Google Earth Engine. Introduction to satellite data analysis in cloud-based environment. Written by: Petr Lukeš

Transcription

1 Google Earth Engine Introduction to satellite data analysis in cloud-based environment Written by: Petr Lukeš

2 1. Introduction Google Earth engine is cloud-based platform for visualisation, processing and interpretation of remote sensing data. It is conceptually different from traditional desktop remote sensing software - in contrast to local data storage and analysis, data are stored remotely in cloud platform on Google servers, processing and visualisation of data is distributed on many Google computers to provide real-time analysis of data for the entire Earth. This allows for the first time to process enormous amounts of satellite data in high spatial resolution and work with satellite data in time series. Google Earth Engine (GEE) environment can be accessed either via webpage, or as so-called application programming interface (API) through different code editors. Google Earth Engine analysis is performed as the series of commands executed through code editor, the code syntax is similar to widely-used Javascript programming language. The usage is free for non-commercial and academic use, commercial use is possible upon agreement with Google. The aim of this document is to introduce the basic principles of GEE and demonstrate its potential for large-scale visualisation and analysis of high spatial resolution data. We will learn step-by-step to visualize selected satellite image and explore its information content via vegetation indices, to mask the image using global mask of forest cover and finally to export the data to commonly used file formats. Figure 1. Example of Google Earth Engine web-based environment. Upper left - scripts, documentation and assets (own data sources), upper middle - main window with code editor, upper right - console with text output, inspector - pixel value query and task - running commands, lower - analysis output overlayed on Google maps. 2

3 2. How to sign-up In this exercise we will work in web-based environment of GEE. In order to access the editor and GEE environment, we have to sign-up for the service. In order to grant the access, enter the following webpage: Here, you have to describe the purpose of your GEE usage - all requests are evaluated so try to include all relevant information here. Once confirmed, you should be able to log-in to to the following web-page: eeapi.appspot.com using your Google credentials. Working environment similar to Figure 1 should appear. 3. Datasets and Assets As already mentioned, in contrast to locally-stored raster (Geotiff, Erdas Imagine) and vector (mainly shapefiles) data, the GEE access the data in cloud. This means, that data are present only in form of abstract ImageCollections (for raster data) and FeatureCollections (for vector data). Access to ImageCollection and FeatureCollection content and data analysis is granted via definition of new variable (see examples below). Own raster data and vector FeatureCollections can be added to GEE. For raster data, geotiff files with the size up to 10 Gb can be added via Assets - New, while vector data are accessed via Google Fusion Table - special Google table accessible via Google Drive environment. Google Fusion Table can be created in Google Drive when selecting New - Google Fusion Tables. Then, the Google KML file can be imported (please note, that commonly used shapefiles are not supported, in order to import shapefile you have to export the data to KML first). Both ImageCollections and FeatureCollections has its unique ID - to access our vector data in Google Fusion Table, select File - About this table and look for table Id. Raster import has its Id according to storage location. We will start with selection of Sentinel-2 high-spatial resolution satellite data. These are already included in GEE. Try to search for the data in the upper search bar and type Sentinel-2. You will retrieve description of the dataset with the number of bands and ImageCollection ID (COPERNICUS/S2). The dataset is initialized by the command ee.imagecollection: var S2 = ee.imagecollection('copernicus/s2') To select the dates from the ImageCollection (all Sentinel-2 satellite data acquisitions) use command filterdate: var S2select = S2.filterDate(' ', ' '); This will limit our ImageCollection only to Sentinel-2 data acquisitions between and Please note, that output of first command (variable S2) is used as input to second command (S2.filterDate). Finally, the most recent image from the ImageCollection is visualized using command Map.addLayer: Map.addLayer(S2select, {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}, 'Sentinel-2 image cloudy'); Here, the section in the curly brackets describes the visualisation of the dataset - minimum and maximum pixel value and the bands used for RGB image composition. 3

