Exploring Wetland Dynamics in Large River Floodplain Systems with Unsupervised Machine Learning: A Case Study of the Dongting Lake, China

Lei Jing,Li Wen,Yan Zhou,Qing Zeng,Cai Lu,Shuguang Liu,Guangchun Lei

doi:10.3390/rs12182995

Abstract

Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain because the spatial distribution of LRFS is never static due to inter- and intra-annual changes in environmental conditions. In this study, we developed and tested a methodological framework to re-construct the long-term wetland dynamics in Dongting Lake, China, utilizing an unsupervised machine-learning algorithm (UMLA) on the basis of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) time series. Our results showed that the UMLA achieved comparable performance to the time-consuming satellite image segmentation method with a Kappa coefficient of agreement greater than 0.75 and an overall accuracy over 85%. With the re-constructed annual wetland distribution maps, we found that 31.35% of wet meadows, one of most important ecological assets in the region, disappeared at an average rate of c.a. 1660 ha year−1 during the past two decades, which suggests that the Dongting Lake is losing its ecological function of providing wintering ground for migratory water birds, and remediation management actions are urgently required. We concluded that UMLA offers a fast and cost-efficient alternative to monitor ecological responses in a rapidly changing environment.

Highlights

The largest freshwater wetlands are associated with floodplains of large rivers [1,2]
We applied an unsupervised machine-learning algorithm—the k-medoids algorithm—to re-construct the long-term floodplain wetland dynamics using land phenology metrics derived from the 16-day composite MODIS Enhanced Vegetation Index (EVI) time series
Despite the relatively coarse spatial resolution of MODIS data, our approach achieved comparable performance to the time-consuming satellite image segmentation method with a Kappa coefficient of agreement greater than 0.75 and an overall accuracy over 85%

Summary

Introduction

The largest freshwater wetlands are associated with floodplains of large rivers [1,2]. Water resources development, including river regulation and land reclamation, has changed the natural flow and flooding regimes across most of the world’s large river floodplain systems (LRFS), causing dramatical changes in the distribution, seasonality, and functionality of floodplain wetlands [14,15] These changes have in term led to detrimental ecological consequences, such as widespread local and regional biodiversity loss in the freshwater realm [16,17] and increasing flooding and droughts [18], undermining the well-being of billions of people [19] and highlighting the pressing need for wetland restoration in large floodplains [17,20]. It is necessary for rapidly identifying areas that require adaptive management and for best allocating limited conservation resources

Methods

Results

Conclusion