Quantifying the spatial correlations between landscape pattern and ecosystem service value: A case study in Ebinur Lake Basin, Xinjiang, China
Abstract
Human activities and environmental degradation have resulted in landscape structure changes and can eventually affect the ecosystem service value (ESV) of its region. Nevertheless, research on the spatial correlations between ESVs and landscape pattern changes is lacking. Thus, 13 landscape metrics and nine ESV types in Ebinur Lake Basin were chosen and used to analyse their spatial correlations using multiple linear regression models in this study. The results revealed that eight out of the 13 landscape metrics showed direct spatial correlations with ESV type, and there were landscape metrics that were positively and negatively correlated with the different ESV types. The interspersion and juxtaposition index (IJI), patch richness (PR), and patch richness density (PRD) had no effects on the provision of ESVs. The results also showed that the land-use/land-cover classification types play a linking role, as changes in land-use/land-cover affect the provision of ESVs and the fragmentation of landscape patterns. At the same time, the total ESV of Ebinur Lake Basin was 21.21 × 109 CNY in 2014. Wood and grassland contributed the highest ESV in Ebinur Lake Basin, i.e., 16.29 × 109 CNY, followed by water bodies and farmland, i.e., 1.785 × 109 CNY and 1.239 × 109 CNY, respectively. The regression models that were obtained quantitatively assessed how the changes in landscape patterns have affected the provision of ESVs. These models greatly contribute to the application of the ecosystem service approach in research as well as in practice and provide a better understanding of landscape planning in Ebinur Lake Basin.
Publication Title
Ecological Engineering
Recommended Citation
Yushanjiang, A., Zhang, F., Yu, H., & Kung, H. (2018). Quantifying the spatial correlations between landscape pattern and ecosystem service value: A case study in Ebinur Lake Basin, Xinjiang, China. Ecological Engineering, 113, 94-104. https://doi.org/10.1016/j.ecoleng.2018.02.005