │ └── readme.md │ ├── max_ndvi │ │ ├── max_ndvi.py │ │ └── readme.md │ └── medoid │ │ ├── __init__.py │ │ ├── medoid.py │ │ ├── readme.md │ │ └── requirements.txt ├── test_udf │ ├── __init__.py │ ├── test.py │ ├── test_ard_medoid_block_numba.py │ ├── test_ard_medoid_pixel_simple.py │ ├── test_ts_clear_observation_sequence.py │ ├── test_utils.py │ └── utils.py └── ts │ ├── __init__.py │ ├── dynamic-habitat-indices │ ├── dhi.py │ └── readme.md │ ├── harmonic-fit │ ├── __init__.py │ ├── harmonic.py │ ├── readme.md │ └── test_ts_harmonic.py │ └── mowingDetection │ ├── mowingDetection_UDF.py │ ├── profileAnalytics.JPG │ ├── profileAnalytics_broken.JPG │ ├── readme.md │ ├── scheme.jpg │ └── visualize_mowingDetection_UDF.py └── rstats ├── ard └── .gitkeep └── ts ├── .gitkeep ├── dynamic-habitat-indices ├── dhi.r └── readme.md └── spline-reconstruction ├── Bolten_Spline_FORCE_UDF.R └── readme.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Set the default behavior, in case people don't have core.autocrlf set. 2 | * text eol=lf 3 | 4 | # Explicitly declare text files you want to always be normalized and converted 5 | # to native line endings on checkout. 6 | *.py text 7 | *.md text 8 | *.r text 9 | *.R text 10 | 11 | # Denote all files that are truly binary and should not be modified. 12 | *.png binary 13 | *.jpg binary 14 | *.tif binary 15 | *.gif binary 16 | 17 | -------------------------------------------------------------------------------- /.github/workflows/dispatch.yml: -------------------------------------------------------------------------------- 1 | name: dispatch to FORCE 2 | 3 | on: 4 | push: 5 | branches: [ main ] 6 | 7 | jobs: 8 | dispatch: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - name: Repository Dispatch 12 | uses: peter-evans/repository-dispatch@v3 13 | with: 14 | token: ${{ secrets.DISPATCH }} 15 | repository: davidfrantz/force 16 | event-type: udf-dispatch 17 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; 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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 | # FORCE UDF 2 | 3 | **User-Defined Functions for FORCE** 4 | 5 | ![FORCE Logo](/images/force-udf.png) 6 | 7 | This repository collects *user-defined functions* (**UDF**) that can be plugged into [FORCE](https://github.com/davidfrantz/force). 8 | 9 | **Everybody** is encouraged to push their UDFs to this repository, such that the community as a whole benefits and has access to a broad variety of workflows. 10 | 11 | This extra step of publishing your workflow is a small step to overcome the ["*Valley of Death*"](https://twitter.com/gcamara/status/1127887595168514049) in Earth observation applications! Only a minimal documentation is needed, see [this example](https://github.com/davidfrantz/force-udf/blob/main/python/ard/medoid/readme.md). 12 | 13 | Supported are UDFs written in **Python** (needs FORCE v. >= 3.7.0) and **R** (needs FORCE v. >= 3.11.0). 14 | 15 | Tutorials [here (Python)](https://force-eo.readthedocs.io/en/latest/howto/udf_py.html) and [here (R).](https://force-eo.readthedocs.io/en/latest/howto/udf_r.html). -------------------------------------------------------------------------------- /images/force-udf.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/images/force-udf.png -------------------------------------------------------------------------------- /python/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/__init__.py -------------------------------------------------------------------------------- /python/ard/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ard/__init__.py -------------------------------------------------------------------------------- /python/ard/exposed_soil/exp_soil.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | """ 4 | >>> Copyright (C) 2022, Max Gerhards, Henning Buddenbaum, David Frantz 5 | """ 6 | 7 | def forcepy_init(dates, sensors, bandnames): 8 | """ 9 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 10 | sensors: numpy.ndarray[nDates](str) 11 | bandnames: numpy.ndarray[nBands](str) 12 | """ 13 | 14 | return bandnames 15 | 16 | 17 | def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 18 | """ 19 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16), nrows & ncols always 1 20 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 21 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 22 | sensors: numpy.ndarray[nDates](str) 23 | bandnames: numpy.ndarray[nBands](str) 24 | nodata: int 25 | nproc: number of allowed processes/threads (always 1) 26 | Write results into outarray. 27 | """ 28 | 29 | # remove nodata 30 | inarray = inarray[:, :, 0, 0] 31 | valid = np.where(inarray[:, 0] != nodata)[0] # skip no data; just check first band 32 | if len(valid) == 0: 33 | return 34 | 35 | # subset 36 | vals = inarray[valid,:] 37 | 38 | # band indices 39 | green = np.argwhere(bandnames == b'GREEN')[0][0] 40 | red = np.argwhere(bandnames == b'RED')[0][0] 41 | nir = np.argwhere(bandnames == b'NIR')[0][0] 42 | swir1 = np.argwhere(bandnames == b'SWIR1')[0][0] 43 | 44 | # remove "high" NDVI 45 | ndvi = (vals[:,nir]-vals[:,red]) / (vals[:,nir]+vals[:,red]) 46 | valid = np.where(ndvi < 0.3)[0] 47 | if len(valid) == 0: 48 | return 49 | 50 | # subset again 51 | vals = vals[valid,:] 52 | 53 | # Dry Bare Soil Index, Rasul et al. 2018 54 | inds = ((vals[:,swir1]-vals[:,green]) / (vals[:,swir1]+vals[:,green])) - ((vals[:,nir]-vals[:,red]) / (vals[:,nir]+vals[:,red])) 55 | 56 | if np.all(inds == 0): 57 | return 58 | 59 | # weighted average 60 | # there still is an error when weights are 0. Need to fix 61 | outarray[:] = np.average(vals, weights = inds, axis = 0) 62 | -------------------------------------------------------------------------------- /python/ard/exposed_soil/readme.md: -------------------------------------------------------------------------------- 1 | # Exposed soil synthesis image 2 | 3 | © 4 | Copyright 2022, Max Gerhards, Henning Buddenbaum, David Frantz 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``UDF`` 10 | - UDF type: ``PYTHON_TYPE = PIXEL`` 11 | - required parameters: ``none`` 12 | - required Python libraries: ``numpy`` 13 | 14 | -------------------------------------------------------------------------------- /python/ard/max_ndvi/max_ndvi.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | """ 4 | >>> Copyright (C) 2021-2022, Franz Schug, David Frantz 5 | """ 6 | 7 | def forcepy_init(dates, sensors, bandnames): 8 | """ 9 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 10 | sensors: numpy.ndarray[nDates](str) 11 | bandnames: numpy.ndarray[nBands](str) 12 | """ 13 | 14 | return bandnames 15 | 16 | 17 | def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 18 | """ 19 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16), nrows & ncols always 1 20 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 21 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 22 | sensors: numpy.ndarray[nDates](str) 23 | bandnames: numpy.ndarray[nBands](str) 24 | nodata: int 25 | nproc: number of allowed processes/threads (always 1) 26 | Write results into outarray. 27 | """ 28 | 29 | 30 | # remove nodata 31 | inarray = inarray[:, :, 0, 0] 32 | valid = np.where(inarray[:, 0] != nodata)[0] # skip no data; just check first band 33 | if len(valid) == 0: 34 | return 35 | 36 | # subset 37 | vals = inarray[valid,:] 38 | 39 | # NDVI 40 | red = np.argwhere(bandnames == b'RED')[0][0] 41 | nir = np.argwhere(bandnames == b'NIR')[0][0] 42 | ndvi = (vals[:,nir]-vals[:,red]) / (vals[:,nir]+vals[:,red]) 43 | 44 | outarray[:] = vals[np.argmax(ndvi),:] 45 | -------------------------------------------------------------------------------- /python/ard/max_ndvi/readme.md: -------------------------------------------------------------------------------- 1 | # Maximum NDVI composite 2 | 3 | © 4 | Copyright 2021-2022, Franz Schug, David Frantz 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``UDF`` 10 | - UDF type: ``PYTHON_TYPE = PIXEL`` 11 | - required parameters: ``none`` 12 | - required Python libraries: ``numpy`` 13 | 14 | -------------------------------------------------------------------------------- /python/ard/medoid/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ard/medoid/__init__.py -------------------------------------------------------------------------------- /python/ard/medoid/medoid.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.spatial.distance import squareform, pdist 3 | 4 | """ 5 | >>> Medoid 6 | >>> Copyright (C) 2021 Andreas Rabe 7 | """ 8 | 9 | def forcepy_init(dates, sensors, bandnames): 10 | """ 11 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 12 | sensors: numpy.ndarray[nDates](str) 13 | bandnames: numpy.ndarray[nBands](str) 14 | """ 15 | 16 | return bandnames 17 | 18 | 19 | def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 20 | """ 21 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16), nrows & ncols always 1 22 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 23 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 24 | sensors: numpy.ndarray[nDates](str) 25 | bandnames: numpy.ndarray[nBands](str) 26 | nodata: int 27 | nproc: number of allowed processes/threads (always 1) 28 | Write results into outarray. 