Xinjiang in China is one of the areas worst affected by coal fires. Coal fires cannot only waste a large amount of natural resources and cause serious economic losses, but they also cause huge damage to the atmosphere, the soil, the surrounding geology, and the environment. Therefore, there is an urgent need to effectively explore remote sensing based detection of coal fires for timely understanding of their latest development trend. In this study, in order to investigate the distribution of coal fires in an accurate and reliable manner, we exploited both Landsat-8 optical data and Sentinel-1A synthetic aperture radar (SAR) images, using the generalized single-channel algorithm and the InSAR time-series analysis approach, respectively, for coal fire detection in the southern part of the Fukang region of Xinjiang, China. The generalized single-channel algorithm was used for land surface temperature information extraction. Meanwhile, the time-series InSAR analysis technology was employed for estimating the surface micro deformation information, which was then used for building a band-pass filter. The suspected coal fire locations could then be established by a band-pass filtering operation on the obtained surface temperature map. Finally, the locations of the suspected coal fires were validated by the use of field survey data. The results indicate that the integration of thermal infrared remote sensing and radar interferometry technologies is an efficient investigation approach for coal fire detection in a large-scale region, which would provide the necessary spatial information support for the survey and control of coal fires.

Based on soil under seven vegetation types, the carbon sequestration potential in the Ebinur Lake wetland was estimated using the maximum value method, the saturation value method, and the classification and grading method. Results indicated that: 1) Soil carbon sequestration results for the top 20 cm soil layer were about 1.88 Mt using the maximum value method; the middle level standard of the classification and grading method result was 1.71 Mt. 2) Soil carbon sequestration potential in the top 20 cm layer under different vegetation types, evaluated using the saturation value method and the classification-grading method, ranged from 0.45 to 0.67 Mt, accounting for about 5/16 of the ideal carbon sequestration potential. 3) Carbon sequestration potential calculated using the saturation method and the classification method (middle level standard), combining the soil organic carbon increment under different vegetation types in Ebinur Lake wetland, recorded an average growth rate of soil organic carbon around 0.7–1 kg/(hm²·a). Time required to reach its carbon sequestration potential was 41 to 144 a. These results indicate that soil organic carbon content dynamically changes, and different forms of land use affect soil organic carbon content. The potential and ability of soil carbon sequestration and its mechanism of dynamic change are investigated, providing a scientific basis for understanding regional carbon cycle and climate change in wetlands.

The drilling areas in Shenhu and Dongsha, South China Sea, studied from 2007 to 2015, reveal great heterogeneity in the spatial distribution of the gas hydrate reservoir. Various types of mass-transport complexes (MTCs) were developed in the study areas, which served as ideal reservoirs. To conduct exploration in these areas, it is necessary to study the different types of MTCs and the corresponding gashydrate accumulations. By integrating seismic reflection and log coring data, we classified three types of MTCs according to their stress distribution: the tension, extrusion, and shear types, and their corresponding gashydrate accumulation patterns. The results show that the accumulation of the gas-hydrate varies with the type of MTC and stress distribution depending on the MTC’s position (e.g., in the headwall, translational, or toe areas). Owing to this variance of the MTC’s position, the corresponding kinemics situation in the MTCs also varies. Accordingly, we determined the corresponding location in which the gashydrate develops for various types of MTCs. Based on the bottom simulating reflectors (BSRs) and the hydrate core and image logging data, the gashydrate reservoir shows an obvious heterogeneity in various types of MTCs. The gashydrate in the tension-type MTCs are mostly borne in the toe and the headwall parts. In extrusion-type MTCs, the translational and toe parts constitute an ideal hydrate reservoir. In shear-type MTCs, the headwall and toe parts’ coarse-grained sediments show an obviously hydrate response. After comparing the gas-hydrate saturation and MTCs morphology statics data, we were able to quantitatively prove that the main factors determining gashydrate accumulation in the different types of MTCs are the fault displacement, sedimentary rate, and flow erosion rate.

