Search

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH3-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.

The industrial economy occupies a crucial position in China’s national economy, and industrial eco-efficiency (IEE) as a significant indicator of regional green development levels. Balancing the positive interaction between industrial economy and resource environment, and enhancing ecological efficiency in industrial development are vital for achieving sustainable regional economic development. This study measures the IEE of 115 cities in coastal China based on panel data of industrial resources and the environment factors. Subsequently, it further analyzes the influencing mechanisms and future trends of IEE. The results indicate that the overall IEE in coastal China is on an upward trend, with higher efficiency values in provinces and regions characterized by faster economic development and better environmental conditions. Significant changes in spatial patterns are observed, with the gaps between cities narrowing and a “multi-core” development model emerging. Factors such as per capita GDP, the ratio of industrial pollution control investment to GDP, innovation index, the proportion of foreign direct investment to GDP, and industrial labor productivity significantly positively influence IEE. In contrast, the proportion of industrial added value to GDP, urbanization rate, and the number of industrial enterprises exhibit notable negative inhibitory effects. Moreover, the interaction effect between industrialization level and other factors is most significant. In the future, IEE is expected to continue improving, although the sustainability of these changes appears weak. These findings reveal the potential impact mechanisms of resource consumption and environmental pollution caused by industrial activities on economic benefit output. This study provides a scientific basis for optimizing energy development layout, enhancing the comprehensive utilization of energy resources, and improving ecological compensation and protection mechanisms.

On this basis, a basketball movement model based on ORB (Oriented FAST and Rotated BRIEF) local feature extraction has been proposed, and a basketball teaching visualization analysis system has been constructed. By extracting images from athletes’ videos in basketball, and using their training as a standard, tracking the frequency of their various celebration postures appearing and improving their scores in the game, corresponding feature points are extracted, and then the BP (back propagation) neural network algorithm is used to classify feature points. Ten different participants were selected from 10 games. Their movements were tracked, and then the extracted feature actions were imported into the visualization system for visual analysis, comparing the changes before and after psychological intervention. The results showed that there were significant differences in scores, errors, hit rates, steals, and motivation among subjects who received psychological cues and incentives compared to the data before psychological counseling. The scores and positive and negative values increased to 2–4 points, which had a certain positive impact on the competitive results.

The establishment of nature reserves (NRs) is widely acknowledged as one of the most effective measures to mitigate the threats on habitat quality (HB) posed by human activities (HAs). Precise and scientific assessment of the effectiveness of NRs holds crucial significance in improving management and promoting conservation. In this study, key indicators were creatively selected and applied to the propensity score matching (PSM) model to comprehensively assess the variations in HAs and HB within national NRs on the Qinghai-Tibet Plateau. The results indicated that between 2000 and 2020, 67.4 % of the NR area experienced a decline in HA-related impacts, while 53.8 % of the area saw improvements in HB. Additionally, with the exclusion of external environmental factors, in 2020, the difference in HAs and HB between NRs and non-protected areas was −0.131 and 0.179, respectively. Finally, based on an assessment of the overall conservation effectiveness, seven NRs were classified as “Class I'', 18 as “Class II'', and another seven as “Class III''. These results not only confirmed the effectiveness of national NRs in alleviating anthropogenic pressure and enhancing HB but also served as an important basis for accurately assessing the conservation effectiveness of other NRs and formulating more scientifically sound and appropriate management policies.

The key to managing urban heat island (UHI) is identifying and cooling the core areas of UHI. Studies have shown that spatial differences in urban green space (UGS) indicators have a significant impact on spatial differences in land surface temperature (LST). However, previous studies have mainly explored UGS and LST in the entire region, with few focusing on the urban core area. In this study, we extracted the urban core area based on the Point of Interest (POI) and kernel density methods. Correlation and regression analyses were then used to investigate the correlation between UGS indicators and seasonal LST in the urban core area. Our results confirm the significant cooling effect of UGS at the urban core scale. The effects of UGS landscape composition and configuration on LST varied across seasons and across urban green space ratio (UGSR) gradients. In the warm season, increasing the UGSR proved to be the most effective way to mitigate the urban thermal environment. In urban core areas where expanding UGS is not possible or in cores with high UGSR, designing UGS with complex shapes and clusters can better achieve the cooling effect. These results suggest that the influence of UGS landscape features on the urban surface thermal environment at the urban core area scale is very complex. Therefore, implementing urban greening to mitigate the UHI problem requires considering the premise of multifactorial analysis.

