Glaciers are a critical freshwater resource in the arid northwest of China, where vulnerability to glacier change is closely linked to regional water security, ecological stability, and socio-economic development. Using remote sensing imagery, reanalysis of meteorological data, and socio-economic statistics, we construct an Exposure-Sensitivity-Adaptive Capacity (ESA) assessment framework and corresponding indicator system to evaluate glacier change vulnerability in the Chinese Altai Mountains. We analyze its spatiotemporal patterns from 2000 to 2020 and apply an obstacle model to identify key factors impeding vulnerability reduction. Our results indicate that vulnerability to glacier change increased consistently during 2000–2020, with notable spatial heterogeneity: lower vulnerability in the southwestern and central areas, and higher vulnerability in the northern and eastern regions. As socio-economic conditions improved over this period, the primary obstacles to reducing vulnerability shifted from factors related to adaptive capacity and sensitivity—such as urban fixed-asset investment and total grain output—to those associated with exposure. By 2020, vulnerability was driven mainly by glacier change dynamics, regional development, and growing population pressure. To regulate regional vulnerability, we propose several mitigation pathways, including increasing urban fixed-asset investment, improving water use efficiency, managing population growth, engaging in climate change mitigation initiatives, and implementing direct glacier protection measures.

Extreme precipitation in Northeast China is often influenced by the Northeast China Cold Vortex (NCCV), yet the microphysical processes operating within such systems remain poorly characterized. In this study, we investigate an NCCV-associated squall line that occurred over Liaoning Province by integrating S-band polarimetric radar with ground-based disdrometer measurements. The analysis focuses on raindrop size distribution (DSD) characteristics and three-dimensional microphysical structure in both convective and stratiform regimes. A comparison is also performed between this squall line and a Mei-yu frontal event, with emphasis on DSD differences and the underlying mechanisms. Observations indicate that convective precipitation within the NCCV squall line exhibits a continental-type DSD, marked by relatively low drop concentrations but larger raindrops when compared with other heavy rainfall regimes across China. In contrast, the Mei-yu frontal convection displays a transitional DSD, falling between maritime and continental types, characterized by smaller but more numerous raindrops. Vertically, the mature squall line features well-defined columns of differential reflectivity (ZDR) and specific differential phase (KDP) extending above the melting level within convective regions, signaling vigorous riming growth of graupel and hail sustained by strong updrafts. Meanwhile, the stratiform region is dominated by ice crystals and aggregates, formed primarily through deposition and aggregation. As these ice-phase particles melt, subsequent collision-coalescence and evaporation-driven size sorting collectively shape the observed surface DSD, which is large in size yet sparse in number. In contrast to the Mei-yu frontal system, the NCCV squall line develops under drier and more unstable atmospheric conditions that favor deep convection and active ice-phase microphysics. The Mei-yu environment, by contrast, is relatively moist and stable, promoting shallower convection where warm-rain processes prevail. These differences in thermodynamic settings directly account for the distinct DSD signatures observed between the two systems. Future research involving multi-case analyses with integrated observational datasets will be essential to quantitatively assess how environmental and aerosol factors modulate these heavy precipitation events.

This paper treats the launch of high-speed rail as a quasi-natural experiment in improving transportation accessibility. Based on 2008-2024 matched data on Chinese cities and firms, it employs a multi-period difference-in-differences model to systematically examine the impact of transportation accessibility on labor skill premiums. The study finds that the launch of high-speed rail significantly increases firms’ labor skill premiums. Mechanism tests indicate that transportation accessibility primarily strengthens the labor skill premium by increasing firms’ relative demand for highly skilled labor, promoting inter-city human capital mobility, and enhancing the operational efficiency of firms’ supply chains. The effect of transportation accessibility on skill premiums is more pronounced for non-state-owned enterprises, high-tech enterprises, and manufacturing firms; at the regional level, its impact is more pronounced on enterprises in areas with high population density, intense market competition, and in the eastern region. This study provides new empirical evidence for understanding the relationship between transportation infrastructure and income distribution in the labor market.

