Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

• Published in: International Journal of Digital Earth
• Main Authors: Hengxing Xiang, Yanbiao Xi, Dehua Mao, Tianyuan Xu, Ming Wang, Fudong Yu, Kaidong Feng, Zongming Wang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2023-12-01
• Subjects: potential wetland distribution; machine learning algorithms; geographic big data; china; wetland geographic features
• Online Access: http://dx.doi.org/10.1080/17538947.2023.2256723
• ISSN: 1753-8947; 1753-8955

Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China. To protect and restore wetlands, it is urgent to predict the spatial distribution of potential wetlands. In this study, the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms. Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic, soil, vegetation, and topographic factors, a simulation model was constructed by machine learning algorithms. The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good, with an area under the receiver operating characteristic curve value of 0.851. The area of potential wetlands was 332,702 km2, with 39.0% of potential wetlands in Northeast China. Geographic features were notable, and potential wetlands were mainly concentrated in areas with 400–600 mm precipitation, semi-hydric and hydric soils, meadow and marsh vegetation, altitude less than 700 m, and slope less than 3°. The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.