2018年第1期
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东莞市针叶类森林生物量遥感模型研究

阮兰君,杨燕琼
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
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基金项目:
 
The Study on the Remote Sensing Model of Dongguan Conifer Forest Biomass
RUAN Lanjun and YANG Yanqiong
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.
Key words:   remote sensing; coniferous forest; forest biomass; regression analysis