Rapid urban expansion in the Chaohu Lake Basin (Anhui Province, China) has profoundly altered the land use and ecosystem characteristics over the past two decades. This study investigates the spatiotemporal dynamics of this expansion and its coupled relationship with ecological sensitivity. Using Landsat imagery on the Google Earth Engine platform, we quantified land use and ecological sensitivity changes from 2000 to 2020. The land use change was dramatic, driven by urban expansion: the built-up area increased from 311.0 to 3885.9 km(2), while cropland decreased by similar to 41 % (4112.48 km(2)). Concurrently, the proportion of the ecologically insensitive areas (dominated by new built-up land) increased from 2.91 % to 28.35 % of the basin, while the extremely sensitive areas (protected forests and water bodies) remained at similar to 4 %. Geodetector analysis revealed that land use type was the dominant driver (q > 0.75) of the spatial variations in ecological sensitivity. The coupling coordination modeling revealed a marked increase in the synergy between land use and ecological sensitivity, especially from 2010 to 2020. Overall, 45.8 % of the basin experienced improved coordination, underscoring that targeted land use planning and conservation policies can be effective in mitigating ecological pressure even during periods of rapid urbanization. These results clarify the co-evolution of urban-driven land use dynamics and ecological vulnerability, providing a scientific basis for achieving targeted ecological protection and sustainable development.

A precise understanding of the status and causal mechanisms of regional ecological environment quality (EEQ) is fundamental to ecological conservation. This is especially crucial in ecologically fragile arid regions. The unique artificial oasis and desert-mountain natural landscape units on the northern slope of the Tianshan Mountains (NSTM) have formed human activity zones (HAZ) and non-human activity zones (NHAZ) with obvious natural-human characteristics. This study, using the region as an example, constructs an improved remote sensing ecological index (IRSEI) tailored for arid regions and leverages the google earth engine (GEE) platform to analyze the spatiotemporal variations in EEQ in both HAZ and NHAZ. Innovatively, it integrates multiple analytical models to propose a comprehensive analytical framework that encompasses “overall explanatory power—spatial heterogeneity—interaction relationship” for the influencing factors, thereby revealing the causal mechanisms of EEQ. The results indicate that: (1) the levels of EEQ across different zones in NSTM are ordered as follows: HAZ (0.387) > Study area (0.345) > NHAZ (0.307), with the NHAZ showing a decreasing trend. Notably, 93.01 % of the significant improvement areas are distributed in the HAZ. (2) From the perspective of the overall explanatory power of factors, ecological factors have the most significant impact on EEQ, followed by climatic factors, with some human activity factors having a relatively weaker explanatory power. The interaction effects between land use intensity, ecological factors, and other factors stand out as particularly influential in explaining EEQ. (3) The spatial heterogeneity of various factors on EEQ is significant, yet there are also similar characteristics. The influence exerted by factors within the HAZ and mountainous forest belts differs significantly from that of the surrounding areas. (4) The interactive relationships among factors vary across different zones. Human activity factors have a significant positive indirect effect on the EEQ of HAZ through their impact on ecological factors, whereas in NHAZ, it is the topographic factors that exert a significant positive indirect effect on EEQ through their influence on climatic factors. This study furnishes novel perspectives and methodologies for the assessment of EEQ as well as the comprehension of its causal mechanisms within arid regions. Moreover, it supplies valuable scientific evidence to support regional ecological environment management and sustainable development.

Renewable energy is one of the key factors in mitigating climate change and achieving sustainable development. With digital technology at its core, the digital economy has gradually become a new driving force for renewable energy development. However, few studies have examined the impact of the digital economy on renewable energy from a global perspective and explored the transmission mechanisms. Based on the cross-country data of 68 countries (regions) from 2013 to 2021, this paper adopts a panel model to study the impacts of the digital economy on renewable energy. The results show that (1) digital economy has a positive impact on renewable energy; (2) the impact of digital economy on renewable energy is asymmetric and heterogeneous; (3) the impact of digital economy on renewable energy development has obvious threshold characteristics; (4) digital economy indirectly affects renewable energy through technological innovation and financial development. The research in this paper provides a theoretical basis for promoting renewable energy development and a reference and guidance for countries to realize sustainable development in the context of the digital economy.

Rock glaciers are distinctive debris landforms found worldwide in cold mountainous regions. They express the long-term movement of perennially frozen ground. Rock Glacier Velocity (RGV), defined as the time series of the annualized surface velocity of a rock glacier unit or a part of it, has been accepted as an Essential Climate Variable Permafrost Quantity in 2022. This review aims to highlight the relationship between rock glacier velocity and climatic factors, emphasizing the scientific relevance of interannual rock glacier velocity in generating RGV products within the context of observed rock glacier kinematics. Under global warming, rock glacier velocity exhibits widespread (multi-)decennial acceleration. This acceleration varies regionally in onset timing (from the 1950s to the 2010s) and magnitude (up to a factor of 10), and has been observed in regions such as the European Alps, High Mountain Asia, and the Andes. Despite different local conditions, a synchronous interannual velocity pattern prevails in the European Alps since the 2000s, highlighting the primary influence of climate. A common pattern is the seasonal velocity rhythm, which peaks in late summer to autumn and declines in spring. RGV assesses permafrost evolution via (multi-)decennial and interannual changes in rock glacier velocity, influenced by air temperature shifts with varying time lags and snow cover effects. Although not integrated into the RGV products, seasonal variations should be examined. This rhythmic behavior is attributed to alterations in pore water pressure influenced by air temperature, snow cover, and ground water conditions.

