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Proceedings Paper

Monitoring tropical by deforestation processing high-frequency and high-resolution satellite data
Author(s): Agnes Begue; Christophe Zanardo; Jacques Imbernon; Michel Deshayes
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Paper Abstract

Approximately two-thirds of the tropical forest destroyed each year are caused by small-scale farmers that slash and burn the forest vegetation to plant crops and pastures. Characterization and diagnosis of these land-use systems are the main theme of this <<VEGETATION Preparatory Programme >> project based on three regions of the equatorial areas: Brazil, Peroo and Cameroon. To fulfill the requirements of the project, forest cover proportion was assessed from high (SPOT, Landsat) and low (NOAA-AVHRR) resolution images. Classification of high resolution images in forest/crop land classes gave good results, but with some confusions between classes (open forest/dense forest for example). These confusions were reduced when the short wavelength infrared was included in the classification process. In comparison, classification of the low resolution images gave a poor estimation of the forest cover which is all the more over- estimated that the forest is fragmented. Forest cover proportion was then estimated from low resolution images, using spatial modeling and spectral mixture modeling, namely the 'TREES' and 'COCKTAIL' methods respectively. These two methods gave very close global values of deforestation at a regional scale, but the 'COCKTAIL' method had the advantage to provide maps of forest cover, while 'TREES' provided only a regional value.

Paper Details

Date Published: 30 December 1997
PDF: 12 pages
Proc. SPIE 3222, Earth Surface Remote Sensing, (30 December 1997); doi: 10.1117/12.298151
Show Author Affiliations
Agnes Begue, CA/CIRAD (France)
Christophe Zanardo, CA/CIRAD (France)
Jacques Imbernon, ICRAF/CIRAD (Kenya)
Michel Deshayes, Lab. Commun de Teledetection (France)

Published in SPIE Proceedings Vol. 3222:
Earth Surface Remote Sensing
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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