Search Results (10)

Cotton is increasingly important in global development. The exploration of drivers of spatiotemporal patterns for cotton planting, considering spatial heterogeneity, is essential for optimizing its distribution and supporting sustainable production. This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted regression (GWR) to investigate the factors shaping cotton-planting patterns in the northern slope of the Tianshan Mountains (NSTM), China, from 2000 to 2020. Cotton distribution was derived from long-term Landsat image series, and its expansion showed an average annual growth rate of 2.10 × 10³ km², with intensive cultivation primarily distributed across the central and western counties. The dominant drivers of cotton distribution were elevation (ELE), sunshine duration (SD), slope (SLO), temperature (TEM), runoff (RO), and gross domestic product (GDP). ELE explained about 40% of the spatial heterogeneity. SD showed a declining influence, SLO remained stable, TEM increased in importance, and GDP exhibited a progressive upward trend, although weaker. Moreover, nonlinear weakening interactions, especially between ELE and other factors, as well as between socio-economic and climatic variables, substantially enhanced explanatory power. These findings highlight the significance of accounting for spatial heterogeneity and factor interactions in guiding the spatial optimization and sustainable management of cotton cultivation. Full article

Net primary productivity (NPP) reflects the carbon sequestration capacity of terrestrial ecosystems and it is used as an important indicator for measuring ecosystem quality. However, due to the effects of “warming and humidification” and “oasisization”, the spatiotemporal evolution and driving mechanisms of the NPP of vegetation in the northern slope of the Tianshan Mountains (NSTM), a typical arid area in China, are still unclear. Thus, in this study, we used remote sensing data and meteorological data to construct a Carnegie–Ames–Stanford–Approach (CASA) model for estimating the NPP of vegetation in the study area. Trend analysis, partial correlation analysis, and optimal parameter-based geographic detector (OPGD) methods were combined to explore the spatiotemporal evolution and driving mechanisms to changes in the NPP. The results showed that from 2001 to 2020, the annual average NPP on the NSTM exhibited an overall significant upward trend, increasing from 107.33 gC⋅m⁻²⋅yr⁻¹ to 156.77 gC⋅m⁻²⋅yr⁻¹, with an increase of 2.47 gC⋅m⁻² per year and 46.06% year-on-year. Over the past 20 years, climate change and human activities generally positively affected the changes in NPP in the study area. Human activities in the study area are mainly manifested in the large-scale conversion of other land use types into farmland, with a total increase of 16,154 km² in farmland area, resulting in a net increase of 6.01 TgC in NPP. Precipitation has the strongest correlation with NPP in the study area, with a partial correlation coefficient of 0.30, temperature and solar radiation have partial correlation coefficients with NPPs of 0.17 and 0.09, respectively. Therefore, increases in precipitation, temperature, and solar radiation have a promoting effect on the growth of NPP on the NSTM. During the study period, the land use type and soil moisture were the main factors that affected the spatial differentiation of vegetation NPP, and the effects of human interference on natural environmental conditions had significant impacts on vegetation NPP in the area. Therefore, in this study, we accurately determined the spatiotemporal variations in the NPP on the NSTM and comprehensively explored the driving mechanisms to provide a theoretical basis for sustainable development in arid areas and achieving carbon neutrality goals. Full article

The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for the development of western China, has experienced rapid regional economic development and high population concentration since the twenty-first century. Accompanied by the increase in human activities in the NSTM, it has significantly altered the land use structure, leading to varying levels of habitat disturbance and degradation. In this paper, based on the land use and land cover (LULC) of NSTM from 2000 to 2020. The InVEST model was employed to assess habitat quality, revealing notable spatial and temporal variations. A geoprobe was further employed to explore the key drivers of the spatially distributed pattern of habitat quality in the research region. The results show that (1) from 2000 to 2020, the NSTM was largely characterized by grassland, unused land, and cropland in terms of land use, with a notable expansion of cropland and construction land; (2) the overall habitat quality in the study area is poor, with a clear spatial distribution pattern of high in the south and low in the north, with a predominance of low grades, and a trend of decreasing and then increasing is shown in the temporal direction; (3) under the influence of rapid urbanization in the region, the degradation degree of habitat quality on the NSTM shows a distinct radial structure, with high degradation in the middle and low degradation at the edges, and shows the trend of “increase-decrease-increase” over time; and (4) the results of the geodetector show that altitude and land use type have the greatest influence on habitat quality on the NSTM, indicating that the habitat quality of the research region is primarily influenced by the type of land use. Full article

Rapid population growth and subsequent urbanization pose significant challenges of water shortage in arid regions. As an important area along the One Belt and One Road line, the Northern Slope of Tianshan Mountains (NSTM) has suffered from water shortages owing to rapid urbanization in recent decades. To conserve water resources and protect the ecosystem, understanding the temporal and spatial variations of the domestic water consumption, availability, and its influencing factors is essential. According to water resource regionalization and its characteristics in NSTM, it was divided into three sections, namely the west section, the middle section, and the east section. In addition, this work characterized the temporal and spatial variation of domestic water consumption in NSTM with a focus on the understanding of the influence of urbanization on domestic water consumption from 1990 to 2020 based on three sections. The results showed that during this period of time, construction land use increased by 2256 km² corresponding to the population increase of 158.58 × 10⁴. Subsequently, the total domestic water consumption increased from 7.55 × 10⁷ m³ in 1990 to 2.60 × 10⁸ m³ in 2020. The eastern section demonstrated steady growth, while the western and middle sections experienced larger fluctuations in domestic water consumption. Urbanization has been identified as a significant factor influencing the shift in domestic water consumption. This study offers a scientific foundation for the sustainable management of water resources in arid areas. Full article

The Northern Slope of the Tianshan Mountains (NSTM) is characterized by complex and diverse terrain, which represents a fragile ecological environment. Soil wind erosion is a key factor affecting the natural ecosystem and the social development of the region, but it has not been well understood until now. In this study, the revised wind erosion equation (RWEQ) was employed to display the spatial and temporal characteristics of soil wind erosion in the NSTM from 2000 to 2018. In addition, the main driving factors of wind erosion were analyzed. The results showed that approximately 94.25% of the NSTM experienced soil wind erosion, with a multi-year average actual soil wind erosion modulus of 6556.40 t·km⁻²·a⁻¹. From 2000 to 2018, the actual soil wind erosion modulus in the NSTM showed a trend of fluctuational increase, with an increase rate of 44.65 t·km⁻²·a⁻², but the area affected by soil wind erosion exhibited a downward trend. The wind erosion rate decreased in 76.38% of the total area, except for some areas such as Hami, with an increasing trend of soil wind erosion. The wind factor in RWEQ showed a significant linear relationship with the soil wind erosion modulus (r = 0.62, p < 0.01). Land use changes also have a critical impact on the soil wind erosion. The results of geographical detectors show that the combined effect of weather factor and vegetation factor can explain more than 60% of the changes in soil wind erosion. Full article

Taking the northern slope of the Tianshan Mountains (NSTM) in Xinjiang as the research area, this study analyzes the changes in the Production–Living–Ecological (P-L-E) Spaces and their Ecological Environmental Effects, providing a basis for optimizing the spatial pattern of the P-L-E Spaces and regional ecological environment protection in this area. Based on land use data and DEM data, various methods were used for analysis. These methods include the land use transfer matrix, ecological environment quality index, hot spot analysis, ecological contribution rate, and Terrain Position Index. The analysis focused on changes in the spatial pattern of the P-L-E Spaces from 1980 to 2020. It also examined the spatiotemporal distribution of ecological environment quality (EEQ). Furthermore, it explored the differentiation characteristics of EEQ in terrain gradients. The conclusions are as follows: (1) On the NSTM, Ecological Space decreased while Production and Living Space expanded. From 1980 to 2020, Agricultural Production Space increased rapidly. Industrial Production Space also saw rapid growth during this period. Urban Living Space expanded significantly from 1980 to 2020. Rural Living Space experienced steady growth over the same period. Forest Ecological Space initially increased but later decreased. Water Ecological Space showed an initial increase followed by a decrease from 1980 to 2020. (2) The EEQ first remained stable, declined slightly from 2000 to 2010, improved significantly, and then deteriorated from 2010 to 2020. The distribution of EEQ exhibits a “high in the northwest, low in the southeast” pattern. EEQ hot spots on the NSTM are concentrated in the Tianshan Mountains, with clustering increasing in both northern and southern areas. Cold spots are found in the southern, eastern, and northern NSTMs, with aggregation strengthening in the south and north and slightly weakening in the east. Hot spots of EEQ changes on the NSTM show stable distribution, with stronger aggregation from 2000 to 2020. However, aggregation of cold spots has gradually weakened, yet noticeable aggregation persists throughout the study period. (3) There is a significant gradient difference in EEQ distribution. Higher terrain gradients have a higher EEQ. From 1980 to 2020, lower terrain gradients saw improvement, while higher gradients experienced deterioration. The EEQ on the NSTM has declined, showing significant spatial differences, with better quality on the northern side than the southern side. Future efforts should focus on restoring the environment at lower gradients, mitigating deterioration at higher gradients, and enhancing water conservation in the Tianshan Mountains. Full article