Increasing landslide activities in cold regions have been attributed to rising temperatures and consequent permafrost degradation.While previous studies have linked permafrost degradation to slope instability,the elevation-dependent effects of this degradation on landslide oc-currences in the high-mountain regions of the Qinghai-Tibet Plateau(QTP)remain poorly understood,particularly concerning their spatial distribution and timing.This study addresses this gap by investigating the distribution and timing of landslides in the Babao River catchment,located in the southeastern Qilian Mountains of the northeastern QTP.Our results reveal a substantial increase in landslide events during the study period of 2009-2018:only 14 occurrences were recorded before and in 2009,22 between 2010 and 2015,and 105 during 2016-2018.Notably,we observed an upward shift in the elevation of landslide occurrences,with an average increase of approximately 130 m over the ten-year period.Analysis of annual permafrost distribution maps indicates that this shift coincides with the rising lower altitudinal limit of mountain permafrost in the study area,likely driven by increased temperatures and precipitation.These findings highlight the critical role of elevation-dependent processes in influencing landslide dynamics under changing climatic conditions,particularly the transition from undisturbed permafrost to seasonally frozen ground at higher elevations.This study provides valuable insights for disaster prevention and mitigation in high-altitude regions,emphasizing the heightened risks posed by permafrost degradation under ongoing warmer and wetter climatic conditions.

AbstractRetrogressive thaw slumps (RTSs), formed by abrupt degradation of ice‐rich permafrost, are widely distributed on the Qinghai‐Tibet Plateau, causing infrastructure damage and enhancing soil carbon emissions. We compiled annual RTS inventories across the plateau from 2016 to 2022 using a deep‐learning‐aided method to quantify the spatial‐temporal variations. We found that RTS‐affected locations increased from 1,592 to 3,805 in 2016–2022, which increased affected areas by 2.8 times from 1,714 to 6,507 ha. The most active initiation and expansion periods were in 2016–2017 and 2018–2019. RTSs tend to be clustered, showing local heterogeneity among clusters characterized by various responses toward high temperatures and precipitation and tendencies to be on different topography and vegetation types. This research reveals the rapid development, wide distribution and regional heterogeneity of RTS activities, serving as a crucial step toward understanding how RTSs respond to climate change and regional environmental varieties.

Glacial changes are crucial to regional water resources and ecosystems in the Sawir Mountains. However, glacial changes, including the mass balance and glacial meltwater of the Sawir Mountains, have sparsely been reported. Three model calibration strategies were constructed including a regression model based on albedo and in-situ mass balance of Muz Taw Glacier (A-Ms), regression model based on albedo and geodetic mass balance of valley, cirque, and hanging glaciers (A-Mr), and degree-day model (DDM) to obtain a reliable glacier mass balance in the Sawir Mountains and provide the latest understanding in the contribution of glacial meltwater runoff to regional water resources. The results indicated that the glacial albedo reduction was significant from 2000 to 2020 for the entire Sawir Mountains, with a rate of 0.015 (10a)- 1, and the spatial pattern was higher in the east compared to the west. Second, the three strategies all indicated that the glacier mass balance has been continuously negative during the past 20 periods, and the average annual glacier mass balance was -1.01 m w.e. Third, the average annual glacial meltwater runoff in the Sawir Mountains from 2000 to 2020 was 22 x 106 m3, and its

