Leaf area index retrieval based on Landsat 8 OLI multi-spectral image data and BP neural network
Yang Min, Lin Jie, Gu Zheyan, Tong Guangchen, Wong Yongbing, Zhang Jinchi, Lu Xiaozhen
1.Key Laboratory of Soil & Water Conservation and Ecological Rehabilitation of Jiangsu, Nanjing Forestry University, 210037, Nanjing,China; 2.Collaborative Innovation Center of Sustainable Forestry in Sourthern China of Jiangsu Province, Nanjing Forestry University, 21003,Nanjing, China; 3.Jiangsu Surveying and Design Institute of Water Resources Co. , Ltd. , 225127, Yangzhou, Jiangsu, China
叶面积指数能反映出植被水平覆盖状况和垂直结构,以及枯枝落叶层厚薄和地下生物量多少,这正是植被影响土壤侵蚀的主要方面。及时、准确、有效地获取区域尺度植被LAI,对研究土壤侵蚀与植被的关系至关重要。本文作者以Landsat 8 OLI 多光谱遥感影像和叶面积指数(Leaf Area Index,LAI)实测数据为基础,构建了神经网络隐含层层数分别为1层和2层的神经网络模型,经对比分析,BP 神经网络模型反演叶面积指数具有较高的反演精度,尤其是隐含层为2 层时,平均相对误差(MAPE)是0.201 3、均方根误差(RMSE)是0.52、相关系数R是0.77,均优于非线性回归模型。基于隐含层为2层的BP神经网络模型反演生成了南京市LAI分布图,经分析,LAI分布情况与植被实际分布情况相符,模型的空间可靠性较高。
Horizontal coverage condition, vertical structure of vegetation, litter layer thickness and underground biomass, which can all be reflected by leaf area index, are the principal aspects from which vegetation affects soil erosion. Acquiring the regional Leaf Area Index (LAI) timely, accurately and effectively is crucial to study the relationship between soil erosion and vegetation. The neural network has the incomparable superiority in the complex, nonlinear data fitting and pattern recognition, and has become a common way for the hybrid inversion method. In this study we established two neural network models, one having one hidden layer, and the other having two, based on four bands (blue, green, red, near infrared) of Landsat 8 OLI multi-spectral remote sensing images with four vegetation indexes (NDVI, RVI, SAVI, MSAVI) as input data, and measured data of LAI as output data. Among 111 sampling sites, 80 were used to train the BP neural network and 46 for verification.empirical formula of the number of hidden layer nodes, the hidden layer nodes were set in a range of 3 -20. Then, based on the MATLAB r2009a neural network toolbox, with the method of trial and error,through iterative optimization, the optimal BP network was determined. Comparison and analysis showed that the BP neural network model had a high retrieval precision, especially for the model with two hidden layers, the average relative error (MAPE) was 0.201 3, root mean square error (RMSE) 0.52, and the correlation coefficient (R) 0.77, all of which were better than the precision of nonlinear regression model. Finally, LAI of Nanjing city was estimated using the BP neural network model with two hidden layers. Analysis indicated that the LAI distribution coincided with the actual distribution of getation.The estimated LAI of Nanjing city was tested by 11 measured LAI points in the nonsampled Nanjing Jubaoshan Forest Park in mid August 2014. The MAPE was 0.117 5 and RMSE was 0.986 1. However, due to the large area of the inversion area, the complexity of vegetation types and vegetation community structure, the uneven distribution of samples and so on, the model showed a higher simulation accuracy within the LAI range 2-4, but bigger retrieval error within the LAI ranges smaller than 2 and bigger than 4. The accuracy of LAI remote sensing inversion needs further improvement, and the inversion method needs further study.
杨敏1,2, 林杰1,2,顾哲衍3,佟光臣1,2,翁永兵1,2,张金池1,2,鲁小珍1,2. 基于Landsat 8 OLI多光谱影像数据和BP神经网络的叶面积指数反演[J]. 中国水土保持科学, 2015, 13(4): 86-93.
Yang Min, Lin Jie, Gu Zheyan, Tong Guangchen, Wong Yongbing, Zhang Jinchi, Lu Xiaozhen. Leaf area index retrieval based on Landsat 8 OLI multi-spectral image data and BP neural network. SSWCC, 2015, 13(4): 86-93.
2015, Vol. 13 
