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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7

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Product type Paperback
Published in Sep 2019
Publisher
ISBN-13 9781789959277
Length 456 pages
Edition 3rd 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 (15) Chapters Close

Preface 1. Section 1: The History and the Present of the Industry FREE CHAPTER
2. Learning about Geospatial Analysis with Python 3. Learning Geospatial Data 4. The Geospatial Technology Landscape 5. Section 2: Geospatial Analysis Concepts
6. Geospatial Python Toolbox 7. Python and Geographic Information Systems 8. Python and Remote Sensing 9. Python and Elevation Data 10. Section 3: Practical Geospatial Processing Techniques
11. Advanced Geospatial Python Modeling 12. Real-Time Data 13. Putting It All Together 14. Other Books You May Enjoy

Creating a normalized difference vegetative index

Our first example will be an normalized difference vegetative index (NVDI). NDVIs are used to show the relative health of plants in an area of interest. An NDVI algorithm uses satellite or aerial imagery to show relative health by highlighting the chlorophyll density in plants. NDVIs use only the red and near-infrared bands. The formula of NDVI is as follows:

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

The goal of this analysis is to produce, to begin with, a multispectral image containing infrared and red bands, and end up with a pseudo color image using seven classes, which 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...

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