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Journal of Applied Remote Sensing

Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations
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Paper Abstract

A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging-spectroradiometer (MODIS) and multiangle-imaging-spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.

Paper Details

Date Published: 1 January 2011
PDF: 22 pages
J. Appl. Remote Sens. 5(1) 053530 doi: 10.1117/1.3594171
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Zhuo Fu, Beijing Normal Univ. (China)
Jindi Wang, Beijing Normal Univ. (China)
Jinling Song, Beijing Normal Univ. (China)
Hongmin Zhou, Beijing Normal Univ. (China)
Yong Pang, Chinese Academy of Forestry (China)
Baisong Chen, Beijing Normal Univ. (China)


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