Remote sensing–based urban floodplain mapping: the added value of UAV-LiDAR compared to global and GNSS-derived DEMs

Flood risk management in urban floodplains strongly depends on the spatial resolution of digital elevation models (DEMs), which control floodplain connectivity, flow pathways, and surface storage. In many developing countries, flood-related studies rely predominantly on publicly available global DEM products, whose spatial resolution and vertical accuracy are often insufficient to represent subtle topographic gradients, densely vegetated floodplains, and complex urban microtopography. These limitations are particularly critical in low-relief environments, where small elevation differences exert a disproportionate control on inundation extent and flood dynamics. This issue has become increasingly evident in subtropical lowland regions of southern Brazil, where extreme flood events in 2023–2024 exposed shortcomings of commonly used global DEMs for urban floodplain applications. Therefore, the Piratini River watershed has been the focus of ongoing efforts to develop a real-time hydrological forecasting system to support decision-making during flood emergencies under data-scarce conditions. The urban areas of Pedro Osório and Cerrito along the main floodplain of the Piratini River constitute the core operational domain of this system and are recurrently affected by flooding. The watershed drains approximately 4,700 km² upstream of the municipalities and is characterized by low relief and wide floodplains. This study investigates the applicability of publicly available global DEMs and locally derived high-resolution elevation datasets for floodplain mapping and hydrological–hydrodynamic applications in these urban areas. A comparative assessment was conducted using two global DEM products - ALOS PALSAR (12.5 m) and ANADEM (30 m) - and three locally derived DEMs generated from high-resolution surveys. Local datasets include two Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK)–based surveys (static and kinematic) acquired with an Emlid Reach RS2+ receiver using real-time corrections via NTRIP (Networked Transport of RTCM via Internet Protocol), and an unmanned aerial vehicle (UAV)–based Light Detection and Ranging (LiDAR) survey acquired with a DJI Matrice 350 RTK platform equipped with a Zenmuse L2 sensor. The static GNSS survey comprised 2,921 points, while the kinematic survey yielded approximately 34,000 at a 1-s sampling interval. The UAV–LiDAR survey covered 21.5 km² of the urban floodplain. Raw elevation data from local surveys were converted from ellipsoidal to orthometric altitude using the hgeoHNOR2020 geoid model. GNSS-derived altitudes were interpolated using ordinary kriging in ArcGIS Pro. LiDAR data were processed in DJI Terra, resulting in a high-density point cloud (> 98 points m⁻²) and a terrain model with decimetric spatial resolution. Results reveal clear differences among datasets. Global DEMs show limited capability to represent floodplain connectivity and microtopography, particularly in vegetated areas. GNSS RTK–based DEMs provide intermediate performance but are constrained by survey logistics and GNSS signal degradation. In contrast, the UAV-based LiDAR DEM provides the most detailed and hydrologically meaningful representation of floodplain morphology, including vegetated and off-street areas, enabling improved delineation of flow paths and floodplain storage. These findings highlight the critical role of high-resolution elevation data for floodplain mapping and hydrological–hydrodynamic analyses in low-relief urban environments, reinforcing UAV-based LiDAR as a key remote sensing tool for risk assessment and climate adaptation in data-scarce regions.