Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Training Classifiers, Supervised Classification and Error Assessment • How to add raster and vector data from the catalog in Google Earth Engine; • Train a classifier; • Perform the error assessment; • Download the results.

The Shuttle Radar Topography Mission (SRTM) digital elevation dataset was originally produced to provide consistent, high-quality elevation data at near global scope. This version of the SRTM digital elevation data has been processed to fill data voids, and to facilitate its ease of use.

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Please refer to the associated publication: Fox, L., Peter, B.G., Frake, A.N. and Messina, J.P., 2023. A Bayesian maximum entropy model for predicting tsetse ecological distributions. International Journal of Health Geographics, 22(1), p.31. https://link.springer.com/article/10.1186/s12942-023-00349-0 Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (2023): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (2023) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (2023) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/unsuitable) for day/night temperatures and ndvi var con =...

Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth's surface. Earth Engine is now available for commercial use, and remains free for academic and research use.

The Sea Surface Temperature - WHOI dataset is part of the NOAA Ocean Surface Bundle (OSB) and provides a high quality Climate Data Record (CDR) of sea surface temperature over ice-free oceans. The SST values are found through modeling the diurnal variability in combination with AVHRR observations of sea surface …

Data Management

• Create and edit fusion tables

• Upload imagery, vector, and tabular data using Fusion Tables and KMLs

• Share data with other Google Earth Engine (GEE) users as well as download imagery after manipulation in GEE.

L'outil Hakai Google Earth Engine Kelp (outil GEEK) a été développé dans le cadre d'une collaboration entre l'Institut Hakai, l'Université de Victoria et le ministère des Pêches et des Océans pour tirer parti des capacités de cloud computing pour analyser l'imagerie satellite Landsat (30 m) afin d'extraire l'étendue de la canopée et du varech. La méthodologie originale est décrite dans Nijland et al. 2019*.

Remarque : Ce jeu de données est conçu comme une « lecture seule », car nous continuons à améliorer les résultats. Il vise à démontrer l'utilité de l'archive Landsat pour cartographier le varech. Ces données sont visibles sur la carte Web GEEK disponible ici.

Ce package de données contient deux jeux de données :

Etendue annuelle maximale estivale du varech formant la canopée (1984 - 2019) en tant que rasters. Etendue maximale décennale du varech formant la canopée (1984 - 1990, 1991 - 2000, 2001 - 2010, 2011 - 2020)

Ce jeu de données a été généré à la suite de modifications apportées aux méthodologies GEEK originales. Les paramètres utilisés pour générer les rasters étaient des scènes d'images avec :

Plage de mois Imagescene = 1er mai - 30 septembre Clouds maximum dans la scène = 80% Marée maximale = 3,2 m (+0,5 MWL des marées de la côte centrale selon les méthodes KIM-1) Marée minimale = 0 m Tampon de rivage appliqué au masque de terrain = 1 pixel (30 m) NDVI* minimum (pour qu'un pixel individuel soit classé comme varech) = -0,05 Nombre minimum de fois qu'un pixel de varech individuel doit être détecté en tant que varech au cours d'une seule année = 30 % de toutes les détections d'une année donnée K moyenne minimale (moyenne du NDVI pour tous les pixels à un emplacement donné détecté comme varech) = -0,05 * NDVI = indice de végétation de différence normalisée.

Ces paramètres ont été choisis sur la base d'une évaluation de la précision à l'aide d'une étendue de varech dérivée d'images WorldView-2 (2 m) de juillet 2014 et août 2014. Ces données ont été rééchantillonnées à 30 m. Bien que de nombreuses itérations exécutées pour l'outil aient donné des résultats très similaires, des paramètres ont été sélectionnés qui ont maximisé la précision du varech pour la comparaison de 2014.

Les résultats de l'évaluation de la précision ont été les suivants : Erreur de commission de 50 % Erreur d'omission de 25 %

En termes simples, les méthodes actuelles conduisent à un niveau élevé de « faux positifs », mais elles capturent avec précision l'étendue du varech par rapport au jeu de données de validation. Cette erreur peut être attribuée à la sensibilité de l'utilisation d'un seul NDVI pour détecter le varech. Nous observons des variations des seuils NDVI à la fois au sein d'une seule scène et entre les scènes.

L'objectif du jeu de données de séries chronologiques est censé prendre en compte une partie de cette erreur, car les pixels détectés seulement un par décennie sont supprimés.

