With the new round of western development being pushed forward and territorial spatial planning being put into place, northwest China’s urbanization rate has sped up. Urbanization will inevitably affect the city’s general landscape pattern and features, aggravating the landscape’s fragmentation and destroying the urban ecological environment. That threatens the well-being of the residents and the city’s biodiversity. Urban green space provides a habitat for the creatures in the city, and its connectivity provides corridors. Researchers and planners have developed green space networks to protect urban biodiversity and satisfy urban residents’ needs for recreation and ecologically friendly open space. This study uses RS, GIS, SeNtinel Application Platform (SNAP), and Conefor Sensinode. Applying the landscape connectivity index, least-cost path model, and corridor curvature analysis to identify potential recreation and biodiversity conservation corridors with a reasonable width, identifies good quality green space patches and corridors, or which ones need improvement. The results show that: (1) The patches selected by the possible connectivity index (PC) calculated with a threshold of 100 m in the urban area of Kashgar have higher recreational attributes. (2) There are 24 effective recreational corridors in Kashgar, with a total length of 43.44 km, and 53 effective biodiversity conservation corridors, a total of 78.23 km. Suppose recreational and ecological functions are considered to build a comprehensive green space network. The 50 m recreational corridor is mainly distributed in the center, and the 30 m biodiversity conservation corridor is primarily distributed on edge. (3) We can determine the location of the new green space suitable for protection or development by analyzing the corridor curvature. Through the constructed green space network, we can find that green space planning has severe fragmentation, unfair distribution, and other problems. Based on these issues, optimizing urban green space can promote the connectivity of urban green space. Furthermore, studying the width of corridors suitable for dense urban areas is conducive to protecting urban biodiversity and resident well-being.

The Ili River Basin, a crucial transboundary river in the arid region of Central Asia, plays a significant role in the region’s ecology and water resources. However, current methods for monitoring glacier area changes in this region face challenges in automation and accuracy due to the complex terrain and climatic conditions. This study aims to evaluate the effectiveness of the Google Earth Engine (GEE) platform for monitoring glacier area changes in the Ili River Basin from 1992 to 2020, with a focus on improving data accuracy and processing efficiency. Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. Our results demonstrated that the GEE platform, combined with the RF algorithm, provided high accuracy in glacier monitoring, achieving an overall accuracy of 89% and a kappa coefficient of 0.85. During the study period, the glacier area in the Ili River Basin decreased by 184.76 km², with an average annual retreat rate of 6.84 km², most notably between 3800 and 4400 m in elevation. The analysis revealed that temperature changes had a more pronounced impact on glacier dynamics than precipitation. This approach significantly enhances image utilization efficiency and data processing speed, offering a reliable tool for monitoring glacier dynamics. Future research should focus on integrating additional environmental variables and extending the temporal scope to further refine glacier dynamics modeling and predictions.

Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm⁻²; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies.

Arid regions are considered to be among the most ecologically fragile and highly sensitive to environmental change globally, and land use and land cover conditions in the region directly influence large-scale ecosystem processes. Currently, thanks to diverse remote sensing platforms, geographers have developed an array of land cover products. However, there are differences between these products due to variations in spatio-temporal resolutions. In this context, assessing the accuracy and consistency of different land cover products is crucial for rationalizing the selection of land cover products to study global or regional environmental changes. In this study, Xinjiang Uygur Autonomous Region (XUAR) is taken as the study area, and the consistency and performance (type area deviation, spatial consistency, accuracy assessment, and other indexes) of the five land cover products (GlobeLand30, FROM_GLC30, CLCD, GLC_FCS30, and ESRI) were compared and analyzed. The results of the study show that (1) the GlobeLand30 product has the highest overall accuracy in the study area, with an overall accuracy of 84.06%, followed by ESA with 75.57%, while CLCD has the lowest overall accuracy of 70.05%. (2) The consistency between GlobeLand30 and CLCD (area correlation coefficient of 0.99) was higher than that among the other products. (3) Among the five products, the highest consistency was found for water bodies and permanent snow and ice, followed by bare land. In contrast, the consistency of these five products for grassland and forest was relatively low. (4) The full-consistency area accounts for 49.01% of the total study area. They were mainly distributed in areas with relatively homogeneous land cover types, such as the north and south of the Tianshan Mountains, which are dominated by bare land and cropland. In contrast, areas of inconsistency make up only 0.03% and are mostly found in heterogeneous areas, like the transitional zones with mixed land cover types in the Altai Mountains and Tianshan Mountains, or in areas with complex terrain. In terms of meeting practical user needs, GlobeLand30 offers the best comprehensive performance. GLC_FCS30 is more suitable for studies related to forests, while FROM_GLC30 and ESRI demonstrate greater advantages in identifying permanent ice and snow, whereas the performance of CLCD is generally average.

Nitrogen inputs exert significant impacts on plant species composition and ecosystem stability within alpine grasslands. The exploration of leaf functional traits holds great potential in uncovering plants’ adaptive strategies and competitive edges, and is pivotal in comprehending the ramifications of nitrogen inputs on biodiversity. In this study, the Bayanbulak grassland was selected as the research subject to investigate the impact of nitrogen addition on leaf functional traits of different plant functional groups. Specifically, various gradients of nitrogen addition were established to observe changes in leaf dry matter content (LDMC) and leaf area (LA) among three distinct plant functional groups. Furthermore, structural equation modeling (SEM) was employed to analyze the pathways through which nitrogen addition influenced the LDMC of these plant functional groups. The results were as follows: (1) LA and leaf length (LL) of Poaceae changed significantly (p < 0.05) under different N addition gradients, and leaf nutrient contents of Poaceae, Rosaceae and Fabaceae showed significant changes under different N addition gradients. (2) Pearson correlation analyses showed that total nitrogen (TN), total carbon (TOC) and leaf width (LW) of Rosaceae leaves had a significant positive correlation, and the TOC and total phosphorus (TP) of Fabaceae leaves showed a significant negative correlation. (3) SEM of the three plant functional groups showed direct and indirect effects of N addition on leaf dry matter content of Poaceae and Rosaceae, and only indirect effects on Fabaceae.

Under the background of intensifying global change, the frequent occurrence of agrometeorological disasters has an adverse impact on the social economy. Low-temperature weather in spring is one of the main agrometeorological disasters in Xinjiang. Studying the spatial and temporal characteristics of low temperatures in spring in Xinjiang is of great significance. However, research on the spatiotemporal variation of spring frost in arid areas is currently lacking, which limits our understanding of the occurrence and development mechanism of typical disastrous weather events in arid areas. Therefore, on the basis of the daily meteorological data of 40 meteorological stations in the Xinjiang Province of China from 1971 to 2020, we studied the spatiotemporal heterogeneity of spring frost in Xinjiang through trend analysis, the Mann–Kendall test, Kriging interpolation, and wavelet analysis. Results showed that the frequency of spring frost had the spatial trend from Northern Xinjiang to Southern Xinjiang. The occurrence frequency of spring frost in the entire and northern region of Xinjiang had an upward trend, whereas that in Southern Xinjiang showed a downward trend. Severe and moderate spring frost events mainly occurred in Xinjiang. The duration of spring frost had the characteristics of Northern Xinjiang to Southern Xinjiang. The spring frost in Northern Xinjiang mainly occurred in April, and that of Southern Xinjiang was in March. Obvious 15-, 10-, and 20-year oscillation cycles were observed in the occurrence frequency of spring frost in the entire, southern, and northern regions of Xinjiang, respectively. This study can provide a useful reference for the prediction and research corresponding to the occurrence mechanism of spring frost in arid areas.