Abstract
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.
Highlights
Modelling ecosystem services (ES) allows us to predict the spatial distribution of different ES that sustain and support human life
For example, carbon storage and nutrient delivery ratio, show differences in the model outputs, indicating differences in the spatial modelled ES supply derived from the differences amongst the input LULC datasets
As this study shows, running ES models with different available LULC datasets can help to add an additional layer to the ES maps - it can predict where the distribution of ES can be modelled with high certainty and where uncertainty in spatial distribution occurs, based on LULC
Summary
Modelling ecosystem services (ES) allows us to predict the spatial distribution of different ES that sustain and support human life. Modelling ES has become an essential tool for mapping and assessing ES, which is heavily used, for instance, in the context of the European Union's (EU) Initiative Mapping and Assessing Ecosystems and their Services (MAES*1) which supports the implementation of the EU's Biodiversity Strategies 2020 and 2030. Amongst the most popular open access models, the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST) (Sharp et al 2018), ESTIMAP (Zulian et al 2013), Resource Investment Optimization System (RIOS) or ARIES (Villa et al 2014) are listed, allowing to model ES in either biophysical terms (e.g. Mg of carbon sequestered) or economic terms (e.g. net present value of that sequestered carbon) (Natural Capital Assessment (Sharp et al 2018))
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