Assessment of Water-related Ecosystem Service Supply and Demand Bundles and Their Drivers in Austria: A Multiscale Machine Learning Approach
This paper maps selected water-related Ecosystem Services (ES) and identifies ES Bundles across multiple scales. It uses machine learning to analyse potential socio-economic drivers that influence the spatial arrangement of those bundles at different spatial scales, thus providing insights for targeted, scale-specific management strategies
Comparison of Topography-based Indices for Soil Wetness Mapping in an Austrian Floodplain Area
This study evaluates different ecological indicators and terrain-based indices for soil wetness mapping in the Salzach river floodplains in Austria. We utilize LiDAR-derived high-resolution DEMs (1m) to calculate the indices and compare them with field data represented by groundwater measurements.
This tool calculates the (cartographic) Depth-to-Water (DTW) index, representing the simulated vertical difference (meters) between a landscape cell and the nearest surface water cell along the accumulative least-cost slope path. DTW is commonly used for soil wetness modelling and identifying areas of high or low probability of water accumulation.