4 Figure 2. Original Sentinel-2 image used for the analysis. Note the high cloud cover for most parts of the Europe. 4. Synthesis of best-quality image As you can see, the individual Sentinel-2 image is quite cloudy. One great strength of GEE is its combination of satellite data availability and computational power. This allows to create a synthesis of new images using appropriate algorithms. One very simple approach to generate cloud-free image from ImageCollection of given date range is the qualitymosaic. This is very simple algorithm which uses given quality information (stored as image band). Using the quality information, new image is synthesized from all observations in given time range, so that the new image consist of pixels of highest quality (best quality information). In our case, we will use value of vegetation index NDVI as the quality information - our assumption is, that the pixel with highest NDVI value has lowest probability of being covered by clouds. The problem is, that the NDVI index is not by default present as image band of Sentinel-2 dataset. To add NDVI band for each image in ImageCollection we have to create new function and apply (map) it on the entire ImageCollection. Let s start with adding the NDVI to the ImageCollection: function addndvi (img) { var ndvi = img.normalizeddifference (['B8','B4']).rename('ndvi'); return img.addbands(ndvi); } 4

5 This way we will create a function, where image on input (img) is used to create a new internal variable ndvi as normalized difference of Sentienel-2 bands B8 and B4. The result is named as band ndvi and added to the image on the output. var S2ndvi=S2select.map(addNdvi); The function is applied on selected Sentinel-2 data using the Map command. This creates a new variable S2ndvi, which consist of selected Sentinel-2 images ( ) with new band containing NDVI index values. var S2hq=S2ndvi.qualityMosaic('ndvi'); Finally, best cloud-free image is synthesized using the qualitymosaic function, where band ndvi is used as information on image quality. Map.addLayer(S2hq, {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}, 'Sentinel-2 image best quality'); The best-quality image is visualized in similar way as in previous case with S2select using Map.addLayer. Figure 3. Sentinel-2 image after applying the qualitymosaic function with NDVI values as indicator of pixel quality. 5

6 5. Masking and clipping As foresters interested in Bosnia and Herzegovina (BiH), we would naturally like to limit our analysis to the area of the country and focus on forested areas only. This can be achieved using two datasets - Google fusion table with world borders (originally a shapefile exported into Google KML file and imported into Google Fusion table with known table ID), and Hanen global forest cover dataset. Fusion table with world borders can be accessed via the following ID: 17mMrmy5DaEPNlPabZeKrTmY3e8cP7-qCFrrFyxk (one of many, try to search for Fusion table World borders on Google). Hansen forest change is a global dataset of forest cover, its gains and losses between 2000 and The data can be visualized and downloaded either through the webpage: or within a GEE environment. In our exercise, we will incorporate Hansen forest change data into our analysis. We begin with creating a featurecollection containing geometry of BiH borders. We will do this by filtering the World boundaries dataset using filtermetadata function. Here we will select only geometry (feature) with attribute name equals to Bosnia and Herzegovina. You may explore the attribute table of fusion table to find out its information content. var world = ee.featurecollection('ft:17mmrmy5daepnlpabzekrtmy3e8cp7- qcfrrfyxk'); var bosnia = world.filtermetadata('name', 'equals', 'Bosnia and Herzegovina'); Next, we will add the Hansen forest dataset to our analysis. You may search for the dataset in GEE, the one we will use in our analysis is Hansen Global Forest Change v1.2 ( ) with Image ID UMD/hansen/global_forest_change_2015. We will add this Image similarly to Sentinel-2 data: var hansen = ee.image('umd/hansen/global_forest_change_2015'); Our goal is to create global mask of forest extend in 2014 (i.e. most recent Hansen dataset). To do so, we need to do some basic processing, including new image creation, selection of bands from ImageCollection, filtering pixel values and adding pixels into new dataset. var blank = ee.image(0); First step in global forest mask creation is to create a new (entirely black) image using ee.image(0) function. This will create a new image (named blank ) with all pixel values equal to zero. var hansencover = ee.image(hansen).select('treecover2000'); Map.addLayer(hansencover, {min: 0, max: 100, palette: ['000000', '227755', '00FF00']}, 'Global forest cover year 2000 (% pixel)') var hansengain = ee.image(hansen).select('gain'); var forest_bosnia = blank.where(hansencover.gte(20),1).add(hansengain).clip(bosnia); 6

