Scholarlink

<p>Wetland loss and degradation is a major global issue in which its detailed estimation of spatio-temporal distribution will be a key for understanding the dynamics and subsequent impact assessment studies. This study aimed to estimate the wetland-to-non-wetland transitions (i.e., wetland loss—from surface water-detected areas to non-surface water areas), analyze its geographical characteristics, and quantify the likelihood of loss occurrence for existing wetlands in the lower Mekong River Basin in Cambodia. Using the global surface water detection datasets, the spatiotemporal distribution of wetland loss with high resolution (30&#xa0;m) over the entire study area (140&#xa0;km×210&#xa0;km) during 1984–2021 was estimated. Statistically significant differences were found in the distance from urban areas and distance from river channels for the existing wetlands and lost wetlands as of 2021, in which the lost wetlands tend to locate closer to urban areas. Subsequent Land Use/Land Cover after the wetland loss was found to be mainly croplands (72.2%) in the study area. Though our estimate overall agrees with the recent global-scale estimate, our estimate resulted in notable ratio of rangelands (11.3%), which represents the unique characteristics of floodplain wetlands in the lower Mekong River Basin. The Random Forest and Light GBM algorithms-based wetland loss prediction models resulted in good statistical evaluation metrics. In both models, the distance from river channels was found to be the most important feature for classifying existing wetlands and lost wetlands. Application of the developed models successfully provided the map of likelihood of wetland loss for existing wetlands in the study area.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatio-Temporal Analysis of Wetland Loss in the Lower Mekong River Basin Based on Surface Water Detection Datasets and Machine Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月25日

查看全文
  • Yusuke Hiraga,
  • Mayu Aoki,
  • So Kazama

摘要

Wetland loss and degradation is a major global issue in which its detailed estimation of spatio-temporal distribution will be a key for understanding the dynamics and subsequent impact assessment studies. This study aimed to estimate the wetland-to-non-wetland transitions (i.e., wetland loss—from surface water-detected areas to non-surface water areas), analyze its geographical characteristics, and quantify the likelihood of loss occurrence for existing wetlands in the lower Mekong River Basin in Cambodia. Using the global surface water detection datasets, the spatiotemporal distribution of wetland loss with high resolution (30 m) over the entire study area (140 km×210 km) during 1984–2021 was estimated. Statistically significant differences were found in the distance from urban areas and distance from river channels for the existing wetlands and lost wetlands as of 2021, in which the lost wetlands tend to locate closer to urban areas. Subsequent Land Use/Land Cover after the wetland loss was found to be mainly croplands (72.2%) in the study area. Though our estimate overall agrees with the recent global-scale estimate, our estimate resulted in notable ratio of rangelands (11.3%), which represents the unique characteristics of floodplain wetlands in the lower Mekong River Basin. The Random Forest and Light GBM algorithms-based wetland loss prediction models resulted in good statistical evaluation metrics. In both models, the distance from river channels was found to be the most important feature for classifying existing wetlands and lost wetlands. Application of the developed models successfully provided the map of likelihood of wetland loss for existing wetlands in the study area.