In recent years, the movement analysis is commonly used to track the risk of injury and strengthen the efficiency of athlete performance. However, most of these devices are costly, found mainly in experimental settings, which analyze a few samples of each movement. In this paper, a new ambulatory movement analysis system with wearable sensors for the precise measurement of all athleteꞌs movements in an actual training scenario is introduced. Initially, an adaptive method categorizes a broad variety of training behaviors by the Differential Finite element Transformation method (DFET) along with a Random Forest Classification (DFET- RF) method. Secondly, the measurement of the absolute identities of the wearable sensor devices placed on the knee bone and pelvic bone is performed with a discrete gradient descent (DGD) algorithm, which calculates a range of motion-extension between the knee and hip angle. Finally, the edge computing is used to process data in real-time and reduce the latency of the system. The next version of wearable technology will know the person's identity, individually - not just physically and actively in a much more significant way; a wearable device that tells the world about the identity of the person and the connected devices. The knee flexion is greater at the terminal swing period (85%) and hip flexion (68%). The development of future device capabilities is based on verification. Once a wearable can validate the wearer's identity, several other things about their activities can be regulated. Such angles are automatically extracted for each movement during jogging at the acceleration of the sacrum effect. Besides, standard data has developed and is used to decide whether the movement methodology for a person varied from the standard data to classify potential instances due to injury. This is done by a gradient-shift recording technique for the joint-related angle details. For precise and automated assessment of athletic movements, effective activity measurement in various uncontrolled conditions for both injuries processing performance progress, the suggested system has been discussed in this paper.

Constructing an ecological security pattern (ESP) is an effective measure to solve current regional ecological problems, alleviate the contradiction between rapid urbanization and ecological protection, and provide an important spatial path for effective management of regional ecosystems. This study integrated the importance of ecosystem services, ecological sensitivity, landscape connectivity, and ecological resistance, and constructed ESP in the Huang-Huai-Hai Plain using analytical hierarchy process (AHP), minimum cumulative model (MCR), and gravity model. A total of 13 ecological sources, 52 ecological corridors (22 first-level corridors, 9 s-level corridors, and 21 third-level corridors), and 201 ecological nodes were identified. The ecological sources were mainly distributed around the Huang-Huai-Hai Plain, and the ecological corridors showed a circular distribution, while the ecological nodes were mainly distributed at the intersection of several ecological corridors. In addition, the built-up land in the Huang-Huai-Hai Plain increased by 40% in the past 20 years, posing a serious threat to ecological sources and corridors, especially ecological sources adjacent to cities such as Beijing, Jinan, and Qingdao, as well as ecological corridors crossing urban areas such as Beijing, Tianjin, and Zhengzhou. The methodological system of regional ESP constructed in this study can provide theoretical foundations and methodological support for the construction of ESP and the implementation of ecological protection in other regions.

Urban vibrancy is an important indicator of urban prosperity. Evaluating the correlation between urban vibrancy and housing prices is increasingly necessary. However, most studies have ignored the temporal characteristics of urban vibrancy and only considered its spatial nature. This study optimizes the identification model of urban vibrancy centers and proposes a novel model for evaluating the time-dynamic accessibility of urban vibrancy centers. The model takes into account real-time changes in travel costs and analyses its spatial correlation with housing prices. The case study of Chengdu shows significant variations in the spatiotemporal distribution of urban vibrancy centers. High-vibrancy areas are concentrated near commercial complexes. The time distribution of urban vibrancy accessibility is more concentrated at night (18:00–24:00), and the high-accessibility areas are located around the old city and the newly planned Central Business District in the south. Housing prices and urban vibrancy centers accessibility are mainly negatively correlated spatially. The study emphasizes the temporal nature of urban vibrancy and accessibility and enriches the evaluation model of urban vibrancy centers. It also contributes to the improvement of the framework of urban vibrancy research and deepens the understanding of the important variable of “time” in urban planning.

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios.

