Vol. 3, No. 3, 405-413, 2004

Derivation of forest leaf area index from multi- and hyperspectral remote sensing data
Martin Schlerf, Clement Atzberger, Michael Vohland, Henning Buddenbaum, Stephan Seeling and Joachim Hill

Abstract
This study evaluated systematically linear predictive models between vegetation indices (VIs) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of leaf area index (LAI) (n=40). Ratio-based and soil-line related broadband VIs were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VIs involved all possible types of 2-band combinations of RVI and PVI. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A perpendicular vegetation index (PVI) based on wavebands at 1,088 and 1,148 nm was linearly related to leaf area index (LAI) (R2=0.67, RMSE=0.69 m2m-2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R2=0.77, RMSE=0.54 m2m-2 (17% of the mean)). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI with good accuracy. Best hyperspectral VIs in relation with LAI are typically based on wavebands related to prominent water absorption features. Such VIs measure the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.

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History
Submitted: 01 June 2004
Revised: 07 October 2004
Accepted: 14 October 2004

Citation
Schlerf M, C Atzberger, M Vohland, H Buddenbaum, S Seeling & J Hill, 2004. Derivation of forest leaf area index from multi- and hyperspectral remote sensing data. EARSeL eProceedings, 3(3), 405-413

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EARSeL European Association of Remote Sensing Laboratories, Paris, France

   
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BIS Library and Information System, Carl von Ossietzky University of Oldenburg

 

ISSN 1729-3782