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ERS INSAR data for remote sensing hilly forested areas

Authors :
Urs Wegmüller
Thierry Castel
Tazio Strozzi
Jean-Michel Martinez
André Beaudoin
Département Amélioration des méthodes pour l'innovation scientifique (AMIS)
Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
Source :
Remote Sensing of Environment, Remote Sensing of Environment, Elsevier, 2000, 73 (1), pp.73-86. ⟨10.1016/S0034-4257(00)00083-3⟩
Publication Year :
2000

Abstract

International audience; ERS INSAR data have proved to be of interest for forest applications. The interferometric coherence was found to be related to various land uses and forest types, while in some special cases (e.g., flat terrain) the interferometric phase has been linked to the forest height. This paper reports an investigation on the information content of the interferometric coherence over a hilly terrain supporting various land use types and large pine plantations. The approach includes the use of a Geographic Information System and multitemporal data to analyze the coherence behavior as a function of forest-type forest parameters and environmental factors such as meteorological and topographic effects. Coherence appears to be efficient to discriminate between forest types. However, topography and environmental conditions strongly affect the coherence and its estimation, pointing out the need for rejection of strong slopes areas (>15°) and the sensitivity to local mteorological/seasonal effects. Based on these observations, forest classification results are presented. Forest/nonforest discrimination is very efficient (accuracy >90%) using one-day interval acquisition. More detailed classification with discrimination between forest themes gives also good results. Then, we investigate the indirect link between coherence and forest parameters. The coherence is sensitive to the forest growth stage, making forest parameter retrieval possible using a simple straight-line model. Finally, the importance of wind upon temporal decorrelation is addressed, and a semiempirical correction is proposed.

Details

Language :
English
ISSN :
00344257 and 18790704
Database :
OpenAIRE
Journal :
Remote Sensing of Environment, Remote Sensing of Environment, Elsevier, 2000, 73 (1), pp.73-86. ⟨10.1016/S0034-4257(00)00083-3⟩
Accession number :
edsair.doi.dedup.....16f927dad4aacf4d691afc193e581c8a
Full Text :
https://doi.org/10.1016/S0034-4257(00)00083-3⟩