Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

作     者：Ying Yu Mingze Li Yu Fu 

作者机构：School of ForestryNortheast Forestry University 

出 版 物：《Journal of Forestry Research》 (林业研究（英文版）)

年 卷 期：2018年第29卷第5期

页      面：1407-1414页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0907[农学-林学] 09[农学] 0829[工学-林业工程] 0903[农学-农业资源与环境] 0901[农学-作物学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.31500518 31500519 and 31470640) 

主　　题：Random forest classification Multitemporal Multisource remote sensing data Polarization decomposition 

摘      要：We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.