29 | """ 30 | 31 | inarray = inarray[:, :, 0, 0] 32 | valid = np.where(inarray[:, 0] != nodata)[0] # skip no data; just check first band 33 | if len(valid) == 0: 34 | return 35 | pairwiseDistancesSparse = pdist(inarray[valid], 'euclidean') 36 | pairwiseDistances = squareform(pairwiseDistancesSparse) 37 | cumulativDistance = np.sum(pairwiseDistances, axis=0) 38 | argMedoid = valid[np.argmin(cumulativDistance)] 39 | medoid = inarray[argMedoid, :] 40 | outarray[:] = medoid 41 | -------------------------------------------------------------------------------- /python/ard/medoid/readme.md: -------------------------------------------------------------------------------- 1 | # Medoid 2 | 3 | © 4 | Copyright 2021, Andreas Rabe 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``PLG`` 10 | - UDF type: ``PYTHON_TYPE = PIXEL`` 11 | - required parameters: ``none`` 12 | - required Python libraries: ``numpy, scipy`` 13 | 14 | ## References 15 | 16 | - Flood, N. (2013): **Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median)**. *Remote Sensing 5(12)*, 6481-6500; [10.3390/rs5126481](https://doi.org/10.3390/rs5126481) 17 | - [wikipedia](https://en.wikipedia.org/wiki/Medoid) 18 | -------------------------------------------------------------------------------- /python/ard/medoid/requirements.txt: -------------------------------------------------------------------------------- 1 | scipy -------------------------------------------------------------------------------- /python/test_udf/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/test_udf/__init__.py -------------------------------------------------------------------------------- /python/test_udf/test.py: -------------------------------------------------------------------------------- 1 | from os import listdir 2 | from os.path import join, dirname, exists 3 | from unittest import TestCase 4 | import numpy as np 5 | from osgeo import gdal 6 | from numba import jit, prange, set_num_threads 7 | 8 | from ard.medoid.pixel_simple.medoid import forcepy_pixel as ardPixelSimple_Medoid 9 | from ard.medoid.block_numba.medoid import forcepy_block as ardBlockNumba_Medoid 10 | from ts.clear_observation_sequence.clear_observation_sequence \ 11 | import forcepy_pixel as tsPixelNumba_ClearObservationSequence 12 | 13 | from enmapboxprocessing.driver import Driver 14 | from enmapboxprocessing.rasterreader import RasterReader 15 | from enmapboxprocessing.utils import Utils as EnmapboxProcessingUtils 16 | 17 | 18 | class TestPixelSimpleMedoid(TestCase): 19 | 20 | def test_handmadePixel(self): 21 | nodata = -9999 22 | inarray = np.array([[0, 0], [0, 10], [10, 0], [10, 10], [5, 5]], dtype=np.int16) 23 | inarray = np.concatenate([inarray, inarray * 0 + nodata, inarray * 0 + nodata]) # add a lot of no data pixel 24 | outarray = np.array([nodata, nodata], dtype=np.int16) 25 | ardPixelSimple_Medoid(inarray, outarray, None, None, None, nodata, 1) 26 | assert np.all(outarray == (5, 5)) 27 | 28 | def test_realPixel(self): 29 | nodata = -9999 30 | inarray = np.array([[0, 0], [0, 10], [10, 0], [10, 10], [5, 5]], dtype=np.int16) 31 | inarray = np.concatenate([inarray, inarray * 0 + nodata, inarray * 0 + nodata]) # add a lot of no data pixel 32 | outarray = np.array([nodata, nodata], dtype=np.int16) 33 | ardPixelSimple_Medoid(inarray, outarray, None, None, None, nodata, 1) 34 | assert np.all(outarray == (5, 5)) 35 | print(Utils.ardBlock()) 36 | 37 | 38 | class TestBlockNumbaMedoid(TestCase): 39 | 40 | def test_handmadeBlock(self): 41 | nodata = -9999 42 | inarray = np.array([[0, 0], [0, 10], [10, 0], [10, 10], [5, 5]], dtype=np.int16) 43 | inarray = np.concatenate([inarray * 0 + nodata, inarray, inarray * 0 + nodata]) # add no data pixel 44 | nDates, nBands = inarray.shape 45 | nY, nX = 300, 300 46 | inarray = inarray.reshape((nDates, nBands, 1, 1)) * np.ones((nDates, nBands, nY, nX), dtype=np.int16) 47 | outarray = np.full((nBands, nY, nX), nodata, dtype=np.int16) 48 | nproc = 4 49 | ardBlockNumba_Medoid(inarray, outarray, None, None, None, nodata, nproc) 50 | print('Done', outarray) 51 | assert np.all(outarray == np.full_like(outarray, 5)) 52 | 53 | 54 | class TestCompleteTile(TestCase): 55 | 56 | def setUp(self): 57 | self.nproc = 8 58 | self.nodata = -9999 59 | self.inarrayBoa, self.dates, self.sensors, self.extent, self.crs = Utils.boaBlock() 60 | self.inarrayNdvi = Utils.ndviBlock(self.inarrayBoa) 61 | self.nDates, self.nBands, self.nY, self.nX = self.inarrayBoa.shape 62 | 63 | def test_blockNumba_Medoid(self): 64 | 65 | self.outarray = np.full((self.nBands, self.nY, self.nX), self.nodata, dtype=np.int16) 66 | 67 | ardBlockNumba_Medoid(self.inarrayBoa, self.outarray, self.dates, None, None, self.nodata, self.nproc) 68 | self.assertEqual(65343043, self.outarray.flatten()[::100].sum()) 69 | self.filename = 'c:/vsimem/blockNumba_Medoid.tif' 70 | 71 | def test_pixelNumba_ClearObservationSequence(self): 72 | self.outarray = np.full((self.nDates * 5, self.nY, self.nX), self.nodata, dtype=np.int16) 73 | self.bandNames = list() 74 | for date, sensor in zip(self.dates, self.sensors): 75 | datestamp = str(np.datetime64('1970-01-01') + np.timedelta64(date, 'D')).replace('-', '') 76 | for name in ['COS-1', 'COS0', 'COS+1', 'COS-1_DTIME', 'COS1_DTIME']: 77 | self.bandNames.append(f'{name}_NDV_{datestamp}_{sensor}') 78 | [print(n) for n in self.bandNames] 79 | utilsMapPixelNumba( 80 | tsPixelNumba_ClearObservationSequence, self.inarrayNdvi, self.outarray, self.dates, None, None, self.nodata, 81 | self.nproc 82 | ) 83 | #self.assertEqual(1, self.outarray.flatten()[::100].sum()) 84 | self.filename = 'c:/vsimem/pixelNumba_ClearObservationSequence.tif' 85 | 86 | def tearDown(self): 87 | print('write data') 88 | print(self.outarray.shape) 89 | writer = Driver(self.filename).createFromArray(self.outarray, extent=self.extent, crs=self.crs) 90 | for bandNo, bandName in enumerate(self.bandNames, 1): 91 | writer.setBandName(bandName, bandNo) 92 | writer.setNoDataValue(-9999) 93 | print('created', self.filename) 94 | 95 | 96 | class Utils(object): 97 | 98 | @classmethod 99 | def ndviBlock(cls, arrayBoa): 100 | red = arrayBoa[:, 2:3] 101 | nir = arrayBoa[:, 3:4] 102 | ndvi = np.clip((nir - red) / (nir + red) * 10000, -10000, 10000) 103 | ndvi[red == -9999] = -9999 104 | ndvi = ndvi.astype(np.int16) 105 | return ndvi 106 | 107 | @classmethod 108 | def boaBlock(cls): 109 | 110 | pickleFilename = join(dirname(__file__), 'ard_block.dat') 111 | print('read data') 112 | if not exists(pickleFilename): 113 | noData = -9999 114 | xsize, ysize = 1000, 100 115 | # test qai masking 116 | # reader = RasterReader('C:/Work/data/FORCE/deu/ard/X0069_Y0043/20190102_LEVEL2_SEN2B_QAI.tif') 117 | # qai = reader.gdalDataset.ReadAsArray(buf_xsize=100, buf_ysize=100) 118 | # Driver('c:/vsimem/mask.tif').createFromArray(mask[None], extent=reader.extent(), crs=reader.crs()) 119 | 120 | rootArd = r'C:\Work\data\FORCE\deu\ard\X0069_Y0043' 121 | array = list() 122 | dates = list() 123 | sensors = list() 124 | for name in listdir(rootArd): 125 | if name.endswith('BOA.tif') and name: 126 | date = np.datetime64(f'{name[:4]}-{name[4:6]}-{name[6:8]}') 127 | sensor = name[16:21] 128 | dates.append(date) 129 | sensors.append(sensor) 130 | print('read', name, sensor) 131 | if 'LND' in name: 132 | bandList = [1, 2, 3, 4, 5, 6] 133 | elif 'SEN2' in name: 134 | bandList = [1, 2, 3, 8, 9, 10] 135 | else: 136 | assert 0 137 | 138 | ds: gdal.Dataset = gdal.Open(join(rootArd, name.replace('BOA', 'QAI'))) 139 | qaiArray = ds.ReadAsArray(buf_xsize=xsize, buf_ysize=ysize) 140 | bit, code, mask = 1, 0, 3 # gives CloudState = clear 141 | cloudfree = np.right_shift(qaiArray, bit) & mask == code 142 | bit, code, mask = 3, 0, 1 # gives CloudShadow = no 143 | noshadow = np.right_shift(qaiArray, bit) & mask == code 144 | invalid = np.logical_not(np.logical_and(cloudfree, noshadow)) 145 | 146 | ds: gdal.Dataset = gdal.Open(join(rootArd, name)) 147 | boaArray = list() 148 | for bandNo in bandList: 149 | boaBandArray = ds.GetRasterBand(bandNo).ReadAsArray(buf_xsize=xsize, buf_ysize=ysize) 150 | boaBandArray[invalid] = noData 151 | boaArray.append(boaBandArray) 152 | array.append(boaArray) 153 | #if len(dates) == 5: break 154 | array = np.array(array, dtype=np.int16) 155 | assert dates == sorted(dates) 156 | dates = np.array([(date - np.datetime64('1970-01-01')).item().days for date in dates]) 157 | sensors = np.array(sensors) 158 | anyInputFilename = join(rootArd, name) 159 | EnmapboxProcessingUtils.pickleDump((array, dates, sensors, anyInputFilename), filename=pickleFilename) 160 | else: 161 | array, dates, sensors, anyInputFilename = EnmapboxProcessingUtils.pickleLoad(filename=pickleFilename) 162 | 163 | print(array.shape) 164 | reader = RasterReader(anyInputFilename) 165 | return array, dates, sensors, reader.extent(), reader.crs() 166 | 167 | 168 | @jit(nopython=True, nogil=True, parallel=True) 169 | def utilsMapPixelNumba(f, inblock, outblock, dates, sensors, bandnames, nodata, nproc): 170 | set_num_threads(nproc) 171 | 172 | nDates, nBands, nY, nX = inblock.shape 173 | for iYX in prange(nY * nX): 174 | iX = iYX % nX 175 | iY = iYX // nX 176 | inarray = inblock[:, :, iY, iX] 177 | outarray = outblock[:, iY, iX] 178 | f(inarray, outarray, dates, sensors, bandnames, nodata, nproc) 179 | -------------------------------------------------------------------------------- /python/test_udf/test_ard_medoid_block_numba.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | import numpy as np 3 | 4 | from .utils import Utils 5 | 6 | from ard.medoid.block_numba.medoid import forcepy_block, forcepy_init 7 | 8 | nodata = Utils.NO_DATA 9 | 10 | 11 | class TestArdBlockNumba_Medoid(TestCase): 12 | 13 | def test_handmade(self): 14 | inarray = np.array([[0, 0], [0, 10], [10, 0], [10, 10], [5, 5]], dtype=np.int16) 15 | inarray = np.concatenate([inarray * 0 + nodata, inarray, inarray * 0 + nodata]) # add no data pixel 16 | nDates, nBands = inarray.shape 17 | nY, nX = 300, 300 18 | inarray = inarray.reshape((nDates, nBands, 1, 1)) * np.ones((nDates, nBands, nY, nX), dtype=np.int16) 19 | outarray = np.full((nBands, nY, nX), nodata, dtype=np.int16) 20 | nproc = 4 21 | forcepy_block(inarray, outarray, None, None, None, nodata, nproc) 22 | self.assertTrue(np.all(outarray == np.full_like(outarray, 5))) 23 | 24 | def test_noData(self): 25 | inarray = np.array([[nodata, nodata]], dtype=np.int16).reshape((1, 2, 1, 1)) 26 | outarray = np.array([nodata, nodata], dtype=np.int16).reshape((2, 1, 1)) 27 | forcepy_block(inarray, outarray, None, None, None, nodata, 1) 28 | self.assertTrue(np.all(outarray == nodata)) 29 | 30 | def test_applyToRaster(self): 31 | outarray = Utils.applyBlockUdf( 32 | 'c:/vsimem/blockNumba_Medoid.tif', forcepy_init, forcepy_block, '20190101', '20191231' 33 | ) 34 | self.assertEqual(631050686, outarray.flatten().sum()) 35 | -------------------------------------------------------------------------------- /python/test_udf/test_ard_medoid_pixel_simple.