The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images
Operational use of remote sensing as a tool for post-fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non-burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field-based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post-fire management of forests in complex Mediterranean landscapes.
Document Type: Research Article
Affiliations: 1: Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, P.O. Box 248, Thessaloniki, GR 541 24, Greece 2: Unit for Landscape Modelling, University of Cambridge, Sir William Hardy Building, Tennis Court Road, Cambridge CB2 1QB, UK
Publication date: 10 January 2006
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