Introduction

This chapter shows you how to bring raster data into a GIS and create derivative raster products using QGIS and Python. QGIS is equally adept at working with raster data as with vector data, by incorporating leading-edge open source libraries and algorithms, including GDAL, SAGA, and the Orfeo Toolbox. QGIS provides a consistent interface to for large array of remote sensing tools. We will switch back and forth between visually working with raster data and using QGIS as a processing engine via the Processing Toolbox, to completely automating remote sensing workflows.

Raster data consists of rows and columns of cells or pixels, with each cell representing a single value. The easiest way to think of raster data is as images, which is how they are typically represented by software. However, raster datasets are not necessarily stored as images. They can also be ASCII text files or binary large objects (BLOBs) in databases.

Another difference between geospatial raster data and regular...