Book Image

QGIS Python Programming Cookbook, Second Edition - Second Edition

By : Joel Lawhead
Book Image

QGIS Python Programming Cookbook, Second Edition - Second Edition

By: Joel Lawhead

Overview of this book

QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we’ll also share a few tips and tricks based on different aspects of QGIS.
Table of Contents (16 chapters)
QGIS Python Programming Cookbook - Second Edition
About the Author
About the Reviewer
Customer Feedback

Mosaicing rasters

Mosaicing rasters is the process of fusing multiple geospatial images with the same resolution and map projection into one raster. In this recipe, we'll combine two overlapping satellite images into a single dataset.

Getting ready

You will need to download the overlapping dataset from if you haven't downloaded it from a previous recipe.

Place the two images in your /qgis_data/rasters/ directory.

How to do it...

This process is relatively straightforward and has a dedicated algorithm within the Processing Toolbox. Perform the following steps:

  1. Start QGIS.

  2. From the Plugins menu, select Python Console.

  3. Run the gdalogr:merge algorithm, specifying the process name, two images, a boolean to use the pseudocolor palette from the first image, a boolean to stack each image into a separate band, and the output filename: