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Modeling Forest Productivity Using Data Acquired Through Remote Sensing

  • Chapter
Remote Sensing of Forest Environments

Abstract

The increased need for information on the growth of the worlds forests has led to a range of new approaches beyond traditional ground-based forest inventory surveys supplemented with aerial stereo-photography. The development of physiologically-based process models, which predict forest growth based on underlying physiological processes, and digital remote sensing in combination improve our ability to interpret and to predict forest growth patterns across landscapes. In this Chapter we review applications of these two rapidly maturing technologies. We cite selected examples of how data acquired through remote sensing have served to initialize, update, and validate models. From these studies we document improvements in our ability to assess, project, and track present and future forest growth patterns in changing environments across landscapes.

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Authors and Affiliations

  1. CSIRO Forestry and Forest Products, Private Bag 10, 3169, Clayton South, Victoria, Australia

    Nicholas C. Coops

  2. Deptartment of Biology, Baylor University, P.O. Box 97388, Waco, Texas, USA

    Joseph D. White

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  1. Nicholas C. Coops
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Editors and Affiliations

  1. Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, V8Z 1M5, Victoria, British Columbia, Canada

    Michael A. Wulder

  2. Department of Geography, University of Calgary, T2N 1N4, Calgary, Alberta, Canada

    Steven E. Franklin

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Coops, N.C., White, J.D. (2003). Modeling Forest Productivity Using Data Acquired Through Remote Sensing. In: Wulder, M.A., Franklin, S.E. (eds) Remote Sensing of Forest Environments. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0306-4_15

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