A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major d...A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major difficulties in generating land classification maps at the regional scale:the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time.Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens.As one of the most well-known VGI initiatives,OpenStreetMap(OSM),contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns.Whereas,most large-scale mapping approaches-including regional and national scales–confuse“land cover”and“land-use”,or build up the land-use database based on modeled land cover data-sets,in this study,we clearly distinguished and differentiated land-use from land cover.By focusing on our prime objective of mapping land-use and management practices,a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery.Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.Furthermore,our novel approach in regional scale land-use mapping produced robust results in our study area:the overall internal accuracy of the classifier was 95.2%and the external accuracy of the classifier was measured at 74.8%.展开更多
文摘A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major difficulties in generating land classification maps at the regional scale:the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time.Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens.As one of the most well-known VGI initiatives,OpenStreetMap(OSM),contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns.Whereas,most large-scale mapping approaches-including regional and national scales–confuse“land cover”and“land-use”,or build up the land-use database based on modeled land cover data-sets,in this study,we clearly distinguished and differentiated land-use from land cover.By focusing on our prime objective of mapping land-use and management practices,a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery.Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.Furthermore,our novel approach in regional scale land-use mapping produced robust results in our study area:the overall internal accuracy of the classifier was 95.2%and the external accuracy of the classifier was measured at 74.8%.
