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

Fu, Zhuo; Wang, Jindi; Song, Jin L.; Zhou, Hong M.; Pang, Yong; Chen, Bai S.

doi:doi:10.1117/1.3594171

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Journal of Applied Remote Sensing / Volume 5 / Research Papers

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

J. Appl. Remote Sens. 5, 053530 (Jun 07, 2011); http://dx.doi.org/10.1117/1.3594171

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Abstract

References (37)

Zhuo Fu, Jindi Wang, Jin L. Song, Hong M. Zhou, and Bai S. Chen

Beijing Normal University, State Key Laboratory of Remote Sensing Science, School of Geography, No.19 Xinjiekouwai Street, Beijing, 100875, China wangjd@bnu.edu.cn

Ministry of Environmental Protection, Satellite Environment Center, Beijing, 100094, China

Yong Pang

Chinese Academy of Forestry, Institute of Forest Resource Information Techniques, Beijing, 100091, China

National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China

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.

History

Received Apr 07, 2010
Accepted May 03, 2011
Revised Apr 18, 2011
Published online Jun 07, 2011

Digital Object Identifier

http://dx.doi.org/10.1117/1.3594171

Citation

Zhuo Fu, Jindi Wang, Jin L. Song, Hong M. Zhou, Yong Pang and Bai S. Chen, "Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations", J. Appl. Remote Sens. 5, 053530 (Jun 07, 2011); http://dx.doi.org/10.1117/1.3594171