A Comparison of Landforms and Processes Detection Using Multisource Remote Sensing Data: The Case Study of the Palinuro Pine Grove (Cilento, Vallo di Diano and Alburni National Park, Southern Italy)

Higher-resolution LiDAR datasets enable the detection of more geomorphological features and processes at finer scales than photogrammetric Digital Terrain Models (DTMs), but they also introduce greater noise due to natural or anthropogenic disturbances. This study compared grid-based methods for landform extraction using a regional-scale photogrammetric DTM, an airborne LiDAR dataset from a decade ago, and a UAV-acquired LiDAR dataset. Findings demonstrate that while LiDAR provides more detailed geomorphological mapping—useful for natural hazard assessment—its higher resolution increases the risk of noise, complicating accurate feature recognition. The research underscores the trade-offs between detail and data quality in remote sensing applications for geo-hydrological hazard studies.

Valiante, M., Di Benedetto, A., & Aloia, A. (2024). A Comparison of Landforms and Processes Detection Using Multisource Remote Sensing Data: The Case Study of the Palinuro Pine Grove (Cilento, Vallo di Diano and Alburni National Park, Southern Italy). Remote Sensing, 16(15), 2771. https://doi.org/10.3390/rs16152771