Mangroves serve many important ecological functions and consequently represent a dominant coastal ecosystem. However, coastal regions are very susceptible to ecological damage due to their high population density, urban expansion being one of the most important influencing factors. Accordingly, it is vital to ascertain how urban expansion endangers mangrove ecosystems. This study used the decision-tree classification method based on classification and regression tree (CART) algorithm to extract areas of mangrove and built-up land from Landsat images. A correlation analysis was performed between the change in the area of mangroves and the change in the area of built-up land at the cell scale. This study aimed to reveal the magnitude of the influence of urban expansion on mangrove forests in different periods and in different regions, and to identify the places that are seriously affected by urban expansion. The results demonstrate that this approach can be used to quantitatively analyze the impact of urban expansion on mangrove forests, and show that larger areas of mangrove were affected by urban expansion in the past 30 years. The effects of urban expansion were stronger over time, with approximately 12% of cells containing mangroves showing a negative correlation between the increase in the area of built-up land and the change in the area of mangrove forests to different degrees from 2005 to 2015. The same quantitative analysis was also carried out in three subregions of Guangdong Province, namely western Guangdong Province, the Pearl River Delta, and eastern Guangdong Province. It was found that the situations in these three regions were very different due to discrepancies in the distribution of mangroves, the rate of urban expansion, and the awareness of the local government regarding environmental protection. These results can assist in the management of coastal cities and the protection of mangrove ecosystems.

The performance of the Revised Universal Soil Loss Equation (RUSLE) as the most widely used soil erosion model is a challenging issue. Accordingly, the objective of this study is investigating the estimated sediment delivery by the RUSLE method and Sediment Delivery Distributed (SEDD) model. To this end, the Talar watershed in Iran was selected as the study area. Further, 700 paired sediment-discharge measurements at Valikbon and Shirgah-Talar hydrometric stations between the years 1991 and 2011 were collected and used in sediment rating curves. Nine procedures were investigated to produce the required RUSLE layers. The estimated soil erosion by RUSLE was evaluated using sediment rating curve data by two methods including least squares and quantile regression. The average annual suspended sediment load was calculated for each sub-watershed of the study area using the SEDD model. Afterwards, a sediment rating curve was estimated by least squares and quantile regression methods using paired discharge-sediment data. The average annual suspended sediment load values were calculated for two hydrometric stations and were further evaluated by the SEDD model. The results indicated that the first considered procedure, which utilized 15-min rainfall measurements for the rainfall factor (R), and the classification method of SENTINEL-2 MSI image for the cover management factor (C), offered the best results in producing RUSLE layers. Furthermore, the results revealed the advantages of utilizing satellite images in producing cover management layer, which is required in the RUSLE method.

Landslide dam, always triggered by the strong earthquake and heavy rain, is a common natural disaster around the world. In this study, a coupled model was built by combining DB-IWHR model and the two-dimensional hydrodynamic model to simulate the landslide dam flood discharge. We mapped the maximum Baige landslide dam flood inundated area based on Gaofen-1 imagery, and then simulated the process of Baige landslide dam flood discharge using this coupled model. It was proved that, with 80.05% F values, the coupled model was suitable to simulate the process of landslide dam flood discharge. Lastly, multiple scenarios were simulated respectively by setting varying width and depth of spillway. The results of scenarios 1–4 indicated that spillway width presented low sensibility to the peak flow in spillway and the time of its arrival, and similarly to the water depth at river cross-section and the inundated area. Water depth at river cross-section and the inundated area decreased as spillway width increased. Even if spillway width varied at 10 m interval, the average variation of water depth was less than 1.82 m and the variation of inundated area was less than 2.85%. However, the results of scenarios 5–8 indicated that spillway depth was sensitive to the peak flow in spillway and its arrival time, and also to water depth at river cross-section and the inundated area. Water depth at river cross-section and the inundated area increased first and then started to drop with spillway depth kept decreasing. When spillway depth varied at only 2 m interval, the average variation of water depth at river cross-section basically exceeded 2 m and the variation of inundated area was more than 2.85%.

Urban growth does not always strictly follow historical trends; the government may reshape urban growth patterns with considerations of ecological conservation or other plans. Both urban dynamic rules and landscape characteristics are the two main factors influencing the spatial patterns of cities, and obtaining an optimized spatial pattern is very important for sustainable urban growth. Therefore, in this study, we integrated logistic regression (LR) with the ant colony optimization (ACO) model to analyze the optimal scenario for smart urban growth. The LR model was used to discuss the relationship between urban patterns and environmental variables such as topography, development centers, and traffic conditions. Then, the urban growth probability was generated using the parameters obtained from LR. The ACO model was further integrated to optimize urban land allocation, which can meet the requirement of high growth probability, and a connected and compacted landscape pattern. This can solve the problem of urban land only being allocated by LR from being distributed fragmentarily in the space. With this integrated model, Guangzhou City, a rapidly developing area in China, was selected as a case study. The urban patterns derived from LR, as well as a simulation scenario using logistic regression-based cellular automata (LR-CA), were used in the comparison. Six landscape metrics were chosen to validate the performance of this proposed model at the pattern level. The results show that the LR-ACO model has a better performance in urban land allocation. This study demonstrated that models that couple dynamic rules and planning objectives can provide plausible scenarios for smart urban growth planning.

