【摘要】 针对传统k-最近邻法（k-nearest neighbor,k-NN）在搜索最近邻单元时赋予特征变量相等的权重,缺少对特征变量加权优化等不足问题,在云南省香格里拉市,以高山松Pinus densata为研究对象,基于49块实测标准地,116株高山松样木和Landsat 8/OLI影像,在前期进行基于遗传算法（genetic algorithm,GA）优化的k-NN模型实现的基础上,对k-NN的3个参数（k,t和d）进行反复测试优化组合,在像元尺度上对研究区高山松地上生物量进行遥感估算。结果表明:基于遗传算法优化的k-NN模型精度优于传统的k-NN模型,优化前均方根误差为30.0 t·hm^-2,偏差为-0.418 t·hm^-2,相对标准误差百分比(R_MSE)为54.8%;优化后均方根误差为24.0 t·hm^-2,偏差为-0.123 t·hm^-2,R_MSE为43.7%。基于优化k-NN模型的研究区高山松地上生物量总储量估测结果为0.89×10⁷t。图7表6参20更多还原

【Abstract】 For the traditional k-nearest neighbor（k-NN）,there are insufficient problems that give the weight of the feature variables equally when searching the nearest neighbor population units and a lack of weight vectors for the feature variables.In this study,Shangri-la City,Yunnan Province,was selected as the research area,and Pinus densata was taken as the research object.Based on 49 field data plots,116 P.densata data samples,and Landsat 8/Operational Land Imager（OLI）imaging,a genetic algorithm was used to optimize the k-nearest neighbor model in the early stages,and the aboveground biomass of P.densata in the study area was estimated at the pixel scale after the k-NN three parameters（k,t,and d）were repeatedly tested and optimized.Results showed that accuracy of the k-NN model optimized by a genetic algorithm was better than the traditional k-NN model.Before optimization,the root mean square error was 30.0 t·hm^-2,deviation was-0.418 t·hm^-2,and R_MSE was 54.8%;after optimization,the root mean square error was 24.0 t·hm^-2,deviation was-0.123 t·hm^-2,and R_MSEwas 43.7%.Finally,the estimated total aboveground biomass of P.densata in the study area was 0.89×10⁷t based on the optimized k-NN model.[Ch,7 fig.6 tab.20 ref.]更多还原