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当前位置: 首页 > 文章 > 基于GF-2号遥感影像的天山云杉林郁闭度估测研究 中南林业科技大学学报 2019 (8) 48-54
Position: Home > Articles > 基于GF-2号遥感影像的天山云杉林郁闭度估测研究 Journal of Central South University of Forestry & Technology 2019 (8) 48-54

基于GF-2号遥感影像的天山云杉林郁闭度估测研究

作  者：
李擎;王振锡;王雅佩;刘梦婷;杨勇强
单  位：
新疆农业大学林学与园艺学院
关键词：
郁闭度;遥感反演;天山云杉林;逐步线性回归
摘  要：
森林郁闭度作为林业综合评价的一个重要指标,对评价和监测森林生态系统的稳定性具有十分重要的意义.本研究通过提取GF-2号遥感影像的光谱信息、纹理特征因子和地形因子,结合地面样方的实测郁闭度数据,为了筛选出对郁闭度反演影响较大的因子和构建反演精度较高的估测模型,首先对各个因子与郁闭度之间的相关性进行分析,剔除相关性较低的因子;其次对各个纹理特征因子之间进行相关性分析,利用主成分法对各个波段的纹理特征因子进行分析,最终筛选出合理的纹理特征因子与影像的光谱信息、地形因子等特征,并以此作为自变量构建郁闭度估测的逐步回归模型.研究表明:以纹理特征+光谱信息+地形因子为自变量构建的估测模型拟合度为0.823,经精度检验EA%达到89.82%.总体来看,该模型基本上满足了郁闭度反演的需要,为新疆天山云杉森林生态系统的评估和实现精准数字林业提供理论依据和技术支撑.
作  者：
LI Qing;WANG Zhenxi;WANG Yapei;LIU Mengting;YANG Yongqiang;College of Forestry and Horticulture, Xinjiang Agricultural University;Key Laboratory of Forestry Ecology and Industrial Technology in the Arid Area of Xinjiang Education Department;
单  位：
LI Qing%WANG Zhenxi%WANG Yapei%LIU Mengting%YANG Yongqiang%College of Forestry and Horticulture, Xinjiang Agricultural University%Key Laboratory of Forestry Ecology and Industrial Technology in the Arid Area of Xinjiang Education Department
关键词：
canopy density;;remote sensing inversion;;Picea schrenkiana;;stepwise linear regression
摘  要：
As a significant index of forest comprehensive evaluation, forest canopy density plays an important role in monitoring and assessing forest ecosystem stability. In this study, spectral information, texture feature factor and topographic factor of GF-2 remote sensing image were extracted, and combined with the measured canopy density data of ground sample, in order to screen out the factors that have a greater impact on canopy density inversion and construct the estimation model with a higher inversion accuracy, the correlation between each factor and canopy density was firstly carried out, eliminating low correlation factor. Secondly, the correlation among texture feature factors was analyzed, the method of principal component factor analysis was carried out on the texture feature factors of each band. Finally, a stepwise regression model for canopy density estimation was constructed by screening out reasonable texture feature factors, spectral information and terrain factors. The experimental results showed that compared with the traditional method only based 0 n spectrum or topographical features, the method combined with texture features greatly improved the estimation accuracy. The optimal R2 value is 0.823, The estimation accuracy increased to 89.82%. Overall, the model basically meets the need of canopy density inversion, provides theoretical basis and technical support for the assessment of the forest ecosystem of Picea schrenkiana in Xinjiang and the realization of accurate digital forestry.

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