Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin

Floodplain wetlands in the China side of the Amur River Basin (CARB) undergone consistent decreases because of both natural and anthropogenic drivers. Monitoring floodplain wetlands dynamics and conversions over longtime periods is thus fundamental to sustainable management and protection. Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. Based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990-2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90% +/- 0.001-97% +/- 0.005), suggesting that COHRF is a robust classification approach; (2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km(2) to 6630 km(2) during 1990-2018; (3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km(2) and 256 km(2) of wetlands rehabilitated from croplands during periods of 2000-2010 and 2010-2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. The resultant dataset will contribute to sustainable use and conservation of wetlands in the Amur River Basin and provide essential information for related researches.