Estimation of forest canopy height by integrating multisensor data

Lixin Dong; Bingfang Wu; Zhenhua Guo

doi:10.1117/12.836304

9 October 2009 Estimation of forest canopy height by integrating multisensor data

Lixin Dong, Bingfang Wu, Zhenhua Guo

Proceedings Volume 7471, Second International Conference on Earth Observation for Global Changes; 74710H (2009) https://doi.org/10.1117/12.836304
Event: Second International Conference on Earth Observation for Global Changes, 2009, Chengdu, China

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.

Citation Download Citation

Lixin Dong, Bingfang Wu, and Zhenhua Guo "Estimation of forest canopy height by integrating multisensor data", Proc. SPIE 7471, Second International Conference on Earth Observation for Global Changes, 74710H (9 October 2009); https://doi.org/10.1117/12.836304

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available