20 August 2009 Forest canopy structural parameters and Leaf Area Index retrieval using multi-sensors synergy observations
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Leaf Area Index (LAI) is a key vegetation structural parameter in ecosystem. Our new approach is on forest LAI retrieval by GOMS model (Geometrical-Optical model considering the effect of crown shape and Mutual Shadowing) inversion using multi-sensor observations. The mountainous terrain forest area in Dayekou in Gansu province of China is selected as our study area. The model inversion method by integrating MODIS, MISR and LIDAR data for forest canopy LAI retrieval is proposed. In the MODIS sub-pixel scale, four scene components' spectrum (sunlit canopy, sunlit background, shaded canopy and shaded background) of GOMS model are extracted from SPOT data. And tree heights are extracted from airborne LIDAR data. The extracted four scene components and tree heights are taken as the a priori knowledge applied in GOMS model inversion for improving forest canopy structural parameters estimation accuracy. According to the field investigation, BRDF data set of needle forest pixels is collected by combining MODIS BRDF product and MISR BRF product. Then forest canopy parameters are retrieved based on GOMS. Finally, LAI of forest canopy is estimated by the retrieved structural parameters and it is compared with ground measurement. Results indicate that it is possible to improve the forest canopy structural parameters estimation accuracy by combining observations of passive and active remote sensors.
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Zhuo Fu, Zhuo Fu, Jindi Wang, Jindi Wang, Jinling Song, Jinling Song, Hongmin Zhou, Hongmin Zhou, Yong Pang, Yong Pang, Wenwen Cai, Wenwen Cai, Baisong Chen, Baisong Chen, } "Forest canopy structural parameters and Leaf Area Index retrieval using multi-sensors synergy observations", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 74540R (20 August 2009); doi: 10.1117/12.825928; https://doi.org/10.1117/12.825928


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