基于PCA与SPOT-5的森林碳储量估测  

Forest carbon storage estimation based on PCA and SPOT-5

作　　者：涂云燕[1] 彭道黎[1] 

机构地区：[1]北京林业大学,北京100083

出　　处：《中南林业科技大学学报》2012年第6期101-103,共3页Journal of Central South University of Forestry & Technology

基　　金：国家"十一五"林业科技支撑计划(2006BAD23B05)

摘　　要：以北京市延庆县为研究对象,利用遥感和地学数据,分析其与地面碳储量的相关关系,探讨基于遥感和地学信息的森林碳储量遥感估测。利用森林资源二类调查数据和2004年SPOT-5遥感影像,选取B1、B2、B3、B4 4个单波段,IDVI、IRVI、INDVI 3种植被指数以及海拔、坡度共9个因子,对这9个因子进行降维,并提取主成分,建立基于主成分回归的森林碳储量估测方程。结果表明:模型复相关系数为0.892;用30个独立样本检验模型的可靠性与精度,相关系数为0.769,精度达到91.60%。该方程可用于森林地上部分碳储量估测。A study of remote sensing estimation of aboveground forest carbon storage was conducted in Yanqing county of Beijing by using the remote sensing and geo-science data, and the relationship between remote sensing and geosciencas information and the ground forest carbon storage was analyzed, investigate forest carbon storage estimation method based on remote sensing and geosciances information. By using forest resource inventory data and SPOT5 images taken in 2004, and selecting 9 factors, which included 4 multi- spectral bands of B~, B2, B3, B4, 3 vegetation indexes of IDvx, 1RVl, INDVl, and slope and elevation, firstly the dimension reduction of the 9 factors were executed, then the principal components were extracted out, and finally a forest carbon storage model based on the analysis and the SPOT5 images was set up by regression method. The results show that the multiple correlation coefficient R2 was 0.892. the reliability and accuracy of the model were examined with 30 independent samples, the correlation coefficient was 0.769, the accuracy reached 91,60%o The model cart be used to estimate forest carbon stocks in aboveground. Under the same conditions, principal component regression is superior to stepwise regression estimation.

关 键 词：森林 遥感信息 森林碳储量 主成分回归法 估测 

分 类 号：S718.55[农业科学—林学]