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Refining the Tiered Approach for Mapping and Assessing Ecosystem Services at the Local Scale: A Case Study in a Rural Landscape in Northern Germany

Abstract : Spatially explicit assessments of ecosystem services (ES) potentials are a key component in supporting a sustainable land use management. The ES matrix method is a commonly used approach as it allows for a comparably fast, comprehensible and accessible ES assessment. As it is often based on land use/land cover data (LULC) with no spatial variability, a main critique is that the results fail to assess spatial variability at landscape levels, which limits the reliability of the outputs for spatial planning applications. By using the case study area of Bornhöved in northern Germany, we analysed three assessment methods that combine expert judgments, LULC data with different resolutions and ecosystem condition indicators, in order to find the required resolution and data for ES assessment and mapping at a local scale. To quantify map discrepancies, we used the structural similarity index (SSIM) and analysed the differences in local mean, variance and covariance between the maps. We found that using different spatial resolutions led to a relatively small difference in the outcomes, in which regulation and maintenance services are more affected than the other services categories. For most regulation, maintenance and cultural ES, our results indicate that assessments based only on LULC proxies are not suitable for a local quantitative assessment of ES, as they cannot sufficiently cover the spatial heterogeneity of ES capacities that arise from different ecosystem conditions.
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https://hal.inrae.fr/hal-03166275
Contributor : Raymond Schiano <>
Submitted on : Thursday, March 11, 2021 - 11:02:15 AM
Last modification on : Saturday, June 5, 2021 - 3:35:56 AM

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Marie Perennes, C. Sylvie Campagne, Felix Müller, Philip Roche, Benjamin Burkhard. Refining the Tiered Approach for Mapping and Assessing Ecosystem Services at the Local Scale: A Case Study in a Rural Landscape in Northern Germany. Land, MDPI, 2020, 9 (10), pp.348. ⟨10.3390/land9100348⟩. ⟨hal-03166275⟩

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