The Finite Volume Community Ocean Model (FVCOM) was adapted to the Northern South China Sea (NSCS) to investigate the seasonality of coastal circulation, as well as along-shelf and cross-shelf transport. In fall and winter, southwestward current dominates the NSCS shelf, while the current’s direction shifts to northeast in summer. The circulation pattern in spring is more complicated: both southwestward and northeastward currents are detected on the NSCS shelf. The mean shelf circulation pattern in winter does not show the permanent counter-wind South China Sea Warm Current (SCSWC) along the 100–200 m isobaths. Meanwhile, the model results indicate a northeastward current flowing along 50–100 m isobaths in spring. Southwestward along-shelf transport varies from 0.30–1.93 Sv in fall and winter, and it redirects to northeast in summer ranging from 0.44–1.09 Sv. Onshore transport is mainly through the shelf break segment southeast of the Pearl River Estuary.

Attention has recently increased on the use of hydrological signatures as a potential tool for assessing the fidelity of model structures and providing insights into the transfer of model parameters. The utility of hydrological signatures as model performance/reliability indicators in a calibration-validation testing scenario (i.e., the temporal transfer of model parameters) is the focus of this study. The Probability Distributed Model, a flexible conceptual hydrological model, is used to test the approach across a number of catchments included in the MOPEX data set. We explore the change in model performance across calibration and validation time periods and contrast it to the corresponding change in several hydrological signatures to assess signature worth. Results are explored in finer detail by utilizing a moving window approach to calibration and validation time periods. The results of this study indicated that the most informative signature can vary, both spatially and temporally, based on physical and climatic characteristics and their interaction to the model parameterization. Thus, one signature could not adequately illustrate complex watershed behaviors nor predict model performance in new analysis periods.

Analyzing the spatial patterns of net primary productivity (NPP) and its driving forces in transnational areas provides a solid basis for understanding regional ecological processes and ecosystem services. However, the spatial patterns of NPP and its driving forces have been poorly understood on multiple scales in transnational areas. In this study, the spatial patterns of NPP in the transnational area of the Tumen River (TATR) in 2016 were simulated using the Carnegie Ames Stanford Approach (CASA) model, and its driving forces were analyzed using a stepwise multiple linear regression model. We found that the total amount of NPP in the TATR in 2016 was approximately 14.53 TgC. The amount of NPP on the Chinese side (6.23 TgC) was larger than those on the other two sides, accounting for 42.88% of the total volume of the entire region. Among different land-use and land-cover (LULC) types, the amount of NPP of the broadleaf forest was the largest (11.22 TgC), while the amount of NPP of the bare land was the smallest. The NPP per unit area was about 603.21 gC/(m²·yr) across the entire region, while the NPP per unit area on the Chinese side was the largest, followed by the Russian side and the DPRK’s side. The spatial patterns of NPP were influenced by climate, topography, soil texture, and human activities. In addition, the driving forces of the spatial patterns of NPP in the TATR had an obvious scaling effect, which was mainly caused by the spatial heterogeneity of climate, topography, soil texture, and human activities. We suggest that effective land management policies with cooperation among China, the DPRK, and Russia are needed to maintain NPP and improve environmental sustainability in the TATR.

Residential quarters in Chinese cities are usually walled off from their surrounding roads for security purposes. Recently, the Chinese government has decided to thoroughly open gated residential communities in order to improve traffic capacity and coordinate major roads in the road network, which will inevitably pose challenges, such as environmental pollution, for community members. Unfortunately, before this decision, there were no comprehensive investigations into whether this measure works for road traffic or how much the adverse impact exerts upon residents. Here, we propose a comprehensive method combining microscopic traffic simulation with a vehicle exhaust emission and dispersion model and a noise emission and attenuation model, in addition to a consideration of social cost, to evaluate the possible influence of opening an enclosed residential community to surrounding roads. The validity of the hybrid model was assessed by an assumptive case of two rectangular gated communities under varying traffic flow and five community opening modes. Preliminary results indicate that the opened community outperforms the gated in the most of 49 percent reduction in comprehensive cost. A more detailed analysis reveals that the appropriate extent of openness should rely on the actual situation, and potentially serves as a foundation for the healthy development of communities and cities. Based on the case study results, this paper outlines some strategical suggestions for improving enclosed residential areas by striking a better balance between traffic capacity and environmental risks.

