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Wetland Change Mapping Using Machine Learning Algorithms, and Their Link with Climate Variation and Economic Growth: A Case Study of Guangling County, China

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  • Gadisa Fayera Gemechu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
    Faculty of Natural Sciences, Salale University, Fiche 245, Ethiopia)

  • Xiaoping Rui

    (College of Earth and Engineering, Hohai University, Nanjing 211100, China)

  • Haiyue Lu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

Abstract

Wetlands are a distinctive terrestrial ecosystem that benefits living things, including people, in various ways. Sustainable wetland ecosystem resources are needed to protect the global environment. Wetlands in China have undergone positive and negative changes in response to several factors, but studies documenting their long-term dynamicity have been few, particularly in Guangling County. This study examines the change of wetlands area based on remotely sensed data while exploring trends associated with climate variations and economic growth in Guangling County, China. Analysis of remotely sensed imagery, mainly in hilly and nonhomogeneous environments is problematic, largely as a result of interference and their high spectral non-homogeneity. We conducted experiments using five classical machine learning algorithms based on the Google Earth Engine (GEE) and obtained the greatest robustness and accuracy using a Support Vector Machine (SVM)—Radial Basis Function (RBF) kernel approach, with overall accuracy and kappa statistics ranging from 86% to 98.1% and from 0.789 to 0.960, respectively. Based on the SVM-RBF model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends. We found that 45.71 km 2 of wetland area was lost over the past 3.7 decades (January 1984–December 2020), or 81.82% of wetland area coverage. In this paper, we explore how factors such as county economic growth (GDP), humidity, and temperature variations are tightly linked with wetland change.

Suggested Citation

  • Gadisa Fayera Gemechu & Xiaoping Rui & Haiyue Lu, 2021. "Wetland Change Mapping Using Machine Learning Algorithms, and Their Link with Climate Variation and Economic Growth: A Case Study of Guangling County, China," Sustainability, MDPI, vol. 14(1), pages 1-25, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:439-:d:715634
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    References listed on IDEAS

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    1. Sanneke van Asselen & Peter H Verburg & Jan E Vermaat & Jan H Janse, 2013. "Drivers of Wetland Conversion: a Global Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
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    Cited by:

    1. Haiyue Lu & Xiaoping Rui & Gadisa Fayera Gemechu & Runkui Li, 2022. "Quantitative Evaluation of Psychological Tolerance under the Haze: A Case Study of Typical Provinces and Cities in China with Severe Haze," IJERPH, MDPI, vol. 19(11), pages 1-23, May.

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