Assessment of ecosystem services for planning of green infrastructure at the regional level
2019
Usca, M., Institute of Agricultural Resources and Economics, LLU APP, Priekuli, Priekuli parish, Priekuli Municipality (Latvia) | Vinogradovs, I., University of Latvia, Riga (Latvia). Faculty of Geography and Earth Sciences | Reke, A., University of Latvia, Riga (Latvia). Faculty of Geography and Earth Sciences | Immurs, D.V., University of Latvia, Riga (Latvia). Faculty of Geography and Earth Sciences | Zarina, A., University of Latvia, Riga (Latvia). Faculty of Geography and Earth Sciences
Ecosystem services (ES) are defined as the benefits that human beings derive from ecosystem functions. Assessment and mapping of these benefits are crucial for sustainable environmental planning and future natural capital. Green infrastructure (GI) is natural or semi-natural territories that provide wide range of ES. Mapping of ES, as well as assessing the interactions among various ES and analysing their supply potential’s cold/hot spots considerably enhances and substantiates the planning process of GI, particularly at the regional scale and for the territories with diverse landscape potential. The aim of this paper is to discuss the assessment of ES supply potential and analyse its spatial distribution to reveal cold/hot spots of ecosystem capacity to provide wide range services and functions for GI. The study presents GIS based assessment of ES in a case study of Zemgale Planning Region. ES supply potential was assessed for 27 Corine land use classes (CLC2018) together with 10 regulatory, 12 provisioning and 6 cultural ES. An expert-based ranking approach using a two-dimensional ES matrix and a geospatial analysis was applied to determine total ES supply potential, spatial patterns and relations among multiple ES. Additional statistical analysis (Getis-Ord Gi*) was performed on spatial distribution of regulatory ES to disclose statistically significant capacity of ecosystems to function as GI in given surroundings. Preliminary results show uneven distribution of ES, trade-offs between regulatory and provisioning ES and landscape dependent spatial clustering of these trade-offs supported by result of Getis-Ord Gi* analysis, thus laying a foundation for further planning of GI at the regional scale.
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