Search Results (12)

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

The Upper Yellow River Region plays an irreplaceable role in water conservation and ecological protection in China. Due to both natural and human-induced factors, this area has experienced significant grassland deterioration, land desertification, and salinization. Consequently, evaluating the region’s environmental status plays a vital role in promoting ecological conservation and sustainable growth in the Upper Yellow River Basin. This study constructed an ecological index based on remote-sensing data and examined its spatiotemporal changes from 1990 to 2020. Future ecological dynamics were predicted using the Hurst index, while key influencing factors were examined through an optimal-parameter-based GeoDetector and geographically weighted regression. The findings revealed the following: (1) RSEI values were generally lower in the north and increased progressively toward the south, indicating a notable spatial disparity. (2) Ecological conditions remained largely stable, with notable improvements observed in 65.47% of the study area. (3) It was anticipated that 52.76% of the region would continue to improve, whereas 24% is expected to experience further degradation. (4) Precipitation, temperature, elevation, and land cover were major factors contributing to ecological variation. Their impact on ecological quality varies across different geographic locations. These research findings provided references for the sustainable development and ecological civilization construction of the Upper Yellow River Region. Full article

Desert ecosystems, particularly in arid regions like the Tengger Desert, are highly sensitive to both anthropogenic activities and climate change, making the monitoring and evaluation of ecological quality critical for sustainable management and restoration efforts. This study analyses the spatiotemporal evolution of ecological quality in the Tengger Desert from 2001 to 2021 using the Remote Sensing Ecological Index (RSEI), incorporating meteorological factors (temperature, precipitation, wind speed), topographical factors (elevation, slope, relief) and anthropogenic indices (land use and land cover). The mean RSEI fluctuated between 0.1542 and 0.2906, indicating poor ecological quality, with a peak in 2008 attributed to national ecological projects. Despite initial improvements, overall ecological quality declined at a rate of 0.0008 a⁻¹ from 2008 to 2021. Spatially, degradation was most pronounced in the central and southern areas. Due to sand-binding engineering in the Tengger Desert in 2008 and the mountain climate suitable for vegetation growth, improvements occurred in the northeast and southwest. Moran’s I and Hurst index analyses revealed significant spatial clustering of ecological quality and persistence of degradation trends, with over 49.53% of the area projected to experience further deterioration. Geodetector analysis identified land use and land use cover as the most influential factors on RSEI, especially in combination with wind speed, temperature, and precipitation, underscoring the role of both human activities and climate. The study highlights the need for sustained ecological management, particularly in areas showing continuous degradation, to prevent further ecological deterioration. Full article

Vibrations caused by downhole operations often induce coal and gas outburst accidents in tectonic zone coal seams. To clarify how vibration affects the pore structure, gas desorption, and diffusion capacity of tectonic coal, isothermal adsorption-desorption experiments under different vibration frequencies were carried out. In this study, high-pressure mercury intrusion experiments and low-pressure liquid nitrogen adsorption experiments were conducted to determine the pore structures of tectonic coal before and after vibration. The pore distribution of vibration-affected tectonic coal, including local concentration, heterogeneity, and connectivity, was analyzed using multifractal theory. Further, a correlation analysis was performed between the desorption diffusion characteristic parameters and the pore fractal characteristic parameters to derive the intrinsic relationship between the pore fractal evolution characteristics and the desorption diffusion characteristics. The results showed that the vibration increased the pore volume of the tectonic coal, and the pore volume increased as the vibration frequency increased in the 50 Hz range. The pore structure of the vibration-affected tectonic coal showed multifractal characteristics, and the multifractal parameters affected the gas desorption and diffusion capacity by reflecting the density, uniformity, and connectivity of the pore distribution in the coal. The increases in the desorption amount (Q), initial desorption velocity (V₀), initial diffusion coefficient (D₀), and initial effective diffusion coefficient (D_e) of the tectonic coal due to vibration indicated that the gas desorption and diffusion capacity of the tectonic coal were improved at the initial desorption stage. Q, V₀, D₀, and D_e had significant positive correlations with pore volume and the Hurst index, and V0, D0, and De had negative correlations with the Hausdorff dimension. To a certain extent, vibration reduced the local density regarding the pore distribution in the coal. As a result, the pore size distribution was more uniform, and the pore connectivity was improved, thereby enhancing the gas desorption and diffusion capacity of the coal. Full article

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context. Full article

Coalburst, a frequent and severe dynamic disaster, poses significant challenges to the safety and sustainable development of coal mines during deep excavation. To investigate the mechanical behaviors and precursory characteristics of coalburst subjected to in situ stress conditions, multiaxial cyclic loading experiments were conducted on cubic coal specimens, and the effects of different confining pressures on the mechanical parameters and energy evolution were analyzed. Acoustic emission (AE) technology was utilized to study the accumulation process of stress-induced damage and identify the source modes of microcracks. Then, nonlinear fractal theory and critical slowing theory were used to investigate the time-varying precursory characteristics of catastrophic failure in coalburst. The results show that as the confining pressure increases, the coal samples exhibit higher levels of elastic strain energy and dissipative energy, indicating an enhancement of plasticity. The AE count and accumulated energy show a strong correlation with cyclic loads. With an increasing number of cycles, the AE Felicity ratio gradually decreases, indicating a progressive increase in irreversible damage. Shear-mode microcracks also become more prominent with applied stress and confining pressures, as supported by varying AF/RA values of AE signals. The AE signals also follow the Hurst statistical law, and increasing applied stress and confining pressure strengthen this statistical pattern with a higher Hurst index. Throughout the cyclic loading process, certain AE varying trends were observed: the autocorrelation coefficient increased, the fractal dimension gradually decreased, and the variance suddenly increased. These trends serve as early, middle, and short–imminent warning signals, respectively, for the catastrophic failure of the loaded coal sample. These research findings contribute to a deeper understanding of coal failure evolution and provide a basis for early detection and warning of coalburst disasters, which are also essential for promoting the safe and sustainable development of deep coal mining operations. Full article