Elucidating the relationships between production-living-ecological space (PLES) and ecological environments can help identify new strategies to promote sustainable development. The northern slope of the Tianshan Mountains represents a typical complex ecosystem characterized by mountain-oasis-desert interactions in arid regions with prominent human-land conflicts and intricate ecological environments. Here, we analyzed the spatiotemporal variations in land use in the study area from the perspective of PLES. Based on moderate resolution imaging spectroradiometer (MODIS) data, an improved remote sensing ecological index (IRSEI) suitable for arid areas was established to evaluate the eco-environment quality (EEQ) by introducing salinity and cleanliness indexes. A stepwise regression model was then used to identify and quantify the impact of the PLES transfer modes on EEQ. The results showed that: (1) The oasis area was dominated by agricultural production land, whereas the desert and alpine areas were dominated by other and pasture ecological lands, respectively. The oasis area primarily involved the conversion of pasture ecological land to agricultural production land, whereas the desert and alpine areas showed mutual transformation of ecological space. (2) The size relationship of the EEQ in each sub-region was alpine region (0.555) > oasis region (0.509) > desert region (0.424). The EEQ in the study area showed an overall upward trend but decreased in the alpine region. (3) Changes in the PLES and IRSEI primarily occurred in the oasis region. Among the 21 PLES transfer modes that affect EEQ, an increase in agricultural production land was the primary mode that enhanced IRSEI, whereas an increase in impervious surfaces significantly decreased IRSEI. The degree of influence was as follows: industrial and mining production (-0.594) > urban living (-0.462) > rural living land (-0.316). In addition, EEQ is also affected by the combined effects of climate, topography, and other factors. By proposing IRSEI, we highlight the impact of land-use transfer mode on ecological environment quality from the perspective of PLES, which can provide a reference for ecological environment evaluation in arid areas and is of great significance for land-use planning and ecological environment protection.

As an important part of the cryosphere, glaciers provide important freshwater for the arid region of western China and the glacier change vulnerability is closely related to regional socioeconomic development. Based on remote sensing images, reanalysis meteorological data, relevant socio-economic data, etc., this study constructed a framework and indicator system for evaluating the glacier change vulnerability in the Chinese Altai Mountains, analyzed the spatiotemporal changes pattern of glacier change vulnerability from 2000 to 2020, and discussed the factors affecting the glacier change vulnerability by using the obstacle degree model. The results showed that firstly the glacier change vulnerability in the Chinese Altai Mountains had decreased and then increased during the period 2000-2020, and the regional differences decreased slowly in the period 2000-2010 and increased in the period 2010-2020. Secondly, the glacier change vulnerability aggregation was high for the spatial scale, showing a distribution pattern of low vulnerability in the southwest and central regions, and high vulnerability in the northern and eastern regions. Thirdly, as the socio-economic conditions had been improving, the main factors hindered the reduction of glacier change vulnerability gradually shifted from the adaptability and sensitivity factors, such as the amount of urban fixed assets investment and total grain output, to those related to exposure. By 2020, glacier change and development were the main reasons for vulnerability.

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we mainly used regression analysis, GIS spatial analysis, correlation analysis, and center-of -gravity model, to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020. Furthermore, we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern. The results indicate the following: (1) From 2001 to 2020, the LST climate average in China is 9.6°C, with a general pattern of higher temperatures in the southeast and northwest regions, lower temperatures in the northeast and Qinghai-Tibet Plateau, and higher temperatures in plains compared to lower temperatures in mountainous areas. Generally, LST has a significant negative correlation with elevation, with a correlation coefficient of −0.66. China’s First Ladder has the most pronounced negative correlation, with a correlation coefficient of −0.76 and the lapse rate of LST is 0.57°C/100 m. (2) The change rate of LST in China during the study is 0.21°C/10 a, and the warming area accounts for 78

As one of the major water supply systems for inland rivers, especially in arid and semi-arid regions, snow cover strongly affects hydrological cycles. In this study, remote sensing datasets combined with in-situ observation data from a route survey of snow cover were used to investigate the changes in snow cover parameters on the Chinese Altai Mountains from 2000 to 2022, and the responses of snow cover to climate and hydrology were also discussed. The annual snow cover frequency (SCF), snow cover area, snow depth (SD), and snow density were 45.03%, 2.27 × 104 km2, 23.4 cm, and ~0.21 g·cm−3, respectively. The snow water equivalent ranged from 0.58 km3 to 1.49 km3, with an average of 1.12 km3. Higher and lower SCF were mainly distributed at high elevations and on both sides of the Irtysh river. The maximum and minimum snow cover parameters occurred in the Burqin River Basin and the Lhaster River Basin. In years with high SCF, abnormal westerly airflow was favorable for water vapor transport to the Chinese Altai Mountains, resulting in strong snowfall, and vice versa in years with low SCF. There were significant seasonal differences in the impact of temperature and precipitation on regional SCF changes. The snowmelt runoff ratios were 11.2%, 25.30%, 8.04%, 30.22%, and 11.56% in the Irtysh, Kayit, Haba, Kelan, and Burqin River Basins. Snow meltwater has made a significant contribution to the hydrology of the Chinese Altai Mountains.

