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当前位置: 首页 > 文章 > 基于主成分分析的BP神经网络估算藏北高寒草地覆盖度变化 中国农学通报 2018 (11) 48-53
Position: Home > Articles > Coverage Change of Alpine Grasslands in Northern Tibet:Based on PCA-BP Neural Network Estimation Chinese Agricultural Science Bulletin 2018 (11) 48-53

基于主成分分析的BP神经网络估算藏北高寒草地覆盖度变化

作  者：
罗布;拉巴;尤学一
单  位：
中国气象局成都高原气象研究所拉萨分部;西藏高原大气环境科学研究所;天津大学环境科学与工程学院
关键词：
BP神经网络;主成分分析;藏北;草地覆盖度;NDVI
摘  要：
藏北高寒牧区草地是中国高寒草地分布面积最大的地区。为了及时准确地获得该区域草地覆盖度的变化趋势,本研究利用多年气象数据、社会统计数据、GIMMS、MODIS 2种归一化植被指数(NDVI)数据作为参数,构建BP神经网络模型,估算2010—2014年藏北高寒草地年际变化趋势,并用主成分分析方法优化参数来改进模型。结果表明:(1)BP神经网络模型及其改进模型对藏北高寒草地覆盖度年际变化趋势与遥感值的相关系数分别为0.16、0.47,表明通过主成分分析优化参数后的BP神经网络模型具有较好的模拟效果;(2)2种BP神经网络估算的植被指数值与NDVI值平均误差率分别为2.36%、2.20%,均有较高的模拟精度;(3)从神经网络训练步数上看,BP神经网络结果训练收敛步长为5000,基于主成分分析的BP神经网络模型训练收敛步长为454,表明后者提高了计算效率,体现出良好的收敛性。因此,无论从年际变化趋势拟合程度、植被指数估算值精度还是从计算效率来看,改进的BP神经网络模型对于估算藏北高寒草地覆盖度变化更加行之有效。
译  名：
Coverage Change of Alpine Grasslands in Northern Tibet:Based on PCA-BP Neural Network Estimation
作  者：
Luo Bu;La Ba;You Xueyi;Tibet Institute of Plateau Atmospheric and Environmental Science;Lhasa Branch of Chengdu Plateau Meteorological Institute,China Meteorological Administration;College of Environmental Science and Engineering, Tianjin University;
关键词：
BP neural network;;PCA;;northern Tibet;;the grassland coverage;;NDVI
摘  要：
Alpine grassland in northern Tibet is the largest alpine grassland area of China. The paper aims totimely and accurately obtain the change trend of grassland coverage in northern Tibet. We built the BP neuralnetwork model and estimated the trend of annual changes of alpine grassland in northern Tibet from 2010 to2014, and used the principal component analysis method to optimize the parameters to improve the model byusing the meteorological data, social statistics data, GIMMS NDVI and MODIS NDVI data as parameters. Theresults showed that:(1) the correlation coefficient of BP neural network model and its improved model to alpinegrassland coverage change value and the remote sensing value in northern Tibet was 0.16 and 0.47,respectively, indicating that the BP neural network model had good simulation effect after optimizing theparameters through principal component analysis;(2) the average error rate of vegetation index and NDVIestimated by 2 BP neural networks was 2.36% and 2.20%, respectively, with high simulation accuracy;(3) from the training steps of neural networks, the training convergence step length was 5000 based on the BP neuralnetwork model, and the training convergence step length was 454 based on PCA-BP neural network model, itwas shown that the latter improved the computational efficiency and had good convergence. Hence, theimproved BP neural network model is more effective to estimate the alpine grassland coverage changes innorthern Tibet whether from the fitting degree of annual variation trend, the precision value of vegetation indexestimation, or the computational efficiency.

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