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

Creating an NDVI

A Normalized Difference Vegetation Index (NDVI) is one of the oldest remote sensing algorithms used to detect green vegetation in an area of interest, using the red and near-infrared bands of an image. The chlorophyll in plants absorbs visible light, including the red band, while the cell structures of plants reflect near-infrared light. The NDVI formula provides a ratio of near-infrared light to the total incoming radiation, which serves as an indicator of vegetation density. This recipe will use Python to control the QGIS raster calculator in order to create an NDVI using a multispectral image of a farm field.

Getting ready

Download the image from and place it in your qgis_data in a directory named rasters.

How to do it...

We will load the raster as a QGIS raster layer, perform the NDVI algorithm, and finally apply a color ramp to the raster so that we can easily visualize the greener vegetation in the image...