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Remote Sensing for Ecology and ConservationA Handbook of Techniques$
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Ned Horning, Julie A. Robinson, Eleanor J. Sterling, Woody Turner, and Sacha Spector

Print publication date: 2010

Print ISBN-13: 9780199219940

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199219940.001.0001

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Working with images

Working with images

(p.37) 3 Working with images
Remote Sensing for Ecology and Conservation

Ned Horning

Julie A. Robinson

Eleanor J. Sterling

Woody Turner

Sacha Spector

Oxford University Press

There are two very different ways to envision a satellite image: as a photograph taken with a camera, or as a visual representation of spectral intensity data quantifying the light reflecting off of objects on a planet’s surface. In working with satellite images, sometimes the objective is to highlight and accent the information in the image using tools to enhance the way the image looks—the same goal that a professional photographer might have when working in the darkroom with film or using Photoshop to manipulate digital photographs. Another objective could be to manipulate the image using automated processing methods within a remote sensing package that rely on a set of equations that quantify information about reflected light. With either approach the goal is to gain information about conditions observed on the ground. At first glance, the image in Fig. 3.1 bears little resemblance to what most people would recognize as a terrestrial landscape. After all, its predominant colors are orange and bright turquoise. The use of colors in creating a visual image allows great breadth in the types of things one can identify on the ground, but also makes image interpretation an art. Even an inexperienced interpreter can make some sense of the image; more experienced interpreters with knowledge of the color scheme in use are able to determine finer details. For example, in Fig. 3.1 some of the more prominent features are a river (blue line on the left side of the image) a gradient of different vegetation (orange colors throughout the image that go from light to dark), and burn scars (turquoise patches). Fig. 3.2 shows a portion of landscape represented in the satellite image in Fig. 3.1. The red dot in Fig. 3.1 indicates the location where the photograph was taken. This photograph shows what a human observer would see looking south (in this case toward the top of the satellite image) from the point represented by the red dot. The view in the photograph differs from the satellite image in two important ways.

Keywords:   atmospheric correction, biodiversity, classification accuracy, detector, extent, frequency, geometric processing, hyperspectral, image calibration

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