remote sensing Article Exploring Wetland Dynamics in Large River Floodplain Systems with Unsupervised Machine Learning: A Case Study of the Dongting Lake, China Lei Jing 1,2 , Yan Zhou 3, Qing Zeng 2, Shuguang Liu 1, Guangchun Lei 2, Cai Lu 2 and Li Wen 4,* 1 National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, China; [email protected] (L.J.); [email protected] (S.L.) 2 School of Nature Conservation, Beijing Forestry University, Beijing 100083, China; [email protected] (Q.Z.); [email protected] (G.L.); [email protected] (C.L.) 3 College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China; [email protected] 4 Science, Economics, and Insights Division, Department of Planning, Industry and Environment, Lidcombe, NSW 2141, Australia * Correspondence: [email protected] Received: 18 August 2020; Accepted: 7 September 2020; Published: 15 September 2020 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, 1 disappeared at an average rate of c.a. 1660 ha year− 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. Keywords: large river floodplain system; long-term dynamics; k-medoids algorithm (PAM); unsupervised machine-learning algorithm (UMLA) 1. Introduction Globally, the largest freshwater wetlands are associated with floodplains of large rivers [1,2]. Some well-known examples including the Amazon floodplains (over 800,000 km2 of wetlands) [3], middle-lower Yangtze floodplains [4,5], and the Okavango Delta in northern Botswana [6]. These wetlands are highly dynamic systems displaying great inter- and intra-annual expansion and shrinkage in extent driven by flow regimes of their associated rivers [7–10]. Moreover, variations in topography will dictate the ability of the floodplain to drain, as well as the direction of flow and the ponding of river water. The variations of hydrological regimes and topography in space and time Remote Sens. 2020, 12, 2995; doi:10.3390/rs12182995 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 2995 2 of 19 determines the development and zonation of vegetation communities [11], which play vital roles in maintaining local and regional biodiversity [12,13]. 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]. Indeed, there are many large-scale ongoing restoration projects aiming to reverse the trend of ecosystem degradation in LRFS and returning ecosystem structures, functions, and processes to more natural state [21,22], for example the environmental water program in the Murray-Darling Basin, Australia from the late 1990s [23], the reconnecting rivers to their floodplains campaign in America since the 1970s [24], the converting farmland to lake strategy in the Yangtze Basin, China, since the 1990s [25], and Water Framework Directives in Europe since 2000 [26,27]. Consistent spatiotemporal quantification of wetland dynamics (i.e., the extent and distribution of wetland through time) is an integrated part of floodplain restoration projects, as it is critical for not only program designing and implementation [28,29] but also for evaluation and reporting [30,31]. Moreover, it is necessary for rapidly identifying areas that require adaptive management and for best allocating limited conservation resources. Floodplain wetlands can be mapped through field-based (normally a combination of field work and aerial image interpretation) and remote-sensing-based methods. Although field measurements offer a high level of detail, such as vegetation community composition and structure [32], they are labor-intensive and expensive, thus limited to site-scale mapping [11,33,34]. In contrast, remote sensing offers many possibilities for basin, regional, continental, and global scale mapping [35,36]. In recent years, the availability of free satellite time-series data, the enhanced computational capacities, and the development of novel image classification algorithms enabled periodical large-scale mapping of land surface [33,37,38] to explore land-cover dynamics and their drivers over multi-decadal time periods [39,40]. Among the freely available satellite products, Landsat (5, 7, and 8) and MODIS (Moderate Imaging Spectroradiometer) remain the most suitable and commonly used data for wetland mapping due to their regularity and spatial resolution [29,41–43]. The spatial distribution of wetlands is never static due to inter- and intra-annual changes in environmental conditions [44], and wetlands can sometimes be in transitional states [45]. Wetland plants show rapid response to water level fluctuations, which can substantially change their reflectance and energy backscatter properties [46] in a short period of time (e.g., within days) [3], presenting one of main challenges for wetland mapping from space [47]. This challenge is more compelling for detecting temporal wetland changes [48], which is critical for global and national wetland inventories [49], exploring the role of local and regional factors contributing to wetland loss (or gain) and evaluating restoration efforts [11]. To overcome these restrictions, a time series of remote sensing data is analyzed to capture the land-surface phenology [50] to identify long-term trends and short-term variability of wetland [51]. However, the acquisition and analysis of time series of multispectral imagery is a difficult task. For example, the imagery must be acquired under similar environmental conditions (e.g., same time of year and sun angle) and in the same or similar spectral bands [52]. One way to approach this problem is to reduce the spectral information to a single index, for example vegetation index (VI), such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), reducing the multispectral imagery into one index [53]. Many mapping algorithms have been developed for land-cover classification using remote sensing (RS) data [54]. These algorithms can be divided into three main categories: unsupervised, such as Iterative Self-Organizing Data Analysis Techniques (ISODATA) [55] and k-means clustering [56]; supervised, for example decision tree [57] and Support Vector Machine [58], and hybrid approaches that combine unsupervised and supervised methods [59]. The unsupervised method groups together Remote Sens. 2020, 12, 2995 3 of 19 pixels according to a defined distance measure and labels clusters based on ancillary information. In contrast,Remote Sens. with 2020, labelled 12, x FOR PEER reference REVIEW (training) data—a supervised method—trains a classifier,3 of which 19 is then used to predict the classes of the un-labelled data [60]. The major advantage of unsupervised classifier, which is then used to predict the classes of the un-labelled data [60]. 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