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

Gadisa Fayera Gemechu,Xiaoping Rui,Haiyue Lu

doi:10.3390/su14010439

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 km2 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.

Highlights

Sustainable wetland ecosystems can offer direct monetary value to human beings and indirectly serve human beings [1]
Based on the Support Vector Machine (SVM)-Radial Basis Function (RBF) model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends
Several previous studies have demonstrated the potential of classical Machine learning (ML) algorithms such as SVM, random forest (RF), gradient boosted trees, and Classification and Regression Trees (CART) in wetland mapping and detecting wetland spatial distributions, showing the capacity of machine learning to handle high dimensionality with complex characteristics [50]

Summary

Introduction

Sustainable wetland ecosystems can offer direct monetary value to human beings and indirectly serve human beings [1]. Wetlands are highly productive natural resources that provide globally substantial social, economic, and environmental benefits. They are regarded as the “kidneys of the Earth” and are its most important ecosystems [2]. Wetlands perform a wide range of stabilizing functions, including by improving water quality; recharging groundwater; storing water and natural products; offering aesthetic and recreational opportunities; mitigating storms and flooding; controlling erosion and stabilizing shorelines; helping support fish, timber, peat, and wildlife resources; and increasing tourism opportunities [3,4]. The key role of many wetlands in supporting biological communities means that they are ecosystems that provide valuable goods. As an intact land cover, they are associated with environmental monitoring and national economies

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