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.

Basin ecosystems are vulnerable to natural resource depletion, ecological damage, and environmental pollution due to their fragile natural environment. Assessing the value of basin ecosystem services (ES) can facilitate informed decision making by policy makers and stakeholders in the context of competing resource use. The Konqi River Basin in China, an arid inland river basin, has suffered from degraded ecosystems due to overexploitation of soil and water resources. In response, the local government launched an ecological water conveyance (EWC) project in 2016 to enhance ecological restoration efforts. This paper analyzes and evaluates the value of ES in the Konqi River Basin based on land use and land cover (LULC) change characteristics before and after EWC in 2013 and 2020, respectively. Remote sensing data and related socioeconomic statistics data are used to assess a typical river basin from three unique locations in the Konqi River Basin, divided into upper and lower reaches. The results show that cropland and unused land are the most important land use types in the upper and lower reaches. The characteristics of ecosystem service value (ESV) changes in the study area are consistent with land use structure changes. The total ESV shows a decreasing trend in the upper reaches from 2013 to 2020, while the lower reaches show an increasing trend. The total ESV increases in the typical river reaches of the Konqi River Basin. Spatially, low-ESV areas are mainly located in ecologically fragile areas that are difficult to develop and use. The sensitivity indexes of the study area are all less than 1, making the results of this study credible. The Moran index shows a significant spatial correlation in the study area, indicating that the distribution characteristics of high-ESV areas are agglomerative. Hot spot areas in the upper reaches show an overall increasing trend, while in the lower reaches, former sub-hot spot areas transform into hot spot areas. Due to data limitations, this study is limited to demonstrating that the value of ES in the area changes due to a combination of EWC policies and other factors. Nevertheless, the analysis shows that EWC policies actively change the ESV of a typical river basin in Konqi. This study can provide a reference for evaluating ESV in inland river basins in the northwest arid region and a scientific basis for the rational development and utilization of water and soil resources in the study area, located in an arid and ecologically fragile area.

As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 10⁵ km²) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain.

The valuation of ecosystem services (ESs) is crucial for preserving ecosystems, assessing natural resources, and making decisions regarding compensation. In this study, we employed the InVEST model’s habitat quality (HQ) module to calculate the HQ and degradation levels in the study area using land use/land cover (LULC) data from 2000 to 2020. Our analysis utilized quantitative methods, including spatial correlation, hotspot analysis, and geo-probing, to determine the value of ESs and identify trends. Furthermore, we examined the spatial and temporal variation in the significance of ESs and their driving factors. The results show the following. (1) The primary LULC types in the Zhundong coalfield from 2000 to 2020 are grassland and barren areas. (2) The average value of the HQ index in the study area exhibited a generally decreasing trend. Between 2000 and 2010, HQ significantly declined, particularly in the region’s large barren industrial and mining zones. However, over time, the proportion of sites with minimal degradation improved steadily, resulting in better overall HQ in the study area by 2020. This pertains to the measures put in place by the local government to safeguard and rehabilitate the ecosystem. (3) The spatial distribution of the ecosystem service value (ESV) aligns with changes in HQ and LULC, with significant hotspots primarily observed in forest and grassland areas, nature reserves, and areas around water sources. (4) LULC, temperature, annual precipitation, and elevation are the main drivers of spatial variation in the ESV in the Zhundong area; the spatial variation in the ESV in the Zhundong coalfield is primarily influenced by the interaction between human factors and natural factors, in which LULC plays a dominant role. This study’s findings can guide the development of rational ecological planning, integrating resource conservation mining with effective zoning management.

In recent years, increasing urbanization has profoundly impacted the quality of regional habitats, presenting a severe risk to the ability of a region to develop in a high-quality manner. Therefore, the scientific assessment of the features of habitat quality (HQ) evolution over time and space and the prediction of future trends in changes in the HQ are of great significance for the formulation of effective ecological protection policies. Based on five periods of land use and land cover (LULC) data from 2000 to 2020, InVEST model was used to estimate both geographical and chronological trends in the HQ in the Tuha region, China. Spatial autocorrelation analysis methods were used to assess HQ and spatial aggregation of habitat degradation, and ecological zoning was delineated in conjunction with the Human Footprint Index (HFI). Based on the results of ecological zoning, the study predicted changes in habitat quality (HQ) in 2040 under three scenarios: natural development (ND), ecological preservation (EP), and urban development (UD) by applying the Patch-Generating Land Use Simulation (PLUS) model. The results demonstrated that (1) from 2000 to 2020, the habitat quality in the Tuha region exhibited a downward trend, with the proportion of low HQ increasing from 83.63% to 84.24%. Spatially, high habitat quality (HQ) is mainly concentrated in the Tianshan Mountains. From 2000 to 2020, the Moran index for habitat quality (HQ) decreased from 0.967 to 0.959, while the Moran index for habitat degradation declined from 0.805 to 0.780. The habitat quality (HQ) and degradation exhibit significant spatial aggregation, and the degree of degradation has increased incrementally. (2) From 2000 to 2020, human activities in the Tuha area increased continuously and were mainly concentrated in Tuha district and counties. The proportion of high Human Footprint Index (HFI) increased from 0.66% to 1.32%, while the proportion of medium HFI increased from 3.13% to 7.46%. (3) The expansion of urbanized land has exacerbated habitat degradation. The proportion of high HQ in the EP scenario is higher than that in ND and UD scenario. The results show that the ecological protection scenario is more conducive to the sustainable development of habitat quality in the Tuha region. The results can provide a scientific basis for ecological management and protection in the Tuha area.

The Zhundong coalfield in Xinjiang, China, is rich in resources and has great significance to the development of the Xinjiang region, but its local ecological environment is fragile and its climate is particularly dry, so mining is very likely to introduce a series of ecological risks; there is an urgent need for us to provide scientific and feasible guidance for the conservation and development of coal resources in this region. Therefore, this paper is based on the land-use-type data concerning the Zhundong Economic and Technological Development Zone from 2000 to 2020, exploring the land use change characteristics in the Zhundong area during these 20 years and calculating the ecological risk index of each risky district according to an ecological risk index model. Afterward, this article uses kriging interpolation to carry out a risk classification analysis to explore changes in ecological risk in the Zhundong area during the last 20 years and to put forward ecological risk partition and control measures for areas of different levels of risk. Our research shows the following features: (1) The land use type in the Zhundong area changed obviously from 2000 to 2020, in which unused land has always occupied most of the area of the Zhundong coalfield. Grassland was the land use type with the greatest area transferred, 211,412.35 hm², accounting for 68.11% of the total transferred area, and it was mainly converted into unused and construction land. (2) In the last 20 years, the Zhundong coalfield has been dominated by higher-risk and high-risk areas, with obvious changes in the distribution of ecological risk levels. The low-risk, medium-risk, and higher-risk areas in the research zone have decreased and then increased; the lower-risk area has declined yearly, and the high-risk area has increased and then declined. Furthermore, overall, the ecological environment has transformed toward good condition. (3) High-risk and higher-risk areas still account for most of the research zone, and there is an urgent need for scientific and feasible programs to carry out ecological restoration in areas with different ecological risk levels to avoid further deterioration of the local environment.