China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures.

Topography and soil factors are known to play crucial roles in the species composition of plant communities in subtropical evergreen-deciduous broadleaved mixed forests. In this study, we used a systematic quantitative approach to classify plant community types in the subtropical forests of Hubei Province (central China), and then quantified the relative contribution of drivers responsible for variation in species composition and diversity. We classified the subtropical forests in the study area into 12 community types. Of these, species diversity indices of three communities were significantly higher than those of others. In each community type, species richness, abundance, basal area and importance values of evergreen and deciduous species were different. In most community types, deciduous species richness was higher than that of evergreen species. Linear regression analysis showed that the dominant factors that affect species composition in each community type are elevation, slope, aspect, soil nitrogen content, and soil phosphorus content. Furthermore, structural equation modeling analysis showed that the majority of variance in species composition of plant communities can be explained by elevation, aspect, soil water content, litterfall, total nitrogen, and total phosphorus. Thus, the major factors that affect evergreen and deciduous species distribution across the 12 community types in subtropical evergreen-deciduous broadleaved mixed forests include elevation, slope and aspect, soil total nitrogen content, soil total phosphorus content, soil available nitrogen content and soil available phosphorus content.

Danjiangkou reservoir area is the main water source and the submerged area of the Middle Route South-to-North Water Transfer Project of China. Soil erosion is a factor that significantly influences the quality and transfer of water from the Danjiangkou reservoir. The objective of this study is to assess the water erosion (rill and sheet erosion) risk and dynamic change trend of spatial distribution in erosion status and intensity between 2004 and 2010 in the Danjiangkou reservoir area using a multicriteria evaluation method.The multicriteria evaluation method synthesizes the vegetation fraction cover, slope gradient, and land use. Based on the rules and erosion risk assessment results of the study area in 2004 and 2010, the research obtained the conservation priority map. This study result shows an improvement in erosion status of the study area, the eroded area decreased from 32.1% in 2004 to 25.43% in 2010. The unchanged regions dominated the study area and that the total area of improvement grade erosion was larger than that of deterioration grade erosion. The severe, more severe, and extremely severe areas decreased by 4.71%, 2.28%, and 0.61% of the total study area, respectively. The percentages of regions where erosion grade transformed from extremely severe to slight, light and moderate were 0.18%, 0.02%, and 0.30%, respectively. However, a deteriorated region with a 2,897.60 km² area was still observed. This area cannot be ignored in the determination of a general governance scheme. The top two conservation priority levels cover almost all regions with severe erosion and prominent increase in erosion risk, accounting for 7.31% of the study area. The study results can assist government agencies in decision making for determining erosion control areas, starting regulation projects, and making soil conservation measures.

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery.

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km²). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ² of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

The Yangtze River ecosystem is one of the most important ecosystems in central China. Due to the substantial increase in China’s economy and the improvement of living conditions in rural areas over the past decade, domestic wastewater discharges in rural areas have increased dramatically, seriously affecting the Yangtze River Basin environment, and becoming one of the main obstacles to achieving global sustainable development goals (SDGs). Rural environmental management is very important for the prevention and control of water pollution in the Yangtze River Basin. In order to address the problem of rural domestic sewage treatment, this paper proposes a domestic sewage collection model applicable to rural areas. An operable village sewage treatment plan was developed for Jiangxia District, Wuhan City, specific for each village, based on a previous village sewage treatment plan in Jiangxia District. The plan is cost-effective and ensures a suitable quality of the treated domestic wastewater that meets the local discharge and reuse standards, thus reducing pollution of the Yangtze River and contributing to the restoration of the Yangtze ecological environment.