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Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities. [ABSTRACT FROM AUTHOR]

Wetlands provide vital ecological services for both humans and environment, necessitating continuous, refined and up-to-date mapping of wetlands for conservation and management. In this study, we developed an automated and refined wetland mapping framework integrating training sample migration method, supervised machine learning and knowledge-driven rules using Google Earth Engine (GEE) platform and open-source geospatial tools. We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland (DLW) during 2015–2021. First, the continuous change detection (CCD) algorithm was utilized to migrate stable training samples. Then, annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples. Ultimately, annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories, with Overall Accuracy (OA) ranging from 81.82% (2015) to 93.84% (2020) and Kappa Coefficient (KC) between 0.73 (2015) and 0.91 (2020), demonstrating satisfactory performance and substantial potential for accurate, timely and type-refined wetland mapping. Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring, conservation and sustainable development of wetland ecosystem. [ABSTRACT FROM AUTHOR]

• Integrating phenology and geometry features boosts wetland classification accuracy. • Developing a novel algorithm for detailed wetland types classification. • Mapping 5 inland wetlands, 6 coastal wetlands, and 3 human-made wetlands. • First wetland map at 10-m resolution covering northern, central and southern Asia. Wetlands have suffered from considerable degradation due to anthropogenic and natural disturbances in recent decades. Although some advancements have been made, effective methods that can produce large-scale wetland maps with detailed categories are still lacking due to the diversity and complexity of wetland ecosystems. To address this issue, we developed a novel algorithm for detailed wetland types classification integrating k-fold random forest and hierarchical decision tree, and so named two-step classification algorithm. Firstly, the phenology-based features were composited based on time series Sentinel-1/2 images, and the k-fold random forest was used to extract five rough wetland types in Google Earth Engine platform. Secondly, the hierarchical decision tree designed based on geometric features was used to separate the rough wetland types into fourteen detailed types. Application of the two-step classification method in Northern, Central and Southern Asia (NCSA) resulted in a continental-scale wetland map with an overall accuracy of 90.0 ± 0.5%. Wetland types, including inland marsh, lake, river, coastal swamp, estuarine water, lagoon, shallow marine water, reservoir, canal/channel and agricultural pond, had good accuracy with both UA and PA over 77%. The remaining wetland types had moderate accuracy, with both UA and PA over 58%. As we calculated, total wetland areas of NCSA were 1,375,489.27 km2. Among the fourteen wetland categories, the inland marsh had the largest area (544,584.38 km2) and was primarily distributed in subarctic and humid continental climates, while the canal/channel had the smallest area (1,651.57 km2) and was primarily scattered in desert, semiarid and humid subtropical climates. The lake and floodplain shared generally large areas with value of 392,413.55 km2 and 173,255.71 km2 respectively, which were typically distributed across desert and semiarid climates. This study successfully mapped continental-scale wetlands with detailed categories at a 10-m spatial resolution, which can provide valuable information for the management of wetland ecosystems and facilitate the implementation of wetland-related sustainable development goals. [ABSTRACT FROM AUTHOR]

• Wetland ecological risk is related to external hazard and internal vulnerability. • An index system consisting of 22 indexes was founded from hazard and vulnerability. • The changes of wetland ecological risk in 181 counties were quantitatively evaluated. • Protection policies have played a substantial role in wetland restoration. Wetlands are some of the most highly productive ecosystems on earth and are one of the most important environments for human survival and development. However, with the acceleration of industrialization and urbanization, wetlands have been constantly occupied and destroyed and have even disappeared in some areas, so it is necessary to establish a reasonable and scientific ecological risk assessment system for wetlands. In this study, 181 counties in Beijing-Tianjin-Hebei urban agglomeration (Jing-Jin-Ji) were selected as the research objects, and an index system with 22 indexes was established from two aspects: the external hazard degree and the vulnerability of the wetland itself. The ecological risk of the wetlands during 6 periods from 1990 to 2015 was comprehensively evaluated by long-term data and multiple index factors. The results showed that (1) due to the decrease in the hazard degree and the increase in the vulnerability, the ecological risk of the wetlands in Jing-Jin-Ji first increased and then decreased; (2) more than 60% of the counties were at the middle or middle-high risk level, especially in the center of urban development, which generally had higher risk level than other regions; (3) most wetland parks were at the middle-high risk level, while wetland reserves, reservoirs and lakes were mostly in the low risk or middle-low risk level. Therefore, in recent years, the implementation of wetland protections and restoration policies and projects has played a significant role in reducing the ecological risk of the wetlands. In the future, more targeted management and protection measures need to be formulated and followed up in time. [ABSTRACT FROM AUTHOR]

• A method for wetland simulation was proposed by coupling random forest and CLUE-S. • Three scenarios were designed to explore different modes for urban development. • The wetland patterns in WUA were predicted from 2015 to 2040 under three scenarios. • Future wetland losses/gains were examined by transition matrix and overlay analysis. Wetlands are one of the most productive ecosystems and play an important role in supporting a wide range of biodiversity and providing various kinds of ecosystem services. Rapid urbanization and climate change, however, have resulted in the disappearance of large amounts of wetlands. In this research, we developed a spatial allocation model by coupling random forest regression and the CLUE-S algorithm to simulate the spatial dynamics of wetlands in the Wuhan Urban Agglomeration. Then, the calibrated model was used to predict the wetland distributions from 2015 to 2040 under three scenarios, i.e. natural increase scenario (NIS), economic development scenario (EDS), and wetland protection scenario (WPS). The results showed that: (1) the natural wetlands exhibit a slight decreased trend under EDS, and a slight increased trend under WPS. But regardless of scenarios, the natural wetlands will suffer from degradation in densely built-up areas during 2015–2040; (2) The ponds will clearly expand under all scenarios, and most of their expansion distribute in Xiantao city; (3) The paddy will continue to degradation under three scenarios, and the area decreases most under EDS with value of 2866.94 km2. Most of the paddy degradation are located in Wuhan, Xiantao and Ezhou city. The proposed framework offered an effective tool to explore the urban wetland dynamics in the future, and revealed wetland distribution under different scenarios, which could provide support for the protection of urban wetlands and sustainable future develop in the Wuhan Urban Agglomeration. [ABSTRACT FROM AUTHOR]

Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong'an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km2, with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km2 in the Xiong'an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km2, 20.71 km2 and 80.05 km2 by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas. [ABSTRACT FROM AUTHOR]

• Spatiotemporal evolution and destruction degree of wetlands from 1990 to 2018. • Comparison and Selection of Four Intelligent Algorithms for Land Cover Classification. • CA-Markov, support vector machine algorithm, wetland evolution prediction in 2018–2035. Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS). [ABSTRACT FROM AUTHOR]

Highlights • Reviewed the principles, developments, and applications of various WIs. • Included applications in surface water, vegetation water, soil water, and wetland. • Summarized the terminology of WIs in different applications. • Designed a field experiment to examine the sensitivities of various WIs. Abstract Water status is an important indicator of ecosystem health. Based on the spectral signatures of liquid water and other land surface features (e.g., soil and vegetation), many water-related spectral reflectance indices (WIs) have been developed to characterize the presence of liquid water. These WIs are widely used in agricultural and ecological applications including surface water body characterization, vegetation water status estimation, soil water content assessment, and wetland (e.g., paddy rice) monitoring. However, choosing the correct WIs for specific studies is confusing due to arbitrary selections of WIs in respective applications and the mixing of WIs names in different studies. To increase the clarity of appropriate usage of WIs in specific conditions, this study reviewed the principles, developments, and applications of various WIs to identify the suitability of WIs for different applications. We started with the discussion of the underlying principles and developments (e.g., from ratio to normalized difference of two bands) of different WIs, based on the spectrum features of water, vegetation, and soil from widely used spectral libraries. Applications from different studies, in each category, were then compared to show the performances of different WIs. This is followed by a brief summary about the terminology of WIs in different studies to further reduce the confusion caused by the mixed use of WIs terms. A field experiment was designed to investigate the dynamics of various WIs in response to different mixing ratios of water and soil or plant. The effectiveness of combining WIs and greenness indices for detecting surface water body and wetland were evaluated using the field experiment data. Finally, we identified the major gaps and pointed out the potential improvements for the future. [ABSTRACT FROM AUTHOR]

The digital elevation model data from traditional stereo photogrammetric methods are inadequate in providing accurate vertical parameters to feed hydrologic models for low-lying, extremely flat areas. High-resolution light detection and ranging (Li DAR) data provide the robust capability of capturing small variations in low-relief playa wetlands. The Rainwater Basin in south-central Nebraska includes a complex of seasonally shallow playa wetlands that attract millions of migratory waterfowl every spring and fall. This research focuses on the development of a procedure with applicable protocols to produce Li DAR-derived three-dimensional wetland maps and to extract the critical surface parameters (i.e., watershed boundaries, flow direction, flow accumulation, and drainage lines) for playa wetlands. The topo-hydrologic conditions of playa wetlands were evaluated at the watershed level. The results show that in the Rainwater Basin, 70.7% of the historic hydric soil footprints identified in the Soil Survey Geographic ( SSURGO) database were not functioning as topographically depressional wetlands. This finding was confirmed by a recent five-year Annual Habit Survey showing that 69.8% of the historic hydric soil footprints did not function during the spring migratory bird seasons between 2004 and 2009. The majority of playa wetlands' topographic conditions have been substantially changed and the SSURGO data cannot fully reflect current topographic reality in the Rainwater Basin. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (NSAT) directly reflects the thermal conditions above the ground and has been considered as a relevant indicator of resident health in urban regions. The rapid retrieval of NSAT data is necessary to assess urban environments. In this paper, a method of NSAT retrieval is developed that employs Landsat Thematic Mapper images using an Energy Balance Bowen Ratio model. This model is established based on the energy balance over land and the Bowen ratio. The degree of retrieval error obtained when using this model is determined on the basis of a comparison with the observed values obtained from weather stations; the mean error is approximately 2.21 °C. Moreover, the spatial relationship between NSAT and urban wetlands is analyzed using Geographical Information System technology. The results show that wetlands have an obvious influence on atmospheric temperature and that this influence decreases as the distance from the wetland increases. When that distance is less than 300 m, its influence on the NSAT is significant. [ABSTRACT FROM AUTHOR]

Based on multi-source data, this paper evaluated the economic value of ecological services in the Yeyahu Wetland Nature Reserve, Beijing, China. The ecological services of wetland included gas regulation, water quality improvement, biodiversity maintenance, erosion control, water supply, recreational opportunity, raw material supply and existence value. Multiple conventional evaluation methods were used to calculate the value of eight wetland services. The results showed that significant values came from biodiversity maintenance and recreational opportunity. The main reasons were as follows. Firstly, Yeyahu Wetland Nature Reserve was the habitat for migrant birds, and government had payed more efforts to protect precious birds. Secondly, the population is large in Beijing. People enjoyed going outside and enjoyed the natural and artificial wetland scenes. At the same time, most people were prepared to pay for wetland conservation. The decline of vegetation cover made the economic value of erosion control the lowest. While the shrink of water resource and the deteriorative water quality caused the economic value of water supply lower. The evaluation results could help decision-makers understand the present status of the Yeyahu Wetland Nature Reserve and provide a scientific basis for strategic decision. [ABSTRACT FROM AUTHOR]