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Promoting the coupling coordinated development between ecological protection and high-quality development is an important path to achieve sustainable regional development around the world. Taking Guizhou, China as a typical case, this study builds a comprehensive evaluation index system for ecological protection and high-quality development, and then uses the coupling coordination degree model to assess the spatiotemporal characteristics of ecological protection and high-quality development. According to the findings, Guizhou's ecological protection index and high-quality development index both exhibit an upward trend and a center-edge spatial pattern, but they also exhibit a "spatial misalignment relationship". The coupling coordination between ecological protection and high-quality development in Guizhou has shown a continuous growth trend from near-disorder to a primary coordination, with a spatial pattern of "central > western > eastern". This study enriches the knowledge body of sustainable development, helps Guizhou make decisions that are best for ecological protection and high-quality development, and is useful as a reference for other regions and countries. [ABSTRACT FROM AUTHOR]

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin. [ABSTRACT FROM AUTHOR]

Under the backdrop of achieving carbon neutrality and accelerating urbanization, China's forests face unprecedented pressures. This study explored the spatiotemporal characteristics of forest loss in the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR). The dynamic mechanism of forest loss caused by fire, logging, construction, and pollution was also analyzed using spatial database development, polygon superposition analysis, grid system construction, and coordinate system calculation. The results show that the forest loss in the UAMRYR experienced three stages: continuous acceleration (1990–2010), peak (2010–2015), and slight decline (2015–2020). Rapid urban expansion is the primary cause of forest loss, and the three metropolitan areas had the fastest urban expansion and the most severe forest loss. Due to the success of afforestation efforts, the forest loss caused by fire, logging, and pollution was restored by 80%, while most of the forest losses caused by construction are permanent. Given the current forest loss trends, large expanses of forests in the UAMRYR are at risk of being destroyed and causing serious damage to the region's ecological environment. Forest losses can be significantly reduced by guiding the rational expansion of cities, supporting afforestation for urban construction projects, strengthening forest fire risk investigation, and implementing ecological reconstruction of polluted areas. [ABSTRACT FROM AUTHOR]

Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals' activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns. [ABSTRACT FROM AUTHOR]

Optical remote sensing data are used widely in many fields (such as agriculture, resource management and the environment), especially for the vast territory of China; however, the application of these data is usually limited by clouds. Although it is valuable to analyze the probability of acquiring cloud-free imagery (PACI), PACI using different sensors at the pixel level across China has not been reported. In this study, the PACI of China was calculated with daily Advanced Very High Resolution Radiometer (AVHRR) cloud mask data from 1990 to 2019. The results showed that (1) PACI varies dramatically in different regions and months in China. The value was larger in autumn and winter, and the largest figure reached 49.55% in October in Inner Mongolia (NM). In contrast, relatively small values occurred in summer, and the minimum value (5.26%) occurred in June in South China (SC). (2) As the climate changes, the PACI has increased significantly throughout the country and most regions, especially in North China (NC), with a growth rate of 1.9% per decade. The results can be used as a reference for selecting appropriate optical sensors and observation times in areas of interest. [ABSTRACT FROM AUTHOR]

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity; however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.