Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Creating an NDVI


Our first example will be a Normalized Differential Vegetative Index or NDVI. NDVIs are used to show the relative health of plants in an area of interest. An NDVI algorithm shows relative health by highlighting chlorophyll density in plants. NDVIs use only the red and infrared bands. The formula is:

NDVI = (Infrared – Red) / (Infrared + Red)

The goal of this analysis is to begin with a multispectral image containing those two bands and end up with a pseudo-color image using seven classes that color the healthier plants darker green, less-healthy plants lighter green, and bare soil brown.

Because the health index is relative, it is important to localize the area of interest. You could perform a relative index for the entire globe, but vast areas like the Sahara Desert on the low-vegetation extreme and densely forested areas like the Amazon Jungle skew the results for vegetation in the middle range. However, that being said, climate scientists do routinely create global NDVIs...