森林冠层氮含量遥感估算

使用高光谱数据估算叶片与冠层尺度的森林氮含量.首先采用基于高斯误差函数的BP神经网络Erf-BP建立叶片尺度氮含量的遥感估算模型；其次根据几何光学模型原理, 推导冠层光谱与叶片光谱的尺度转化函数, 将Hyperion影像的冠层光谱转换到叶片尺度并反演叶片尺度的氮含量；最后, 利用森林结构参数LAI得到研究区域冠层尺度氮含量.结果表明, 隐含层包含8个神经元的Erf-BP模型最优, 检验精度为76.8597%；利用尺度转化函数估算670 nm和865 nm冠层光谱与实测光谱决定系数为0.5203和0.4117；反演叶片尺度氮含量与实测数据的决定系数为0.7019；该方法为高精度快速估算叶片和冠层尺度森林氮含量提供参考.

Abstract

Hypespectral data was used to estimate leaf and canopy nitrogen content. Erf-BP, an improved model based on the Gaussian error function of BP neural network, was used to develop remote sensing models for estimating leaf nitrogen content. Then the scaling conversion function during downscales from canopy to leaf spectral was derived according to principles of geometric optics model. These relations were used during downscales from the canopy reflectance of Hyperion image to leaf spectral for leaf nitrogen content estimation. Finally, forest structural parameter leaf area index (LAI) was used to obtain canopy nitrogen content from leaf level. The results showed that the best Erf-BP neural network model with testing accuracy of 76.8597% includes 8 neurons in hidden layer. Using scaling conversion function to estimate canopy spectra at 670nm and 865nm, correlations (R2) between modeling spectra and measurements were 0.5203 and 0.4117 respectively. Correlation coefficient between estimated leaf nitrogen content and measurements was 0.7019. This method provides a good reference for more rapid and accurate estimation of leaf and canopy nitrogen.

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