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OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Dryland ecosystems are highly vulnerable to extreme droughts under climate change. Yet, response of vegetation productivity across global drylands to changes in drought stress in a warming climate remains obscure. Here, we investigated future changes in drought stress, characterized by low soil moisture (SM) and high vapor pressure deficit (VPD), under severe drought conditions and its impact on gross primary productivity (GPP) deviations in drylands, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system model (ESM) simulations. Under both intermediate (SSP2-4.5) and high (SSP5-8.5) emission scenarios, the dryland ecosystems are projected to experience more intense, extensive and frequent severe drought events owing to increasing VPD. The probabilities of high VPD-dominated drought stress in the end of the 21st century would be nearly double (2.1–2.4 times) of the present-day (39%). Excluding the carbon dioxide (CO2) fertilization effect, the annual GPP loss caused by severe drought is projected to further deteriorate over more than half fraction (56.9–70.9%) of global vegetated dryland areas, reaching 2.0 (1.9–2.2) times of the present-day (with an area-weighted total of −21.5 KgC m−2 yr−1) by the end of the 21st century. Such aggravating reduction is predominantly induced by drought stress with higher-than-usual VPD anomaly. The high VPD-dominated drought stress would lead to approximately 100% (95–102%) of annual aggregated dryland GPP loss by the end of 21st century from the present-day 68%. Our results suggest an increasing risk of high atmospheric aridity-dominated drought stress on dryland ecosystems. It is of great urgency to make adaption and mitigation strategies for the natural and cultivated vegetation in drylands.

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons’ NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced “greening” process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant “browning” trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of “greening in the east and browning in the west” of Central Asia. During spring, the dominant theme was the “greening” of vegetation NDVI, although there was noticeable “browning” observed in southwest region of Central Asia. During summer, the “browning” of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the “browning” trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the “greening” trend of vegetation NDVI in arid regions, which further aggravates the “browning” trend of vegetation NDVI.

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.

The ecosystem service value (ESV) is an important basis for measuring an ecological environment’s quality and the efficient management of ecosystems. It is particularly necessary to explore a proven methodology for assessing and predicting ESV dynamics coupled with policy-oriented scenarios that can provide a theoretical groundwork for macro decision, particularly in the context of implementing ecological protection and restoration projects. This study selected the land cover (LC) of Sichuan Province at five periods and the spatiotemporal dynamic equivalent factor method to assess the ESVs from 2000 to 2020. Additionally, the study coupled the Markov chain and GeoSOS-FLUS model, and predicted the future pattern of ESVs under four future development scenarios. The results show that (a) the areas of forests, shrubs, waters, wastelands, wetlands, and impervious areas showed a continuous increase from 2000 to 2020, with the most frequent interchanges occurring among croplands, forests, and grasslands. (b) The implementation of ecological protection and restoration projects led to a 13,083.32 × 108 yuan increase in ESV, and barycenter of the ESVs is located in the northeastern part of Ya’an and exhibits a tendency to move towards the northeast. (c) The ESV aggregation pattern of each city has remained unchanged, with Ganzi being the only city with a high aggregation. Overall, there are more conflict cities than coordination cities between economic development and the ecological environment. (d) The total ESV in 2025 will continue to increase under all development scenarios, reaching a maximum of 50,903.37 × 108 yuan under the EP scenario. This study can provide insights for ecological planning decisions and sustainable regional socio-economic development.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Spatiotemporal variations in Central Asian vegetation phenology provide insights into arid ecosystem behavior and its response to environmental cues. Nevertheless, comprehensive research on the integrated impact of meteorological factors (temperature, precipitation, soil moisture, saturation vapor pressure deficit), topography (slope, aspect, elevation), and greenhouse gases (carbon dioxide, methane, nitrous oxide) on the phenology of Central Asian vegetation remains insufficient. Utilizing methods such as partial correlation and structural equation modeling, this study delves into the direct and indirect influences of climate, topography, and greenhouse gases on the phenology of vegetation. The results reveal that the start of the season decreased by 0.239 days annually, the length of the season increased by 0.044 days annually, and the end of the season decreased by 0.125 days annually from 1982 to 2021 in the arid regions of Central Asia. Compared with topography and greenhouse gases, meteorological factors are the dominant environmental factors affecting interannual phenological changes. Temperature and vapor pressure deficits (VPD) have become the principal meteorological elements influencing interannual dynamic changes in vegetation phenology. Elevation and slope primarily regulate phenological variation by influencing the VPD and soil moisture, whereas aspect mainly affects the spatiotemporal patterns of vegetation phenology by influencing precipitation and temperature. The findings of this study contribute to a deeper understanding of how various environmental factors collectively influence the phenology of vegetation, thereby fostering a more profound exploration of the intricate response relationships of terrestrial ecosystems to environmental changes.

