全球变化知识资源中心

Relict landforms provide a wealth of information on the evolution of the modern landscape and climate change in the past. To improve understanding of the origin and development of these landforms we need better spatial measurements across a variety of scales. This can be challenging using conventional surveying techniques due to difficulties in landform recognition on the ground (e.g. weak visual/topographic expression) and spatially variable areas of interest. Here we explore the appropriateness of existing remote sensing datasets (aerial LiDAR and aerial photography) and newly acquired unmanned aerial vehicle (UAV) imagery of a test site on the upland of Dartmoor in SW England (Leeden Tor) for the recognition and automated mapping of relict patterned ground composed of stripes and polygons. We find that the recognition of these landforms is greatly enhanced by automated mapping using spectral two-dimensional imagery. Image resolution is important, with the recognition of elements (boulders) of <1 m maximised from the highest resolution imagery (UAV red-green-blue (RGB)) and recognition of landforms (10-100 m scale) maximised on coarser resolution aerial imagery. Topographic metrics of these low relief (0.5 m) landforms are best extracted from structure-from-motion (SfM) processed UAV true-colour imagery, and in this context the airborne LiDAR data proved less effective. Integrating automated mapping using spectral attributes and SfM-derived digital surface models from UAV RGB imagery provides a powerful tool for rapid reconnaissance of field sites to facilitate the extraction of meaningful topographic and spatial metrics that can inform on the origin of relict landform features. Care should be given to match the scale of features under consideration to the appropriate scale of datasets available.