Geoprocessing with Python by Chris Garrard

Author:Chris Garrard
Language: eng
Format: epub, pdf
Publisher: Manning Publications
Published: 2018-09-25T16:00:00+00:00

Note to Print Book Readers: Color Graphics

Many graphics in this book are best viewed in color. The eBook versions display the color graphics, so they should be referred to as you read. To get your free eBook in PDF, ePub, and Kindle formats, go to https://www.manning.com/books/geoprocessing-with-python to register your print book.

Satellite imagery, on the other hand, contains measurements of various wavelengths, many of which aren’t visible to our eyes. While the image might contain bands corresponding to visible red, green, and blue wavelengths, there might also be bands for infrared or thermal radiation. False color images such as those shown in figure 9.2 are created by displaying an infrared band along with visible light. This figure illustrates another use case for satellite imagery in the form of raster data. The image on the left was created using visible light wavelengths, like a traditional photograph. The camera also captured a near infrared wavelength as another band at the same time, and it was used along with the visible red and green bands to create the false color image on the right. The near infrared band is brighter for growing vegetation, and it’s displayed as red, so red areas are vegetation. This band combination is useful for monitoring vegetation. The field in the stadium and the practice fields outside are both green and look like grass in the natural color image, but the field inside the stadium is dark gray in the false color image, so it must be artificial. The practice fields outside the stadium are bright red, however, so they must be grass.

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