Quantifying and mapping land use changes and regulating ecosystem service potentials in a data-scarce peri-urban region in Kenya

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Wangai, P.W.; Burkhard, B.; Müller, F.: Quantifying and mapping land use changes and regulating ecosystem service potentials in a data-scarce peri-urban region in Kenya. In: Ecosystems and People 15 (2019), Nr. 1, S. 11-23. DOI: https://doi.org/10.1080/21513732.2018.1529708

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Sum total of downloads: 112

Recent scientific developments are advancing to link land use and land cover (LULC) change with ecosystem service (ES) potentials. Such links within peri-urban ecosystems are scanty due to methodological and expertise challenge, and data limitation. The study applies the ‘ES matrix approach’ to spatially display potentials for regulating ES in mainly overlooked data-scarce peri-urban areas, whereby LULC classes and qualitative ES values are the main data inputs. The LULC maps are based on LANDSAT satellite images from the years 1990, 2000 and 2010. ES potentials were assessed qualitatively on a relative scale ranging between 0 and 5 by use of interview data from local people. Results show that with exception of settlements, the area for all LULC classes decreased between 1990 and 2010. The ‘matrix approach’ successfully generated ES potential maps for the different LULC classes. Grasslands, forests and wetlands have comparatively high potentials for regulating ES, whereas settlements and ‘otherlands’ showed lower potentials. The main uncertainties of the study relate to study area selection, data accuracy and reliability, and ‘matrix approach’ adaptability. Results indicate that the potential of the area to provide regulating ES is declining over time. To realize suitable and reliable results, it is necessary to conduct data accuracy-check during and after the fieldwork exercise.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Naturwissenschaftliche Fakultät

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pos. country downloads
total perc.
1 image of flag of Germany Germany 65 58.04%
2 image of flag of United States United States 13 11.61%
3 image of flag of Indonesia Indonesia 8 7.14%
4 image of flag of Russian Federation Russian Federation 6 5.36%
5 image of flag of Cambodia Cambodia 3 2.68%
6 image of flag of India India 2 1.79%
7 image of flag of France France 2 1.79%
8 image of flag of Czech Republic Czech Republic 2 1.79%
9 image of flag of Kenya Kenya 1 0.89%
10 image of flag of Italy Italy 1 0.89%
    other countries 9 8.04%

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