The risk of compound drought heatwave events (CDHEs) and their persistence has intensified in recent decades and is expected to increase faster in the future. Projecting future changes in the CDHEs and the area of the cropland exposed to CDHEs under different scenarios is critical for climate adaptation and sustainable development. In this study, we analyze cropland exposure to extreme CDHEs at the global and continental scales under different Shared Socioeconomic Pathways (SSPs) scenarios for the mid-term (2041–2060) and long-term (2081–2100) of the 21st century by using 14 CMIP6 GCMs and LUH2 land-use data. We find that the extreme CDHEs with high frequency are mainly located in tropical areas. The frequency of extreme CDHEs in the future will be much higher than that of the baseline period (1995–2014), and the increased frequency will be more obvious with the emissions increase. Overall, the global total cropland exposure in warm season during the 1995–2014 period will be 148.05 × 103 km2 month−1. Exposure in the mid-term and long-term will be 868.68–1801.25 and 1058.58–3887.54 × 103 km2 month−1 under the different SSP scenarios. The climate effect will be the dominant driving factor for the increase in cropland exposure. Cropland exposure to extreme CDHEs will increase on all continents, especially in Asia and Africa. Our findings provide scientific evidence of the benefit of limiting low-emission scenarios which will effectively reduce cropland exposure to CDHEs under future climate change.

Understanding the mechanisms that influence changes in ecosystem services (ESs) is critical to the sustainable management of ecosystems. However, existing studies ignore the different importance of influencing factors of ESs in different periods and do not consider the spatiotemporal heterogeneity of influence factors. In this study, we first quantified six ESs for the Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2000 and 2020 based on remote sensing data, including water yield, grain production, climate regulation, air purification, biodiversity, and recreation. Then, eight factors influencing ESs were selected from natural and human perspectives, and random forest was used to determine the importance level of factors influencing ESs. Finally, the GTWR model was used to explore the spatial and temporal differentiation of factors influencing ESs. The results showed that the spatial variation of the six ESs in the YRD and PRD was irregular from 2000 to 2020. In 2000, natural factors (forest, topography, climate) dominated the regional ESs, while in 2020 human factors (population, economy, human activities) gradually replaced the dominance of natural factors on ESs. The spatial and temporal heterogeneity of multiple influence factors on ESs in the YRD and PRD is significant, and we interpret the ecological implications in detail and propose a series of policy recommendations. The results of this study could provide an important reference for scientific guidance to enhance ecological sustainability in developed regions.

Accurate and timely urban boundaries can effectively quantify the spatial characteristics of urban evolution and are essential for understanding the impacts of urbanization processes and land-use changes on the environment and biodiversity. Currently, there is a lack of long time-series, high-resolution, nationally consistent Chinese urban boundary data for urban research. In this study, the city clustering algorithm was used to generate urban settlement boundaries in China based on the local density, size, and spatial relationships of impervious surfaces. The results showed that both the area and the number of urban settlements in China revealed an upward trend from 1985 to 2020, with East China (EC) being much higher than other regions and South China showing the most significant growth rate. The average area ratio of urban green space in China was 41.68%, with the average area ratio in EC being higher than in other regions. Meanwhile, Zipf’s law was used to verify the universality of urban settlement rank–size; the changes in the Zipf index from 1985 to 2020 also revealed that China’s urban size tended to be concentrated, and the development of large urban settlements was relatively prominent. The urban definition method we propose in this study can divide urban boundaries efficiently and accurately, identify urban expansion hotspots, and promote research on farmland loss and ecological land degradation, further exploring the impacts of urbanization on food security, biodiversity, and carbon sequestration. By coupling big data such as economy, energy, and population with urban evolution patterns, urban managers can analyze current and future problems in urban development, thereby providing scientific decision-making for urban sustainability.

Abstract Background NAC (NAM, ATAF and CUC) transcription factors (TFs) play vital roles in plant development and abiotic stress tolerance. Salt stress is one of the most limiting factors for rice growth and production. However, the mechanism underlying salt tolerance in rice is still poorly understood. Results In this study, we functionally characterized a rice NAC TF OsNAC3 for its involvement in ABA response and salt tolerance. ABA and NaCl treatment induced OsNAC3 expression in roots. Immunostaining showed that OsNAC3 was localized in all root cells. OsNAC3 knockout decreased rice plants’ sensitivity to ABA but increased salt stress sensitivity, while OsNAC3 overexpression showed an opposite effect. Loss of OsNAC3 also induced Na+ accumulation in the shoots. Furthermore, qRT-PCR and transcriptomic analysis were performed to identify the key OsNAC3 regulated genes related to ABA response and salt tolerance, such as OsHKT1;4, OsHKT1;5, OsLEA3–1, OsPM-1, OsPP2C68, and OsRAB-21. Conclusions This study shows that rice OsNAC3 is an important regulatory factor in ABA signal response and salt tolerance.

