This study analysed the spatial pattern change of land use from 2005 to 2015 in Xinjiang and proposed a Markov–Future Land Use Simulation composite model to predict land-use change by 2025 under natural growth and ecological protection scenarios. The Criteria Importance Though Intercrieria Correlation method was proposed to construct the landscape ecological risk index based on land-use data. By calculating the risk index centroid and standard deviation ellipses, the spatio-temporal patterns and changing characteristics of landscape ecological risk in different scenarios in 2005, 2015, and 2025 were evaluated, and the driving forces of their evolution were also analysed. The results indicated that: (1) The characteristics of land use pattern change under the natural growth scenario from 2015 to 2025 are similar to those from 2005 to 2015. Under an ecological protection scenario, restricting the expansion of construction land, forest land, and grassland, which are the central ecological lands, can be effectively protected. Compared to the natural growth scenario, these areas increased by 507 and 724 km², respectively. (2) From 2005 to 2015, landscape ecological risk in Xinjiang generally showed a decreasing trend. Compared to the natural growth scenario in 2025, ecological protection can effectively reduce the ecological risk of the landscape. The highest, higher, and medium ecological risks were reduced by 0.39%, 3.11%, and 4.43%, respectively, while the areas with the lowest and lower ecological risks increased by 0.16% and 7.77%, respectively. (3) Under the natural growth scenario from 2005 to 2025, the centroid of landscape ecological risk in Xinjiang generally shifted to the southwest. To some extent, the ecological protection scenario can make the distribution of the highest, the higher, and the medium ecological risk areas more dispersed and the distribution of the lower ecological risk areas more concentrated. (4) Temperature is positively correlated with landscape ecological risk, while rainfall, Digital Elevation Model, slope, and aspect are negatively correlated with landscape ecological risk. There is also a correlation between soil types and landscape ecological risk. This research can reference the theory and method of landscape ecological risk and provide decision support for the sustainable development of the ecological environment of Xinjiang.

Timely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-series UAV images, Euclidian distance (ED) was utilized to calculate the separability of samples under various vegetation indices. Second, co-occurrence measures and the gray-level co-occurrence matrix (GLCM) were employed to derive texture characteristics, and the spectral and texture features of the crops were successfully fused. Finally, random forest (RF) and other algorithms were utilized to classify crops, and the confusion matrix was applied to assess the accuracy. The experimental results indicate the following: (1) Time-series UAV remote sensing images considerably increased the accuracy of crop classification. Compared to a single-period image, the overall accuracy and kappa coefficient increased by 26.65% and 0.3496, respectively. (2) The object-oriented classification method was better suited for the precise classification of crops. The overall accuracy and kappa coefficient increased by 3.13% and 0.0419, respectively, as compared to the pixel-based classification results. (3) RF obtained the highest overall accuracy and kappa coefficient in both pixel-based and object-oriented crop classification. RF’s producer accuracy and user accuracy for cotton, spring wheat, cocozelle, and corn in the study area were both more than 92%. These results provide a reference for crop area statistics and agricultural precision management.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm—1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800–1000 nm), red region (620–700 nm), and blue-green region (420–570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

Land-use changes significantly influence carbon storage capacity by altering the structure, layout, and function of terrestrial ecosystems. Predicting the relationship between future land-use changes and carbon storage is essential for optimizing land-use patterns and making rational, ecology-based decisions. Using multi-period land-use data from Xinjiang, we analyzed the spatial pattern of carbon storage. Based on land-use change patterns in Xinjiang from 2000 to 2020, we coupled the Markov-Future Land Use Simulation (FLUS)-Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to simulate and predict land-use spatial patterns in Xinjiang for 2035 under two scenarios: natural growth and ecological protection. Carbon storage and its spatiotemporal dynamic changes under these scenarios were evaluated, and the Geodetector was employed to analyze the spatial heterogeneity of carbon storage from a statistical perspective, revealing the influence of various driving factors. The results showed that: (1) From 2000–2020, grassland and unused land were the primary land-use types in Xinjiang, accounting for over 28.85% and 60.17% of the total area, respectively. By 2035, cropland, forest, water, and construction land areas are expected to increase, while grassland and unused land areas are projected to decrease. Under the ecological protection scenario, cropland, forest land, and grassland—major main contributors to carbon storage—will be effectively conserved to some extent. (2) From 2000 to 2020, Xinjiang’s carbon storage capacity exhibited an overall increasing trend, with a cumulative increase of 137.515×10⁵ t and a growth rate of 1.58%. However, this capacity is projected to decline by 2035, with an estimated reduction of 168.344×10⁵ t compared to that in 2020. Ecological protection is anticipated to mitigate this decline, increasing carbon storage by 13.227×10⁵ t relative to the natural growth scenario. (3) Geodetector analysis indicated that land-use types had the greatest carbon storage explanatory power for carbon storage (q = 0.80), followed by soil types (q = 0.41), net primary productivity (q = 0.32), and geomorphology (q = 0.22). This highlights land-use types as the most critical environmental factor determining the spatial pattern of carbon storage. These findings provide scientific insights and recommendations for the sustainable development management and the enhancement of carbon storage functions.

Accurately portraying the mechanism of the flow of natural resource consumption between regions and its impact on ecology is of crucial value in deepening the understanding of the coordinated relationship between population, resources, environment and development. Consequently, this promotes the sustainable development of the natural economy and society. Based on a regional-industrial perspective, this study used a localized three-dimensional ecological footprint model to measure and decompose natural resources in Xinjiang from 2005 to 2020. In doing so, the study clarified the supply, demand, and flow utilization of natural capital in Xinjiang, the balance of spatial and temporal allocation of resources, the coupling between economic growth and resource consumption, and the coordination between industrial structure and ecological environment. The results showed that (1) Xinjiang's per capita ecological deficit grew from 2.096 to 11.667 in 2005-2020. Moreover, the energy footprint was a decisive part of the ecological deficit throughout the study period. Furthermore, the trend of increased ecological pressure was higher in northern and eastern Xinjiang than in southern Xinjiang. (2) The overall Gini coefficient of Xinjiang's ecological carrying capacity was at the critical value of spatial equilibrium (0.4), with differences between the groups: Northern & Southern Xinjiang > Northern & Eastern Xinjiang > Eastern & Southern Xinjiang. The reasons for this inter-regional economic disparity are related to fiscal expenditure/GDP, level of urbanization, and regional industrial output. Overall, the decoupling relationship between environmental pressures and economic growth was optimistic. (3) From an industrial perspective, the levels of industrial structural efficiency and the industrial ecological harmony index were still relatively low, but the overall trend was on the rise. (4) Resource endowment, economic development, consumption structure, and population had significant driving effects on the ecological footprint, whereas environmental protection, science, and technology could inhibit its growth to a certain extent. This study aimed to provide an in-depth analysis of the current situation and problems of natural resource use in Xinjiang and provide theoretical and practical references for sustainable development in the region.