文献类型：期刊文献

英文题名：Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

作者：Meng, Jinghui[1] Li, Shiming[2] Wang, Wei[3] Liu, Qingwang[2] Xie, Shiqin[1] Ma, Wu[4]

第一作者：Meng, Jinghui

通信作者：Meng, JH[1];Li, SM[2]

机构：[1]Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource & Informat Tech, Beijing 100091, Peoples R China;[3]State Forestry Adm, Survey & Planning Inst, Beijing 100714, Peoples R China;[4]West Virginia Univ, Sch Nat Resources, Morgantown, WV 26506 USA

年份：2016

卷号：8

期号：9

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20172203711102);Scopus(收录号：2-s2.0-85019721002);WOS:【SSCI(收录号:WOS:000385488000029)，SCI-EXPANDED(收录号:WOS:000385488000029)】；

基金：This study was supported by the National Natural Science Foundation of China (31370635), the project National Science and Technology Major Projects of China (21-Y30B05-9001-13/15) and the National Natural Science Foundation of China (31300532). We thank the Survey & Planning Institute of the State Forestry Administration, China, who provided data support during our research.

语种：英文

外文关键词：forest health; spectral and textural measures; Pearson's correlation analysis; all-subsets regression; forest health management

摘要：Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson's correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R-2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management.