DeltaDTM: A global coastal digital terrain model

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels due to Sea Level Rise (SLR), subsidence and changing extreme weather patterns. However, current freely available elevation data sets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 30 m and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects the CopernicusDEM with space borne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods (including machine learning) recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.

海岸高程数据广泛应用于海岸管理、洪水建模与适应规划等诸多场景，具备重要的实用价值。海拔处于平均海平面（Mean Sea Level, MSL）上方10米以内的低洼沿海区域，将因海平面上升（Sea Level Rise, SLR）、地面沉降以及极端天气格局变化，面临未来极端水位的威胁。然而，当前可免费获取的高程数据集精度不足，无法可靠模拟此类风险。本研究提出的DeltaDTM是一款面向全球沿海区域的公开共享数字地形模型（Digital Terrain Model, DTM），其水平空间分辨率为30米，整体垂直平均绝对误差（Mean Absolute Error, MAE）为0.45米。DeltaDTM基于ICESat-2与GEDI任务搭载的星载激光雷达数据，对哥白尼DEM（CopernicusDEM）进行校正。具体而言，研究团队首先校正CopernicusDEM中的高程偏差，通过滤波操作剔除非地形单元格，并利用插值方法填补数据空白。值得注意的是，相较于近期其他研究用于校正DEM的回归方法（含机器学习方法）所能达到的最优整体平均绝对误差0.72米，本研究采用的分类方法可获得更精准的校正结果。综上，DeltaDTM将成为沿海洪水影响模拟及其他相关应用的宝贵数据资源。