| 基于ANN的森林蓄积遥感估测研究 |
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| 引用本文： | 曾明宇,陈振雄,刘庭威.基于ANN的森林蓄积遥感估测研究[J].中南林业调查规划,2010,29(3):36-39. |
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| 作者姓名： | 曾明宇 陈振雄 刘庭威 |
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| 作者单位： | 国家林业局中南林业调查规划设计院,长沙,410014 |
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| 摘 要： | 利用TM遥感图像光谱信息良好的综合性和现势性以及地理信息系统(GIS)强大的空间分析功能,结合人工神经网络(ANN)可优化求解非线性复杂系统的功能,对海南省抱龙林场森林蓄积进行遥感估测研究。结果表明:ANN可有效地估测森林蓄积量,研究区森林蓄积量的预测值与实际值的一致性较好,其相关系数达0.914;以遥感特征纹理(Skewness)Band2对蓄积估测的贡献率最大。
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| 关 键 词： | ANN 森林蓄积 遥感 预测 |
Study on the Remote Sensing Estimation of Forest Volume Based on ANN |
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| ZENG Mingyu,CHEN Zhenxiong,LIU Tingwei.Study on the Remote Sensing Estimation of Forest Volume Based on ANN[J].Central South Forest Inventory and Planning,2010,29(3):36-39. |
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| Authors: | ZENG Mingyu CHEN Zhenxiong LIU Tingwei |
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| Institution: | (Central South Forest Inventory and Planning Institute of State Forestry Administration, Changsha 410014, China) |
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| Abstract: | Based on TM remote sensing （RS） which had integrated and realistic characteristics, geograph- ic information system （GIS） which had powerful spatial analysis ability, and artificial neutral network （ANN） which can optimize nonlinear complex systems, the forest volume in Hainan Baolong forest farm was estimated. The results showed that： ANN was effective in estimating forest volume in study area, the predicted forest volume was in good agreement with the actual value, the correlation coefficient was 0.914. The maximal contribution factor to forecast forest volume was Band2（Skewness）. |
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| Keywords: | ANN |
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