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | import numpy as np 3 | 4 | from .utils import Utils 5 | 6 | from ard.medoid.pixel_simple.medoid import forcepy_pixel, forcepy_init 7 | 8 | 9 | class TestArdPixelSimple_Medoid(TestCase): 10 | 11 | def test_handmade(self): 12 | nodata = -9999 13 | inarray = np.array([[0, 0], [0, 10], [10, 0], [10, 10], [5, 5]], dtype=np.int16) 14 | nDates, nBands = inarray.shape 15 | inarray = inarray.reshape((nDates, nBands, 1, 1)) 16 | outarray = np.full((nBands), nodata, dtype=np.int16) 17 | forcepy_pixel(inarray, outarray, None, None, None, nodata, None) 18 | self.assertTrue(np.all(outarray == np.full_like(outarray, 5))) 19 | 20 | def test_applyToRaster(self): 21 | outarray = Utils.applyPixelSimpleUdf( 22 | 'c:/vsimem/pixelSimple_Medoid.tif', forcepy_init, forcepy_pixel, '20190101', '20191231' 23 | ) 24 | self.assertEqual(648523542, outarray.flatten().sum()) 25 | -------------------------------------------------------------------------------- /python/test_udf/test_ts_clear_observation_sequence.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | import numpy as np 3 | 4 | from .utils import Utils 5 | 6 | from ts.clear_observation_sequence.clear_observation_sequence import forcepy_block, forcepy_init 7 | 8 | nodata = Utils.NO_DATA 9 | 10 | 11 | class TestTsBlockNumba_ClearObservationSequence(TestCase): 12 | 13 | def test_handmade(self): 14 | assert 0 15 | 16 | def test_applyToRaster(self): 17 | outarray = Utils.applyNdviBlockUdf( 18 | 'c:/vsimem/blockNumba_ClearObservationSequence.tif', forcepy_init, forcepy_block, '20190101', '20191231' 19 | ) 20 | self.assertEqual(631050686, outarray.flatten().sum()) 21 | -------------------------------------------------------------------------------- /python/test_udf/test_utils.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | 3 | from test_udf.utils import Utils 4 | 5 | 6 | class TestUtils(TestCase): 7 | 8 | def test_createTestRaster(self): 9 | Utils.createTestRaster() 10 | 11 | def test_boaBlock(self): 12 | boaArray, dates, sensors, bandNames = Utils.boaBlock(dateMin='19810101', dateMax='19841231') 13 | self.assertEqual(4, boaArray.ndim) 14 | self.assertEqual((14, 6, 100, 1000), boaArray.shape) 15 | 16 | def test_ndviBlock(self): 17 | boaArray, dates, sensors, bandNames = Utils.boaBlock(dateMin='19810101', dateMax='19841231') 18 | ndviArray = Utils.ndviBlock(boaArray) 19 | self.assertEqual((14, 1, 100, 1000), ndviArray.shape) 20 | -------------------------------------------------------------------------------- /python/test_udf/utils.py: -------------------------------------------------------------------------------- 1 | import builtins 2 | from datetime import date as Date 3 | from multiprocessing.pool import Pool 4 | from os import listdir 5 | from os.path import join, dirname, exists, abspath 6 | from warnings import warn 7 | 8 | import numpy as np 9 | from osgeo import gdal 10 | from numba import jit, prange, set_num_threads 11 | from qgis._core import QgsRectangle, QgsCoordinateReferenceSystem, QgsRasterLayer 12 | 13 | from enmapboxprocessing.driver import Driver 14 | from enmapboxprocessing.rasterreader import RasterReader 15 | 16 | 17 | class Utils(object): 18 | TILE_DIRECTORY = r'\\141.20.140.222\dagobah\dc\deu\ard\X0069_Y0043' 19 | TILE_DIRECTORY = r'C:\Work\data\FORCE\deu\ts\X0069_Y0043' 20 | TSS_DIRNAME = dirname(__file__) 21 | BOA_NAMES1 = ['BLU', 'GRN', 'RED', 'NIR', 'SW1', 'SW2'] 22 | BOA_NAMES2 = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'] 23 | NO_DATA = -9999 24 | 25 | #CRS = QgsCoordinateReferenceSystem('EPSG:3035') 26 | #EXTENT = QgsRectangle( 27 | # 4526026.36304164957255125, 3266319.60796480439603329, 4556026.36304164957255125, 3269319.60796480439603329 28 | #) 29 | NPROC = 8 30 | 31 | @classmethod 32 | def applyPixelNumbaUdf(cls, filename, udf_init, udf_pixel, dateMin, dateMax, sensorFilter=None): 33 | inarray, dates, sensors = cls.boaBlock(dateMin, dateMax, sensorFilter) 34 | bandNames = np.array(udf_init(dates, sensors, Utils.BOA_NAMES2)) 35 | outarray = Utils.outBlock(len(bandNames), *inarray.shape[-2:]) 36 | mapPixelNumba(udf_pixel, inarray, outarray, dates, sensors, bandNames, cls.NO_DATA, cls.NPROC) 37 | cls.writeOutput(outarray, bandNames, filename) 38 | return outarray 39 | 40 | @classmethod 41 | def applyPixelSimpleUdf(cls, filename, udf_init, udf_pixel, dateMin, dateMax, sensorFilter=None): 42 | inarray, dates, sensors = cls.boaBlock(dateMin, dateMax, sensorFilter) 43 | bandNames = np.array(udf_init(dates, sensors, Utils.BOA_NAMES2)) 44 | outarray = Utils.outBlock(len(bandNames), *inarray.shape[-2:]) 45 | mapPixelSimple(udf_pixel, inarray, outarray, dates, sensors, bandNames, cls.NO_DATA, cls.NPROC) 46 | cls.writeOutput(outarray, bandNames, filename) 47 | return outarray 48 | 49 | @classmethod 50 | def applyBlockUdf(cls, filename, udf_init, udf_block, dateMin, dateMax, sensorFilter=None): 51 | inarray, dates, sensors = cls.boaBlock(dateMin, dateMax, sensorFilter) 52 | bandNames = udf_init(dates, sensors, cls.BOA_NAMES2) 53 | outarray = cls.outBlock(len(bandNames), *inarray.shape[-2:]) 54 | udf_block(inarray, outarray, dates, None, None, cls.NO_DATA, cls.NPROC) 55 | cls.writeOutput(outarray, bandNames, filename) 56 | return outarray 57 | 58 | @classmethod 59 | def applyNdviBlockUdf(cls, filename, udf_init, udf_block, dateMin, dateMax, sensorFilter=None): 60 | inarray, dates, sensors = cls.boaBlock(dateMin, dateMax, sensorFilter) 61 | inarray = cls.ndviBlock(inarray) 62 | bandNames = udf_init(dates, sensors, ['NDVI']) 63 | outarray = cls.outBlock(len(bandNames), *inarray.shape[-2:]) 64 | udf_block(inarray, outarray, dates, None, None, cls.NO_DATA, cls.NPROC) 65 | cls.writeOutput(outarray, bandNames, filename) 66 | return outarray 67 | 68 | 69 | @classmethod 70 | def writeOutput(cls, outBlock, bandNames, filename): 71 | layer = QgsRasterLayer(join(cls.TSS_DIRNAME, 'BLU_TSS.tif')) 72 | driver = Driver(filename, 'GTiff', 'INTERLEAVE=BAND COMPRESS=LZW PREDICTOR=2 TILED=YES BIGTIFF=YES'.split()) 73 | writer = driver.createFromArray(outBlock, layer.extent(), layer.crs()) 74 | for bandNo, bandName in enumerate(bandNames, 1): 75 | writer.setBandName(bandName, bandNo) 76 | writer.setNoDataValue(cls.NO_DATA) 77 | 78 | @classmethod 79 | def outBlock(cls, nBands, nY, nX): 80 | return np.full((nBands, nY, nX), cls.NO_DATA, dtype=np.int16) 81 | 82 | @classmethod 83 | def ndviBlock(cls, arrayBoa): 84 | red = arrayBoa[:, 2:3] 85 | nir = arrayBoa[:, 3:4] 86 | ndvi = np.clip((nir - red) / (nir + red) * 10000, -10000, 10000) 87 | ndvi[red == cls.NO_DATA] = cls.NO_DATA 88 | ndvi = ndvi.astype(np.int16) 89 | return ndvi 90 | 91 | @classmethod 92 | def boaBlock(cls, dateMin, dateMax, sensorFilter=None): 93 | readers = [RasterReader(join(cls.TSS_DIRNAME, name + '_TSS.tif')) for name in cls.BOA_NAMES1] 94 | array = list() 95 | dates = list() 96 | sensors = list() 97 | for bandNo in range(1, readers[0].bandCount() + 1): 98 | name = readers[0].bandName(bandNo) 99 | datestamp = name[:8] 100 | sensor = name[16:21] 101 | if dateMin is not None: 102 | if datestamp < dateMin: 103 | continue 104 | if dateMax is not None: 105 | if datestamp > dateMax: 106 | continue 107 | if sensorFilter is not None: 108 | if sensor not in sensorFilter: 109 | continue 110 | daysSinceEpoch = (Date(int(name[:4]), int(name[4:6]), int(name[6:8])) - Date(1970, 1, 1)).days 111 | dates.append(daysSinceEpoch) 112 | sensors.append(sensor) 113 | for reader in readers: 114 | array.append(reader.array(bandList=[bandNo])[0]) 115 | array = np.array(array).reshape((-1, 6, readers[0].height(), readers[0].width())) 116 | dates = np.array(dates, np.int16) 117 | sensors = np.array(sensors) 118 | return array, dates, sensors 119 | 120 | @classmethod 121 | def createTestRaster_OLD(cls): 122 | if exists(cls.ARD_FILENAME): 123 | warn(f'Creation skipped, ARD file already exists: {cls.ARD_FILENAME}') 124 | return 125 | 126 | names = list() 127 | for name in listdir(cls.TILE_DIRECTORY): 128 | if name.endswith('BOA.tif') and name: 129 | names.append(name) 130 | #if len(names) == 3: break 131 | 132 | array = list() 133 | dates = list() 134 | sensors = list() 135 | namesSelected =list() 136 | for name in sorted(names): 137 | date = np.datetime64(f'{name[:4]}-{name[4:6]}-{name[6:8]}') 138 | sensor = name[16:21] 139 | dates.append(date) 140 | sensors.append(sensor) 141 | print('read', name, sensor) 142 | buf_xsize, buf_ysize = 1000, 100 143 | if 'LND' in name: 144 | xoff, yoff = 0, 520 145 | xsize, ysize = 1000, 100 146 | bandList = [1, 2, 3, 4, 5, 6] 147 | elif 'SEN2' in name: 148 | xoff, yoff = 0 * 3, 520 * 3 149 | xsize, ysize = 1000 * 3, 100 * 3 150 | bandList = [1, 2, 3, 8, 9, 10] 151 | else: 152 | assert 0 153 | 154 | ds: gdal.Dataset = gdal.Open(join(cls.TILE_DIRECTORY, name.replace('BOA', 'QAI'))) 155 | qaiArray = ds.ReadAsArray(xoff, yoff, xsize, ysize, None, buf_xsize, buf_ysize) 156 | 157 | bit, code, mask = 1, 0, 3 # gives CloudState = clear 158 | valid = np.right_shift(qaiArray, bit) & mask == code 159 | 160 | bit, code, mask = 3, 0, 1 # gives CloudShadow = no 161 | valid = np.logical_and(np.right_shift(qaiArray, bit) & mask == code, valid) 162 | 163 | bit, code, mask = 4, 0, 1 # gives Snow = no 164 | valid = np.logical_and(np.right_shift(qaiArray, bit) & mask == code, valid) 165 | 166 | bit, code, mask = 8, 0, 1 # gives Subzero = no 167 | valid = np.logical_and(np.right_shift(qaiArray, bit) & mask == code, valid) 168 | 169 | bit, code, mask = 9, 0, 1 # gives Saturation = no 170 | valid = np.logical_and(np.right_shift(qaiArray, bit) & mask == code, valid) 171 | 172 | validFraction = np.sum(valid) / buf_xsize * buf_ysize 173 | if validFraction < 0.1: 174 | print('skip', name) 175 | continue 176 | 177 | namesSelected.append(name) 178 | 179 | invalid = np.logical_not(valid) 180 | 181 | ds: gdal.Dataset = gdal.Open(join(cls.TILE_DIRECTORY, name)) 182 | for bandNo in bandList: 183 | rb: gdal.Band = ds.GetRasterBand(bandNo) 184 | boaBandArray = rb.ReadAsArray(xoff, yoff, xsize, ysize, buf_xsize, buf_ysize) 185 | boaBandArray[invalid] = cls.NO_DATA 186 | array.append(boaBandArray) 187 | 188 | driver = Driver( 189 | cls.ARD_FILENAME, 'GTiff', 'INTERLEAVE=BAND COMPRESS=LZW PREDICTOR=2 TILED=YES BIGTIFF=YES'.split() 190 | ) 191 | writer = driver.createFromArray(array, cls.EXTENT, cls.CRS) 192 | writer.setNoDataValue(cls.NO_DATA) 193 | bandNo = 0 194 | for name in sorted(namesSelected): 195 | for boaName in cls.BOA_NAMES: 196 | bandNo += 1 197 | writer.setBandName(name.replace('BOA.tif', boaName), bandNo) 198 | 199 | @classmethod 200 | def createTestRaster(cls): 201 | if exists(join(cls.TSS_DIRNAME, 'BLU_TSS.tif')): 202 | warn('Creation skipped, TSS files already exists: '+ join(cls.TSS_DIRNAME, '*_TSS.tif')) 203 | return 204 | bandList = list() 205 | 206 | for name in ['BLU', 'GRN', 'RED', 'NIR', 'SW1', 'SW2']: 207 | ds: gdal.Dataset = gdal.Open(join(cls.TILE_DIRECTORY, f'1984-2020_001-365_HL_TSA_LNDLG_{name}_TSS.tif')) 208 | translateOptions = gdal.TranslateOptions( 209 | format='GTiff', bandList=bandList, srcWin=[0, 520, 1000, 100], 210 | creationOptions='INTERLEAVE=BAND COMPRESS=LZW PREDICTOR=2 BIGTIFF=YES'.split() 211 | ) 212 | outds: gdal.Dataset = gdal.Translate(join(cls.TSS_DIRNAME, name + '_TSS.tif'), ds, options=translateOptions) 213 | outds.SetMetadata(ds.GetMetadata('FORCE'), 'FORCE') 214 | for bandNo in range(1, ds.RasterCount + 1): 215 | rb: gdal.Band = ds.GetRasterBand(bandNo) 216 | outrb: gdal.Band = outds.GetRasterBand(bandNo) 217 | outrb.SetDescription(rb.GetDescription()) 218 | outrb.SetMetadata(rb.GetMetadata('FORCE'), 'FORCE') 219 | 220 | 221 | def mapPixelSimple(f, inblock, outblock, dates, sensors, bandnames, nodata, nproc): 222 | 223 | argss = list() 224 | nDates, nBands, nY, nX = inblock.shape 225 | for iYX in range(nY * nX): 226 | iX = iYX % nX 227 | iY = iYX // nX 228 | inarray = inblock[:, :, iY: iY + 1, iX:iX + 1] 229 | outarray = outblock[:, iY, iX] 230 | f(inarray, outarray, dates, sensors, bandnames, nodata, nproc) 231 | 232 | 233 | @jit(nopython=True, nogil=True, parallel=True) 234 | def mapPixelNumba(f, inblock, outblock, dates, sensors, bandnames, nodata, nproc): 235 | set_num_threads(nproc) 236 | 237 | nDates, nBands, nY, nX = inblock.shape 238 | for iYX in prange(nY * nX): 239 | iX = iYX % nX 240 | iY = iYX // nX 241 | inarray = inblock[:, :, iY: iY + 1, iX:iX + 1] 242 | outarray = outblock[:, iY, iX] 243 | f(inarray, outarray, dates, sensors, bandnames, nodata, nproc) 244 | -------------------------------------------------------------------------------- /python/ts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ts/__init__.py -------------------------------------------------------------------------------- /python/ts/dynamic-habitat-indices/dhi.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import warnings 3 | 4 | """ 5 | >>> Dynamic Habitat Indices 6 | >>> Copyright (C) 2021 Andreas Rabe 7 | """ 8 | 9 | def forcepy_init(dates, sensors, bandnames): 10 | """ 11 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 12 | sensors: numpy.ndarray[nDates](str) 13 | bandnames: numpy.ndarray[nBands](str) 14 | """ 15 | 16 | return ['cumulative', 'minimum', 'variation'] 17 | 18 | 19 | def forcepy_block(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 20 | """ 21 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16) 22 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 23 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 24 | sensors: numpy.ndarray[nDates](str) 25 | bandnames: numpy.ndarray[nBands](str) 26 | nodata: int 27 | nproc: number of allowed processes/threads 28 | Write results into outarray. 29 | """ 30 | 31 | # prepare data 32 | inarray = inarray[:, 0].astype(np.float32) # cast to float ... 33 | invalid = inarray == nodata 34 | if np.all(invalid): 35 | return 36 | inarray[invalid] = np.nan # ... and inject NaN to enable np.nan*-functions 37 | 38 | # calculate DHI 39 | with warnings.catch_warnings(): 40 | warnings.simplefilter("ignore", RuntimeWarning) 41 | cumulative = np.nansum(inarray, axis=0) / 1e2 42 | minimum = np.nanmin(inarray, axis=0) 43 | variation = np.nanstd(inarray, axis=0) / np.nanmean(inarray, axis=0) * 1e4 44 | 45 | # store results 46 | for arr, outarr in zip([cumulative, minimum, variation], outarray): 47 | valid = np.isfinite(arr) 48 | outarr[valid] = arr[valid] 49 | -------------------------------------------------------------------------------- /python/ts/dynamic-habitat-indices/readme.md: -------------------------------------------------------------------------------- 1 | # Dynamic Habitat Indices 2 | 3 | © 4 | Copyright 2021, Andreas Rabe 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``TSA`` 10 | - UDF type: ``PYTHON_TYPE = BLOCK`` 11 | - required parameters: 12 | - ``INTERPOLATE = RBF`` with very large kernels 13 | - ``DATE_RANGE``: set to one(!) full year 14 | - required Python libraries: ``numpy`` 15 | 16 | ## References 17 | 18 | - Radeloff, V. C., M. Dubinin, N. C. Coops, A. Allen, T. M. Brooks, M. Clayton, G. Costa, C. H. Graham, D. Helmers, A. R. Ives, D. Kolesov, A. M. Pidgeon, G. Rapacciuolo, E. Razenkova, N. Suttidate, B. E. Young, L. Zhu, and M. Hobi. (2019): **The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity**. *Remote Sensing of Environment, 222*, 204-214. [10.1016/j.rse.2018.12.009](https://doi.org/10.1016/j.rse.2018.12.009) 19 | - Hobi, M.L., Dubinin, M., Graham, C.H., Coops, N.C., Clayton, M.K., Pidgeon, A.M., & Radeloff, V.C. (2017): **A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness.** *Remote Sensing of Environment, 195*, 142-152. [10.1016/j.rse.2017.04018](https://doi.org/10.1016/j.rse.2017.04018) 20 | -------------------------------------------------------------------------------- /python/ts/harmonic-fit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ts/harmonic-fit/__init__.py -------------------------------------------------------------------------------- /python/ts/harmonic-fit/harmonic.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime, timedelta 2 | 3 | import numpy as np 4 | from scipy.optimize import curve_fit 5 | 6 | """ 7 | >>> Harmonic time series fit 8 | >>> Copyright (C) 2021 Andreas Rabe 9 | """ 10 | 11 | # some global config variables 12 | date_start = 16436 # days since epoch (1970-01-01) 13 | date_end = 18627 # days since epoch (1970-01-01) 14 | step = 16 # days 15 | 16 | 17 | def forcepy_init(dates, sensors, bandnames): 18 | """ 19 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 20 | sensors: numpy.ndarray[nDates](str) 21 | bandnames: numpy.ndarray[nBands](str) 22 | """ 23 | 24 | bandnames = [(datetime(1970, 1, 1) + timedelta(days=days)).strftime('%Y%m%d') + ' sin-interpolation' 25 | for days in range(date_start, date_end, step)] 26 | return bandnames 27 | 28 | 29 | # regressor 30 | # define all three models from the paper 31 | def objective_simple(x, a0, a1, b1, c1): 32 | return a0 + a1 * np.cos(2 * np.pi / 365 * x) + b1 * np.sin(2 * np.pi / 365 * x) + c1 * x 33 | 34 | 35 | def objective_advanced(x, a0, a1, b1, c1, a2, b2): 36 | return objective_simple(x, a0, a1, b1, c1) + a2 * np.cos(4 * np.pi / 365 * x) + b2 * np.sin(4 * np.pi / 365 * x) 37 | 38 | 39 | def objective_full(x, a0, a1, b1, c1, a2, b2, a3, b3): 40 | return objective_advanced(x, a0, a1, b1, c1, a2, b2) + a3 * np.cos(6 * np.pi / 365 * x) + b3 * np.sin( 41 | 6 * np.pi / 365 * x) 42 | 43 | 44 | # - choose which model to use 45 | objective = objective_full 46 | 47 | 48 | def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 49 | """ 50 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16), nrows & ncols always 1 51 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 52 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 53 | sensors: numpy.ndarray[nDates](str) 54 | bandnames: numpy.ndarray[nBands](str) 55 | nodata: int 56 | nproc: number of allowed processes/threads (always 1) 57 | Write results into outarray. 58 | """ 59 | 60 | # prepare dataset 61 | profile = inarray.flatten() 62 | valid = profile != nodata 63 | if not np.any(valid): 64 | return 65 | xtrain = dates[valid] 66 | ytrain = profile[valid] 67 | 68 | # fit 69 | popt, _ = curve_fit(objective, xtrain, ytrain) 70 | 71 | # predict 72 | xtest = np.array(range(date_start, date_end, step)) 73 | ytest = objective(xtest, *popt) 74 | 75 | outarray[:] = ytest 76 | -------------------------------------------------------------------------------- /python/ts/harmonic-fit/readme.md: -------------------------------------------------------------------------------- 1 | # Harmonic Time Series fitting 2 | 3 | © 4 | Copyright 2021, Andreas Rabe 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``TSA`` 10 | - UDF type: ``PYTHON_TYPE = PIXEL`` 11 | - required parameters:``none`` 12 | - required Python libraries: ``numpy, scipy`` 13 | 14 | ## References 15 | 16 | - Zhu, Z., Woodcock, C.E., Hoden, C., Yang, Z. (2015): **Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time**. *Remote Sensing of Environment, 162*, 67-83. [10.1016/j.rse.2015.02.009](https://doi.org/10.1016/j.rse.2015.02.009) 17 | -------------------------------------------------------------------------------- /python/ts/harmonic-fit/test_ts_harmonic.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | # todo this is just a saved version - clean this up! 3 | from scipy.optimize import curve_fit 4 | from harmonic import objective 5 | 6 | def test(): # pure python test 7 | from matplotlib.pylab import plot, show 8 | from test_udf.utils import Utils 9 | boaArray, dates, sensors = Utils.boaBlock(dateMin='20100101', dateMax='20130101') # 3 years of data 10 | ndviArray = Utils.ndviBlock(boaArray) 11 | profile = ndviArray[:, 0, 10, 10].astype(float) 12 | valid = profile != -9999 13 | xtrain = dates[valid] 14 | ytrain = profile[valid] 15 | 16 | # fit 17 | popt, _ = curve_fit(objective, xtrain, ytrain) 18 | print(min(xtrain), max(xtrain)) 19 | # predict 20 | xtest = np.linspace(min(xtrain), max(xtrain), 100) 21 | ytest = objective(xtest, *popt) 22 | 23 | plot(xtrain, ytrain, '*') 24 | plot(xtest, ytest, '-') 25 | show() 26 | 27 | 28 | def test2(): # forcepy test 29 | from matplotlib.pylab import plot, show 30 | from test_udf.utils import Utils 31 | boaArray, dates, sensors = Utils.boaBlock(dateMin='20100101', dateMax='20130101') # 3 years of data 32 | ndviArray = Utils.ndviBlock(boaArray) 33 | inarray = ndviArray[:, :, 10:11, 10:11] 34 | 35 | # doit 36 | bandnames = forcepy_init(dates, sensors, Utils.BOA_NAMES1) 37 | outarray = np.full(shape=(len(bandnames), 1, 1), fill_value=-9999) 38 | forcepy_pixel(inarray, outarray, dates, sensors, bandnames, -9999, 1) 39 | 40 | # plot result 41 | profile = inarray.flatten() 42 | valid = profile != -9999 43 | xtrain = dates[valid] 44 | ytrain = profile[valid] 45 | xtest = list(range(date_start, date_end, step)) 46 | ytest = outarray.flatten() 47 | plot(xtrain, ytrain, '*') 48 | plot(xtest, ytest, '-') 49 | show() 50 | 51 | if __name__ == '__main__': 52 | test() 53 | -------------------------------------------------------------------------------- /python/ts/mowingDetection/mowingDetection_UDF.py: -------------------------------------------------------------------------------- 1 | from scipy import interpolate 2 | from datetime import datetime, timedelta 3 | import time 4 | import numpy as np 5 | import warnings 6 | 7 | """ 8 | >>> Mowing detection 9 | >>> Copyright (C) 2021 Marcel Schwieder and Max Wesemeyer 10 | """ 11 | 12 | 13 | def get_cso(x, y, nodata=-9999, verbose=False, SoS=2018.2, EOS=2018.85): 14 | # if no gap is found it will return 5 days as gap 15 | # in case the last potential observation misses the function calculates the gap to the EOS 16 | if np.all(y == nodata): 17 | nodata_ratio = 0 18 | return nodata_ratio, (x[-1] - x[0])*365, nodata 19 | nodata_sum = np.sum(np.where(y==nodata, True, False)) 20 | 21 | nodata_ratio = 1-(nodata_sum/len(y)) 22 | data_gap = 0 23 | data_gap_indeces = [] 24 | data_gap_dates_list = [] 25 | for index, value in enumerate(y): 26 | if value == nodata: 27 | if index < 1: 28 | continue 29 | data_gap += 1 30 | if data_gap == 0: 31 | data_gap_indeces.append(index-1) 32 | data_gap_indeces.append(index) 33 | else: 34 | if len(x[data_gap_indeces]) >= 1: 35 | data_gap_indeces.append(index) 36 | gap_dates = x[data_gap_indeces] 37 | gap_days = (gap_dates[-1]-gap_dates[0])*365 38 | data_gap_dates_list.append(gap_days) 39 | else: 40 | data_gap_dates_list.append(0) 41 | data_gap = 0 42 | data_gap_indeces = [] 43 | ######################### 44 | # calculating gap to EOS 45 | index_to_end_save = -1 46 | for indeces_to_end in range(1, len(y)): 47 | if y[-indeces_to_end] == nodata: 48 | index_to_end_save = -(indeces_to_end + 1) 49 | continue 50 | else: 51 | break 52 | gap_to_EOS = (EOS - x[index_to_end_save]) * 365 53 | data_gap_dates_list.append(gap_to_EOS) 54 | ######################### 55 | # calculating gap to SOS 56 | index_to_start_save = 0 57 | for indeces_to_start in range(len(y)): 58 | if y[indeces_to_start] == nodata: 59 | index_to_start_save = (indeces_to_start + 1) 60 | continue 61 | else: 62 | break 63 | gap_to_SOS = (x[index_to_start_save]-SoS) * 365 64 | data_gap_dates_list.append(gap_to_SOS) 65 | ######################### 66 | if int(max(data_gap_dates_list)) == 0: 67 | data_gap_dates_list.append(5) 68 | if verbose: 69 | print(max(data_gap_dates_list), 'MAX GAP') 70 | print(x, y) 71 | return nodata_ratio, max(data_gap_dates_list), len(y)-nodata_sum 72 | 73 | 74 | def toYearFraction(date): 75 | 76 | def sinceEpoch(date): # returns seconds since epoch 77 | return time.mktime(date.timetuple()) 78 | 79 | s = sinceEpoch 80 | 81 | year = date.year 82 | startOfThisYear = datetime(year=year, month=1, day=1) 83 | startOfNextYear = datetime(year=year + 1, month=1, day=1) 84 | 85 | yearElapsed = s(date) - s(startOfThisYear) 86 | yearDuration = s(startOfNextYear) - s(startOfThisYear) 87 | fraction = yearElapsed / yearDuration 88 | 89 | return date.year + fraction 90 | 91 | 92 | def detectMow_S2_new(xs, ys, clearWd, yr, type='ConHull', nOrder=3, model='linear'): 93 | warnings.simplefilter('ignore') 94 | another_thrs = 0.15 95 | 96 | Y = np.asarray(ys)/10000 97 | X = np.asarray(xs) 98 | 99 | Season_min_frac = yr + GLstart 100 | Season_max_frac = yr + GLend 101 | Start_frac = yr + PSstart 102 | End_frac = yr + PSend 103 | 104 | if type == 'ConHull': 105 | validIndex = Y < 1 106 | Y = Y[validIndex] 107 | X = X[validIndex] 108 | validIndex_2 = Y > 0 109 | Y = Y[validIndex_2] 110 | X = X[validIndex_2] 111 | 112 | ############################################## 113 | # averages duplicates in the time series 114 | records_array = X 115 | vals, inverse, count = np.unique(records_array, return_inverse=True, return_counts=True) 116 | 117 | idx_vals_repeated = np.where(count > 1)[0] 118 | 119 | vals_repeated = vals[idx_vals_repeated] 120 | 121 | new_x_ = np.unique(X) 122 | new_y_ = np.zeros(shape=vals.shape) 123 | for repeated_value in vals_repeated: 124 | where = np.where(X == repeated_value) 125 | new_y_[np.where(new_x_ == repeated_value)] = np.mean(Y[where]) 126 | 127 | rows, cols = np.where(inverse == idx_vals_repeated[:, np.newaxis]) 128 | 129 | mask = np.ones(shape=X.shape, dtype=bool) 130 | mask[cols] = False 131 | result = Y[mask] 132 | 133 | mask = np.ones(new_x_.shape, dtype=bool) 134 | mask[idx_vals_repeated] = False 135 | new_y_[mask] = result 136 | Y = new_y_ 137 | X = new_x_ 138 | 139 | ############################################## 140 | 141 | # filter time series to season (check if needed or a code legacy) 142 | SoGLSdiff = np.abs(X - Season_min_frac) 143 | SoGLS = np.where(SoGLSdiff == np.nanmin(SoGLSdiff)) 144 | EoGLS = np.abs(X - Season_max_frac) 145 | EoGLS = np.where(EoGLS == np.nanmin(EoGLS)) 146 | Y = np.asarray(Y[SoGLS[0][0]:EoGLS[0][0]]) 147 | X = np.asarray(X[SoGLS[0][0]:EoGLS[0][0]]) 148 | 149 | # calculate NDVI difference (t1) - (t-1) 150 | yT1 = np.asarray(Y[1:]) 151 | yT2 = np.asarray(Y[:-1]) 152 | 153 | YDiffzero = [0] 154 | YDiff = yT1 - yT2 155 | YDiff = np.append(YDiffzero, YDiff) 156 | 157 | EVI_STD = np.nanstd(Y) 158 | EVI_mean = np.nanmean(Y) 159 | EVI_obs = sum(~np.isnan(Y)) 160 | EVI_obs_pot = EVI_obs / len(Y) 161 | 162 | LoS = int(X[len(X) - 1] * 365 - X[0] * 365) 163 | EVI_obs_potII = EVI_obs / (LoS / 5) 164 | 165 | # identify first peak somewhere around the "mid" of the season 166 | # DOY 120 167 | MoSStart = np.abs(X - Start_frac) 168 | MoSStart = np.where(MoSStart == np.min(MoSStart)) 169 | 170 | # DOY 240 171 | MoSEnd = np.abs(X - End_frac) 172 | MoSEnd = np.where(MoSEnd == np.min(MoSEnd)) 173 | 174 | YPeakSub = Y[MoSStart[0][0]:MoSEnd[0][0]] 175 | 176 | if len(YPeakSub) == 0: 177 | return 178 | 179 | MoSPeak = np.nanmax(YPeakSub) 180 | MoSIndex = np.where(YPeakSub == MoSPeak)[0][0] 181 | IndexDiff = len(X[0:MoSStart[0][0]]) 182 | MoSIndex = MoSIndex + IndexDiff 183 | 184 | earlyIndex2 = [] 185 | lateIndex2 = [] 186 | 187 | # todo check if early and late peak equals Y0 188 | Y0 = np.argwhere(np.isfinite(Y)) 189 | Y0 = np.min(Y0) 190 | 191 | if MoSIndex <= 2: 192 | if MoSIndex == 0: 193 | earlyPeak1 = Y[0] 194 | else: 195 | earlyPeak1 = np.nanmax(Y[0:MoSIndex]) 196 | earlyIndex1 = np.min(np.where(Y == earlyPeak1)) 197 | else: 198 | searchInd = np.argwhere(X <= X[MoSIndex] - clearWd * 0.00273973) 199 | if np.any(searchInd): 200 | searchInd = searchInd.max() 201 | earlyPeak1 = np.nanmax(Y[0:searchInd]) 202 | earlyIndex1 = np.min(np.where(Y == earlyPeak1)) 203 | else: 204 | earlyIndex1 = 0 205 | 206 | if MoSIndex + 2 == len(X): 207 | latePeak1 = np.nanmax(Y[MoSIndex + 1:len(X)]) 208 | lateIndex1 = np.max(np.where(Y == latePeak1)) 209 | else: 210 | searchInd2 = np.argwhere(X >= X[MoSIndex] + clearWd * 0.00273973) 211 | if np.any(searchInd2): 212 | searchInd2 = searchInd2.min() 213 | if searchInd2 != len(X)-1: 214 | latePeak1 = np.nanmax(Y[searchInd2:len(X)-1]) 215 | lateIndex1 = np.max(np.where(Y == latePeak1)) 216 | else: 217 | lateIndex1 = 0 218 | else: 219 | lateIndex1 = 0 220 | 221 | if (earlyIndex1 != 0) and (earlyIndex1 - 2) > 0 and np.any(Y[0:earlyIndex1 - 2]): 222 | searchInd3 = np.argwhere(X <= X[earlyIndex1] - clearWd * 0.00273973) 223 | if np.any(searchInd3): 224 | searchInd3 = searchInd3.max() 225 | earlyPeak2 = np.nanmax(Y[0:searchInd3]) 226 | earlyIndex2 = np.min(np.where(Y == earlyPeak2)) 227 | 228 | if (lateIndex1 != 0) and lateIndex1 + 2 <= len(X) and np.any(Y[lateIndex1 + 2:len(X)]): 229 | searchInd4 = np.argwhere(X >= X[lateIndex1] + clearWd * 0.00273973) 230 | if np.any(searchInd4): 231 | searchInd4 = searchInd4.min() 232 | latePeak2 = np.nanmax(Y[searchInd4:len(X)]) 233 | lateIndex2 = np.max(np.where(Y == latePeak2)) 234 | 235 | Xarr = [X[Y0], X[earlyIndex1], X[MoSIndex], X[lateIndex1], X[len(X) - 1]] 236 | Yarr = [Y[Y0], Y[earlyIndex1], Y[MoSIndex], Y[lateIndex1], Y[len(Y) - 1]] 237 | 238 | if earlyIndex2: 239 | Xarr = [X[Y0], X[earlyIndex2], X[earlyIndex1], X[MoSIndex], X[lateIndex1], X[len(X) - 1]] 240 | Yarr = [Y[Y0], Y[earlyIndex2], Y[earlyIndex1], Y[MoSIndex], Y[lateIndex1], Y[len(Y) - 1]] 241 | if lateIndex2: 242 | Xarr = [X[Y0], X[earlyIndex2], X[earlyIndex1], X[MoSIndex], X[lateIndex1], X[lateIndex2], X[len(X) - 1]] 243 | Yarr = [Y[Y0], Y[earlyIndex2], Y[earlyIndex1], Y[MoSIndex], Y[lateIndex1], Y[lateIndex2], Y[len(Y) - 1]] 244 | 245 | if lateIndex2: 246 | Xarr = [X[Y0], X[earlyIndex1], X[MoSIndex], X[lateIndex1], X[lateIndex2], X[len(X) - 1]] 247 | Yarr = [Y[Y0], Y[earlyIndex1], Y[MoSIndex], Y[lateIndex1], Y[lateIndex2], Y[len(Y) - 1]] 248 | if earlyIndex2: 249 | Xarr = [X[Y0], X[earlyIndex2], X[earlyIndex1], X[MoSIndex], X[lateIndex1], X[lateIndex2], X[len(X) - 1]] 250 | Yarr = [Y[Y0], Y[earlyIndex2], Y[earlyIndex1], Y[MoSIndex], Y[lateIndex1], Y[lateIndex2], Y[len(Y) - 1]] 251 | 252 | if model == 'linear': 253 | # model and fit spline 254 | polyVal = np.interp(X, xp=Xarr, fp=Yarr) 255 | 256 | if model == 'poly': 257 | # model and fit polynom of n-th order 258 | poly = np.polyfit(Xarr, Yarr, nOrder) 259 | polyVal = np.polyval(poly, X) 260 | 261 | if model == 'spline': 262 | tck = interpolate.splrep(x=Xarr, y=Yarr, s=0) 263 | 264 | # predict values with spline and write to array 265 | polyVal = interpolate.splev(X, tck, der=0) 266 | 267 | # difference between polynom and values 268 | diff = np.abs(polyVal - Y) 269 | diff_sum = np.nansum(diff) 270 | diff_mean = np.nanmean(diff) 271 | testVal = diff_sum * EVI_obs_potII 272 | 273 | thresh = diff_mean 274 | NDVIthresh = -EVI_STD 275 | NDVIthresh_list = list(np.random.normal(NDVIthresh, GFstd, 100)) 276 | 277 | # create empty array for neighborhood indices 278 | clearWidth = [] 279 | 280 | mow_date_index = [] 281 | mowingEvents = [] 282 | mowingDoy = [] 283 | 284 | if len(diff) > 0: 285 | i = 1 286 | for evIndex, ev in enumerate(diff): 287 | ndvi_diff_check = False 288 | NDV_Check_list = [YDiff[evIndex]] * 100 289 | # print(NDVIthresh_list) 290 | result = [a for a, b in zip(NDV_Check_list, NDVIthresh_list) if a < b] 291 | 292 | if len(result) >= posEval: 293 | ndvi_diff_check = True 294 | else: 295 | continue 296 | #ndvi_diff_check = False 297 | 298 | eventDate = X[evIndex] 299 | 300 | if evIndex == len(X)-1: 301 | eventDate_next = X[evIndex] + 1 302 | else: 303 | eventDate_next = X[evIndex + 1] 304 | 305 | if i == 1: 306 | if ev > thresh: 307 | # check NDVI difference and compare to threshold 308 | if