Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different propert...Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.展开更多
采用基于短波红外波段的Vanhellemont和Ruddick算法对乌梁素海水体的Landsat-8业务陆地成像仪(Operational Land Imager,OLI)数据进行了大气校正。用该算法得到的OLI反射率与ENVI Flaash大气校正结果之间具有很好的一致性,且R^2为0.8。...采用基于短波红外波段的Vanhellemont和Ruddick算法对乌梁素海水体的Landsat-8业务陆地成像仪(Operational Land Imager,OLI)数据进行了大气校正。用该算法得到的OLI反射率与ENVI Flaash大气校正结果之间具有很好的一致性,且R^2为0.8。经大气校正后得到的OLI反射率与实测值吻合得较好,而且483 nm、561 nm和655 nm波段的误差在19.3%一36.5%之间,表明该算法适用于乌梁素海水体。基于时间序列OLI数据,得到了悬浮物浓度的时空分布特征。乌梁素海的悬浮物浓度反演结果存在一定的不确定性,其主要原因是底质、沉水植物和藻华对离水反射率有很大影响。展开更多
基金funded by the National Key R&D Program of China(Grant No.2017YFE0100800)the International Partnership Program of the Chinese Academy of Sciences(Grant No.131551KYSB20160002/131211KYSB20170046)the National Natural Science Foundation of China(41701481)。
文摘Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.
