[1]李晗,陈新云,白彦锋,等. 基于SPOT-5光谱和纹理信息的湘西森林生态功能指数遥感预测模型构建[J].西北林学院学报,2019,34(5):147-153.[doi:10.3969/j.issn.1001-7461.2019.05.23]
　LI Han,CHEN Xin-yun,BAI Yan-feng,et al. Estimation of the Forest Ecological Function Index in Western Hunan Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2019,34(5):147-153.[doi:10.3969/j.issn.1001-7461.2019.05.23]
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 基于SPOT-5光谱和纹理信息的湘西森林生态功能指数遥感预测模型构建()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第34卷
期数:
2019年第5期
页码:
147-153
栏目:
出版日期:
2019-09-30

文章信息/Info

Title:
 Estimation of the Forest Ecological Function Index in Western Hunan Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images
文章编号:
1001-7461(2019)05-0147-07
作者:
 李晗1; 陈新云2; 白彦锋3; 姜春前3; 孟京辉1*
 （1.北京林业大学 省部共建森林培育与保护教育部重点实验室，北京 100083；2.国家林业局 调查规划设计院，北京 100714；3.中国林业科学研究院 林业研究所，北京 100091）
Author(s):
 LI Han1; CHEN Xin-yun2; BAI Yan-feng3; JIANG Chun-qian3; MENG Jing-hui1*
 （1.Key Laboratory for Siviculture and Conservation of Ministry of Education,Beijing Forestry University，Beijing 100083,China; 2.Academy of Forest Inventory and Planning,State Forestry Administration,Beijing 100714,China; 3.Research Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,China）
关键词:
 ; 森林生态功能指数; SPOT-5; Pearson相关分析; 全子集回归
Keywords:
 ; forest ecological function index;  SPOT-5;  Pearsons correlation analysis;  all subsets multiple linear regression
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2019.05.23
文献标志码:
A
摘要:
 以湘西区域SPOT-5遥感影像为基础，提取样地纹理和光谱信息，以一类调查数据的生态指数为因变量，所对应的纹理和光谱指数为自变量，采用全子集回归来构建预估模型，并采用留一交叉验证进行模型检验。结果表明，构建模型的判定系数R2adj为0.507 1，留一交叉验证结果R2cv=0.486 0，模型的残差呈均匀的分布在0附近，没有明显的变化趋势。此外，SW检验和NCV检验结果显示残差的正态性和等方差性，表明构建的森林生态功能指数遥感预估模型能够预估森林生态功能指数，为生态功能的快速、经济和定量的评价提供数据支持，为有效森林管理以及决策的制定提供理论支持。
Abstract:
 In the present study,a remote sensing estimation model for forest ecological function index is proposed to provide a basis for the evaluation of forest ecosystem in western Hunan.Based on SPOT 5 remote sensing images in western Hunan,the texture and spectral information of field plots were extracted.The all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables and the produced model was further validated for its performance using the leave-one-out cross-validation approach.The results indicated that the adjusted coefficient of determination R2adj was 0.507 1,the leave-one-out cross-validation approach R2cv was 0.486 0,any particular patterns or trends were not observed from the residual plot of the model.In addition,the results of SW and NCV tests demonstrated the normal distribution.It was concluded that the produced model could predict forest ecological function index,which could provide data support for the rapid,economic and quantitative evaluation of ecological function,and to provide theoretical support for effective forest management and decision-making.

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备注/Memo

备注/Memo:
 收稿日期：2018-11-07修回日期：2019-01-04
基金项目：国家重点研发计划（2017YFC0505604）。
作者简介：李晗，女，在读硕士，研究方向：林业遥感与信息技术。E-mail:lihan0112@126.com
*通信作者：孟京辉，男，副教授，研究方向：森林经营理论与技术、林业信息技术等。E-mail:jmeng@bjfu.edu.cn
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