Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

Abstract

This article explores a non-linear partial least square (NLPLS) regression method for bamboo forest carbon stock estimation based on Landsat Thematic Mapper (TM) data. Two schemes, leave-one-out (LOO) cross validation (scheme 1) and split sample validation (scheme 2), are used to build models. For each scheme, the NLPLS model is compared to a linear partial least square (LPLS) regression model and multivariant linear model based on ordinary least square (LOLS). This research indicates that an optimized NLPLS regression mode can substantially improve the estimation accuracy of Moso bamboo (Phyllostachys heterocycla var. pubescens) carbon stock, and it provides a new method for estimating biophysical variables by using remotely sensed data.

Acknowledgements

The authors acknowledge funding support from the National Natural Science Foundation (grant, 30700638), the ‘948’ item of the National Forestry Bureau (grants, 2008-4-49), the National ‘863’ Programme (grant, 2006AA12Z104) and the Items of Science and Technology Department of Zhejiang province (grants, 2007C13041 and 2008C12068). The authors also thank Anji Forestry Bureau for the assistance of field work.