Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km², and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km²) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon emissions.

Hydrologic regime plays a major role in structuring biotic diversity within river ecosystems by controlling key habitat conditions within the river channel and floodplain. Daily flow records from seven hydrological stations and the range of variability approach were utilized to investigate the variability and spatial pattern of the hydrologic alterations induced by the construction of the Three Gorges Dam (TGD) in the middle and lower reaches of the Yangtze River, China. Results show that the impoundment of the TGD disturbed the hydrologic regime downstream and directly affected the streamflow variations. The rate of changes and the annual extreme conditions were more affected by the TGD, particularly the low-flow relevant parameters. The alterations in the hydrologic regime were mainly caused by the TGD storing water during early autumn and releasing water during winter and spring. The effects on spatial patterns decreased as the distance from the dam increased, which was mainly attributed to the inflows from large tributaries along the Yangtze River as well as the interaction with the two largest natural lakes (i.e., Dongting Lake and Poyang Lake). These hydrologic alterations not only break the natural balance of eco-flow regimes but also result in undesirable ecological effects, particularly in terms of habitat availability for the fish community.

Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

In China, the Lower Jingjiang Reach (LJR) of the Yangtze River could be one of the most complicated areas in terms of channel migration. The river had undergone many channel changes in the reach since the 18th century. Intensive human activities in recent decades, such as the construction of upstream dams and revetments, had directly affected the channel migration characteristics. The revetment would significantly diminish migration, whereas the reduced sediment caused by dams would increase bank erosion and bank failure risks. Satellite imageries of Landsat Multi Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) were employed to quantify the planform migration of the reach from 1983 to 2013 and to investigate the possible effect of human activities on the LJR channel evolution. Apart from the commonly used planform morphometric parameters, the migration direction was introduced to predict the future migration trends. Results showed that the LJR was gradually changing to a straighter channel, with sinuosity reducing from 2.09 to 1.9 and river length decreasing from 125.32 km to 113.31 km in the past 30 years. Planform morphometric parameters, such as migration rate of the channel centerline and erosion and deposition areas and rates, also decreased drastically in the past 30 years. The migration rate of the channel centerline decreased from 31.05 m·year⁻¹ in 1983–1988 to 11.62 m·year⁻¹ in 2009–2013. The lateral erosion and deposition areas decreased from 21.32 and 25.73 km² in 1983–1988 to 4.83 and 5.83 km² in 2009–2013. All of these findings indicate that the LJR tended to be in a steady state from 1983 to 2013 and was totally controlled by the bank revetments. However, the undercutting was strengthened because of the restrictive effect of revetments on lateral migration in the LJR. Moreover, the channel migrated to the left bank with a visible tendency as the total migration area to the left bank was approximately two times that of the right bank during the period. Consequently, the left bank of the LJR should be the focus of more attention in future migrations, and bank revetments of the left bank should be kept reinforced and adjusted with the change of water and sediment conditions.