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

被引：52

作者：

Du, Huaqiang ^{[1
,2
]}

Zhou, Guomo ^{[1
,2
]}

Ge, Hongli ^{[1
,2
]}

Fan, Wenyi ^{[3
]}

Xu, Xiaojun ^{[1
,2
]}

Fan, Weiliang ^{[1
,2
]}

Shi, Yongjun ^{[1
,2
]}

机构：

[1] Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China

[2] Zhejiang A&F Univ, Sch Environm Sci & Technol, Linan 311300, Peoples R China

[3] NE Forestry Univ, Forest coll, Harbin 150040, Heilongjiang Pr, Peoples R China

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 2012年 / 33卷 / 06期

关键词：

LANDSAT TM DATA; BIOMASS ESTIMATION; ABOVEGROUND BIOMASS; MOSO BAMBOO; STRATEGY; IMAGERY; ETM+;

D O I：

10.1080/01431161.2011.603379

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

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.

引用

页码：1917 / 1933

页数：17

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