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Investigating the use of Landsat thematic mapper data for estimation of forest
 

Summary: Investigating the use of Landsat thematic
mapper data for estimation of forest
leaf area index in southern Sweden
L. Eklundh, K. Hall, H. Eriksson, J. Ardö, and P. Pilesjö
Abstract. The study aims at investigating the use of Landsat thematic mapper (TM) for mapping leaf area index (LAI) in
coniferous and deciduous forests in southern Sweden. LAI has been estimated in the field with optical measurements,
allometric equations, and litter-trap data, and empirical relationships between LAI estimates and satellite-measured
reflectances have been analysed. Several common vegetation indices and multiple regressions where estimated LAI is
predicted as a function of various spectral bands are tested. The results indicate significant relationships between Landsat
TM reflectances and parameters related to LAI, and the relationships are improved when separating coniferous and
deciduous stands. The best relationships occur between Landsat TM data and the product of effective LAI as estimated with
the LAI-2000 instrument and a needle clumping factor (LG), which explains about 80% of the variation in coniferous stands
and about 50% of the variation in deciduous stands. The best single bands in coniferous stands are the middle-infrared
bands (TM5 and TM7), and the best vegetation index is the moisture stress index (TM5/TM4). The best single band in
deciduous stands is TM4, and the best vegetation index is the simple ratio (SR).
Résumé. L'objectif de cette étude est de faire le point sur l'utilisation de Landsat-TM pour la cartographie de l'indice de
surface foliaire (LAI) dans des forêts de conifères et de feuillus du Sud de la Suède. L'indice LAI a été estimé sur le terrain
à l'aide de mesures optiques, d'équations allométriques et des données de pièges à litière, et des relations empiriques entre
les estimations de LAI et les réflectances mesurées par satellite ont été analysées. Plusieurs indices de végétation
couramment utilisés et des régressions multiples basées sur des estimations de LAI dérivées en fonction de diverses bandes

  

Source: Ardö, Jonas - Department of Earth and Ecosystem Sciences, Lunds Universitet

 

Collections: Environmental Sciences and Ecology; Geosciences