Research Papers

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

[+] Author Affiliations
Zhuo Fu

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

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

Jindi Wang

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

Jin L. Song, Hong M. Zhou

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

Yong Pang

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

Bai S. Chen

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

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

J. Appl. Remote Sens. 5(1), 053530 (June 07, 2011). doi:10.1117/1.3594171
History: Received April 07, 2010; Revised April 18, 2011; Accepted May 03, 2011; Published June 07, 2011; Online June 07, 2011
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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.

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Zhuo Fu ; Jindi Wang ; Jin L. Song ; Hong M. Zhou ; Yong Pang, et al.
"Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations", J. Appl. Remote Sens. 5(1), 053530 (June 07, 2011). ; http://dx.doi.org/10.1117/1.3594171


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