[1]邹琪,孙华,王广兴*,等. 基于Landsat 8的深圳市森林碳储量遥感反演研究[J].西北林学院学报,2017,32(4):164-171.[doi:10.3969/j.issn.1001-7461.2017.04.29]
 ZOU Qi,SUN Hua,WANG Guang-xing*,et al. Remote Sensing Retrieval of Forest Carbon Storage in Shenzhen Based on Landsat 8 Images[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2017,32(4):164-171.[doi:10.3969/j.issn.1001-7461.2017.04.29]
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 基于Landsat 8的深圳市森林碳储量遥感反演研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第32卷
期数:
2017年第4期
页码:
164-171
栏目:
出版日期:
2017-07-31

文章信息/Info

Title:
 Remote Sensing Retrieval of Forest Carbon Storage in Shenzhen Based on Landsat 8 Images
文章编号:
1001-7461(2017)04-0164-08
作者:
 邹琪12孙华12王广兴123*林辉12谭一凡4马中刚12
 (1.中南林业科技大学 林业遥感信息工程研究中心,湖南 长沙 410004;2.林业遥感大数据与生态安全湖南省重点实验室,湖南 长沙 410004;3.Department of Geography,Southern Illinois University at Carbon dale,IL 62901 USA;4.深圳市仙湖植物园,广东 深圳 518004)
Author(s):
 ZOU Qi12SUN Hua12WANG Guang-xing123*LIN Hui12TAN Yi-fan4MA Zhong-gang12
 (1.Research Center of Forestry Remote Sensing and Information Engineering,Central South University of Forestry and Technology,Changsha,Hunan 410004,China; 2.Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province,Changsha,Hunan 410004,China; 3.Deptment of Geography,Southern Illinois University at Carbondale,IL 62901,USA; 4.Xianhu Botanic Garden of Shenzhen,Shenzhen,Guangdong 518004,China)
关键词:
 碳储量多元线性回归模型Logistic回归模型RBF径向基函数神经网络遥感影像深圳市
Keywords:
 carbon storage multivariate stepwise regression Logistic model radical basis function (RBF) neutral network remote sensing image Shenzhen
分类号:
S127
DOI:
10.3969/j.issn.1001-7461.2017.04.29
文献标志码:
A
摘要:
 以2014年Landsat 8遥感影像为数据源,研究了深圳市森林碳储量遥感反演模型的构建及其空间分布情况,对城市生态系统碳循环研究具有重要意义。采用分层随机抽样的方式布设168个样地,结合外业样地数据,从遥感影像中提取31个植被指数作为自变量,分别构建了多元线性回归模型、Logistic回归模型和Radical Basis Function(RBF)径向基函数神经网络模型,进而估算该地区的森林碳储量并比较分析。结果表明,RBF神经网络模型的估算精度最高,决定系数最大且均方根误差最小,分别为0.829 t·hm-2和9.131 t·hm-2;Logistic回归模型估算精度次之,决定系数和均方根误差分别为0.523 t·hm-2和11.821 t·hm-2;多元线性回归模型估算精度最低,决定系数最小,均方根误差最大,分别为0.438 t·hm-2和12.870 t·hm-2。可见,RBF神经网络模型能更好地模拟森林碳储量与各个因子之间的关系。研究区森林碳储量的空间分布特点表现为东南沿海部分碳储量大,中西部城市经济开发区碳储量小,与实际森林分布基本一致。
Abstract:
 With Landsat 8 remote sensing images acquired in 2014 as datum source,remote sensing retrieval of forest carbon storage in Shenzhen was conducted and the spatial distribution was analyzed.A total of 168 sample plots were selected by a stratified random sampling procedure.A total of 31 vegetation indices were extracted from the images and used as independent variables,and urban forest carbon storage from field sampling plots was a dependent variable.Multivariate-stepwise regression model,Logistic regression model and radical basis function (RBF) neutral network model were developed to estimate forest carbon storage of the study area.The results showed that:the estimation accuracy of RBF neutral network model was the highest with the greatest determination coefficient and smallest root mean square error (RMSE) of 0.829 and 9.131 t·hm-2,respectively; the determination coefficient and RMSE of Logistic regression model were 0.723 and 11.821 t·hm-2,taking the second place.Multi-stepwise regression model had the lowest estimation accuracy with the determination coefficient and RMSE of 0.662 and 12.870 t·hm-2.Therefore,the relationship between urban forest carbon and image derived spectral variables could be modeled and described better by RBF neutral network model.The spatial distribution of forest carbon storage of the study area was characterized by larger estimates in the southeast coast and smaller estimates in the development zones of the mid-west city,being consistent with the actual spatial patterns of forests.

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备注/Memo

备注/Memo:
 收稿日期:2016-11-06修回日期:2017-03-06
基金项目:中国博士后科学基金项目(2014M562147);湖南省百人计划特聘教授资助项目(0990);深圳市仙湖植物园课题:“深圳市绿地碳汇计量与方法研究”(8858)。
作者简介:邹琪,女,硕士,研究方向:森林碳汇模拟。E-mail:zouqi9011@163.com
*通信作者:王广兴,男,教授,博士,研究方向:遥感与地理信息系统、全球气候变化和森林碳汇模拟。E-mail:gxwang@siu.edu
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