| 基于遥感技术的宜良县云南松蓄积量反演 |
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| 引用本文： | 李紫荆,胥辉.基于遥感技术的宜良县云南松蓄积量反演[J].绿色科技,2022(2):1-6. |
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| 作者姓名： | 李紫荆 胥辉 |
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| 作者单位： | 西南林业大学 |
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| 基金项目： | 国家自然科学基金项目(编号:31760206,31770677,31660202);云南省王广兴专家工作站(编号:2018IC100);云南省万人计划青年拔尖人才专项(编号:YNWR-QNBJ-2018-184)。 |
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| 摘 要： | 以云南省宜良森林二类调查数据为样本,基于Google Earth Engine云平台Landsat 8 OLI影像,结合植被因子、纹理特征以及K-T变化为自变量,构建了多元线性回归和随机森林的建模方法,建立了森林蓄积量反演模型.以宜良县云南松为研究对象,运用Landsat8 OLI遥感影像数据结合地面角规控制样地调查数...
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| 关 键 词： | LandsatOLI GEE 森林蓄积量 随机森林 多元线性回归 |
Inversion of Yiliang Pinus Yunnan Ensis Forest Stock Volume Based on 3S Technology |
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| Li Zijing,Xu Hui.Inversion of Yiliang Pinus Yunnan Ensis Forest Stock Volume Based on 3S Technology[J].LVSE DASHIJIU,2022(2):1-6. |
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| Authors: | Li Zijing Xu Hui |
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| Institution: | (Southwest Forestry University,Kunming,Yunan 650224,China) |
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| Abstract: | In this paper,the second-class forest survey data in Yiliang,Yunnan Province is used as a sample,based on the Google Earth Engine cloud platform Landsat8 OLI image.Combined with vegetation factors,texture features and KT changes as independent variables,a multivariate linear regression and random forest modeling method is constructed to establish a forest accumulation volume inversion model.Taking Yunnan pine in Yiliang County as the research object,Landsat8 OLI remote sensing image data combined with ground angle gauge is used to control the plot survey data and establish multiple linear regression and random forest estimation models.The results show that the accuracy of the multiple linear regression model is average,and its R2 and RMSE are 0.259 and 34.5579,respectively.The accuracy of the random forest model is extremely high,and its R2 and RMSE are 0.887 and 1.1954 respectively.There are many uncertain factors in using Landsat8 OLI image data to estimate the aboveground volume of forests.The random forest estimation model can be used as a comparison method for remote sensing estimation of the aboveground volume of Yunnan pine and other tree species,which will be used for future forest volume estimation and provide reference for testing. |
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| Keywords: | Landsat OLI GEE forest stock volume random forest multiple linear regression |
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