中国农业大学学报2012，Vol.17Issue(4)：148-153,6.
基于PCA-RBF神经网络的森林碳储量遥感反演模型研究
Remote sensing retrieval model of forest carbon storage based on principal components analysis and radial basis function neural network
摘要
Abstract
Aiming at the problem of multicollinearity and low precision predictions by the regression prediction model of carbon storage, this study used forest resource inventory data and SPOT5 image to retrieve the aboveground forest carbon storage of Populus forests in Yanqing County. Firstly, 10 factors were analyzed by principal components analysis. Then this paper introduced a method based on PCA and radial basis function （RBF） neural network for predicting forest carbon storage. The research results show that forest resource inventory data combined SPOT5 image is very useful for retrieving study of carbon storage of Populus forests~ the fitting precision of the PCA-RBF neural network model was 99.90% ,and the average prediction reached 96.71%. The model has a good retrieval accuracy, which can be well used for retrieval of regional aboveground forest carbon storage.关键词
森林碳储量/SPOT5/主成分分析/遥感反演/RBF神经网络Key words
forest carbon storage/SPOT5/principal component analysis/remote sensing retrieval/RBF neuralnetwork分类
农业科技引用本文复制引用
张超,彭道黎..基于PCA-RBF神经网络的森林碳储量遥感反演模型研究[J].中国农业大学学报,2012,17(4):148-153,6.基金项目
国家“十一五”科技支撑计划 （）
国家级林业推广项目 （）