ndvi_diff_check: 309 | # check next observation 310 | if eventDate_next - eventDate <= 6 * 0.00273973: 311 | if YDiff[evIndex + 1] > another_thrs: 312 | continue 313 | # get julian date 314 | doy = ((eventDate - yr) * 365) + 1 315 | if doy > 305: 316 | continue 317 | else: 318 | dt = datetime(yr, 1, 1) 319 | dtdelta = timedelta(days=doy) 320 | dates = str(dt + dtdelta) 321 | date = dates[0:10] 322 | mowingEvents.append(date) 323 | mowingDoy.append(np.int(doy)) 324 | mow_date_index.append(evIndex) 325 | i = i + 1 326 | else: 327 | if ev > thresh: 328 | dec_date_preceding = X[np.array(mow_date_index)[-1]] 329 | dec_date_current_iter = X[evIndex] 330 | # delta days in decimal format 331 | delta_days = dec_date_current_iter - dec_date_preceding 332 | # clearwd (days) divided by 365 = minimum distance from preceding mowing event as decimal number 333 | clearWd_days = clearWd / 365 334 | if delta_days > clearWd_days: 335 | # if evIndex not in clearWidth: 336 | # date of event when threshold was crossed 337 | eventDate = X[evIndex] 338 | if ndvi_diff_check: 339 | if eventDate_next - eventDate <= 6 * 0.00273973: 340 | if YDiff[evIndex + 1] > another_thrs: 341 | continue 342 | # get julian date 343 | doy = ((eventDate - yr) * 365) + 1 344 | if doy > 305: 345 | continue 346 | else: 347 | ############################# 348 | # check if there is one observation that is higher than the preceding between 349 | # two mowing events 350 | time_mask = np.where((X >= X[mow_date_index[-1]]) & (X <= eventDate), True, False) 351 | any_preced_lower = np.any(np.ediff1d(Y[time_mask]) > 0) 352 | # in case there is no increase in EVI values between two mowing events 353 | # "any_preced_lower" will be False 354 | # print('Any observation higher than preceding between DOY ', mowingDoy[-1], 'and ', 355 | # int(doy), '?', any_preced_lower) 356 | ############################# 357 | if any_preced_lower: 358 | dt = datetime(yr, 1, 1) 359 | dtdelta = timedelta(days=doy) 360 | dates = str(dt + dtdelta) 361 | date = dates[0:10] 362 | mowingEvents.append(date) 363 | mowingDoy.append(np.int(doy)) 364 | mow_date_index.append(evIndex) 365 | i = i + 1 366 | else: 367 | None 368 | 369 | if profileAnalytics: 370 | return mowingEvents, mowingDoy, diff_sum, EVI_obs, EVI_obs_pot, testVal, Xarr, Yarr, X, polyVal 371 | else: 372 | return mowingEvents, mowingDoy, diff_sum, EVI_obs, EVI_obs_pot, testVal 373 | 374 | # new version 375 | def forcepy_init(dates, sensors, bandnames): 376 | """ 377 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 378 | sensors: numpy.ndarray[nDates](str) 379 | bandnames: numpy.ndarray[nBands](str) 380 | """ 381 | bandnames = ['mowingEvents', 'max_gap_days', 'CSO_ABS', 'Data_Ratio', 382 | 'Mow_1', 'Mow_2', 'Mow_3', 'Mow_4', 'Mow_5', 'Mow_6', 'Mow_7', 'Mean', 'Median', 'SD', 'diff_sum', 383 | 'diff_sum_dataavail', 'Error'] 384 | 385 | return bandnames 386 | 387 | 388 | def serial_date_to_string(srl_no): 389 | new_date = datetime(1970,1,1,0,0) + timedelta(int(srl_no) - 1) 390 | return new_date 391 | 392 | 393 | def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc): 394 | """ 395 | inarray: numpy.ndarray[nDates, nBands, nrows, ncols](Int16), nrows & ncols always 1 396 | outarray: numpy.ndarray[nOutBands](Int16) initialized with no data values 397 | dates: numpy.ndarray[nDates](int) days since epoch (1970-01-01) 398 | sensors: numpy.ndarray[nDates](str) 399 | bandnames: numpy.ndarray[nBands](str) 400 | nodata: int 401 | nproc: number of allowed processes/threads (always 1) 402 | Write results into outarray. 403 | """ 404 | global GLstart, GLend, GLendII, PSstart, PSend, GFstd, posEval, clrwd, profileAnalytics 405 | 406 | ################# user defined parameters ################# 407 | # define if you want to run the UDF in FORCE or display the result of the algorithm per pixel using QGIS-Plugin Profile Analytics 408 | # see details: https://enmap-box.readthedocs.io/en/latest/usr_section/usr_manual/eo4q.html?highlight=profile#profile-analytics 409 | # make sure to append an environmental variable in QGIS following this example: 410 | # Settings --> Options --> System --> Environment: Apply: Append | Variable: PYTHONPATH | Value: PATH\TO\mowingDetection_UDF.py 411 | 412 | profileAnalytics = False 413 | 414 | # define the approximate length of grassland season in which you expect the main mowing activity; in decimal years = DOY / 365; make sure too include a temporal buffer --> here end of December 415 | GLstart = 0.2 # DOY 73 416 | GLend = 1 # DOY 365 417 | 418 | # define end of grassland season 419 | GLendII = 0.85 # DOY 420 | 421 | # define the approximate length of the main vegetation season; i.e., time of the year in which you expect at least one peak 422 | PSstart = 0.33 # DOY 120 423 | PSend = 0.66 # DOY 240 424 | 425 | # adjust sensitivity of thresholds; i.e., width of gaussian function and number of positive evaluations needed 426 | GFstd = 0.02 427 | posEval = 40 428 | 429 | # define minimum distance between two consecutive mowing eventsin days 430 | clrwd = 15 431 | ########################################################### 432 | 433 | np.seterr(all='ignore') 434 | ts = inarray.squeeze() 435 | 436 | nodata = nodata 437 | 438 | all_no_data = np.all(ts == nodata) 439 | all_zero = np.all(ts == 0) 440 | 441 | if all_no_data: 442 | return 443 | elif all_zero: 444 | return 445 | else: 446 | 447 | dateList = [] 448 | 449 | if profileAnalytics: 450 | for imgDate in dates: 451 | dateList.append(imgDate) 452 | else: 453 | for imgDate in dates: 454 | dateList.append(serial_date_to_string(imgDate)) 455 | 456 | date = np.array(dateList) 457 | 458 | try: 459 | if profileAnalytics: 460 | x = date 461 | else: 462 | x = np.array(list(map(toYearFraction, date))) 463 | 464 | yr = int(str(x[0])[:4]) 465 | ################################# 466 | # get sd mean median 467 | Season_min_frac = yr + GLstart 468 | Season_max_frac = yr + GLendII 469 | subsetter = np.where((Season_min_frac < x) & (x < Season_max_frac), True, False) 470 | 471 | Y = np.array(ts[subsetter]) 472 | X = x[subsetter] 473 | nodata_ratio, max_gap_days, cso_abs = get_cso(X, Y, nodata=nodata, verbose=False, SoS=Season_min_frac, EOS=Season_max_frac) 474 | Y = np.array(ts[subsetter], dtype=np.float) 475 | Y[Y == nodata] = np.nan 476 | mean = np.nanmean(Y) 477 | median = np.nanmedian(Y) 478 | sd = np.nanstd(Y) 479 | 480 | Season_min_frac = yr + GLstart 481 | Season_max_frac = yr + GLend 482 | subsetter = np.where((Season_min_frac < x) & (x < Season_max_frac), True, False) 483 | X = x[subsetter] 484 | Y = ts[subsetter] 485 | 486 | if profileAnalytics: 487 | mowingEvents, mowingDoy, diff_sum, EVI_obs, EVI_obs_pot, diff_sum_dataavail, xPeak, yPeak, xPol, yPol = detectMow_S2_new( 488 | X, Y, clearWd=clrwd, yr=yr, type='ConHull', nOrder=3, model='linear' 489 | ) 490 | else: 491 | mowingEvents, mowingDoy, diff_sum, EVI_obs, EVI_obs_pot, diff_sum_dataavail = detectMow_S2_new( 492 | X, Y, clearWd=clrwd, yr=yr, type='ConHull', nOrder=3, model='linear' 493 | ) 494 | 495 | mowing_doy_out = [0] * 7 496 | 497 | for index, doys in enumerate(mowing_doy_out): 498 | try: 499 | mowing_doy_out[index] = mowingDoy[index] 500 | except: 501 | break 502 | outarray[:] = [int(len(mowingEvents)), int(max_gap_days), int(cso_abs), int(nodata_ratio * 100), 503 | mowing_doy_out[0], 504 | mowing_doy_out[1], mowing_doy_out[2], mowing_doy_out[3], mowing_doy_out[4], 505 | mowing_doy_out[5], mowing_doy_out[6], mean, median, sd, 506 | int(diff_sum * 100), 507 | int(diff_sum_dataavail * 100), 0] 508 | if profileAnalytics: 509 | return mowingEvents, mowing_doy_out, xPeak, yPeak, xPol, yPol 510 | except: 511 | #print('ERROR') 512 | outarray[-1] = 1 513 | 514 | 515 | 516 | ''' 517 | # test data set 518 | if __name__ == '__main__': 519 | profileAnalytics = True 520 | 521 | bandnames = forcepy_init(None, None, None) 522 | sensors = None 523 | 524 | text = '2021.0 7681.0, 2021.013698630137 7813.0, 2021.027397260274 7842.0, 2021.041095890411 7823.0, 2021.054794520548 7670.0, 2021.0684931506848 7567.0, 2021.0821917808219 7237.0, 2021.0958904109589 7237.0, 2021.109589041096 6993.0, 2021.123287671233 6916.0, 2021.13698630137 6863.0, 2021.150684931507 6853.0, 2021.164383561644 6937.0, 2021.1780821917807 7011.0, 2021.1917808219177 7022.0, 2021.2054794520548 7292.0, 2021.2191780821918 7541.0, 2021.2328767123288 7722.0, 2021.2465753424658 7667.0, 2021.2602739726028 7544.0, 2021.2739726027398 7145.0, 2021.2876712328766 7010.0, 2021.3013698630136 7457.0, 2021.3150684931506 7894.0, 2021.3287671232877 7927.0, 2021.3424657534247 7779.0, 2021.3561643835617 7655.0, 2021.3698630136987 7879.0, 2021.3835616438357 7926.0, 2021.3972602739725 8093.0, 2021.4109589041095 7964.0, 2021.4246575342465 7666.0, 2021.4383561643835 7035.0, 2021.4520547945206 7176.0, 2021.4657534246576 7406.0, 2021.4794520547946 7606.0, 2021.4931506849316 7740.0, 2021.5068493150684 7410.0, 2021.5205479452054 7269.0, 2021.5342465753424 7127.0, 2021.5479452054794 7101.0, 2021.5616438356165 7049.0, 2021.5753424657535 6826.0, 2021.5890410958905 6723.0, 2021.6027397260275 6510.0, 2021.6164383561643 6122.0, 2021.6301369863013 5919.0, 2021.6438356164383 6295.0, 2021.6575342465753 6443.0, 2021.6712328767123 7090.0, 2021.6849315068494 6990.0, 2021.6986301369864 6767.0, 2021.7123287671234 6507.0, 2021.7260273972602 6385.0, 2021.7397260273972 6284.0, 2021.7534246575342 6277.0, 2021.7671232876712 6243.0, 2021.7808219178082 6193.0, 2021.7945205479452 5828.0, 2021.8082191780823 5633.0, 2021.8219178082193 5479.0, 2021.835616438356 5426.0, 2021.849315068493 5425.0, 2021.86301369863 5554.0, 2021.876712328767 6390.0, 2021.890410958904 6638.0, 2021.9041095890411 6879.0, 2021.9178082191781 6934.0, 2021.9315068493152 7222.0, 2021.945205479452 7267.0, 2021.958904109589 7528.0, 2021.972602739726 7370.0, 2021.986301369863 7179.0' 525 | text = '2018.035616438356 2983.0, 2018.0849315068492 3342.0, 2018.0986301369862 3106.0, 2018.1041095890412 3160.0, 2018.1178082191782 3011.0, 2018.1178082191782 -9999, 2018.13698630137 2731.0, 2018.145205479452 2857.0, 2018.1616438356164 2782.0, 2018.1671232876713 2572.0, 2018.2054794520548 -9999, 2018.2082191780821 2436.0, 2018.2246575342465 2881.0, 2018.227397260274 -9999, 2018.2493150684932 2825.0, 2018.2493150684932 2890.0, 2018.2630136986302 -9999, 2018.268493150685 