“宝钢湛江项目”的实施对近十年湛江东海岛的地物分布产生剧烈影响,尤其是工业用地。本文基于2013年、2017年和2021年的陆地卫星8号(Landsat-8)数据对湛江东海岛进行地物分类,研究该区域近十年的用地变化趋势。以2013年数据为参照:采...“宝钢湛江项目”的实施对近十年湛江东海岛的地物分布产生剧烈影响,尤其是工业用地。本文基于2013年、2017年和2021年的陆地卫星8号(Landsat-8)数据对湛江东海岛进行地物分类,研究该区域近十年的用地变化趋势。以2013年数据为参照:采用归一化水体指数(Normalized Difference Water Index,NDWI)模型和谱间关系模型实现水陆分离,比对选择分离效果较优者以提取东海岛岸线;对比最大似然法、神经网络法和支持向量机法3种监督分类方法,选择提取地物效果最优者应用于其余数据。基于Google earth在线地图及无人机实测数据构建验证点集,使用混淆矩阵进行精度评价。结果表明:谱间关系模型的水陆分离效果较优,提取海岛岸线的精确度有明显提升;支持向量机法的分类总体精度和Kappa系数最高,分类结果能较好地反映研究区的真实地物分布;汇总三年数据的分类结果,发现用于发展工业的土地面积增长突出且处于持续增长趋势。谱间关系模型与支持向量机法分别实现了对东海岛岸线和地物类型的准确提取,得出近十年研究区的用地变化趋势,能为研究区的用地规划提供参考。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
鉴于传统调制传递函数(modulation transfer function,MTF)空间域分析结果受实验目标选择影响很大,应用EROS提出的频率域线状地物法对Landsat-8卫星OLI成像仪MTF进行在轨评测,并通过对比实验验证方法。基于目标和传感器特性分析,分别建...鉴于传统调制传递函数(modulation transfer function,MTF)空间域分析结果受实验目标选择影响很大,应用EROS提出的频率域线状地物法对Landsat-8卫星OLI成像仪MTF进行在轨评测,并通过对比实验验证方法。基于目标和传感器特性分析,分别建立目标模型和系统传递函数模型,利用拟合数据求解模型参数,进而计算得到MTF值。并将EROS方法与传统T choi方法测得的MTF值进行对比。实验结果表明,EROS方法能有效应用于Landsat-8卫星OLI成像仪的MTF在轨测量,且明显减少了目标选择对于MTF评测结果的影响。展开更多
为精准识别水体信息并实时监测湖泊水体时空特征及其环境特征变化情况,以洞庭湖为例,基于Landsat-8影像数据分别使用改进的归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI)、自动水体提取指数(Automated Water E...为精准识别水体信息并实时监测湖泊水体时空特征及其环境特征变化情况,以洞庭湖为例,基于Landsat-8影像数据分别使用改进的归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI)、自动水体提取指数(Automated Water Extraction Index,AWEI sh)、支持向量机(Support Vector Machine,SVM)、人工神经网络(Artificial Neural Networks,ANNs)、随机森林(Random Forest,RF)等5种方法提取枯、丰水期水体分布信息,通过精度指标评价及影响因素分析,旨在找到提取精度高、鲁棒性强的水体提取方法。结果表明:5种方法中SVM法水体提取总精度最高且泛化能力良好。研究成果可为各方法适用性提供一定参考,并通过定量分析揭示漏提率在提取精度评价指标中的重要性。展开更多
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
文摘“宝钢湛江项目”的实施对近十年湛江东海岛的地物分布产生剧烈影响,尤其是工业用地。本文基于2013年、2017年和2021年的陆地卫星8号(Landsat-8)数据对湛江东海岛进行地物分类,研究该区域近十年的用地变化趋势。以2013年数据为参照:采用归一化水体指数(Normalized Difference Water Index,NDWI)模型和谱间关系模型实现水陆分离,比对选择分离效果较优者以提取东海岛岸线;对比最大似然法、神经网络法和支持向量机法3种监督分类方法,选择提取地物效果最优者应用于其余数据。基于Google earth在线地图及无人机实测数据构建验证点集,使用混淆矩阵进行精度评价。结果表明:谱间关系模型的水陆分离效果较优,提取海岛岸线的精确度有明显提升;支持向量机法的分类总体精度和Kappa系数最高,分类结果能较好地反映研究区的真实地物分布;汇总三年数据的分类结果,发现用于发展工业的土地面积增长突出且处于持续增长趋势。谱间关系模型与支持向量机法分别实现了对东海岛岸线和地物类型的准确提取,得出近十年研究区的用地变化趋势,能为研究区的用地规划提供参考。
文摘鉴于传统调制传递函数(modulation transfer function,MTF)空间域分析结果受实验目标选择影响很大,应用EROS提出的频率域线状地物法对Landsat-8卫星OLI成像仪MTF进行在轨评测,并通过对比实验验证方法。基于目标和传感器特性分析,分别建立目标模型和系统传递函数模型,利用拟合数据求解模型参数,进而计算得到MTF值。并将EROS方法与传统T choi方法测得的MTF值进行对比。实验结果表明,EROS方法能有效应用于Landsat-8卫星OLI成像仪的MTF在轨测量,且明显减少了目标选择对于MTF评测结果的影响。
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.
