Suchergebnisse

Modelling ecosystem services (ES) has become a new standard for the quantification and assessment of various ES. Multiple ES model applications are available that spatially estimate ES supply on the basis of land-use/land-cover (LULC) input data. This paper assesses how different input LULC datasets affect the modelling and mapping of ES supply for a case study on Terceira Island, the Azores (Portugal), namely: (1) the EU-wide CORINE LULC, (2) the Azores Region official LULC map (COS.A 2018) and (3) a remote sensing-based LULC and vegetation map of Terceira Island using Sentinel-2 satellite imagery. The InVEST model suite was applied, modelling altogether six ES (Recreation/Visitation, Pollination, Carbon Storage, Nutrient Delivery Ratio, Sediment Delivery Ratio and Seasonal Water Yield). Model outcomes of the three LULC datasets were compared in terms of similarity, performance and applicability for the user. For some InVEST modules, such as Pollination and Recreation, the differences in the LULC datasets had limited influence on the model results. For InVEST modules, based on more complex calculations and processes, such as Nutrient Delivery Ratio, the output ES maps showed a skewed distribution of ES supply. Yet, model results showed significant differences for differences in all modules and all LULCs. Understanding how differences arise between the LULC input datasets and the respective effect on model results is imperative when computing model-based ES maps. The choice for selecting appropriate LULC data should depend on: 1) the research or policy/decision-making question guiding the modelling study, 2) the ecosystems to be mapped, but also on 3) the spatial resolution of the mapping and 4) data availability at the local level. Communication and transparency on model input data are needed, especially if ES maps are used for supporting land use planning and decision-making. ; The research leading to these results has received funding from the European Union, under the programme Pilot Project — Mapping and ...