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Proceedings Paper

Time-series tropical forest change detection: a visual and quantitative approach
Author(s): Steven A. Sader; Thomas Sever; James C. Smoot
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Paper Abstract

Forest change detection over a decadal time frame was conducted for the Maya Biosphere Reserve in northern Guatemala. A simple and logical method of visualizing and quantifying forest change is presented. Analysis of time- series Landsat-Thematic Mapper imagery provided estimates of forest change at three time periods; prior to 1990, 1990 to 1993 and 1993 to 1995. Four dates of Landsat imagery were pre-processed, co-registered to a UTM projection and the normalized difference vegetation index was computed for each date. An unsupervised classification was performed and cluster classes were grouped into time-series change/no change categories. A color coded image was generated which resembled the RBG-NDVI color composite of the 1990, 1993, and 1995 imagery. Land cover information and Geographic Information System (GIS) editing techniques were applied to resolve some confusions between forest change and change in non-forest types. Results indicated that forest clearing rates in the reserve were less than 0.5 percent per year in the early to mid 1990s but the buffer zone clearing rates, at over two percent, were much higher.

Paper Details

Date Published: 4 November 1996
PDF: 11 pages
Proc. SPIE 2818, Multispectral Imaging for Terrestrial Applications, (4 November 1996); doi: 10.1117/12.256076
Show Author Affiliations
Steven A. Sader, Univ. of Maine (United States)
Thomas Sever, NASA Stennis Space Ctr. (United States)
James C. Smoot, Lockheed Martin Missiles and Space Co., Inc. (United States)


Published in SPIE Proceedings Vol. 2818:
Multispectral Imaging for Terrestrial Applications
Brian Huberty; Joan B. Lurie; Jule A. Caylor; Pol Coppin; Pierre C. Robert, Editor(s)

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