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Proceedings Paper

Estimation of forest canopy height by integrating multisensor data
Author(s): Lixin Dong; Bingfang Wu; Zhenhua Guo
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Paper Abstract

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 extract 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 helps to extract the forest canopy height over a range of slopes. Regression models explained for 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 model 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.

Paper Details

Date Published: 10 October 2009
PDF: 11 pages
Proc. SPIE 7471, Second International Conference on Earth Observation for Global Changes, 74710H (10 October 2009); doi: 10.1117/12.836304
Show Author Affiliations
Lixin Dong, China Meteorological Administration (China)
Institute of Remote Sensing Applications, CAS (China)
Bingfang Wu, Institute of Remote Sensing Applications, CAS (China)
Zhenhua Guo, Ministry of Land and Resources of China (China)


Published in SPIE Proceedings Vol. 7471:
Second International Conference on Earth Observation for Global Changes
Xianfeng Zhang; Jonathan Li; Guoxiang Liu; Xiaojun Yang, Editor(s)

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