期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B1
页码：379-384
出版社：Copernicus Publications
摘要：Forest canopy height is an important input for ecosystem and highly correlated with aboveground biomass at the landscape scale. In this paper, we make efforts to extracte the maximum canopy height using GLAS waveform combination with the terrain index in sloped area where LiDAR data were present. Where LiDAR data were not present, the optical remote sensing data were used to estimate the canopy height at broad scale regions. we compared four aspatial and spatial methods for estimating canopy height integrating large footprint Lidar system (GLAS) and Landsat ETM+: ordinary least squares regression, ordinary kriging, cokriging, and cokriging of regression residuals. The results show that (1) the terrain index will help to extract the forest canopy height over a range of slopes. Regression modles explained 51.0% and 84.0% of variance for broadleaf and needle forest respectively.(2) some improvements were achieved by adding additional remote sensing data sets. The integrated models that cokriged regression residuals were preferable to either the aspatial or spatial models alone.The integrated modeling strategy is most suitable for estimating forest canopy height at locations unsampled by lidar
关键词：Forest Canopy Height; Lidar; Multisensor Integration; Three Gorges; Spatial models