3965.0, 2018.268493150685 3975.0, 2018.2904109589042 5382.0, 2018.2931506849316 5290.0, 2018.295890410959 5898.0, 2018.304109589041 -9999, 2018.317808219178 -9999, 2018.323287671233 7505.0, 2018.33698630137 7889.0, 2018.33698630137 8057.0, 2018.345205479452 8228.0, 2018.3506849315067 8488.0, 2018.3643835616438 9036.0, 2018.3780821917808 -9999, 2018.3808219178081 9042.0, 2018.386301369863 9182.0, 2018.3917808219178 -9999, 2018.4 -9999, 2018.4054794520548 9255.0, 2018.4136986301369 -9999, 2018.427397260274 8679.0, 2018.4328767123288 8533.0, 2018.441095890411 8628.0, 2018.495890410959 5672.0, 2018.5013698630137 -9999, 2018.5123287671233 5107.0, 2018.5287671232877 5261.0, 2018.531506849315 6430.0, 2018.5369863013698 6234.0, 2018.5424657534247 6375.0, 2018.5506849315068 -9999, 2018.5561643835617 -9999, 2018.5561643835617 -9999, 2018.5643835616438 6787.0, 2018.5698630136985 7416.0, 2018.5753424657535 7059.0, 2018.5780821917808 7079.0, 2018.5972602739726 7322.0, 2018.6 7888.0, 2018.6109589041096 -9999, 2018.6383561643836 7313.0, 2018.6657534246576 -9999, 2018.6739726027397 -9999, 2018.6794520547944 7208.0, 2018.6876712328767 5541.0, 2018.6876712328767 4451.0, 2018.7150684931507 6746.0, 2018.731506849315 7893.0, 2018.7616438356165 2303.0, 2018.7753424657535 3070.0, 2018.7753424657535 3107.0, 2018.7835616438356 3265.0, 2018.7890410958903 3461.0, 2018.8027397260273 3743.0, 2018.8301369863013 -9999, 2018.8438356164384 -9999, 2018.8794520547945 2259.0, 2018.9068493150685 2873.0, 2018.9068493150685 2686.0, 2018.9260273972602 2832.0, 2018.9260273972602 2874.0' 526 | text = text.replace(', ', ' ').split(' ') 527 | data = np.array(text, float).reshape(-1, 2) 528 | dates = data[:, 0] 529 | inarray = data[:, 1] 530 | 531 | nodata = -9999 532 | nproc = 1 533 | outarray = np.ones(len(bandnames)) 534 | result = forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc) 535 | print('Done:', result) 536 | ''' 537 | -------------------------------------------------------------------------------- /python/ts/mowingDetection/profileAnalytics.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ts/mowingDetection/profileAnalytics.JPG -------------------------------------------------------------------------------- /python/ts/mowingDetection/profileAnalytics_broken.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ts/mowingDetection/profileAnalytics_broken.JPG -------------------------------------------------------------------------------- /python/ts/mowingDetection/readme.md: -------------------------------------------------------------------------------- 1 | # Mowing detection 2 | 3 | © 4 | Copyright 2021 Marcel Schwieder and Max Wesemeyer 5 | 6 | This algorithm was developed to estimate mowing events on grasslands from dense vegetation index time series derived from Sentinel-2 and Landsat data. 7 | It was developed and tested for grasslands in Germany (see maps here: https://ows.geo.hu-berlin.de/webviewer/mowing-detection/). 8 | While the thresholds used to identify mowing events are derived from the time series itself, some parameters might be adjusted for your specific 9 | use case. Details regarding the indivídual processing steps are described in the Schwieder et al., 2022 (open accesss). 10 | 11 | ![mowing detection scheme](scheme.jpg) 12 | 13 | # Output 14 | The algorithm is pixel based. The output is a raster stack with 17 bands that contain: 15 | 16 | - B1: Sum of mowing events 17 | - B2: Maximum data gap in original time series 18 | - B3: absolute clear sky observations (CSO) 19 | - B4: CSO/potential observations (*100) 20 | - B5 - B11: DOY of detected mowing events 21 | - B12: Mean VI value of the defined grassland season 22 | - B13: Median VI value of the defined grassland season 23 | - B14: VI standard deviation of the defined grassland season 24 | - B15: Sum of differences between interpolated and original values (*100) 25 | - B16: Sum of differences between interpolated and original values * data availability (*100) 26 | - B17: Processing error [0,1] 27 | 28 | ## Usage 29 | 30 | Run with the FORCE higher-level module and the TSA parameter file. Choose a vegetation index of your choice (tested with EVI and NDVI). 31 | Make sure not to output TSI unless you do not want to analyze the original time series. 32 | It is recommended to not use the above und below noise filters (FORCE TSA), as they might filter out potential mowing events. 33 | 34 | - program: ``force-higher-level`` 35 | - submodule: ``TSA`` 36 | - UDF type: ``PYTHON_TYPE = PIXEL`` 37 | - required parameters: ``none`` 38 | - suggested parameters: ``INTERPOLATE = NONE, BELOW_NOISE = 0, ABOVE_NOISE = 0`` 39 | - required Python libraries: ``numpy, scipy`` 40 | 41 | The following parameters might be changed in the mowingDetection.py UDF (search for the function: detectMow_S2_new): 42 | - GLstart and GLend (defines the approximate length of grassland season in which you expect the main mowing activity; make sure too include a buffer) 43 | - PSstart and PSend (defines the approximate length of the main vegetation season; i.e., time of the year in which you expect at least one peak) 44 | - GFstd and posEval (sensitivity of thresholds; i.e., width of gaussian function and number of positive evaluations) 45 | 46 | ## Visualization 47 | 48 | You can visualize the pixelwise results of the mowingDetection_UDF using the QGIS-Plugin Profile Analytics which comes with the installation of the EnMAP-Box Plugin 49 | (van der Linden et al., 2015; implemented since v.3.12-alpha.2; tested in QGIS 3.26; further details: https://enmap-box.readthedocs.io/en/latest/usr_section/usr_manual/eo4q.html?highlight=profile#profile-analytics). 50 | 51 | Please save a local copy of the UDF and make sure to set ``profileAnalytics = True`` in the main code. Additionally you need to set an environmental variable in QGIS 52 | following this example: 53 | 54 | Settings --> Options --> System --> Environment: ``Apply = Append`` | ``Variable = PYTHONPATH`` | ``Value = ;PATH\TO\mowingDetection_UDF.py`` 55 | (Note: Make sure to set the correct separator for appended variables (before the defined path) according to your system; here ";") 56 | 57 | Finally you need to download /force-udf/pyhton/ts/mowingDetection/visualize_mowingDetection_UDF.py and prompt to this file in the Profile Analytics GUI. 58 | Choose a FORCE TSS output file (vegetation index of your choice) as raster input and set the x-axis to Date Time (decimal years) in the Profile Analytics GUI. Once you 59 | click a pixel with the "Select current location" tool the vegetation index time series will be visualized along with the results of the mowingDetection_UDF. You can now 60 | adjust parameters in the main code and directly investigate the impacts of your changes. Printouts can be checked in the QGIS Python console. 61 | 62 | 63 | ![Profile Analytics scheme](https://github.com/davidfrantz/force-udf/blob/main/python/ts/mowingDetection/profileAnalytics.JPG) 64 | 65 | 66 | ## References 67 | 68 | - Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795. 69 | - van der Linden, S., Rabe, A., Held, M., Jakimow, B., Leitão, P.J., Okujeni, A., Schwieder, M., Suess, S., & Hostert, P. (2015). The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing. Remote Sensing, 7, 11249-11266. 70 | 71 | 72 | -------------------------------------------------------------------------------- /python/ts/mowingDetection/scheme.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/python/ts/mowingDetection/scheme.jpg -------------------------------------------------------------------------------- /python/ts/mowingDetection/visualize_mowingDetection_UDF.py: -------------------------------------------------------------------------------- 1 | import enmapbox.qgispluginsupport.qps.pyqtgraph.pyqtgraph as pg 2 | from enmapbox.qgispluginsupport.qps.plotstyling.plotstyling import PlotStyle, MarkerSymbol 3 | from profileanalyticsapp.profileanalyticsdockwidget import Profile 4 | from qgis.PyQt.QtCore import Qt 5 | from qgis.PyQt.QtGui import QColor 6 | from enmapboxprocessing.utils import Utils 7 | import mowingDetection_UDF 8 | import importlib 9 | importlib.reload(mowingDetection_UDF) 10 | from mowingDetection_UDF import * 11 | import numpy as np 12 | from typing import List 13 | 14 | 15 | def updatePlot(profile: Profile, profiles: List[Profile], plotWidget: pg.PlotItem): 16 | 17 | # get x (decimal dates) and y (vegetation index values from QGIS) 18 | xValues = np.array(profile.xValues) 19 | yValues = np.array(profile.yValues) 20 | 21 | # default values for running mowingDetection_UDF.py 22 | bandnames = forcepy_init(None, None, None) 23 | sensors = None 24 | dates = xValues 25 | inarray = yValues 26 | nodata = -9999 27 | nproc = 1 28 | outarray = np.ones(len(bandnames)) 29 | mowingEvents, mowing_doy_out, xPeak, yPeak, xPol, yPol = forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc) 30 | 31 | # plot indentified verticies (Start, end and peak values) 32 | style = PlotStyle() 33 | style.setMarkerSymbol(MarkerSymbol.Cross) 34 | style.markerBrush.setColor(QColor('#ff0000')) 35 | style.markerSize = 15 36 | plotDataItem = plotWidget.plot(xPeak, [i * 10000 for i in yPeak], name='Vertices') 37 | style.apply(plotDataItem) 38 | 39 | # plot the interpolated "convex hull" 40 | style = PlotStyle() 41 | style.setMarkerSymbol(MarkerSymbol.No_Symbol) # options: Circle, Triangle_Down, Triangle_Up, Triangle_Right, Triangle_Left, Pentagon, Hexagon, Square, Star, Plus, Diamond, Cross, ArrowUp, ArrowRight, ArrowDown, ArrowLeft, No_Symbol 42 | style.linePen.setColor(QColor('#0000ff')) 43 | style.linePen.setWidth(2) 44 | style.linePen.setStyle(Qt.SolidLine) 45 | plotDataItem = plotWidget.plot(xPol, [i * 10000 for i in yPol], name='Interpolation') 46 | style.apply(plotDataItem) 47 | 48 | # plot identified mowing events 49 | style = PlotStyle() 50 | style.setMarkerSymbol(MarkerSymbol.No_Symbol) 51 | style.linePen.setColor(QColor('#00ff00')) 52 | style.linePen.setWidth(2) 53 | style.linePen.setStyle(Qt.DashLine) 54 | 55 | dateTimes = [Utils.parseDateTime(mowingEvent) for mowingEvent in mowingEvents] 56 | dyears = [Utils.dateTimeToDecimalYear(dateTime) for dateTime in dateTimes] 57 | 58 | for i, dyear in enumerate(dyears,1): 59 | plotDataItem = plotWidget.plot(x=[dyear, dyear], y=[0,10000], name=f'Mowing event {i}') 60 | style.apply(plotDataItem) 61 | 62 | # print dates in pyhton console 63 | print(dyears) 64 | -------------------------------------------------------------------------------- /rstats/ard/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/rstats/ard/.gitkeep -------------------------------------------------------------------------------- /rstats/ts/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/davidfrantz/force-udf/666981ba5c761aae9cc66d4ccc1d19e2343e2f9b/rstats/ts/.gitkeep -------------------------------------------------------------------------------- /rstats/ts/dynamic-habitat-indices/dhi.r: -------------------------------------------------------------------------------- 1 | # global header, e.g., for loading libraries 2 | # library(...) 