The elevation image is a raster at 7.5 arc-second spatial resolution containing a continuous measure of elevation in meters in each pixel. We will start by loading the Global Multi-Resolution Terrain Elevation Data 2010 and the Global Administrative Unit Layers 2015 dataset to focus on Colombia. In this section we will convert an elevation image (raster) to a feature collection (vector). In this exercise, we’ll use topographic elevation and forest change images in Colombia as well as a protected area feature collection to practice the conversion between raster and vector formats, and to identify situations in which this is worthwhile. Each format lends itself to distinctive analytical operations, and combining them can be powerful. Rasters can store data efficiently where each pixel has a numerical value, while vector data can more effectively represent geometric features where homogenous areas have shared properties. In making such conversions, it is important to consider the key advantages of each format. Raster and vector data are commonly combined (e.g., extracting image information for a given location or clipping an image to an area of interest) however, there are also situations in which conversion between the two formats is useful. Each data format has its advantages, and both will be encountered as part of GIS operations. Vector data contains geometry features (i.e., points, lines, and polygons) describing locations and areas. Raster data consists of regularly spaced pixels arranged into rows and columns, familiar as the format of satellite images. This code base is collection of codes that are freely available from different authors for google earth engine. Use reduceRegions to summarize an image in irregular shapes (Chap. Write a function and map it over an ImageCollection (Chap.Understand the filter, map, reduce paradigm (Chap.Perform image morphological operations (Chap.Perform basic image analysis: select bands, compute indices, create masks (Part F2).Understand distinctions among Image, ImageCollection, Feature and FeatureCollection Earth Engine objects (Part F1, Part F2, Part F5).Import images and image collections, filter, and visualize (Part F1).Write a function and map it over a FeatureCollection.Knowing how and why to convert from vector to raster.Knowing how and why to convert from raster to vector.Understanding raster and vector data in Earth Engine and their differing properties. By way of example, this chapter focuses on topographic elevation and forest cover change in Colombia, but note that these are generic methods that can be applied in a wide variety of situations. The purpose of this chapter is to review methods of converting between raster and vector data formats, and to understand the circumstances in which this is useful. This Part introduces you to the vector framework in Earth Engine, shows you how to create and to import your vector data, and how to combine vector and raster data for analyses. In addition to raster data processing, Earth Engine supports a rich set of vector processing tools. Cloud-Based Remote Sensing with Google Earth Engine
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