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.
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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