7 Figure 4. Hansen global forest cover dataset. Brighter green color corresponds to higher canopy cover of the forest. Next, we create a new images hansencover and hansengain by selecting (ee.image(image).select) the bands treecover forest extend in 2000 with pixel values representing the percentage forest cover of the pixel, and hansengain - forest gains between 2000 and 2014 (gain = pixel value 1). Finally, mask of BiH forest cover is created by adding forest cover > 20% (image.where(image.gte(value),newvalue) and forest gains (.add(image) into blank dataset with originally zero pixel values and clipping (function.clip) our area of interest to BiH extend. Map.addLayer(forest_bosnia,{'min': 0, 'max': 1},'Forest mask of Bosnia and Herzegovina'); The binary mask of global forest cover can we visualized in similar way to other Images using Map.addLayer function. 7

8 Figure 5. Mask of forests of Bosnia and Herzegovina derived from Hansen global forest cover dataset, including forest cover from 2000 and all forest gains between 2000 and Finally, we have two datasets needed for 1) clipping our area of interest to BiH and 2) masking all non-forest classes. We will create our last image by masking all non-forest classes (function.mask). var S2hq_forest=S2hq.mask(forest_bosnia); Again, the result - map of high-quality NDVI index values calculated from Sentinel-2 observations in summer 2016 of the forests of BiH can be visualized using Map.addLayer function. In this case, visualization of the data is performed using color palette stretching from red colors for low NDVI values to green for high NDVI values. Map.addLayer(S2hq_forest.select('ndvi'), {min: 0.3, max: 1, palette: ['FF0000', '227755', '00FF00']}, 'NDVI index values - forests of Bosnia and Herzegovina') 8

9 Figure 6. Final product - NDVI values of the forest of Bosnia and Herzegovina calculated above high-quality (low cloud cover) image of Sentinel-2 acquired in summer Data export So far, all our outputs were visualized as map layers in GEE environment. In order to export the results as raster image files, which can be opened and used on traditional desktop GIS/remote sensing software (e.g. ERDAS Imagine), GEE offers a function Export.image. This will save the desired output as geotiff file in Google drive storage linked to your Google account. Export.image(S2hq_forest.select('ndvi')); We simply call the function Export.image and select the band (NDVI index values), the result is new dialogue window under the Task section of GEE, where we specify the name of new file. Please note, that the export can take some time to finish. 7. Final remarks Many interesting features and algorithms can be found under the Help - User guide section of GEE. Best way to learn the code and all the functions available is to explore someone else s codes - plenty of examples are found in ready-made script under the Examples section of Scripts section. Please feel free to experiment, copy part of the codes in your own script and change the parameters according to your needs. 9

10 8. Complete Google Earth Engine code // DATASET AND ASSETS var S2 = ee.imagecollection('copernicus/s2'); var S2select = S2.filterDate(' ', ' '); Map.addLayer(S2select, {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}, 'Sentinel-2 image cloudy'); // SYNTHESIS OF BEST-QUALITY IMAGE function addndvi (img) { var ndvi = img.normalizeddifference (['B8','B4']).rename('ndvi'); return img.addbands(ndvi); } var S2ndvi=S2select.map(addNdvi); var S2hq=S2ndvi.qualityMosaic('ndvi'); Map.addLayer(S2hq, {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}, 'Sentinel-2 image best quality'); //MASKING AND CLIPPING var world = ee.featurecollection('ft:17mmrmy5daepnlpabzekrtmy3e8cp7- qcfrrfyxk'); var bosnia = world.filtermetadata('name', 'equals', 'Bosnia and Herzegovina'); var hansen = ee.image('umd/hansen/global_forest_change_2015'); var blank = ee.image(0); var hansencover = ee.image(hansen).select('treecover2000'); Map.addLayer(hansencover, {min: 0, max: 100, palette: ['000000', '227755', '00FF00']}, 'Global forest cover year 2000 (% pixel)') var hansengain = ee.image(hansen).select('gain'); var forest_bosnia = blank.where(hansencover.gte(20),1).add(hansengain).clip(bosnia); Map.addLayer(forest_bosnia,{'min': 0, 'max': 1},'Forest mask of Bosnia and Herzegovina'); var S2hq_forest=S2hq.mask(forest_bosnia); Map.addLayer(S2hq_forest.select('ndvi'), {min: 0.3, max: 1, palette: ['FF0000', '227755', '00FF00']}, 'NDVI index values - forests of Bosnia and Herzegovina') //DATA EXPORT Export.image(S2hq_forest.select('ndvi')); NOTE ON COLORS - bold blue text highlights the variables (Images, ImageCollection, Features), whereas bold black text highlights the GEE functions. 10