文献类型：期刊文献

英文题名：Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived 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, Minist Educ, Key Lab Silviculture & Conservat, 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]W Virginia Univ, Sch Nat Resources, Morgantown, WV 26506 USA

年份：2016

卷号：8

期号：2

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20161302145780);Scopus(收录号：2-s2.0-84962505100);WOS:【SCI-EXPANDED(收录号:WOS:000371898800013)】；

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 State Forestry Administration, China for their data support during our research.

语种：英文

外文关键词：uneven-aged forest management; forest structural diversity; spectral and textural measures; Pearson's correlation analysis; all subsets multiple linear regression

摘要：Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.