3 | 4 | # dates: vector with dates of input data (class: Date) 5 | # sensors: vector with sensors of input data (class: character) 6 | # bandnames: vector with bandnames of input data (class: character) 7 | force_rstats_init <- function(dates, sensors, bandnames){ 8 | 9 | return(c("cumulative", "minimum", "variation")) 10 | } 11 | 12 | 13 | # inarray: 2D-array with dim = c(length(dates), length(bandnames)) 14 | # No-Data values are encoded as NA. (class: Integer) 15 | # dates: vector with dates of input data (class: Date) 16 | # sensors: vector with sensors of input data (class: character) 17 | # bandnames: vector with bandnames of input data (class: character) 18 | # nproc: number of CPUs the UDF may use. Always 1 for pixel functions (class: Integer) 19 | force_rstats_pixel <- function(inarray, dates, sensors, bandnames, nproc){ 20 | 21 | s <- sum(inarray[,1], na.rm = TRUE) / 1e2 22 | m <- min(inarray[,1], na.rm = TRUE) 23 | v <- sd(inarray[,1], na.rm = TRUE) / mean(inarray[,1], na.rm = TRUE) * 1e4 24 | 25 | return(c(s, m, v)) 26 | } 27 | -------------------------------------------------------------------------------- /rstats/ts/dynamic-habitat-indices/readme.md: -------------------------------------------------------------------------------- 1 | # Dynamic Habitat Indices 2 | 3 | © 4 | Copyright 2023, David Frantz 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``TSA`` 10 | - UDF type: ``RSTATS_TYPE = PIXEL`` 11 | - required parameters: 12 | - ``INTERPOLATE = RBF`` with very large kernels 13 | - ``DATE_RANGE``: set to one(!) full year 14 | - required R libraries: none 15 | 16 | ## References 17 | 18 | - Radeloff, V. C., M. Dubinin, N. C. Coops, A. Allen, T. M. Brooks, M. Clayton, G. Costa, C. H. Graham, D. Helmers, A. R. Ives, D. Kolesov, A. M. Pidgeon, G. Rapacciuolo, E. Razenkova, N. Suttidate, B. E. Young, L. Zhu, and M. Hobi. (2019): **The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity**. *Remote Sensing of Environment, 222*, 204-214. [10.1016/j.rse.2018.12.009](https://doi.org/10.1016/j.rse.2018.12.009) 19 | - Hobi, M.L., Dubinin, M., Graham, C.H., Coops, N.C., Clayton, M.K., Pidgeon, A.M., & Radeloff, V.C. (2017): **A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness.** *Remote Sensing of Environment, 195*, 142-152. [10.1016/j.rse.2017.04018](https://doi.org/10.1016/j.rse.2017.04018) 20 | -------------------------------------------------------------------------------- /rstats/ts/spline-reconstruction/Bolten_Spline_FORCE_UDF.R: -------------------------------------------------------------------------------- 1 | force_rstats_init <- function(dates, sensors, bandnames){ 2 | 3 | # Year which should be reconstructed 4 | year_to_interpolate <- 2023 5 | # Days to predict in this year and the intervall: 60 to 330 (1st March to 26th November) 6 | DOYs_to_predict <- seq(60,330,by =10) 7 | dates_to_predict <- as.Date(paste(year_to_interpolate, DOYs_to_predict), format = "%Y %j") 8 | 9 | band_names <- paste(substr(as.character(dates_to_predict),1,4),substr(as.character(dates_to_predict),6,7),substr(as.character(dates_to_predict),9,10), sep ="") 10 | return(band_names) 11 | } 12 | 13 | force_rstats_pixel <- function(inarray, dates, sensors, bandnames, nproc){ 14 | 15 | # Year which should be interpolated (same like above) 16 | year_to_interpolate <- 2023 17 | # Days to predict in this yearand the intervall: 60 to 330 (1st March to 26th November) 18 | DOYs_to_predict <- seq(60,330,by =10) 19 | dates_to_predict <- as.Date(paste(year_to_interpolate, DOYs_to_predict), format = "%Y %j") 20 | 21 | # spline variables 22 | # smoothing factor for the spline reconstruction 23 | smooth_fac <- 0.5 24 | # Bolton's variable of maximum weight to assing for the predessesor years 25 | # the year of reconstruction has a wheight of 1 26 | max_weight <- 0.2 27 | 28 | # cumulate the DOY to the year of interpolation 29 | # start year 2015 (example), because of Sentinel 2 launch date, for e.g. Landsat adjust to your time span 30 | start_year <- 2015 31 | DOYs_to_predict <- (year_to_interpolate - start_year) * 365 + DOYs_to_predict 32 | 33 | tryCatch({ 34 | # grap FORCE no-data case 35 | if (all(is.na(inarray[,1]))){ 36 | return(rep(-9999,length(DOYs_to_predict))) 37 | } 38 | 39 | # calculate cumulative DOYs for the input data 40 | DOYs <- as.numeric(format(dates, "%j")) 41 | years <- as.numeric(substr(as.character(dates),1,4)) 42 | cumulative_DOYs <- (years - start_year) * 365 + DOYs 43 | 44 | # join the data to a dataframe 45 | df <- data.frame(x=cumulative_DOYs,y=inarray[,1]) 46 | 47 | # ------- 1.1 calcualte Mean Function -------------- 48 | euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2)) 49 | 50 | # define Start and endpoints for the three spline reconstuctions 51 | DOY_borders_year <- c((year_to_interpolate-start_year)*365 - 180, (year_to_interpolate-start_year+1)*365+180) 52 | DOY_borders_b <- c((year_to_interpolate-start_year-1)*365 - 180, (year_to_interpolate-start_year)*365+180) 53 | DOY_borders_bb <- c((year_to_interpolate-start_year-2)*365 - 180, (year_to_interpolate-start_year-1)*365+180) 54 | 55 | # create dataframes for reconstuction 56 | data_year <- na.exclude(df[df$x %in% seq(DOY_borders_year[1],DOY_borders_year[2]),c(1,2)]) 57 | data_b <- na.exclude(df[df$x %in% seq(DOY_borders_b[1] ,DOY_borders_b[2]),c(1,2)]) 58 | data_bb <- na.exclude(df[df$x %in% seq(DOY_borders_bb[1],DOY_borders_bb[2]),c(1,2)]) 59 | 60 | # calculate spline model for year of reconstruction and predict 61 | DOYs_target_year <- seq(DOY_borders_year[1],DOY_borders_year[2]) 62 | tryCatch({ 63 | eval( spline_model_year <<- smooth.spline(data_year$x,data_year$y, spar =smooth_fac) ) 64 | eval( predict_year <<- predict(spline_model_year, x = DOYs_target_year)$y ) 65 | }, error = function(err) { 66 | return(rep(-9999,length(DOYs_to_predict))) 67 | }) 68 | 69 | #calculate d_max 70 | mean_year <- mean(na.exclude(data_year$y)) 71 | mean_prediction <- rep(mean_year,length(DOYs_target_year)) 72 | d_max = euc.dist(mean_prediction, predict_year) / 10000 73 | 74 | 75 | # --------- 1.2 spline for precessor years ------------ 76 | # one year before 77 | # predict with DOYs of year of reconstruction, for difference calculation 78 | # between the two spline reconstructions 79 | tryCatch({ 80 | eval( spline_model_b <<- smooth.spline(data_b$x+365,data_b$y, spar =smooth_fac) ) 81 | eval( predict_b <<- predict(spline_model_b, x = DOYs_target_year)$y ) 82 | }, error = function(err) { 83 | return(rep(-9999,length(DOYs_to_predict))) 84 | }) 85 | d_b = euc.dist(predict_year, predict_b) / 10000 86 | 87 | # two years before 88 | # predict with DOYs of year of reconstruction, for difference calculation 89 | # between the two spline reconstructions 90 | tryCatch({ 91 | eval( spline_model_bb <<- smooth.spline(data_bb$x+(365*2),data_bb$y, spar =smooth_fac) ) 92 | eval( predict_bb <<- predict(spline_model_bb, x = DOYs_target_year)$y ) 93 | }, error = function(err) { 94 | return(rep(-9999,length(DOYs_to_predict))) 95 | }) 96 | d_bb = euc.dist(predict_year, predict_bb) / 10000 97 | 98 | # ---------- 1.3 Calculate weights ------------------- 99 | # one year before 100 | if (d_max != 0) { 101 | weight_b = max_weight*(1-(d_b/d_max)) 102 | if (weight_b < 0){ 103 | weight_b = 0 104 | } 105 | } else {weight_b = 0} 106 | 107 | # two years before 108 | if (d_max != 0) { 109 | weight_bb = max_weight*(1-(d_bb/d_max)) 110 | if (weight_bb < 0){ 111 | weight_bb = 0 112 | } 113 | } else {weight_bb = 0} 114 | 115 | #----------- 1.4 apply weights and calculate weighted spline -------------- 116 | # combine the time series to one year and assign weights to the new data points 117 | combined_x <- c(data_year$x , (data_b$x+365)[weight_b>0] , (data_bb$x + (365*2))[weight_bb>0]) 118 | combined_y <- c(data_year$y , data_b$y[weight_b>0] , data_bb$y[weight_bb>0]) 119 | vec_weights <- c(rep(1,length(data_year$x)), 120 | rep(weight_b,length(data_b$x))[weight_b>0], 121 | rep(weight_bb,length(data_bb$x))[weight_bb>0]) 122 | 123 | # calculate weighted spline 124 | tryCatch({ 125 | eval( spline_model_combined <<- smooth.spline(x=combined_x, y=combined_y, w = vec_weights , spar =smooth_fac) ) 126 | eval( predict_combined <<- predict(spline_model_combined, x = DOYs_to_predict)$y ) 127 | }, error = function(err) { 128 | return(rep(-9999,length(DOYs_to_predict))) 129 | }) 130 | return(predict_combined) 131 | 132 | }, error = function(err) { 133 | return(rep(-9999,length(DOYs_to_predict))) 134 | }) 135 | } -------------------------------------------------------------------------------- /rstats/ts/spline-reconstruction/readme.md: -------------------------------------------------------------------------------- 1 | # Time series reconstruction for forest using splines 2 | 3 | © 4 | Copyright 2024, David Klehr 5 | 6 | ## Run with 7 | 8 | - program: ``force-higher-level`` 9 | - submodule: ``TSA`` 10 | - DATE_RANGE: ``xxxx-07-01 yyyy-06-31`` 11 | * xxxx = three years before your target year 12 | * yyyy = one year after your target year 13 | * e.g. for target year 2022: ``2019-07-01 2023-06-31`` 14 | - UDF type: ``RSTATS_TYPE = PIXEL`` 15 | - required parameters:``none`` 16 | - required R libraries: ``none`` 17 | 18 | ## References 19 | 20 | - Bolton, D.K., Gray, J.M., Melaas, E.K., Moon, M., Eklundh, L., Friedl, M.A., 2020. **Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery**. *Remote Sensing of Environment 240*, 111685. https://doi.org/10.1016/j.rse.2020.111685. --------------------------------------------------------------------------------