| 基于Rapideye影像的林木地上生物量估测 |
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| 引用本文： | 吴平,黄庆丰,唐雪海,陆宁辛.基于Rapideye影像的林木地上生物量估测[J].安徽农业大学学报,2016,43(6):891. |
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| 作者姓名： | 吴平 黄庆丰 唐雪海 陆宁辛 |
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| 作者单位： | 安徽农业大学林学与园林学院,合肥,230036;安徽农业大学林学与园林学院,合肥,230036;安徽农业大学林学与园林学院,合肥,230036;安徽农业大学林学与园林学院,合肥,230036 |
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| 基金项目： | 中德合作BMBF-Lin2Value-033L049C; Lin2Value-CAFYBB2012013资助。 |
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| 摘 要： | 以石台县为研究地,结合Rapideye高分遥感影像和不同森林类型样地林木地上生物量调查数据,采用Pearson双变量相关分析方法筛选模型变量,分别用多元线性回归和随机森林算法建立不同森林类型的遥感地上生物量估测模型,并进行模型估测精度对比分析。结果表明,叶绿素红边模型(CRM)与叶绿素绿波模型(CGM)2个指数与针叶林、阔叶林生物量在0.01水平上的相关性极显著,且在其多元线性回归模型和随机森林模型中两者均被挑选为建模变量。另外,与生物量相关性较强的纹理特征主要集中的红光波段和红边波段,且仅MEAN、VAR、SM3个滤波对生物量估测贡献较大,可作为建模变量。阔叶林、针叶林和针阔混交林3种森林类型的地上生物量模型估测精度均表现为随机森林模型优于多元线性回归模型。随机森林模型生物估测绝对均方误差在12.8760~36.5363之间,相对均方误差在20.20%~45.95%之间;多元线性回归生物量估测绝对均方误差在22.0425~46.4494之间,相对均方误差在34.58%~58.42%之间。
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| 关 键 词： | 林木地上生物量 Rapideye影像 随机森林 逐步线性回归 |
| 收稿时间： | 3/2/2016 12:00:00 AM |
Estimation of aboveground tree biomass based on Rapideye Imagery |
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| WU Ping,HUANG Qingfeng,TANG Xuehai and LU Ningxin.Estimation of aboveground tree biomass based on Rapideye Imagery[J].Journal of Anhui Agricultural University,2016,43(6):891. |
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| Authors: | WU Ping HUANG Qingfeng TANG Xuehai and LU Ningxin |
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| Institution: | School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036,School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036,School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036 and School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei 230036 |
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| Abstract: | Based on Pearson bivariate correlation analysis for model variables selection together with the analysis of data from the Rapideye image and sample plot aboveground biomass survey of different forest types in Shitai County, two remote sensing aboveground forest biomass estimation models of different forest types were built using the multiple linear regression and random forest algorithm method, respectively. The estimation precision of the two models was compared. The results showed that both CRM and CGM had significant correlation with the biomass of the coniferous forest and broad-leaved forest at the 0.01 level, indicating that CRM and CGM can be used for forest biomass estimation. In addition, the texture measurements which had strong correlation with biomass were mainly contained in the band of red or red edge and only MEAN, VAR and SM filter greatly contributed to biomass estimation, which can be selected to building model variables. The forest aboveground biomass estimation precision of broad-leaved forest, coniferous forest and coniferous and broad-leaved mixed forest using the random forest biomass model was better than the multiple linear regression model. The absolute mean square error of the random forest biomass estimation was between 12.8760 and 36.5363, while the relative mean square error was between 20.20% and 45.95%. The absolute mean square error of multiple linear regression biomass estimation was between 22.0425 and 46.4494, while the relative mean square error was between 34.58% and 58.42%. |
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| Keywords: | forest biomass Rapideye Image vegetation indices random forest stepwise linear regression |
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