The aim of this study is geomorphometric relief classification of a temperate latitude upland area in Central Europe. The Silesian Upland represents diversified structural relief which contains a fan-shaped configuration of long thresholds and wide erosion depressions. A 20 m × 20 m digital elevation model (DEM) provided input data for the analysis. The k-median method was applied to examine morphometric variables of the relief. The aim of these activities was to identify clusters with objects of similar mathematical characteristics. These clusters were the basis of landform classification. Smaller numbers of clusters 4 transparently show hypsometric relationships. Key elements of the morphology of the area were clearly visible. The division into 6 clusters gives the best results—a detailed but clear image of the morphological diversity by distinguishing characteristic landform elements. The results for 8 clusters show significant background noise and are ambiguous, which makes them difficult to identify. Our research has confirmed that the k-median method is a useful tool for landform classifications. We determined optimal parameters of this method (filtering window size, DEM resolution, number of clusters, aspect influence).

Solar radiation is the primary energy source that drives many of Earth’s physical and biological processes and determines the patterns of climate and productivity on the surface of the Earth. A fundamental proportion of solar radiation is composed of shielded extra-terrestrial solar radiation (SESR), which can be computed using the slope and aspect derived from a digital elevation model (DEM). The objective of this paper is to determine the influence of the grid spacing of the DEM (the influence of the scale of the DEM) on the errors of slope, aspect and SESR. This paper puts forward the concepts of slope representation error, aspect representation error, and SESR representation error and then studies the relations among these errors and the grid spacing of DEMs. We find that when the grid spacing of a DEM becomes coarser, the average SESR increases; the increase in SESR is dominated by the grid cells of the DEM with a negative slope representation error, whereas SESR generally decreases in the grid cells with a positive slope representation error. Although the grid spacing varies, the distribution of the percentages of positive SESR representation errors on the slope, which is classified into 11 slope intervals, is independent of the grid spacing; this distribution is concentrated across some slope intervals. Moreover, the average absolute value and mean square error of the SESR representation error are closely related to those of the slope representation error and the aspect representation error. The findings in this study may be useful for predicting and reducing the errors in SESR measurements and may help to avoid mistakes in future research and in practical applications in which SESR is the data of interest or plays a vital role in an analysis.

Probabilistic inflow forecasts can quantify the uncertainty involved in the forecasting process and provide useful risk information for reservoir management. This study proposed a probabilistic inflow forecasting scheme for the Three Gorges Reservoir (TGR) at 1–3 d lead times. The post-processing method Ensemble Model Output Statistics (EMOS) is used to derive probabilistic inflow forecasts from ensemble inflow forecasts. Considering the inherent skew feature of the inflow series, lognormal and gamma distributions are used as EMOS predictive distributions in addition to conventional normal distribution. Results show that TGR’s ensemble inflow forecasts at 1–3 d lead times perform well with high model efficiency and small mean absolute error. Underestimation of forecasting uncertainty is observed for the raw ensemble inflow forecasts with biased probability integral transform (PIT) histograms. The three EMOS probabilistic forecasts outperform the raw ensemble forecasts in terms of both deterministic and probabilistic performance at 1–3 d lead times. The EMOS results are more reliable with much flatter PIT histograms, coverage rates approximate to the nominal coverage 89.47% and satisfactory sharpness. Results also show that EMOS with gamma distribution is superior to normal and lognormal distributions. This research can provide reliable probabilistic inflow forecasts without much variation of TGR’s operational inflow forecasting procedure.

With a Multi-Regional Input-Output model, this study quantifies global final energy demands’ grey water footprint (GWF) based on the latest available data. In 2009, 9.10 km³ of freshwater was required to dilute the pollutants generated along the life-cycle supply chain of global energy final demands to concentrations permitted by relevant environmental regulations. On a national level, final energy demands in China, USA, India, Japan, and Brazil required the largest GWF of 1.45, 1.19, 0.79, 0.51, and 0.45 km³ respectively, while European countries have the highest energy demands GWF per capita. From the producer perspective, the largest GWF was generated in BRIC countries, i.e., Russia (1.54 km³), China (1.35 km³), India (0.92 km³) and Brazil (0.56 km³) to support global final energy demands. Because of global trading activities, a country or region’s final energy demands also give rise to water pollutants beyond its territorial boundaries. Cyprus, Greece, Luxembourg, and Malta almost entirely rely on foreign water resources to dilute water pollutants generated to meet their final energy demands. Energy demands in BRIC countries have the least dependency on external water resources. On a global average, 56.9% of GWF for energy demands was generated beyond national boundaries. Energy demands in the global north are inducing water pollutions in the global south.

