A Comparison of Estimating Forest Canopy Height Integrating Multi-sensor Data Synergy ——a Case Study in Mountain Area of Three Gorges

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. * Corresponding author. Email: [email protected]..