近几十年来,全球生态系统、气候和生物多样性已不同程度受到人口快速增长和城市持续扩张带来的负面影响.使用低分辨率遥感影像数据和GIS技术定量分析了近40a全球城市时空变化.世界数字化图(Digital Chart of the World,DCW)城市图层代表...近几十年来,全球生态系统、气候和生物多样性已不同程度受到人口快速增长和城市持续扩张带来的负面影响.使用低分辨率遥感影像数据和GIS技术定量分析了近40a全球城市时空变化.世界数字化图(Digital Chart of the World,DCW)城市图层代表1960年至1970年的全球城市面积,利用MODIS影像数据、稳定夜间光数据和城市格网人口密度数据编制的全球土地覆盖数据城市图(GlobalLand Cover by National Mapping Organization,GLCNMO)代表2003年时的全球城市面积.计算了1960年和2003年全球城市的城市用地比重和年城市增长率指数,从区域尺度、国家尺度和单个城市尺度分析了全球城市扩展的时空变化过程.结果表明:在1960年代发达国家城市面积较大,而发展中国家城市面积小得多;到2003年发展中国家有相当大的变化,新增长的城市区域快速扩大.近40a来发展中国家城市区域发展速度远快于发达国家,与欧洲和北美国家相比,发展中国家城市结构更紧凑和密集.展开更多
It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLC...It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.展开更多
文摘近几十年来,全球生态系统、气候和生物多样性已不同程度受到人口快速增长和城市持续扩张带来的负面影响.使用低分辨率遥感影像数据和GIS技术定量分析了近40a全球城市时空变化.世界数字化图(Digital Chart of the World,DCW)城市图层代表1960年至1970年的全球城市面积,利用MODIS影像数据、稳定夜间光数据和城市格网人口密度数据编制的全球土地覆盖数据城市图(GlobalLand Cover by National Mapping Organization,GLCNMO)代表2003年时的全球城市面积.计算了1960年和2003年全球城市的城市用地比重和年城市增长率指数,从区域尺度、国家尺度和单个城市尺度分析了全球城市扩展的时空变化过程.结果表明:在1960年代发达国家城市面积较大,而发展中国家城市面积小得多;到2003年发展中国家有相当大的变化,新增长的城市区域快速扩大.近40a来发展中国家城市区域发展速度远快于发达国家,与欧洲和北美国家相比,发展中国家城市结构更紧凑和密集.
文摘It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.
