Regional assessments of selected ecosystem services in northern Germany

The purpose of this study is to gain a better understanding of the land cover distributions, land cover changes and the differences in ecosystem service provisions resulting from distinctive indicators based on various CORINE land cover classes. By analyzing the land cover dynamics and the annual total Gross Primary Production (GPP) and Net Primary Production (NPP), Soil Organic Carbon (SOC), Carbon Storage (CS), and a qualitative indicator derived from the ecosystem service matrix method (GCR), this study measures the land cover distributions. The land-cover-based global climate regulation potential is assessed to improve global climate regulation. This study uses spatial and statistical analysis tools referring to Geographic Information Systems (GIS), the statistical package R and the software of SPSS are used to estimate the land cover and ecosystem services dynamics in Schleswig-Holstein. The study has found that “non-irrigated arable land” and “pastures” are the dominating land cover classes in Schleswig-Holstein, and the significant land cover changes are among them. “Non-irrigated arable land” and “pastures” have higher annual total stored GPP and NPP values than the other land cover classes. Geest has the largest annual total stored GPP and NPP even though the annual total GPP and NPP are less than the annual total GPP and NPP in Marsch. Simultaneously, Dithmarschen and Rendsburg-Eckernförde hold larger annual total stored GPP and NPP amounts than the other districts. The outcome of the comparison of qualitative and quantitative indicators of the global climate regulation service, the annual total GPP, the annual total NPP, SOC, Carbon Stocks and GCR is that all of them show significant correlations. The interrelations among the different indicators, the annual total GPP, the annual total NPP, and the statistical data of average harvest, the harvests of grain, green corn, root crops and winter rape evaluated based on districts also correlate significantly.


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