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.

Xinjiang grasslands play a crucial role in regulating the regional carbon cycle and maintaining ecosystem stability, and grassland net primary productivity (NPP) is highly vulnerable to drought. Drought events are frequent in Xinjiang due to the impact of global warming. However, there is a lack of more systematic research results on how Xinjiang grassland NPP responds to drought and how its heterogeneity is characterized. In this study, the CASA (Carnegie Ames Stanford Application) model was used to simulate the 1982–2020 grassland NPP in Xinjiang, and the standardized Precipitation Evapotranspiration Index (SPEI) was calculated using meteorological station data to characterize drought. The spatial and temporal variability of NPP and drought in Xinjiang grasslands from 1982 to 2020 were analyzed by the Sen trend method and the Mann-Kendall test, and the response characteristics of NPP to drought in Xinjiang grasslands were investigated by the correlation analysis method. The results showed that (1) the overall trend of NPP in Xinjiang grassland was increasing, and its value was growing season > summer > spring > autumn. Mild drought occurred most frequently in the growing season and autumn, and moderate drought occurred most frequently in spring. (2) A total of 64.63% of grassland NPP had a mainly concurrent effect on drought, and these grasslands were primarily located in the northern region of Xinjiang. The concurrent effect of drought on NPP was strongest in plain grassland and weakest in alpine subalpine grassland. (3) The lagged effect is mainly in the southern grasslands, the NPP of alpine subalpine meadows, meadows, and alpine subalpine grasslands showed mainly a 1-month time lag effect to drought, and desert grassland NPP showed mainly a 3-month time lag effect to drought. This research can contribute to a reliable theoretical basis for regional sustainable development.

With the development of human society, the balance between the minimum ecological instream flow requirement (MEIFR), which is an essential part of the ecological water demand in arid areas, and anthropogenic water depletion has received increasing attention. However, due to the lack of hydrological station data and river information on arid basins, previous researchers usually considered only the individual ecological water demand of rivers, lakes, or oases. To address this issue, a new method that combines river hydraulic parameters and the wet circumference obtained by an unmanned aerial vehicle (UAV) and remote sensing hydrological station (RSHS) technologies was applied to obtain the MEIFR and, then, systematically and quantitatively explore the balance from the perspective of the entire basin of Aiding Lake from 1990 to 2022, which is the lowest point of Chinese terrestrial territory. The results showed the following: (1) since 1990, the discharge of the seven rivers in the study area increased by 1–6%, and the MEIFR of these rivers increased by 15–100%; both quantities decreased by 3–5% from the upper to the lower reaches of the basin; (2) the surface area and water level of Aiding Lake decreased by 5% and 14%, respectively, but the MEIFR first decreased by 25% from 1990 to 2013 and, then, increased by 66.7% from 2013 to 2022; and (3) from 2011 to 2022, the MEIFR and anthropogenic water depletion exhibited a balance. Against the background of climate change, this research revealed that the MEIFR of the rivers in the Aiding Lake Basin have shown an upward trend over the past 30 years and quantitatively determined the above balance relationship and the period of its occurrence. This study supplied a method that could provide guidance for water resource management by decision-makers at a global level, thus helping achieve the sustainable development goals (SDGs).

In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Under global warming, the gradual pattern of spring phenology along elevation gradients (EG) has significantly changed. However, current knowledge on the phenomenon of a more uniform spring phenology is mainly focused on the effect of temperature and neglected precipitation. This study aimed to determine whether a more uniform spring phenology occurs along EG in the Qinba Mountains (QB) and explore the effect of precipitation on this pattern. We used Savitzky-Golay (S-G) filtering to extract the start of season (SOS) of the forest from the MODIS Enhanced Vegetation Index (EVI) during 2001-2018 and determined the main drivers of the SOS patterns along EG by partial correlation analyses. The SOS showed a more uniform trend along EG in the QB with a rate of 0.26 ± 0.01 days 100 m-1 per decade during 2001-2018, but there were differences around 2011. A delayed SOS at low elevations was possibly due to the reduced spring precipitation (SP) and spring temperature (ST) between 2001 and 2011. Additionally, an advanced SOS at high elevations may have been caused by the increased SP and reduced winter temperature (WT). These divergent trends contributed to a significant uniform trend of SOS with a rate of 0.85 ± 0.02 days 100 m-1 per decade. Since 2011, significantly higher SP (especially at low elevations) and rising ST advanced the SOS, and the SOS at lower altitudes was more advanced than at higher altitudes, resulting in greater SOS differences along EG (0.54 ± 0.02 days 100 m-1 per decade). The SP determined the direction of the uniform trend in SOS by controlling the SOS patterns at low elevations. A more uniform SOS may have important effects on local ecosystem stability. Our findings could provide a theoretical basis for establishing ecological restoration measures in areas experiencing similar trends.

