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Conclusion

Most of the Landsat images appearing in the 20 sections of this tutorial are individual TM bands or color composites made from diverse combinations of three TM bands, along with some MSS images. Also appearing are selected images acquired by SPOT and images made from radar and thermal sensors that flew on satellites and Space Shuttle missions. A principal source for Landsat imagery and much of the astronaut space photography is the EROS Data Center (EDC) in Sioux Falls, S.D., operated by the U.S. Geological Survey (USGS). Where appropriate or necessary, principles underlying the operation of those sensors in the text accompanying these sections.

The USGS's EROS Data Center has assembled an Internet on-line collection of satellite images (mainly Landsat) designed to introduce the general viewer to scenes worldwide that focus on several environmental themes (e.g., cities, deserts, forests). We have linked their three-page Index for this Earthshot collection, which you can access here. To further stimulate interest in the practicality of this imagery, in addition to its beauty, we suggest taking time to sample scenes of interest and read the accompanying descriptions. We also recommend returning to this collection for repeat looks or to examine new examples.

We want to introduce some of the computer-based processing techniques that are employed to extract information from space imagery. We preview the main elements of image processing in the extended analysis of Section 1. There we consider a Landsat image subscene that is centered on the oceanside town of Morro Bay, California. For that analysis, and other images in Sections 2 and 5, we apply the Idrisi Program modules, developed in part as a training tool for image processing and Geographic Information Systems (GIS) by the Geography Department at Clark University. To learn more about their system, contact them on Email at: Idrisi@vax.clark.edu. However, the capstone of this instruction in image processing techniques is in Appendix B,where we describe the PhotoInterpretation Toolkit (PIT), an interactive processing software package. Working with a training image, we walk through many of the processing routines, ending in an opportunity to classify images into thematic maps. Before trying these procedures, we suggest you complete Section 1, and perhaps, several sections that follow it.

References to foreign (non-U.S.) remote sensing systems now operating and to U.S. distribution centers, such as EROS and SpaceImaging-Eosat, indicate that remote sensing is now truly a worldwide activity. The big trend in the 1990's and into the next century is the commercialization of space. Remote sensing is becoming a multi-billion dollar industry and new national organizations and private companies merge each year to take advantage of its income-producing aspects, because they identify many applications (some covered in this tutorial) of great practical value. There is even a monthly magazine, EOM, dedicated to these increasing uses (http://www.eomonline.com). We shall more fully touch upon trends and issues in commercialization of remote sensing as we review the outlook for the future of remote sensing in Section 20. In that Section is also short look at NASA's Regional Applications Center Program, being developed by the Applied Information Sciences Branch (sponsor of this tutorial) at NASA-Goddard.

With this introduction into basic principles and to characteristics of Landsat and other systems completed, and, hopefully, digested and understood. You should move on to Section 1, with its protracted tutorial development of the "whys" and "hows" of image processing. There you should gain real insight into and practice in the efficacies of remote sensing from satellites and other types of air and space platforms.

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Primary Author: Nicholas M. Short, Sr. email: nmshort@epix.net

Collaborators: Code 935 NASA GSFC, GST, USAF Academy
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