Estimation of Tree Height and Forest Biomass from GLAS Data(<Special Issue>Silvilaser)
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概要
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The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) is the first spaceborne lidar instrument for routine global observation of the Earth. GLAS records a vertical profile of the returned laser energy from a footprint of approximate 70m diameter. The GLAS waveform data (GLA01) and the Land/Canopy Elevation product (GLA14) provide information on vegetation spatial structure. In this study the use of the GLAS data for forest structural parameters retrieval was evaluated using airborne LVIS (NASA's Laser Vegetation Imaging Sensor) data and field measurements. The tree height indices from airborne large-footprint lidars such as LVIS have been successfully used for estimation of forest structural parameters in many studies. The tree height indices, based on lidar return energy quartiles from GLAS data were compared to similar tree height indices derived from LVIS data within the GLAS footprints. The results show that the tree height indices derived from the GLAS and LVIS waveforms were highly correlated. Our analysis showed that tree height and biomass obtained from field measurements can be predicted from GLAS data. Comparisons of the near-repeat-pass GLAS data acquired in Fall of 2003 (L2A), Fall of 2004 (L3A), and early Summer of 2005 (L3C) and 2006 (L3F) show that the surface elevations from GLAS were consistent. When the mean distance between corresponding points from two 4.5km orbits (260 GLAS shots from L2A and L3F) was 82.6m, the R^2 of the elevations from these two orbits was 0.997, with a RMSE of 4.1m. The top tree heights from the near-repeat-pass GLAS orbits show significant differences, probably due to the heterogeneity of the forests.
著者
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Guo Z.
State Key Laboratory Of Remote Sensing Institute Of Remote Sensing Applications Chinese Academy Of S
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Sun G.
Department of Geography University of Maryland
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Pang Y.
Institute of Forest Resources Information Technology, Chinese Academy of Forestry
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Fu A.
State Key Laboratory of Remote Sensing, Institute of Remote Sensing Applications, Chinese Academy of
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Wang D.
State Key Laboratory of Remote Sensing, Institute of Remote Sensing Applications, Chinese Academy of
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Pang Y.
Institute Of Forest Resources Information Technology Chinese Academy Of Forestry
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Fu A.
State Key Laboratory Of Remote Sensing Institute Of Remote Sensing Applications Chinese Academy Of S