Taking the lower Yellow River basin as the study area, this study aims to construct ecological networks to mitigate the negative impacts of rapid urbanization on the ecosystem. Ecological sources were identified based on morphological spatial pattern analysis (MSPA), habitat quality and landscape connectivity. The ecological resistance surface was constructed and corrected by integrating natural and anthropogenic factors. The spatial range of ecological corridors and some of their key nodes were identified based on circuit theory. The ecological network (EN) was finally optimized using a similarity search and cost connectivity modules. The results show that the optimized ecological network structure is more stable than before. The EN includes 23 ecological sources with a total area of 5464.8 km2 and 30 ecological corridor clusters with a total area of 2205.92 km2. Through the internal landscape heterogeneity of the corridor, 28 ecological node areas and 75 barrier areas were identified as key protection and restoration areas, with a total area of 78.44 km2 and 372.79 km2, respectively. Through the construction and optimization of EN, this study identifies key areas for promoting ecological sustainability and provides a useful framework for coordinating regional ecological conservation and economic development.

The identification of conservation priority areas (CPAs) based on multiple ecosystem services (ESs) is a prerequisite and important basis for decision-makers in implementing regional ecological restoration. However, the trade-offs between different services pose great challenges to determining the locations of priority areas. Based on the identification and assessment of key ESs in the Hengduan Mountain region from 2000 to 2020, a multicriteria ordered weighted average (OWA) method was used to balance the conflicts among different services and determine the location of CPAs. The results indicated that the optimal CPAs in the Hengduan Mountain region totaled 89,699 km2, accounting for 20.30% of the study area, and were located mainly in the southwestern and eastern regions. Moreover, the OWA method balanced the trade-offs among multiple services. In the four CPA scenarios that protected each service exclusively, the protection efficiency (PE) of each respective service was greater, but the PE of the remaining three services was always smaller than the corresponding PEs in the optimal scenario. The comprehensive PE of the optimal CPA scenario was 1.27, and the PE values of net primary productivity, water yield, soil conservation, and cultural service were 1.45, 1.20, 1.08, and 1.39, respectively. The CPA determination method in this study can provide a scientific blueprint for regional ecological restoration and the determination of the CPAs in other regions.

Wetland is one of the most critical ecosystems with various functions, it produces the highest ecosystem services value (ESV) per unit area among all land types, thus, its decline has become a global concern in recent years. Since the 21st century, many developing countries have been continuously encroaching on wetlands due to rapid economic and population growth, resulting in the replacement of a large number of wetlands by cities, and how to reconcile urban expansion and wetland conservation has become a significant problem that needs to be considered. In this context, we mapped the spatial distribution of wetlands occupied by the expansion of global artificial impervious surface area (ISA) for 2001–2018 and discussed its temporal and spatial change characteristics at the global, continental, and national scales using the annual maps of global annual impervious area data set and global dynamic land cover data set based on Google Earth Engine platform, the loss of ESV due to wetland occupation was also measured at each spatial scale level. The results show that: (1) A total of 1301.24 km2 of wetlands were occupied by ISA expansion globally for 2001–2018, and their interannual variation showed an upward trend for 2001–2015, then began to decline rapidly since 2016. The occupation results in a global loss of US$3.46 billion in ecosystem service value; (2) Asia has the highest loss of ESV due to occupation of wetlands by ISA expansion, 624.18 km2 of occupation of wetlands resulted in a loss of US$1.66 billion in ESV, making it the continent with the largest area of occupied wetlands. Taking into account the different sizes of the continents, Oceania is the continent where ISA expansion occupies the wetlands most, while South America is the continent where it is relatively least severe; (3) China, the USA and Russia account for 50.9 % of global ESV loss due to wetland occupation by ISA expansion, a total of 663.95 km2 of wetlands were occupied. Although countries have made great efforts to protect wetland resources and deal with urban land expansion, our results show that the problem of wetlands occupied by global urban land expansion is still severe.

