基于Landsat8 OLI遥感影像的森林蓄积量估测模型研究

本次试验以湖南省湘潭县为研究区,提取Landsat8 OLI影像数据的56个遥感因子作为候选因子,结合皮尔逊相关系数和主成分分析两种方法对变量进行降维,构建多元线性回归模型(MLR)、误差反向传播神经网络(BP-ANN)、K最近邻模型(KNN)和随机森林模型(RF)进行蓄积量反演,并采用决定系数(R2)、均方根误差(R...

Research on estimation model of forest stock volume based on Landsat8 remote sensing image

In this work,Xiangtan County in Hunan Province was collected as the research area.Landsat8 OLI image data was utilized as the remote sensing data source,in which fifty-sixth remote sensing factors were extracted as candidate factors.The Pearson correlation coefficient and principal component analysis were used to reduce the dimensionality of variables.The multiple linear regression model(MLR),error back propagation neural network(BP-ANN),K nearest neighbor model(KNN)and random forest model(RF)were established to inverse volume accumulation.Three coefficients of determination coefficient(R²),root mean square error(RMSE),relative root mean square error(RRMSE%)were used for accuracy evaluation.The results showed that the fitting results of the three machine learning models were better than the multiple linear regression model.Their determination coefficients(R²)were all higher than 0.6,in which the highest RF was 0.65.Among the four models,the estimation accuracy of the three machine learning models was higher than the traditional linear model by more than 10 percentage points,of which the random forest model(RF)has the highest accuracy with root mean square error of 66.7 m³·hm^-2 and the relative root mean square error of 32.3%.