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China’s EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China’s EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China’s geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China’s territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain. Full article

Although continuous-variable quantum key distribution (CVQKD) systems have unconditional security in theory, there are still many cyber attacking strategies proposed that exploit the loopholes of hardware devices and algorithms. At present, few studies have focused on attacks using algorithm vulnerabilities. The low-rate denial-of-service (LDoS) attack is precisely an algorithm-loophole based hacking strategy, which attacks by manipulating a channel’s transmittance T. In this paper, we take advantage of the feature that the power spectral density (PSD) of LDoS attacks in low frequency band is higher than normal traffic’s to detect whether there are LDoS attacks. We put forward a detection method based on the Bartlett spectral estimation approach and discuss its feasibility from two aspects, the estimation consistency and the detection accuracy. Our experiment results demonstrate that the method can effectively detect LDoS attacks and maintain the consistency of estimation. In addition, compared with the traditional method based on the wavelet transform and Hurst index estimations, our method has higher detection accuracy and stronger pertinence. We anticipate our method may provide an insight into how to detect an LDoS attack in a CVQKD system. Full article

This paper investigates the pricing formula for barrier options where the underlying asset is driven by the sub-mixed fractional Brownian motion with jump. By applying the corresponding $It\widehat{o}$’s formula, the B-S type PDE is derived by a self-financing strategy. Furthermore, the explicit pricing formula for barrier options is obtained through converting the PDE to the Cauchy problem. Numerical experiments are conducted to test the impact of the barrier price, the Hurst index, the jump intensity and the volatility on the value of barrier option, respectively. Full article

Fractional Brownian motion (FBM) is a generalization of the classical Brownian motion. Most of its statistical properties are characterized by the self-similarity (Hurst) index $0<H<1$. In nature one often observes changes in the dynamics of a system over time. For example, this is true in single-particle tracking experiments where a transient behavior is revealed. The stationarity of increments of FBM restricts substantially its applicability to model such phenomena. Several generalizations of FBM have been proposed in the literature. One of these is called multifractional Brownian motion (MFBM) where the Hurst index becomes a function of time. In this paper, we introduce a rigorous statistical test on MFBM based on its covariance function. We consider three examples of the functions of the Hurst parameter: linear, logistic, and periodic. We study the power of the test for alternatives being MFBMs with different linear, logistic, and periodic Hurst exponent functions by utilizing Monte Carlo simulations. We also analyze mean-squared displacement (MSD) for the three cases of MFBM by comparing the ensemble average MSD and ensemble average time average MSD, which is related to the notion of ergodicity breaking. We believe that the presented results will be helpful in the analysis of various anomalous diffusion phenomena. Full article

As the main defoliators of coniferous forests in Shandong Province, China, pine caterpillars (including Dendrolimus suffuscus suffuscus Lajonquiere, D. spectabilis Butler, and D. tabulaeformis Tsai et Liu) have caused substantial forest damage, adverse economic impacts, and losses of ecosystem resources. Therefore, elucidating the effects of drought on the outbreak of these pests is important for promoting forestry production and ecological reconstruction. Accordingly, the aim of the present study was to analyse the spatiotemporal variation of drought in Shandong Province, using the Standard Precipitation Index, and to investigate the impact of drought on the outbreak of pine caterpillar infestations. Future trends in drought and pine caterpillar populations were then estimated using the Hurst exponent. The results showed that: (1) Drought decreased gradually and showed a wetting trend from 1981 to 2012, with frequency decreasing on a decadal scale as follows: 1980s > 1990s > 2000s > 2010s; (2) The total area of pine caterpillar occurrence decreased strongly from 1992 to 2012; (3) Long-term or prolonged drought had a greater positive impact on pine caterpillar outbreak than short-term drought; (4) In the future, a greater portion of the province’s area will experience increased wetting conditions (57%) than increased drought (43%), and the area of pine caterpillar outbreak is estimated to decrease overall. These findings help elucidate the relationship between drought and pine caterpillar outbreak in Shandong Province and, hence, provide a basis for developing preventive measures and plans. Full article

The Yellow River Basin has been affected by global climate change. Studying the spatial–temporal variability of the hydrothermal climate conditions in the Yellow River Basin is of vital importance for the development of technologies and policies related to ecological, environmental, and agricultural adaptation in this region. This study selected temperature and precipitation data observed from 118 meteorological stations distributed in the Yellow River Basin over the period of 1957–2015, and used the Mann–Kendall, Pettitt, and Hurst indices to investigate the spatial–temporal variability of the hydrothermal climate conditions in this area. The results indicated: (1) the annual maximum, minimum, and average temperatures have increased. The seasonal maximum, minimum, and average temperatures for the spring, summer, autumn, and winter have also increased, and this trend is statistically significant (p < 0.01) between 1957–2015. The rate of increase in the minimum temperature exceeded that of the maximum temperature, and diurnal warming was asymmetric. Annual precipitation and the total spring, summer, and autumn precipitations declined, while the total winter precipitation increased, although the trend was non-significant (p > 0.05). (2) Based on the very restrictive assumption that future changes will be similar to past changes, according to the Hurst index experiment, the future trends of temperature and precipitation in the Yellow River Basin are expected to stay the same as in the past. There will be a long-term correlation between the two trends: the temperature will continue to rise, while the precipitation will continue to decline (except in the winter). However, over the late stage of the study period, the trends slowed down to some extent. Full article