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

Swapping image bands


Our eyes can only see colors in the visible spectrum as combinations of red, green, and blue (RGB). Air and space-borne sensors can collect wavelengths of the energy outside of the visible spectrum. In order to view this data we move images representing different wavelengths of light reflectance in and out of the RGB channels to make color images. These images often end up as bizarre and alien color combinations that can make visual analysis difficult. An example of a typical satellite image is seen in the following Landsat 7 satellite scene near the NASA Stennis Space Center in Mississippi along the Gulf of Mexico, which is a leading center for Remote Sensing and Geospatial Analysis in general:

Most of the vegetation appears red, and water appears almost black. This image is one type of "false color" image meaning the color of the image is not based on RGB light. However we can change the order of the bands or swap out certain bands to create another type of false-color...