In response to the problems of high energy consumption and high-temperature instability in the preparation of ceramics and the need for low-temperature co-fired processes, some methods of reducing the sintering temperature of ceramics are needed for promising applications. The low-temperature solid-state method is a simple method to prepare materials, and it synthesizes precursors with small particle size and uniform element distribution, which has wide applicability in large-scale materials preparation. To compare the performance differences between Mn–Ni–Cu–O precursors and ceramics prepared using the low-temperature solid-state (LTSS) method and the high-temperature solid-state (HTSS) method under the same sintering condition. The temperature at which the two precursors form a pure spinel phase is revealed by X-ray diffraction analysis. The LTSS precursor forms a pure phase at 650 °C, which is 250 °C lower than that of the HTSS precursor, and the reasons for decreasing the pre-annealing temperature by the LTSS method are discussed. A comparison of the electrical properties of the two ceramics reveals that LTSS ceramics sintered at 1050 °C, which is 100 °C lower than that of the HTSS method, can obtain 96.9% relative density, high sensitivity (− 3.19%K−1), and low activation energy (0.236 eV). The conductivity mechanism is explored by XPS and complex impedance spectroscopy.

The mass elevation effect (MEE) is a thermal effect, in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift, which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas. Based on multi-source remote sensing data and field observations, a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted, and the MEE was estimated and a spatio-temporal analysis was conducted. The GeoDetector model (GDM) and a geographically weighted regression (GWR) model were applied to explore the spatial and temporal heterogeneity of the study area. Four key results can be obtained. (1) The temperature pattern is complex and diverse, and the overall temperature presented a pattern of high in the south and east, but low in the north and west. There were clear zonal features of temperature that were negatively correlated with altitude, and the temperature difference between the internal and external areas of the mountains. (2) The warming effect of mountains was prominent, and the temperature at the same altitude increased in steps from west to east and north to south. Geomorphological units, such as large valleys and intermontane basins, weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude, with the warming effect of mountains in the southern Tianshan Mountains. (3) The dominant factors affecting the overall pattern of the MEE were topography and location, among which the difference between the internal and external areas of the mountains, and the absolute elevation played a prominent role. The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors, and there was a strong interaction between terrain and climate, precipitation, the normalized difference vegetation index (NDVI), and other factors. (4) There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE. Absolute elevation was significantly positively correlated with the change of MEE, while precipitation and the NDVI were dominated by negative feedback. In general, topography had the largest effect on the macroscopic control of MEE, and coupled with precipitation, the underlying surface, and other factors to form a unique mountain circulation system and climate characteristics, which in turn enhanced the spatial and temporal heterogeneity of the MEE. The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.

High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation, and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data. In this study, we used a machine learning algorithm combined with a regression algorithm RF-PLS (Random Forest-Partial Least Squares) to construct a downscaling model to obtain three types of high-resolution TRMM (Tropical Rainfall Measuring Mission) downscaled precipitation data for the years 2000–2017 at 250 m, 500 m, and 1 km. The scale effects with topographic and geomorphological features in the study area were analysed. Finally, we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data. The results showed that: 1) The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models (PLS, RF, and RF-PLS) and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations. The accuracy of the RF-PLS model was better than the other two models. 2) Based on the RF-PLS model, the resolution of the TRMM data was increased to three different scales (250 m, 500 m, and 1 km), considering the scale effects with topographic and geomorphological features. The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales. 3) The annual precipitation was the highest in the areas with extremely high mountains, followed by the mediumhigh mountain, high mountain, medium mountain, medium-low mountain, plain, low mountain, and basin.

In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.

Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects, including fully understanding the causes and mechanisms of climate change, actively adapting to climate change, pursuing rational development, and protecting the ecological environment. Taking the north slope of Tianshan Mountains, located in the arid area of northwestern China and extremely sensitive to climate change, as the research area, this study retrieves the surface temperature of the mountain based on MODIS data, while characterizing the intensity of human activities thereby data on the night light, population distribution and land use. The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed, and the spatio-temporal correlation between them was further explored. It is found that: (1) The average human activity intensity (0.11) in the research area has kept relatively low since this century, and the overall trend has been slowly rising in a stepwise manner (0.0024·a −1 ); in addition, the increase in human activity intensity has lagged behind that in construction land and population by 1–2 years. (2) The annual average surface temperature in the area is 7.18 °C with a pronounced growth. The rate of change (0.02 °C·a −1 ) is about 2.33 times that of the world. The striking boost in spring (0.068 °C·a −1 ) contributes the most to the overall warming trend. Spatially, the surface temperature is low in the south and high in the north, due to the prominent influence of the underlying surface characteristics, such as elevation and vegetation coverage. (3) The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there, showing a strong distribution in the east section and a weak one in the west section. The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities, manifestations, and land-use changes. Vegetation-related human interventions, such as agriculture and forestry planting, urban greening, and afforestation, can effectively reduce the surface warming caused by human activities. This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.