Optical and radar remote sensing improve DEM-based geomorphic change detection in semi-arid landscapes

Abstract

Accurate measurements of geomorphic change are necessary to improve quantitative and conceptual models in geomorphology. New generation high-resolution topography (HRT) is enabling increasingly accurate quantification of surface change via differencing of fine scale (<1 m) multi-temporal digital elevation models (DEMs). The resulting DEMs of difference (DoDs) provide spatially continuous estimates of surface change. However, harnessing the information contained in HRT DoDs requires progressively sophisticated methods for handling the error propagated into a DoD from each DEM. As HRT acquisition increases, and technology to host and distribute the data improves, there is increasing need for reliable and repeatable error handling procedures. We investigate the potential for satellite-borne optical and radar data to improve DEM-based geomorphic change detection in semi-arid landscapes. The primary motivation for this work is to enable improved geomorphic change detection in semi-arid landscapes affected by extensive erosion. We apply the methodology to a ~15 km2 catchment adjacent to the Great Barrier Reef, Australia, where independent end-of-catchment sediment load data is available for comparison. Our goal is to enable improved geomorphic change detection over relatively large areas (>1 km2) by minimising systematic error propagated into a DoD, particularly from DEMs with sparse ground control networks.We find the methodology reliably decreases the systematic error in our DoD and improves the separation of real geomorphic change from noise. However, the presence of grass and consequent point misclassification remains a key challenge even with a relatively high point density (~48 pts·m2) airborne-lidar dataset. This is the first time optical and radar remote sensing have been used alongside airborne-lidar for improved DEM-based geomorphic change detection in semi-arid landscapes.