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Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban wetland extent and change is thus critical for improving urban planning toward sustainability. Here, we have developed a rapid method for continuous mapping of urban wetlands (MUW) by combining automatic sample migration and the random forest algorithm (SM&RF), the so-called MUW_SM&RF. Using time series Landsat images, annual training samples were generated through spectral angular distance (SAD) and time series analysis. Combined with the RF algorithm, annual wetland maps in urban areas were derived. Employing the Google Earth Engine platform (GEE), the MUW_SM&RF was evaluated in four metropolitan areas in different geographical and climatic regions of China from 1990 to 2020, including Tianjin, Hangzhou, Guangzhou, and Wuhan. In all four study areas, the generated annual wetland maps had an overall accuracy of over 87% and a Kappa coefficient above 0.815. Compared with previously published datasets, the urban wetland areas derived using the MUW_SM&RF approach achieved improved accuracy and thus demonstrated its robustness for rapid mapping of urban wetlands. Urban wetlands in all four cities had variable distribution patterns and showed significantly decreased trends in the past three decades. The annual urban wetland data product generated by the MUW_SM&RF can provide invaluable information for sustainable urban planning and management, so as for assessment related to the United Nation's sustainable development goals. [ABSTRACT FROM AUTHOR]

Wetland restoration is important to responding to climate change and ensuring ecological security. In terms of the serious wetland loss and limited wetland restoration in China, there is a need to investigate approaches to identifying potential areas for wetland restoration from cropland, in order to support making spatial decisions at a regional scale. Here, we provide an example of integrating remote sensing (RS) and geographical information systems (GIS) effectively to identify where and how many croplands could be converted into wetlands in the West Songnen Plain (WSNP). The map of potential areas for wetland restoration from croplands generated in this study is expected to help decision makers to implement wetland restoration in the WSNP. Besides the widely highlighted hydrological, topographical, and landscape features, four indicators, namely, flooded area, time under cultivation, human disturbance, and wetland conservation level, were selected to identify the potential areas for wetland restoration—with different priorities—from croplands. Satellite observation revealed that a total of 2753.3 km2 of wetlands have been cultivated into croplands for grain production from 1990 to 2015 in the WSNP. It is estimated that 8882.1 km2 of croplands are suitable for conversion to wetlands, of which 3706 km2 (29.4%) are with high priority, and 44.5% are from dry farmlands. A total of 3284.7 km2 of paddy fields are identified to be potential areas for wetland restoration, of which 1119.6 km2 are high priority, and another 2165.1 km2 are medium priority. [ABSTRACT FROM AUTHOR]