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Global Estimates of Ecosystem Service Value and Change: Taking Into Account Uncertainties in Satellite-based Land Cover Data

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  • Song, Xiao-Peng

Abstract

Global estimates of ecosystem service value (ESV) and change are often produced using satellite-based land cover maps. However, uncertainties in global land cover data and their impacts on ESV estimation have not been fully recognized. Considerably inflated estimates of land cover change and ESV change could be derived using a direct map comparison approach when classification uncertainties are not explicitly taken into account. This study collected all available global land cover datasets and applied an ensemble approach to derive the range and central tendency of terrestrial ESV estimates. Different input data caused ESV estimate varying between 35.0 and 56.5trillionInt$/year. Wetland classes, albeit having the highest per unit value, were the most uncertain classes mapped using satellite data. To reduce uncertainty, a spatial data harmonization procedure was developed, which resulted in an improved ESV estimate at 49.4trillionInt$/year. The study further illustrated the quantification of changes in forest ESV using a high-resolution global forest cover change dataset. An ESV loss of 716.0billionInt$/year was estimated between 2000 and 2012—a result representing one fifth of previous estimates. These findings highlighted the importance of improving the characterization and monitoring of land cover for global ESV and change estimation.

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  • Song, Xiao-Peng, 2018. "Global Estimates of Ecosystem Service Value and Change: Taking Into Account Uncertainties in Satellite-based Land Cover Data," Ecological Economics, Elsevier, vol. 143(C), pages 227-235.
    • Handle: RePEc:eee:ecolec:v:143:y:2018:i:c:p:227-235
    DOI: 10.1016/j.ecolecon.2017.07.019
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