The application of fast Fourier transform filtering to high spatial resolution digital terrain models derived from LiDAR sensors for the objective mapping of surface features and digital terrain model evaluations

Abstract

In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units´ construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM?s capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation.