Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from...Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC （Finer Resolution Observa- tion and Monitoring-Global Land Cover） and FROM-GLC-seg （Segmentation）, with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area （NL-ISA） and MODIS urban extent （MODIS-urban）, to produce an improved 30 m global land cover map-FROM-GLC-agg （Aggregation）. It was pos-processed using additional coarse res- olution datasets （i.e., MCD12Q1, GlobCover2009, MOD44W etc.） to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy （i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km） of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map （at 30 m） and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.展开更多
基金国家自然科学基金重点项目"基于城乡协调发展的农村公共物品管理研究"(编号:71033003)陕西省科学技术研究发展计划项目"陕西省居民幸福指数架构及其对公共服务的满意度评估"(编号:2012KR2-05)+1 种基金美国国家地理资助的垃圾污染和水资源管理项目"Empowering Woman to Sustain Community-based Waste Management and Water conservation in Northwestern Rural China"(编号:GEFC08-15)西北大学研究生创新教育项目"农村公共产品供求错位分析"(编号:YZZ13054)
基金supported by the National High-tech R&D Program of China(Grant No.2009AA12200101)the National Natural Science Foundation of China(Grant No.41301445)+1 种基金an Open Fund from the State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201202)a research grant from Tsinghua University(Grant No.2012Z02287)
文摘Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC （Finer Resolution Observa- tion and Monitoring-Global Land Cover） and FROM-GLC-seg （Segmentation）, with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area （NL-ISA） and MODIS urban extent （MODIS-urban）, to produce an improved 30 m global land cover map-FROM-GLC-agg （Aggregation）. It was pos-processed using additional coarse res- olution datasets （i.e., MCD12Q1, GlobCover2009, MOD44W etc.） to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy （i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km） of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map （at 30 m） and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.
