Very high-resolution (VHR) optical Earth observation (EO) satellites as well as low-altitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both point clouds. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides the most detailed model of the island to date, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

超高分辨率（VHR）光学地球观测（EO）卫星以及低空且易于使用的无人机系统（UAS /无人机）提供了不断完善的数据源，用于生成详细的3维（3D） ）数据，并使用具有密集图像匹配功能的数字摄影测量方法。如今，这两个数据源都是专用机载传感器的经济高效替代品，特别是对于偏远地区。最新一代的EO卫星每年可以多次从不同的角度收集最远位置的0.30 m地面采样距离（GSD）的VHR图像。因此，不断增长的图像档案库中精心挑选的场景可以生成高分辨率的数字高程模型（DEM）。此外，低成本且易于使用的无人机可以快速部署在偏远地区，以捕获本地图像块。从这些方法得出的密集点云提供了宝贵的数据源，填补了全球可用的低分辨率DEM与高精度地面测量之间的空白。在这里，我们调查使用存档的VHR卫星图像的大约 0.5 m GSD以及平均GSD优于0.03 m的低空无人机影像，可使用摄影测量工具在南大西洋的偏远岛屿Tristan da Cunha上生成高质量的DEM，这超出了当前的范围商业载人机载测绘平台。这项研究探索了结合这种异构数据源以生成准确性和分辨率方面改进的DEM的潜力和局限性。低空机载和星载数据集之间的交叉验证描述了两个光学数据集之间的适合性。没有检测到共配准误差，尺度差异或畸变，并且云与云的定量比较显示两点云之间的平均距离为0.26 m。使用常规地理参考方法将两个点云合并。合并的DEM保留了基于无人机的调查中的丰富细节，并提供了整个研究区域的准确3D表示。它提供了迄今为止最详细的岛屿模型，适合于支持实际和科学应用。这项研究表明，结合存档的VHR卫星和基于低空无人机的影像提供了廉价的替代方案来生成高质量的DEM。