Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

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. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. 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. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Elevation data

A Digital Elevation Model (DEM) is a three-dimensional representation of a planet's terrain. In the context of this book, this planet is the Earth. The history of digital elevation models is far less complicated than remotely-sensed imagery but no less significant. Before computers, representations of elevation data were limited to topographic maps created through traditional land surveys. Technology existed to create three-dimensional models from stereoscopic images or physical models from materials such as clay or wood, but these approaches were not widely used for geography.

The concept of digital elevation models began in 1986 when the French space agency, Centre national d'études spatiales (CNES), launched its SPOT-1 satellite, which included a stereoscopic radar. This system created the first usable DEM. Several other U.S. and European satellites followed this model with similar missions. In February, 2000, the Space Shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM), which collected elevation data over 80% of the Earth's surface using a special radar antenna configuration that allowed a single pass. This model was surpassed in 2009 by the joint U.S. and Japanese mission using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor aboard NASA's Terra satellite. This system captured 99% of the Earth's surface but has proven to have minor data issues. As the Space Shuttle's orbit did not cross the Earth's poles, it did not capture the entire surface. SRTM remains the gold standard. The following image from the USGS shows a colorized DEM known as a hillshade. Greener areas are lower elevations while yellow and brown areas are mid-range to high elevations:

Recently, more ambitious attempts at a worldwide elevation dataset are underway in the form of TerraSAR-X and TanDEM-X satellites launched by Germany in 2007 and 2010, respectively. These two radar elevation satellites worked together to produce a global DEM called WorldDEM that was released on April 15, 2014. This dataset has a relative accuracy of two meters and an absolute accuracy of four meters.