| 毛竹林地上部分生物量遥感估算模型的可移植性 |
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| 引用本文： | 余朝林,杜华强,周国模,徐小军,桂祖云.毛竹林地上部分生物量遥感估算模型的可移植性[J].应用生态学报,2012,23(9):2422-2428. |
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| 作者姓名： | 余朝林 杜华强 周国模 徐小军 桂祖云 |
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| 作者单位： | 1. 浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江临安311300;浙江农林大学环境与资源学院,浙江临安311300
2. 安吉县林业局,浙江安吉,313300 |
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| 基金项目： | 浙江省自然科学基金项目(Y3100427);国家自然科学基金项目(31070564);科技部“973”项目(2011CB302705);浙江省重点科技创新团队项目(2012R10030-01);国家林业局948项目(2008-4-49)资助 |
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| 摘 要： | 选择浙江省内临安、安吉、龙泉3个毛竹产区为研究区域,基于野外调查数据和Landsat 5 TM影像,分别建立3个区域的毛竹林生物量遥感估算模型,包括一元线性模型、一元非线性模型、逐步回归模型、多元线性模型和Erf-BP神经网络模型,并对3个区域的模型进行评价；最后,选择精度较好的模型进行移植并对其可移植性进行分析.结果表明:在3个区域,Eff-BP神经网络模型精度均最高,逐步回归模型和一元非线性模型次之.Erf-BP神经网络模型的可移植性最佳.模型类型和模型自变量对统计模型的可移植性有较大影响.
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| 关 键 词： | 地上生物量 遥感估算模型 移植性 毛竹 |
Transferability of remote sensing-based models for estimating moso bamboo forest aboveground biomass |
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| YU Chao-lin,DU Hua-qiang,ZHOU Guo-mo,XU Xiao-jun,GUI Zu-yun.Transferability of remote sensing-based models for estimating moso bamboo forest aboveground biomass[J].Chinese Journal of Applied Ecology,2012,23(9):2422-2428. |
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| Authors: | YU Chao-lin DU Hua-qiang ZHOU Guo-mo XU Xiao-jun GUI Zu-yun |
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| Institution: | 1Zhejiang Province Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration,Zhejiang A & F University,Lin’an 311300,Zhejiang,China;2School of Environmental and Resources Science,Zhejiang A & F University,Lin’an 311300,Zhejiang,China;3Anji County Forestry Bureau,Anji 313300,Zhejiang,China). |
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| Abstract: | Taking the moso bamboo production areas Lin’an,Anji,and Longquan in Zhejiang Province of East China as study areas,and based on the integration of field survey data and Landsat 5 Thematic Mappr images,five models for estimating the moso bamboo(Phyllostachys heterocycla var.pubescens) forest biomass were constructed by using linear,nonlinear,stepwise regression,multiple regression,and Erf-BP neural network,and the models were evaluated.The models with higher precision were then transferred to the study areas for examining the model’s transferability.The results indicated that for the three moso bamboo production areas,Erf-BP neural network model presented the highest precision,followed by stepwise regression and nonlinear models.The Erf-BP neural network model had the best transferability.Model type and independent variables had relatively high effects on the transferability of statistical-based models. |
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| Keywords: | aboveground biomass remote sensing-based model transferability moso bamboo |
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