| 基于MODIS时间序列数据的竹林地上生物量估算 |
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| 引用本文： | 杨绍钦,王翔,许澄,商天其.基于MODIS时间序列数据的竹林地上生物量估算[J].浙江农林大学学报,2022,39(4):734-741. |
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| 作者姓名： | 杨绍钦 王翔 许澄 商天其 |
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| 作者单位： | 1.浙江省森林资源监测中心，浙江 杭州 3100202.浙江省林业勘测规划设计有限公司，浙江 杭州 310020 |
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| 基金项目： | 浙江省森林生态状况年度监测项目(S2033500600018) |
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| 摘 要： | 目的 基于浙江省中分辨率成像光谱仪(MODIS)时间序列数据，对浙江省竹林地上生物量进行估算，为竹林碳汇遥感监测提供参考。 方法 以MODIS叶面积指数(LAI)、增强型植被指数(EVI)和比值指数(RVI)时间序列数据为变量，利用随机森林模型筛选变量，采用支持向量回归(SVR)模型估算研究区竹林地上生物量。 结果 随机森林模型共筛选出43个对竹林地上生物量影响最大的变量；基于43个变量，采用radial核函数构建的SVR模型预测能力最强，模型训练精度和测试精度分别为0.76和0.72，均方根误差分别为5.15和8.03 Mg·hm?2。浙江省全省竹林地上生物量均值为7.85 Mg·hm?2，总地上生物量为3.31×107 Mg；浙江省竹林地上生物量在各市具有明显的差异性，其中，湖州市、杭州市、金华市、绍兴市和宁波市的竹林地上生物量均值均大于全省均值，湖州市竹林地上生物量均值最大，为13.56 Mg·hm?2，舟山市地上生物量均值最小，为5.72 Mg·hm?2。 结论 耦合了MODIS LAI、EVI、RVI时间序列数据的SVR模型可实现浙江省竹林地上生物量较高精度的估算。图3表1参31
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| 关 键 词： | 竹林 地上生物量 SVR模型 随机森林 MODIS产品 |
| 收稿时间： | 2021-06-16 |
Estimating bamboo forest aboveground biomass based on MODIS time series data |
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| YANG Shaoqin,WANG Xiang,XU Cheng,SHANG Tianqi.Estimating bamboo forest aboveground biomass based on MODIS time series data[J].Journal of Zhejiang A&F University,2022,39(4):734-741. |
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| Authors: | YANG Shaoqin WANG Xiang XU Cheng SHANG Tianqi |
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| Institution: | 1.Zhejiang Forest Resources Monitoring Center, Hangzhou 310020, Zhejiang, China2.Zhejiang Forestry Survey Planning and Design Company Limited, Hangzhou 310020, Zhejiang, China |
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| Abstract: | Objective This study, based on the time series data of moderate resolution imaging spectrometer (MODIS) in Zhejiang Province, is aimed to estimate the aboveground biomass (AGB) of bamboo forest in Zhejiang Province to provide reference for remote sensing monitoring of bamboo carbon sink. Method The time series data of MODIS leaf area index (LAI), enhanced vegetation index (EVI) and ratio vegetation index (RVI) were taken as variables before they were screened by random forest model, and then the AGB of bamboo forest in the study area was estimated employing support vector regression (SVR) model. Result Of all the variables screened by random forest model, 43 had the greatest impact on bamboo forest AGB, and the SVR model constructed by radial kernel function based on them has the strongest prediction ability with its training accuracy and test accuracy being 0.76 and 0.72 while the root mean squared error (RMSE) being 5.15 and 8.03 Mg·hm?2 respectively. The average aboveground biomass of bamboo forest in Zhejiang Province was 7.85 Mg·hm?2 whereas the total aboveground biomass was 3.31×107 Mg. There was an evident diversity in the aboveground biomass of bamboo forest among the cities across Zhejiang Province among which the average aboveground biomass of bamboo forest in Huzhou City, Hangzhou City, Jinhua City, Shaoxing City and Ningbo City was greater than the provincial average value, with that of Huzhou City being the largest (13.56 Mg·hm?2) while that of Zhoushan City being the smallest (5.72 Mg·hm?2). Conclusion The SVR Model coupled with MODIS LAI, EVI, RVI time series data could serve to realize the high-precision estimation of bamboo forest AGB in Zhejiang Province. Ch, 3 fig. 1 tab. 31 ref.] |
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