A new planar 5R parallel mechanism with nine driving methods is proposed to solve the problem of system performance degradation caused by different driving methods of parallel mechanisms. Firstly， based on the transmission scheme of the 5R parallel mechanism， the kinematics model of the 5R parallel mechanism is established. Secondly， on the basis of matrix orthogonality， the operating performance index of 5R parallel mechanism is defined， and the operating performance under different driving modes is evaluated according to the index requirements. Finally， the size of the 5R parallel mechanism is optimized based on the atlas method， and the best geometric parameters and the best execution mode transformation diagram of the 5R parallel mechanism are obtained. The experimental results show that the proposed performance evaluation index and optimization method have good universality. The new driveable mechanism constructed can significantly improve the kinematic performance of the parallel mechanism by reasonably changing the drive mode.

The intensity of human pressure (HP) has an important impact on the biodiversity and ecosystem services of nature reserves (NRs), and the conflict and the coordination between NRs and human activities are now key issues to solve in the construction of NR systems. This study improved and applied a human footprint (HF) model that considers population density, land use, night light, grazing intensity, and road construction as indicators of human activity to evaluate the effectiveness of NRs in the Qinghai–Tibet Plateau in mitigating HP from 2000 to 2020. The results indicated that during this period, the average HP in the national NRs of the plateau increased from 1.47646 to 1.76687, where values were generally high in the east and low in the west. The average value in wetland NRs was the largest and had the smallest growth rate, while that in desert NRs was the smallest and had the largest growth rate. From 2000 to 2020, the average HP in the core areas, buffer areas, and experimental areas of the NRs increased by 0.12969, 0.29909, and 0.44244, respectively. It is a challenge for the Chinese government to strengthen the ability of NRs to mitigate HP on the wetland reserves and experimental zones in the Qinghai–Tibet Plateau region.

Abstract The frequent occurrence of local COVID‐19 today gives a strong necessity to better understand the effects of "source‐case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID‐19 cases, which originated in the Xinfadi market in Beijing from June–July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero‐inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID‐19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID‐19 was identified in both global and local modeling estimation. The dominant role of the “source‐case” distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID‐19.

With the rapid development of urbanization, the disorderly expansion of land use for urban construction has led to increasingly severe spatial dislocations of ecological land and construction land, and the topic of rationally planning the scale of urban land use and controlling urban sprawl from the perspective of ecological security has become a challenge for many countries. This paper considers the Jiaodong Peninsula as the study area. The ecological sources were obtained by integrating the results of an ecological importance assessment and the data on nature reserves, a basic ecological resistance surface was built, and ecological corridors and buffers were identified using a minimum cumulative resistance model. Three development scenarios were designed for 2015–2030 based on future land use simulation (FLUS) model, namely, natural development (ND), core ecological patch protection (CEP) and ecological security pattern restriction (ESPR). The ND scenario followed the land expansion rate from 2005 to 2015; the CEP scenario used the ecological sources as a forbidden conversion area, and the ESPR scenario used the ecological security pattern as a forbidden conversion area. A multiscale evaluation system based on the ecological security index (ESI) and the landscape pattern was established to analyze the three scenarios in detail. The main conclusions were as follows: (1) the area of ecological sources of the Jiaodong Peninsula was 3296.85 km2, and 13 ecological corridors were identified, and (2) in the multi-scale ecological security evaluation, the ND scenario destroys the original ecosystem structure of the region, which is not conducive to regional ecological conservation; the CEP scenario still has a trend of landscape fragmentation, and the ESPR scenario performs well in all aspects. Based on the landscape pattern, it was observed that the ESPR scenario had the largest landscape diversity and the most uniform distribution pattern, and the landscape dominance and heterogeneity were both the best. This finding indicated that imposing the ecological security pattern could make it possible to effectively control the spatial form of construction land, avoid excessive dispersion of landscape types, and form a stable and secure ecological pattern. This study can provide an important reference for spatial boundary control of urban development and construction and the formulation of related ecological protection policies.

Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.

