{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","rai":"http://mlcommons.org/croissant/RAI/","data":{"@id":"cr:data","@type":"@json"},"dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","examples":{"@id":"cr:examples","@type":"@json"},"extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform","wd":"https://www.wikidata.org/wiki/"},"@type":"sc:Dataset","conformsTo":"http://mlcommons.org/croissant/1.0","name":"Replication Data for: Fast flood extent monitoring with SAR change detection using Google Earth Engine","url":"https://doi.org/10.7910/DVN/WOTC7E","creator":[{"@type":"Person","givenName":"Ebrahim","familyName":"Hamidi","affiliation":{"@type":"Organization","name":"University of Alabama, The"},"sameAs":"https://orcid.org/0000-0003-3160-6999","@id":"https://orcid.org/0000-0003-3160-6999","identifier":"https://orcid.org/0000-0003-3160-6999","name":"Hamidi, Ebrahim"},{"@type":"Person","givenName":"Brad","familyName":"Peter","affiliation":{"@type":"Organization","name":"The University of Arkansas"},"sameAs":"https://orcid.org/0000-0002-5724-4482","@id":"https://orcid.org/0000-0002-5724-4482","identifier":"https://orcid.org/0000-0002-5724-4482","name":"Peter, Brad"},{"@type":"Person","givenName":"David F.","familyName":"Muñoz","affiliation":{"@type":"Organization","name":"Virginia Polytechnic Institute and State University"},"sameAs":"https://orcid.org/0000-0001-6032-1082","@id":"https://orcid.org/0000-0001-6032-1082","identifier":"https://orcid.org/0000-0001-6032-1082","name":"Muñoz, David F."},{"@type":"Person","givenName":"Hamed","familyName":"Moftakhari","affiliation":{"@type":"Organization","name":"University of Alabama, The"},"sameAs":"https://orcid.org/0000-0003-3170-8653","@id":"https://orcid.org/0000-0003-3170-8653","identifier":"https://orcid.org/0000-0003-3170-8653","name":"Moftakhari, Hamed"},{"@type":"Person","givenName":"Hamid","familyName":"Moradkhani","affiliation":{"@type":"Organization","name":"University of Alabama, The"},"sameAs":"https://orcid.org/0000-0002-2889-999X","@id":"https://orcid.org/0000-0002-2889-999X","identifier":"https://orcid.org/0000-0002-2889-999X","name":"Moradkhani, Hamid"}],"description":"Fast flood extent monitoring with SAR change detection using Google Earth Engine This dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliable tool for assisting decision-makers and emergency planners using near real-time multi-temporal satellite SAR data sets. GEE code was developed by Ebrahim Hamidi and reviewed by Brad G. Peter; Figures were created by Brad G. Peter. This tool accompanies a publication Hamidi et al., 2023: E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari and H. Moradkhani, \"Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine,\" in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3240097. GEE input datasets: Methodology flowchart: Sensitivity Analysis: GEE code (muti-source and multi-temporal flood monitoring): https://code.earthengine.google.com/7f4942ab0c73503e88287ad7e9187150 The threshold sensitivity analysis is automated in the below GEE code: https://code.earthengine.google.com/a3fbfe338c69232a75cbcd0eb6bc0c8e The above scripts can be run independently. The threshold automation code identifies the optimal threshold values for use in the flood monitoring procedure. GEE code for Hurricane Harvey, east of Houston Java script: // Study Area Boundaries var bounds = /* color: #d63000 */ee.Geometry.Polygon( [[[-94.5214452285728, 30.165244882083663], [-94.5214452285728, 29.56024879238989], [-93.36650748443218, 29.56024879238989], [-93.36650748443218, 30.165244882083663]]], null, false); // [before_start,before_end,after_start,after_end,k_ndfi,k_ri,k_diff,mndwi_threshold] var params = ['2017-06-01','2017-06-15','2017-08-01','2017-09-10',1.0,0.25,0.8,0.4] // SAR Input Data var before_start = params[0] var before_end = params[1] var after_start = params[2] var after_end = params[3] var polarization = \"VH\" var pass_direction = \"ASCENDING\" // k Coeficient Values for NDFI, RI and DII SAR Indices (Flooded Pixel Thresholding; Equation 4) var k_ndfi = params[4] var k_ri = params[5] var k_diff = params[6] // MNDWI flooded pixels Threshold Criteria var mndwi_threshold = params[7] // Datasets ----------------------------------- var dem = ee.Image(\"USGS/3DEP/10m\").select('elevation') var slope = ee.Terrain.slope(dem) var swater = ee.Image('JRC/GSW1_0/GlobalSurfaceWater').select('seasonality') var collection = ee.ImageCollection('COPERNICUS/S1_GRD') .filter(ee.Filter.eq('instrumentMode', 'IW')) .filter(ee.Filter.listContains('transmitterReceiverPolarisation', polarization)) .filter(ee.Filter.eq('orbitProperties_pass', pass_direction)) .filter(ee.Filter.eq('resolution_meters', 10)) .filterBounds(bounds) .select(polarization) var before = collection.filterDate(before_start, before_end) var after = collection.filterDate(after_start, after_end) print(\"before\", before) print(\"after\", after) // Generating Reference and Flood Multi-temporal SAR Data ------------------------ // Mean Before and Min After ------------------------ var mean_before = before.mean().clip(bounds) var min_after = after.min().clip(bounds) var max_after = after.max().clip(bounds) var mean_after = after.mean().clip(bounds) Map.addLayer(mean_before, {min: -29.264204107025904, max: -8.938093778644141, palette: []}, \"mean_before\",0) Map.addLayer(min_after, {min: -29.29334290990966, max: -11.928313976797138, palette: []}, \"min_after\",1) // Flood identification ------------------------ // NDFI ------------------------ var ndfi = mean_before.abs().subtract(min_after.abs()) .divide(mean_before.abs().add(min_after.abs())) var ndfi_filtered = ndfi.focal_mean({radius: 50, kernelType: 'circle', units: 'meters'}) // NDFI Normalization ----------------------- var ndfi_min = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.min(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_max = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.max(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_rang = ee.Number(ndfi_max.get('VH')).subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_subtctMin = ndfi_filtered.subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_norm = ndfi_subtctMin.divide(ndfi_rang) Map.addLayer(ndfi_norm, {min: 0.3862747346632676, max: 0.7632898395906615}, \"ndfi_norm\",0) var histogram = ui.Chart.image.histogram({ image: ndfi_norm, region: bounds, scale: 10, maxPixels: 1e13 })...","keywords":["Earth and Environmental Sciences","Engineering","Computer and Information Science","Flood extent monitoring, multi-source remote sensing data, multi-temporal Sentinel-1 radar data, optical remote sensing data, change detection and thresholding, Google Earth Engine, Hurricanes"],"license":"http://creativecommons.org/publicdomain/zero/1.0","datePublished":"2022-08-01","dateModified":"2023-01-30","includedInDataCatalog":{"@type":"DataCatalog","name":"Harvard Dataverse","url":"https://dataverse.harvard.edu"},"publisher":{"@type":"Organization","name":"Harvard Dataverse"},"version":"2.1","citeAs":"@data{DVN/WOTC7E_2022,author = {Hamidi, Ebrahim and Peter, Brad and Muñoz, David F. and Moftakhari, Hamed and Moradkhani, Hamid},publisher = {Harvard Dataverse},title = {Replication Data for: Fast flood extent monitoring with SAR change detection using Google Earth Engine},year = {2022},url = {https://doi.org/10.7910/DVN/WOTC7E}}","citation":[{"@type":"CreativeWork","name":"E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari and H. 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