Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces.

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

Understanding the recent changes in mangrove adjacent to mega-cities is critical for conservation, management, and policymaking in coastal zones with fast population growth and global change. Here we investigated mangrove area changes in one of the world’s largest urban areas near the main estuaries in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Mangrove area changes are quantified for the period 1990–2018 by analyzing multiple sources of satellite images by classification algorithms. We found that estuarine mangrove area dynamics are driven by human actions and are contrasting between these two periods. (1) During 1990–2000, the estuarine mangrove area approximately decreased from 11.5 to 6.9 km², among which the bulk part was transformed into aquaculture ponds (41.1%) and built-up area (29.9%). (2) During 2000–2018, the estuarine mangrove area rapidly increased to 18.3 km² resulting from the protection and restoration efforts. Proportions of mangrove occurring in nature reserves increased from 37.5% in 1990 to >80% in the 2000s. Two major mangrove expansion ways, natural establishment (NE, in protected areas without any human interference) and human afforestation (HA) accounted almost equally (53.1 and 46.9%) for the gained estuarine mangrove area during 2010–2018. A future projection according to the current mangrove increasing rate suggests that all the low-lying land that is theoretically suitable for mangrove afforestation would be used up by 2060. Although afforestation has contributed to important gains in mangrove quantity, we highlight that it may also imply decreased habitat quality. It has resulted in a great occupation of high tidal mudflats and a loss of their valuable ecosystem services, and it may lead to spreading of non-native species, e.g., Sonneratia apetala, used in afforestation programs. Future restoration approaches should adopt more eco-friendly strategies, like reversing (abandoned) aquaculture ponds to native mangrove forests. Knowledge obtained from the GBA in this study may be also instrumental to ecological restorations for mangrove forests in other urbanized estuaries.

High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km²). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes.

Tidal flats provide valuable ecosystem services such as flood protection and carbon sequestration. Erosion and accretion processes govern the ecogeomorphic evolution of intertidal ecosystems (marshes and bare flats) and, hence, substantially affect their valuable ecosystem services. To understand the intertidal ecosystem development, high-frequency bed-level change data are thus needed. However, such datasets are scarce due to the lack of suitable methods that do not involve excessive labour and/or costly instruments. By applying newly developed surface elevation dynamics (SED) sensors, we obtained unique high-resolution daily bed-level change datasets in the period 2013–2017 from 10 marsh–mudflat sites situated in the Netherlands, Belgium, and the United Kingdom in contrasting physical and biological settings. At each site, multiple sensors were deployed for 9–20 months to ensure sufficient spatial and temporal coverage of highly variable bed-level change processes. The bed-level change data are provided with synchronized hydrodynamic data, i.e. water level, wave height, tidal current velocity, medium sediment grain size (D₅₀), and chlorophyll a level at four sites. This dataset has revealed diverse spatial morphodynamics patterns over daily to seasonal scales, which are valuable to theoretical and model development. On the daily scale, this dataset is particularly instructive, as it includes a number of storm events, the response to which can be detected in the bed-level change observations. Such data are rare but useful to study tidal flat response to highly energetic conditions. The dataset is available from 4TU.ResearchData (https://doi.org/10.4121/12693254.v4; Hu et al., 2020), which is expected to expand with additional SED sensor data from ongoing and planned surveys.