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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783281138
Length 364 pages
Edition 1st Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python 2. Geospatial Data FREE CHAPTER 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modelling 9. Real-Time Data 10. Putting It All Together 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...

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