Abstract Modeling and forecasting tourism demand across destinations has become a priority in tourism research. Most tourism demand studies rely on annual statistics with small sample sizes and lack research on spatial heterogeneity and drivers of tourism demand. This study proposes a new framework for measuring inter-provincial tourism demand's spatiotemporal distribution using search engine indices based on a geographic perspective. A combination of spatial autocorrelation and Geodetector is utilized to recognize the spatiotemporal distribution patterns of tourism demand in 2011 and 2018 in 31 provinces of mainland China and detect its driving mechanisms. The results reveal that the spatial distribution of tourism demand manifests a vital stratification phenomenon with significant spatial aggregation in the southwest and northeast of China. Traffic conditions, social-economic development level, and physical conditions compose a constant and robust interaction network, which dominates the spatial distribution of tourism demand in different development stages through different interactions.

With China’s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments.

This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities.

The evolution of a glacial lake is a true reflection of glacial and climatic change. Currently, the study of glacial lakes in the Altai Mountains is mainly concerned with the application of high-resolution remote sensing images to monitor and evaluate the potential hazards of glacial lakes. At present, there is no rapid and large-scale method to monitor the dynamical variation in glacial lakes in the Altai Mountains, and there is little research on predicting its future tendency. Based on the supervised classification results obtained by Google Earth Engine (GEE), combined with an analysis of meteorological data, we analyzed the spatial and temporal variations in glacial lakes in the Altai Mountains between 2000 and 2020, and used the MCE-CA-Markov model to predict their changes in the future. According to the results, as of 2020, there are 3824 glacial lakes in the Altai Mountains, with an area of 682.38 km2. Over the entire period, the glacial lake quantity growth rates and area were 47.82% and 17.07%, respectively. The distribution of glacial lakes in this region showed a larger concentration in the north than in the south. Most glacial lakes had areas smaller than 0.1 km2, and there was minimal change observed in glacial lakes larger than 0.2 km2. Analyzing the regional elevation in 100 m intervals, the study found that glacial lakes were predominantly distributed at elevations from 2000 m to 3000 m. Interannual rainfall and temperature fluctuations in the Altai Mountains have slowed since 2014, and the trends for the area and number of glacial lakes have stabilized. The growth of glacial lakes in both number and surface area is expected to continue through 2025 and 2030, although the pace of change will slow. In the context of small increases in precipitation and large increases in temperature, in the future, glacial lakes with faster surface area growth rates will be located primarily in the southern Altai Mountains.

Pedicularis longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) are alpine plants and traditional Chinese medicines with important medicinal value, and future climate changes may have an adverse impact on their geographic distribution. The maximum entropy (MAXENT) model has the outstanding ability to predict the potential distribution region of species under climate change. Therefore, given the importance of the parameter settings of feature classes (FCs) and the regularization multiplier (RM) of the MAXENT model and the importance of add indicators to evaluate model performance, we used ENMeval to improve the MAXENT niche model and conducted an in-depth study on the potential distributions of these two alpine medicinal plants. We adjusted the parameters of FC and RM in the MAXENT model, evaluated the adjusted MAXENT model using six indicators, determined the most important ecogeographical factors (EGFs) that affect the potential distributions of these plants, and compared their current potential distributions between the adjusted model and the default model. The adjusted model performed better; thus, we used the improved MAXENT model to predict their future potential distributions. The model predicted that P. longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) would move northward and showed a decrease in extent under future climate scenarios. This result is important to predict their potential distribution regions under changing climate scenarios to develop effective long-term resource conservation and management plans for these species.

Background: Pancreatic ductal adenocarcinoma (PDAC) is dominated by an immunosuppressive microenvironment, which makes immune checkpoint blockade (ICB) often non-responsive. Understanding the mechanisms by which PDAC forms an immunosuppressive microenvironment is important for the development of new effective immunotherapy strategies.Methods: This study comprehensively evaluated the cell-cell communications between malignant cells and immune cells by integrative analyses of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC. A Malignant-Immune cell crosstalk (MIT) score was constructed to predict survival and therapy response in PDAC patients. Immunological characteristics, enriched pathways, and mutations were evaluated in high- and low MIT groups.Results: We found that PDAC had high level of immune cell infiltrations, mainly were tumor-promoting immune cells. Frequent communication between malignant cells and tumor-promoting immune cells were observed. 15 ligand-receptor pairs between malignant cells and tumor-promoting immune cells were identified. We selected genes highly expressed on malignant cells to construct a Malignant-Immune Crosstalk (MIT) score. MIT score was positively correlated with tumor-promoting immune infiltrations. PDAC patients with high MIT score usually had a worse response to immune checkpoint blockade (ICB) immunotherapy.Conclusion: The ligand-receptor pairs identified in this study may provide potential targets for the development of new immunotherapy strategy. MIT score was established to measure tumor-promoting immunocyte infiltration. It can serve as a prognostic indicator for long-term survival of PDAC, and a predictor to ICB immunotherapy response.

