以澳大利亚阿德莱德中心城区为研究区,基于高分辨率城市三维建筑物数据计算得到天空开阔度(sky view factor,SVF)与迎风面积指数(frontal area index,FAI),并将其与晴好天气下四季的城市热岛强度进行相关性分析。结果表明:晴好天气下,...以澳大利亚阿德莱德中心城区为研究区,基于高分辨率城市三维建筑物数据计算得到天空开阔度(sky view factor,SVF)与迎风面积指数(frontal area index,FAI),并将其与晴好天气下四季的城市热岛强度进行相关性分析。结果表明:晴好天气下,阿德莱德城市热岛强度(urban heat island intensity,UHII)在2010—2011年四季均呈现出夜间强、白天弱的变化特征。SVF与UHII在夜间呈显著线性负相关,白天呈线性正相关;而FAI与UHII在四季的夜间和早晨时段呈对数关系,白天呈线性负相关。SVF和FAI对不同季节、不同时刻的城市热岛影响不同,在不同空间尺度下的适用性也存在差异,SVF在不同空间尺度下适用性更强。展开更多
Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory m...Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging(Li DAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the Li DAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne Li DAR DEM derivatives. Quasi-3 D relief image maps are generated based on the sky-view factor(SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km^(2) study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of Li DAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m^(2). Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of Li DAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.展开更多
文摘以澳大利亚阿德莱德中心城区为研究区,基于高分辨率城市三维建筑物数据计算得到天空开阔度(sky view factor,SVF)与迎风面积指数(frontal area index,FAI),并将其与晴好天气下四季的城市热岛强度进行相关性分析。结果表明:晴好天气下,阿德莱德城市热岛强度(urban heat island intensity,UHII)在2010—2011年四季均呈现出夜间强、白天弱的变化特征。SVF与UHII在夜间呈显著线性负相关,白天呈线性正相关;而FAI与UHII在四季的夜间和早晨时段呈对数关系,白天呈线性负相关。SVF和FAI对不同季节、不同时刻的城市热岛影响不同,在不同空间尺度下的适用性也存在差异,SVF在不同空间尺度下适用性更强。
基金The research was supported by the National Innovation Research Group Science Fund(No.41521002)the National Key Research and Development Program of China(No.2018YFC1505202)。
文摘Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging(Li DAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the Li DAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne Li DAR DEM derivatives. Quasi-3 D relief image maps are generated based on the sky-view factor(SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km^(2) study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of Li DAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m^(2). Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of Li DAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.
