Raster file types 10112/5/2023 ![]() ![]() Specifically, rasterio represents rasters as numpy arrays associated with a separate object holding the spatial metadata. These two packages differ in their scope and underlying data models. The two most notable approaches for working with rasters in Python are provided by the rasterio (which we learn about in this chapter) and xarray packages. Instead, there are several packages providing alternative (subsets of methods) of working with raster data. As mentioned above, working with rasters in Python is less organized around one comprehensive package (such as the case for vector layers and geopandas). rasterio, like most raster processing software, is based on the GDAL software. rasterio makes raster data accessible in the form of numpy arrays, so that we can operate on them, then write back to new raster files. Rasterio is a third-party Python package for working with rasters. ![]() Import richdem as rd import scipy.ndimage What is rasterio? # To start working with rasterio, first of all, we import the rasterio package, as well as the show function (used for raster plotting) from the ot sub-package: Let us go over the packages and their purpose, and load them, before we start working. ![]() In this chapter we use quite a few more packages than in any of the previous chapters. In the next chapter (see Raster-vector interactions), we are going to explore operations that involve both a raster and a vector layer, such as converting a raster to polygons, or extracting raster values to points or polygons. Rasterstats-For zonal statistics of rasters (see Zonal statistics) Scipy-for focal filtering (see Focal filtering) Richdem-for calculating topographic indices (see DEM calculations) In the second part of this chapter and in the next one, we will demonstrate more specific tasks through additional third-party packages: Therefore, many standard raster-related workflows are complicated to do with rasterio on its own. The rasterio package is not as comprehensive, and is lower level, compared to geopandas for vector layers. Making calculations with raster values (see “No Data” in rasters, and Working with raster values) Writing raster to file (see Writing rasters) Plotting rasters (see Plotting (rasterio))Ĭreating a raster from a numpy array (see Creating raster from array) Reading raster files and examining their properties (see Raster file connection, Raster properties, and Reading raster data) Specifically, we will go over the following raster workflow: The rasterio package is compatible with, and extends, the numpy package. ![]() To work with rasters, we are going to use the rasterio package. Rasters are stored in special formats, such as GeoTIFF (. That is, in additional to the numeric array that contains the image values, that every image has, a raster also has metadata specifying the rectangual extent that the image corresponds to in a particular spatial Coordinate Reference System. Rasters are basically georeferenced images. In this section we move on to the second type of spatial layers, rasters. ![]()
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