Ce jeu de données fait partie du programme de cartographie de l'habitat de Hakai. L'objectif principal du programme de cartographie de l'habitat de Hakai est de générer des inventaires spatiaux des habitats côtiers, d'étudier comment ces habitats évoluent au fil du temps et les moteurs de ce changement.

*Nijland, W., Reshitnyk, L. et Rubidge, E. (2019). Télédétection par satellite de varech formant une canopée sur un littoral complexe : une nouvelle procédure utilisant les archives d'images Landsat. Télédétection de l'environnement, 220, 41-50. doi:10.1016/j.rse.2018.10.032

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Visualization

• How to use the knowledge of how to visualize images that you learned in previous tutorials and embed the visualization parameters inside of the GEE script so that the imagery will appear with the same visualization every time it is run.

Charts, Histograms, and Time Series • Create a histogram graph from band values of an image collection • Create a time series graph from band values of an image collection

This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five categories of ground surface inundation; in addition to not-water (class 0) and water (class 1), the DSWE algorithm distinguishes pixels that are less distinctly inundated (class 2: “moderate confidence”), comprise a mixture of vegetation and water (class 3: “potential wetland”), or are of marginal validity (class 4: “water or wetland - low confidence”). Class 9 is applied to classify clouds, shadows and hill shade. Two additional documents accompany the raster image files and XML metadata. The first provides a key representing the general location of each raster file. The second file includes all Google Earth Engine Javascript code, which can be used online (https://code.earthengine.google.com/) to replicate the monthly DSWE map time series for Cambodia, or for any other location on Earth. The code block includes comments to explain how each step works. These data support the following publication: These data support the following publication: Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing: Remote Sensing, v. 12, no. 6, p. 984, https://doi.org/10.3390/rs12060984.

This project aims to use remote sensing data from the Landsata database from Google Earth Engine to evaluate the spatial extent changes in the Bear Lake located between the US states of Utah and Idaho. This work is part of a term project submitted to Dr Alfonso Torres-Rua as a requirment to pass the Remote Sensing of Land Surfaces class (CEE6003). More information about the course is provided below. This project uses the geemap Python package (https://github.com/giswqs/geemap) for dealing with the google earth engine datasets. The content of this notebook can be used to:

learn how to retrive the Landsat 8 remote sensed data. The same functions and methodology can also be used to get the data of other Landsat satallites and other satallites such as Sentinel-2, Sentinel-3 and many others. However, slight changes might be required when dealing with other satallites then Landsat. Learn how to create time lapse images that visulaize changes in some parameters over time. Learn how to use supervised classification to track the changes in the spatial extent of water bodies such as Bear Lake that is located between the US states of Utah and Idaho. Learn how to use different functions and tools that are part of the geemap Python package. More information about the geemap Pyhton package can be found at https://github.com/giswqs/geemap and https://github.com/diviningwater/RS_of_Land_Surfaces_laboratory Course information:

Name: Remote Sensing of Land Surfaces class (CEE6003) Instructor: Alfonso Torres-Rua (alfonso.torres@usu.edu) School: Utah State University Semester: Spring semester 2023

Pixel Selection • Select pixels from rasters with conditional statements and boolean operators;

• Select from multiple bands and images to create a single selection;

• Create new image and transfer selected pixels to new image.

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. This global map of Local Climate Zones, at 100m pixel size and representative for the nominal year …

CANUE staff developed annual estimates of maximum mean warm-season land surface temperature (LST) recorded by LandSat 8 at 30m resolution. To reduce the effect of missing data/cloud cover/shadows, the highest mean warm-season value reported over three years was retained - for example, the data for 2021 represent the maximum of the mean land surface temperature at a pixel location between April 1st and September 30th in 2019, 2020 and 2021. Land surface temperature was calculated in Google Earth Engine, using a public algorithm (see supplementary documentation). In general, annual mean LST may not reflect ambient air temperatures experienced by individuals at any given time, but does identify areas that are hotter during the day and therefore more likely to radiate excess heat at night - both factors that contribute to heat islands within urban areas.

Data Acquisition • Acquiring data stored on Google’s servers for use in Google Earth Engine.

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

Clipping • How to clip a raster image to the extent of a vector polygon in order to speed up processing times as well to display only the imagery you want.

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 })...