首页> 外文期刊>International journal of remote sensing >Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models
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Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models

机译:基于几何光学和Erf-BP神经网络模型的多源遥感影像毛竹林多尺度冠层闭合反演

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摘要

This article focuses on retrieving the multi-scale crown closure (CC) of Moso bamboo forest using Systeme Pour l'Observation de la Terre (SPOT5) and Landsat Thematic Mapper (TM) satellite remotely sensed imagery based on the geometric-optical model and the artificial neural network (ANN) model. CC at local scale was first retrieved using the Li-Strahler geometric-optical model (LSGM) and images from an unmanned aerial vehicle (UAV). Then, multi-scale CC was retrieved using the Erf-BP model (a kind of back-propagation (BP) feed-forward neural network, which takes a Gaussian error function (Erf) as an activation function of the hidden layer) based on a combination of SPOT5 and Landsat TM images. The results show that by combining multi-source remotely sensed data, the CC of Moso bamboo forest can be retrieved at the local region, township area, and county scale with high accuracy using the Erf-BP model. Estimated values have a linear relationship with the observed values at a significance level of 0.05. The highest accuracy of the retrieval of CC (referred to as LSGM-UAV-CC) was observed at the local region based on LSGM and UAV, with the coefficient of determination (R-2) of 0.63, followed by that at the township area with an R-2 of 0.0.55 based on LSGM-UAV-CC and SPOT5 data using the Erf-BP model (Erf-BP-SPOT5-CC), and that at the county scale with an R-2 of 0.54 based on Erf-BP-SPOT5-CC and Landsat TM data using the Erf-BP model (Erf-BP-TM-CC).
机译:本文着重于利用基于几何光学模型和遥感影像的Systeme Pour l'Observation de la Terre(SPOT5)和Landsat Thematic Mapper(TM)卫星遥感图像检索毛竹林的多尺度冠封(CC)。人工神经网络(ANN)模型。首先使用Li-Strahler几何光学模型(LSGM)和来自无人机(UAV)的图像检索局部尺度的CC。然后,使用Erf-BP模型(一种反向传播(BP)前馈神经网络,将高斯误差函数(Erf)作为隐藏层的激活函数)来检索多尺度CC。 SPOT5和Landsat TM图像的组合。结果表明,通过结合多源遥感数据,利用Erf-BP模型可以在局部地区,乡镇地区和县域范围内高精度地检索到毛竹林的CC。估计值与观察值在0.05的显着性水平上具有线性关系。在基于LSGM和UAV的局部区域,CC的检索精度最高(称为LSGM-UAV-CC),确定系数(R-2)为0.63,其次是乡镇地区使用Erf-BP模型(Erf-BP-SPOT5-CC)基于LSGM-UAV-CC和SPOT5数据得出的R-2为0.0.55,而在县级范围基于RGM为0.54的R-2为0.0.55使用Erf-BP模型(Erf-BP-TM-CC)的Erf-BP-SPOT5-CC和Landsat TM数据。

著录项

  • 来源
    《International journal of remote sensing》 |2015年第22期|5384-5402|共19页
  • 作者单位

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

    Zhejiang A&F Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Peoples R China|Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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