航天飞机雷达地形任务
数字高程模型
哥白尼
遥感
地形
大洪水
地理定位
激光雷达
地理
植被(病理学)
土地覆盖
地质学
仰角(弹道)
地图学
土地利用
计算机科学
数学
考古
物理
土木工程
病理
万维网
工程类
天文
医学
几何学
作者
Michael E. Meadows,Simon Jones,Karin Reinke
标识
DOI:10.1080/17538947.2024.2308734
摘要
Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).
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