Institute of Social Ecology, University of Natural Resources and Life Sciences, Vienna, Schottenfeldgasse 29, 1070 Vienna, Austria.
Geography Department, Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
Environ Sci Technol. 2021 Mar 2;55(5):3368-3379. doi: 10.1021/acs.est.0c05642. Epub 2021 Feb 18.
The dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Two main types of data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites and cadastral information. We present an alternative approach for broad-scale material stock mapping based on freely available high-resolution EO imagery and OpenStreetMap data. Maps of built-up surface area, building height, and building types were derived from optical Sentinel-2 and radar Sentinel-1 satellite data to map patterns of material stocks for Austria and Germany. Using material intensity factors, we calculated the mass of different types of buildings and infrastructures, distinguishing eight types of materials, at 10 m spatial resolution. The total mass of buildings and infrastructures in 2018 amounted to ∼5 Gt in Austria and ∼38 Gt in Germany (AT: ∼540 t/cap, DE: ∼450 t/cap). Cross-checks with independent data sources at various scales suggested that the method may yield more complete results than other data sources but could not rule out possible overestimations. The method yields thematic differentiations not possible with NTL, avoids the use of costly cadastral data, and is suitable for mapping larger areas and tracing trends over time.
社会物质存量(如建筑物和基础设施)及其空间格局的动态变化推动了资源利用和排放的急剧增长。目前主要有两种类型的数据可用于存量制图,即地球观测卫星的夜间灯光(NTL)数据和地籍信息。我们提出了一种基于免费的高分辨率对地观测影像和 OpenStreetMap 数据的大规模物质存量制图的替代方法。利用光学卫星 Sentinel-2 和雷达卫星 Sentinel-1 的卫星数据,以及 OpenStreetMap 数据,我们可以生成建筑占地面积、建筑高度和建筑类型等地图,从而绘制奥地利和德国的物质存量模式。利用物质强度系数,我们计算了不同类型建筑物和基础设施的质量,区分了 8 种材料,空间分辨率为 10 米。2018 年,奥地利的建筑物和基础设施总质量约为 50 亿吨(人均 540 吨),德国的总质量约为 380 亿吨(人均 450 吨)。与各种规模的独立数据源进行交叉检查表明,该方法可能比其他数据源产生更完整的结果,但不能排除可能存在的高估。该方法能够进行 NTL 无法进行的专题区分,避免使用昂贵的地籍数据,并且适合更大区域的制图和随时间推移的趋势追踪。