首页 | 本学科首页   官方微博 | 高级检索  
     检索      

东莞市针叶类森林生物量遥感模型研究
引用本文:阮兰君,杨燕琼.东莞市针叶类森林生物量遥感模型研究[J].广东林业科技,2018,34(1):32-36.
作者姓名:阮兰君  杨燕琼
作者单位:华南农业大学林学与风景园林学院 广东 广州 华南农业大学林学与风景园林学院,华南农业大学林学与风景园林学院 广东 广州 华南农业大学林学与风景园林学院
摘    要:基于 Landsat 8 影像数据,对东莞市松树林 (Pinus sp.)、杉木林 (Cunninghamia lanceolata)、针 叶混交林 3 种针叶类森林生物量进行估算,利用相关分析、主成分分析和逐步回归分析,建立针叶类森 林生物量遥感估算模型,其决定系数 (R2) 值分别为 0.880 9、 0.832 5、 0.964 0,均达显著水平。经适用性 检验,模型均达 0.05 显著水平,可用于东莞市针叶类森林生物量估算。

关 键 词:遥感  针叶林  森林生物量  回归分析
收稿时间:2017/8/2 0:00:00
修稿时间:2017/9/11 0:00:00

The Study on the Remote Sensing Model of Dongguan Conifer Forest Biomass
RUAN Lanjun and YANG Yanqiong.The Study on the Remote Sensing Model of Dongguan Conifer Forest Biomass[J].Forestry Science and Technology of Guangdong Province,2018,34(1):32-36.
Authors:RUAN Lanjun and YANG Yanqiong
Institution:College of Forestry and Landscape Architecture,South China Agriculture University,Guangdong,College of Forestry and Landscape Architecture,South China Agriculture University
Abstract:Based on Landsat 8 image data, this paper estimates the biomass of three coniferous forest in Dongguan, including Pinus forest, Cunninghamia lanceolata and coniferous mixed forest . By using correlation analysis, principal component analysis and stepwise regression, a remote sensing estimation model of coniferous forest biomass was established, and its determining coefficient (R2) value were 0.880 9, 0.832 5 and 0.964 0 respectively, which reached a significant level. The applicability test showed that the model reached 0.05 significant levels and could be used for estimating the biomass of coniferous forest in Dongguan.
Keywords:remote sensing  coniferous forest  forest biomass  regression analysis
点击此处可从《广东林业科技》浏览原始摘要信息
点击此处可从《广东林业科技》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号