Uploading files usually takes a few minutes. 4: Upload a shapefile to specify the area of interest. On the 'New' drop-down menu select 'Table upload', then select your file. Careful: Make sure also to include the .dbf and .shx files, as the shapefile relies on them.įig. Start the import via the 'Assets'-tab in the top left corner. This is recommended when researching a very distinct study area (e.g. 3: Draw a polygon of the area of interest (aoi) by hand.ĭefining the spatial processing extent with a Shapefile (.shp) is the most accurate solution. The geometry will be listed under 'Imports' at the top of the script.įig. Press 'Exit' once you are done setting up your study area. A geometry can consist of more than one polygon. Vertices are created with left clicks and the polygon is completed by double-clicking. The polygon tool can be activated in the top-right corner of the map pane. This is the quickest and easiest option, suitable for exploring and testing the script in different regions. Users can draw an area of interest by hand, upload location information from a file or import country boundaries provided as GEE FeatureCollection.īoundaries can be created interactively. This information is necessary to limit the processing extent of the analysis and avoids redundant calculations. In the following section we will present three different ways to specify the location of your study area. Step 10: Area calculation of flood extent Step 2: Time frame and sensor parameters selection 1.3: In-build country boundary features.Alternatively, you can create a new file in the code editor, download this script and paste it.įig.2: Access the Google Earth Engine script by copy-and-pasting the text-file. There you will find detailed comments along with the code line-by-line. 1: Access the Google Earth Engine script by using the link. The code for this Recommended Practice can be imported by following this link:įig. For a quick orientation around the code editor, click here. A confirmation usually comes within 2-3 work days. While it is free of charge, an activate Google account with Google Earth Engine is required. The platform provides a variety of constantly updated datasets which can be accessed directly within the code editor. The advantage mainly lies in its computational speed, as processing is outsourced to Google’s servers. The following step-by-step procedure uses Google Earth Engine, which is a powerful web-platform for cloud-based processing of remote sensing data on large scales. To assess the number of potentially exposed people, affected cropland and urban areas, additional datasets will be intersected with the derived flood extent layer and visualized. The flood extent is created using a change detection approach on Sentinel-1 (SAR) data. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.The aim of this step-by-step procedure is the generation of a flood extent map for the assessment of affected areas. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. “Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform” Atmosphere 12, no. Mehmood, Hamid, Crystal Conway, and Duminda Perera.
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