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Digital terrain models (DTMs) are a fundamental source of information in Earth sciences. DTM-based studies, however, can contain remarkable biases if limitations and inaccuracies in these models are disregarded. In this work, four freely available datasets, including Shuttle Radar Topography Mission C-Band Synthetic Aperture Radar (SRTM C-SAR V3 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM V2), and two nationwide airborne light detection and ranging (LiDAR)-derived DTMs (at 5-m and 1-m spatial resolution, respectively) were analysed in three geomorphologically contrasting, small (3–5 km2) catchments located in Mediterranean landscapes under intensive human influence (Mallorca Island, Spain). Vertical accuracy as well as the influence of each dataset’s characteristics on hydrological and geomorphological modelling applicability were assessed by using ground-truth data, classic geometric and morphometric parameters, and a recently proposed index of sediment connectivity. Overall vertical accuracy—expressed as the root mean squared error (RMSE) and normalised median deviation (NMAD)—revealed the highest accuracy for the 1-m (RMSE = 1.55 m; NMAD = 0.44 m) and 5-m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of the SRTM data was lower (RMSE = 6.98 m; NMAD = 5.27 m), but considerably higher than for the ASTER data (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. Propagation of these errors and coarse horizontal resolution caused negative impacts on flow routing, stream network, and catchment delineation, and to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for catchment hydrogeomorphological modelling.
In this study, a low-cost unmanned aerial vehicle was used to obtain multi-spectral high-resolution imagery (1.4 cmpx(-1)) from2 microcatchments (3.3 ha) with burned Mediterranean shrubland and pine forests. This imagery was used to calculate the blue normalized differential vegetation index and to generate digital elevation models for estimating the sediment connectivity index. Both indices enabled an integrated approach for deciphering how hydrological and sediment connectivity interact with vegetation as well as soil conservation structures. The application of spatial analysis improves our understanding of the feedback between biological and geomorphological processes. Local spatial data analysis established a significant link between local geomorphological and biological factors, enabling a precise identification of homogeneous areas at micro-catchment scale and the minimal size of vegetation units reacting to geomorphology as natural groups at plot-scale where management strategies and efforts should be applied. Establishing this local relationship between sediment connectivity and vegetation patterns through new and interdisciplinary methodologies represents a new strategy for the assessment of ecosystem dynamics and management.