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river's end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin.

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands; and the benefits of the ecological water diversion through overflowing surface water irrigation are unclear. To address the above issues, this study examines the intra-annual changes and its influencing factors in ecological water diversion (inundation) in the Daliyaboyi Oasis in the lower Keriya River valley within the Tarim Basin, discusses whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands, and assesses the ecological benefits of overflowing surface water irrigation. Inundation was quantified by digitizing monthly changes in the inundated area from 2000 to 2018 in the oasis using 184 Landsat images. The results demonstrate that seasonal changes in the inundated area varied significantly, with maximum peaks occurring in February and August; a period of minimal inundation occurred in May. Differences in the July/August peak (i.e., July or August) in inundation dominated the inter-annual variations in the inundated area over the 19-year study period. The two peaks in the inundation area were temporally consistent with the vegetation’s seasonal water demand. Local residents have used ecological water to irrigate vegetation in different parts of the oasis during different seasons, an approach that expanded the inundated area. The February peak in the inundated area is closely linked to elevated downstream groundwater levels and the melting of ice along the river. The August peak is related to a peak in runoff from headwater areas. The minimum May value is correlated to a relatively low value in upstream runoff and an increase in agricultural water demand. Thus, natural factors control the intra-annual and inter-annual variations in the inundated area. Humans changed the spatial distribution of the inundated area and enhanced the water’s ecological benefits, but did not alter the correlation between peak periods of inundation and vegetation water demand. The results from this study improve our understanding of the benefits of the EWDP in the Tarim River Basin.

Background: Angiotensin-converting enzyme 2 (ACE2) is implicated as a host cell receptor that causes infection in the pathogenesis of coronavirus disease 2019 (COVID-19), and its genetic polymorphisms in the ACE2 gene may promote cardiovascular disease and systemic inflammatory injury in COVID-19 patients. Hence, the genetic background may potentially explain the broad interindividual variation in disease susceptibility and/or severity. Methods: Genetic susceptibility to COVID-19 was analyzed by examining single-nucleotide polymorphisms (SNPs) of ACE2 in 246 patients with COVID-19 and 210 normal controls using the TaqMan genotyping assay. Results: We demonstrated that the ACE2 SNPs rs4646142, rs6632677, and rs2074192 were associated with COVID-19 (for all, p < 0.05), and the differences in the ACE2 SNPs rs4646142 and rs6632677 were correlated with COVID-19-related systemic inflammatory injury and cardiovascular risk. Specifically, rs4646142 was associated with high-sensitivity C-reactive protein (hs-CRP), prealbumin (PAB), apolipoprotein A (APOA), high-density lipoprotein (HDL), and acid glycoprotein (AGP) levels. Rs6632677 was also associated with elevated CRP, acid glycoprotein (AGP), and haptoglobin (HPT). Conclusions: Our results suggest that the ACE2 SNPs rs4646142 and rs6632677 may be common genetic loci and optimal early identification genetic markers for COVID-19 with cardiovascular risk.

Xinjiang, an arid region of China, has experienced a substantial warming–wetting trend over the past five decades. However, climate change has affected vegetation growth/greening in arid Central Asia in unexpected ways due to complex ecological effects. We found a significant greening trend (consistent increase in the normalized difference vegetation index or NDVI) from 1982 to 1996, during the growing season; however, the NDVI consequently decreased and plateaued from 1997 to 2015, especially in naturally vegetated regions. Atmospheric vapor pressure deficit (VPD) is a critical driver of vegetation growth, is a direct measure of atmospheric aridity, and has increased sharply in recent decades. A partial correlation analysis indicated a significant relationship between growing season NDVI and VPD from 1997 to 2015. This implies that decreased VPD corresponds to increasing NDVI, and increasing VPD corresponds to a decrease and plateauing in the NDVI trend. Using the partial derivative equation method, our results suggest that the trend in growing season NDVI was affected primarily by increasing VPD (contributing 87.57%) from 1997 to 2015, especially in the grassland and desert biomes. Rising temperatures lead to a greater VPD, resulting in exacerbated evaporative water loss. Soil drought and atmospheric aridity limit plant stomatal conductance and could effectively lead to a decrease in the greening trend and increased vegetation mortality in arid Xinjiang. Our results emphasize the importance of VPD as a limiting factor of greening trends in arid regions. The influence of VPD on vegetation growth should be considered when evaluating arid ecosystem functioning under global warming.

Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions.

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data. [ABSTRACT FROM AUTHOR]