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The Geoscience Laser Altimeter System(GLAS)accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems.Since the GLAS footprints are discontinuously distributed with a relativity low density,continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone.The MODIS BRDF product provides more forest structural information than other optical remote sensing data.This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data.Firstly,the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis,and the terrain index was calculated using the ASTER GDEM data.Secondly,the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index.Thirdly,the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data,and applied to get the continuous canopy height map over China.Finally,the map was validated by the measured data and the airborne Li DAR data,and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data.
The Geoscience Laser Altimeter System (GLAS) accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems.Since the GLAS footprints are discontinuously distributed with a relativity low density, continuous vegetation height distributions can not be mapped with a high accuracy using GLAS data alone. The MODIS BRDF product provides more forest structural information than other optical remote sensing data. This study aims to map forest canopy heights over China from the GLAS and MODIS BRDF data. Firstly, the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis, and the terrain index was calculated using ASTER GDEM data. Secondarily, the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index.Thirdly, the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data, and applied to get the continuous canopy height map over China. Finally, the map was validated by the measured data and the airborne Li DAR data, and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data.