Despite growing interest in the use of Payments for Ecosystem Services (PES) for both social and ecological benefits, few studies have investigated the feedback and interaction between poverty alleviation and ecosystem protection outcomes. In this study, the poverty reduction effects of PES policies and their subsequent influence on environmental protection outcomes are investigated. To address these questions, 222 local rural households who were involved in PES programs from the Habahu National Nature Reserve in western China were interviewed. The results showed that the social and ecological outcomes of PES policies are neither two separate entities nor a trade-off. While rural households are the key participants in PES programs, the social and ecological outcomes of PES policies are closely related to each other. In addition, poverty reduction results could greatly influence ecosystem conservation effects. Livelihood assets, as well as the attitudes of rural households, play important roles in both of the outcomes. This research provides a new perspective that considers the social and ecological benefits of PES policies, and it also calls for an integrated consideration of social and ecological components in the design of PES policies to achieve enhanced results both for poverty alleviation and ecosystem conservation.

The vegetation in the Ningxia Hui Autonomous Region (henceforth, Ningxia) of north-western China plays an important role in guarding regional ecological safety and sustainable development. However, it is unclear how climate affects vegetation growth in terms of seasonality and various vegetation types in Ningxia. Based on remote sensing vegetation index from 2001 to 2016, climatic parameters, and the Chinese vegetation type data, this article examines the spatiotemporal effects of climate parameters on vegetation. The relative importance to variability in the normalized difference vegetation index (NDVI) for different seasons and various vegetated types is also determined. The results demonstrate that the vegetation increased from 2001 to 2016 in Ningxia. The rate of NDVI increase was fastest in summer and slowest in spring. Areas with significant increases in vegetation occurred primarily in the southern mountain, Liupan Mountain, and central arid areas. Degraded vegetation occurred in the Yellow River irrigation area with intense human activity influence. The vegetation in most areas of Ningxia will continue to increase in the future. The sensitivity of vegetation to temperature, precipitation, sunshine duration, and wind velocity showed significantly seasonal variability. Sunshine duration and wind velocity were important climatic factors affecting vegetation growth in Ningxia. However, the impact of summer precipitation variation on summer NDVI (SMN) demonstrated a time lag effect. The impact of climate variation on vegetation was distinct among various vegetation types. Moreover, the spatial pattern of vegetation in Ningxia was also impacted by human activities.

Global change affected by multiple factors, the consequences of which continue to be far-reaching, has the characteristics of large spatial scale and long-time scale. The demand for Earth observation technology has been increasing for large-scale simultaneous observations and stable global observation over the long-term. A Moon-based observation platform, which uses sensors on the nearside lunar surface, is considered a reasonable solution. However, owing to a lack of appropriate processing methods for optical sensor data, global change study using this platform is not sufficient. This paper proposes two optical sensor imaging processing methods for the Moon-based platform: area imaging processing method (AIPM) and global imaging processing method (GIPM), primarily considering global change characteristics, optical sensor performance, and motion law of the Moon-based platform. First, the study proposes a simulation theory which includes the construction of a Moon–Sun elevation angle model and a global image mosaicking method. Then, coverage images of both image processing methods are simulated, and their features are quantitatively analyzed. Finally, potential applications are discussed. Results show that AIPM, whose coverage is mainly affected by lunar revolution, is approximately between 0% and 50% with a period of 29.5 days, which can help the study of large-scale instant change phenomena. GIPM, whose coverage is affected by Earth revolution, is conducive to the study of long term global-scale phenomena because of its sustained stable observation from 67°N–67°S on the Earth. AIPM and GIPM have great advantages in Earth observation of tripolar regions. The existence of top of the atmosphere (TOA) albedo balance line is verified from the GIPM perspective. These two imaging methods play a significant role in linking observations acquired from the Moon-based platform to Earth large-scale geoscience phenomena, and thus lay a foundation for using this platform to capture global environmental changes and new discoveries.