The spatial distribution and accessibility of urban public service facilities affect socioeconomic factors in the lives of residents, especially housing prices. Given that most previous studies focus on the accessibility of a certain, single type of facility and its impact on housing prices, this research uses improved two-step floating catchment area (2SFCA) methods by considering the differences in the service capacity of different types of public service facilities in real life to evaluate their accessibility to residential communities in Jinan city based on 3117 facilities covering 11 different kinds of facilitates. Then, we assess the spatial distribution of the impact of the accessibility of different public service facilities on housing prices in Jinan city through a local indicator of a spatial association (LISA) cluster diagram generated based on the bivariate local Moran’s index. Our objectives are to assess the accessibility of multiple public service facilities using an improved 2SFCA method and to explore the spatial correlations between the accessibility of public service facilities and housing prices. The results show that the housing prices in Jinan are clustered and that the areas with high housing prices are mainly concentrated in the Lixia District and the center of the downtown area. The accessibility of medical, shopping, educational and bus stop facilities in the Lixia District is better than that in other districts. The accessibility of shopping, medical and tourist attraction facilities has the most significant impact on housing prices, and the number of communities in which the accessibility of these public service facilities and housing prices form a positive correlation cluster accounts for 50.5%, 47.9% and 45.8% of all communities, respectively. On the other hand, educational accessibility and bus stop accessibility have nothing to do with housing prices, and the number of communities in which the accessibility of these public service facilities forms a not-significant cluster with housing prices accounting for 51.1% and 56.5% of the total, respectively. In this study, the combined 2SFCA method is used to improve the method for evaluating the accessibility of a variety of public service facilities, and its applicability is verified by practical application. By analyzing the spatial correlation between accessibility and housing prices, we expand our understanding of accessibility and show that it plays a central role in housing prices, which will help to improve the spatial pattern of urban public places in the future, provide support for decision makers and provide a reference for the government and real estate developers.

In the last few decades, land use/land cover (LULC) has changed significantly under the influence of local planning and policy implementation, and this has had a profound impact on the regional ecological environment. By taking the Hengduan Mountain region as the study area, this study considered the demands of various commodities and services and applied the CLUMondo model to predict the trajectory of change in the land system for the years 2010–2030. The results indicate that the forest system expands significantly in this time, while the grassland and cropland systems are projected to develop intensively under the three scenarios. The high demand for livestock products is the main cause of the intensification of the grassland system under the TREND scenario, the demand for forests leads to the expansion of the forest land system under the FOREST scenario, and the significant intensification of the cropland system under the CONSERVATION scenario is closely related to an increase in the area of ecological land. The results of this study can provide a scientific reference for the optimal management of land systems in other mountainous areas.

Long-term intensive land use/cover changes (LUCCs) of the Yellow River Delta (YRD) have been happening since the 1960s. The land use patterns of the LUCCs are crucial for bio-diversity conservation and/or sustainable development. This study quantified patterns of the LUCCs, explored the systematic transitions, and identified wetland change trajectory for the period 1976–2014 in the YRD. Landsat imageries of 1976, 1984, 1995, 2006, and 2014 were used to derive nine land use classes. Post classification change detection analysis based on enhanced transition matrix was applied to identify land use dynamics and trajectory of wetland change. The five cartographic outputs for changes in land use underlined major decreases in natural wetland areas and increases in artificial wetland and non-wetland, especially aquafarms, salt pans and construction lands. The systematic transitions in the YRD were wetland degradation, wetland artificialization, and urbanization. Wetland change trajectory results demonstrated that the main wetland changes were wetland degradation and wetland artificialization. Coastline change is the subordinate reason for natural wetland degradation in comparison with human activities. The results of this study allowed for an improvement in the understanding of the LUCC processes and enabled researchers and planners to focus on the most important signals of systematic landscape transitions while also allowing for a better understanding of the proximate causes of changes.

In order to improve the security, ease of use and flexibility of software products, this paper proposes a software cloud authorized protection mechanism based on specific time encryption. This mechanism sends the encrypted and packed executable file to the cloud server, so that only legitimate users can unpack, decrypt and run the software in the cloud within a specific time interval. On this basis, this article gives a formal definition of the solution model and constructs a specific implementation solution based on the random oracle model. Security analysis shows that this solution can not only prevent attackers from using cloud servers and time servers to illegally obtain software usage rights, but also reduce time overhead and costs, thereby effectively protecting the legitimate rights and interests of developers and users. [ABSTRACT FROM AUTHOR]