It is of great significance for scientific land use planning and ecological security protection to clarify the impacts of land use changes on an ecosystem’s health. Based on the dynamic evolution of land use and ecosystem health on the Northern Slope of Tianshan Mountain (NSTM) from 2000 to 2020, this study utilized the patch-generating land use simulation (PLUS) model, the Vitality–Organization–Resilience–Services (VORS) model, and the elasticity approach to assess the impacts of land use changes on ecosystem health under four different scenarios: Natural Development Scenario (ND), Farmland Conservation Priority Scenario (FP), Ecological Conservation Priority Scenario (EP), and Urban Development Priority Scenario (UD). The results indicate that (1) land use on the NSTM from 2000 to 2020 was predominantly characterized by barren land and grassland. (2) The overall level of ecosystem health on the NSTM was poor from 2000 to 2020 but showed a gradual improvement trend. (3) Ecosystem health levels vary greatly across scenarios. In general, ecosystem health improves under FP and EP scenarios but deteriorates significantly under ND and UD scenarios. The resilience of ecosystem health varies significantly across different land categories. In the future, optimizing the current land use pattern and refining the ecological protection policy are essential to enhance ecosystem health and services in the NSTM. Full article

Northern Slopes of Tianshan Mountain (NSTM) in Xinjiang hold significance as a principal agricultural hub within the region’s arid zone. Accurate crop mapping across vast agricultural expanses is fundamental for intelligent crop monitoring and devising sustainable agricultural strategies. Previous studies on multi-temporal crop classification have predominantly focused on single-point pixel temporal features, often neglecting spatial data. In large-scale crop classification tasks, by using spatial information around the pixel, the contextual relationships of the crop can be obtained to reduce possible noise interference. This research introduces a multi-scale, multi-temporal classification framework centered on ConvGRU (convolutional gated recurrent unit). By leveraging the attention mechanism of the Strip Pooling Module (SPM), a multi-scale spatial feature extraction module has been designed. This module accentuates vital spatial and spectral features, enhancing the clarity of crop edges and reducing misclassifications. The temporal information fusion module integration features from various periods to bolster classification precision. Using Sentinel-2 imagery spanning May to October 2022, datasets for cotton, corn, and winter wheat of the NSTM were generated for the framework’s training and validation. The results demonstrate an impressive 93.03% accuracy for 10 m resolution crop mapping using 15-day interval, 12-band Sentinel-2 data for the three crops. This method outperforms other mainstream methods like Random Forest (RF), Long Short-Term Memory (LSTM), Transformer, and Temporal Convolutional Neural Network (TempCNN), showcasing a kappa coefficient of 0.9062, 7.52% and 2.42% improvement in Overall Accuracy compared to RF and LSTM, respectively, which demonstrate the potential of our model for large-scale crop classification tasks to enable high-resolution crop mapping on the NSTM. Full article

The diurnal variation characteristics of precipitation in summer (June–August) during the period of 2015–2019 over the Northern Slope of the Tianshan Mountains (NSTM) was analyzed using hourly simulated data from Nanjing University’s real-time forecasting system (WRF_NJU) with 4 km resolution, Automatic Weather Station (AWS) data, and the ERA5-Land data through using methods such as the Rotated Empirical Orthogonal Function (REOF) and Coefficient of Variation (CV). The results show that the diurnal variation pattern of the precipitation over the NSTM simulated by WRF_NJU aligns closely with that of the observational AWS data, and it captured spatial distribution, peak values, and the times of precipitation reasonably well. The hourly precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI) all show characteristics of being greater in the afternoon to nighttime than from early morning to noon, and the diurnal variations of precipitation in this region are significantly influenced by altitude. The PA, PF, and PI peak over the southern edge of the Junggar Basin (JB) below 1000 m occurred at around 2200 Local Solar Time (LST). In contrast, peak PA over the mountainous regions above 3000 m occurred at around 1500 LST. Further analysis with REOF and CV indicated that the difference in diurnal variations of precipitation between the mountainous regions and the JB is most pronounced likely due to the topographical influences. The peak PA over the mountainous regions mainly occurred at around 1500 LST, while that of the JB occurred at around 0100 LST. High CV regions for PI are predominantly found over the area near the central JB and the middle Tianshan mountains, whereas high CV regions for the PF are located in the central and northern parts of Urumqi and Changji. In addition, different land surface categories exhibit distinct patterns of diurnal precipitation variation, i.e., the forests, grasslands, and water bodies exhibit their peak PA in the period from early morning to noon, while the impervious surfaces, croplands, and barren lands exhibit their peak PA in the period from afternoon to nighttime. Full article

Revealing the temporal and spatial evolution pattern of reservoirs and their driving mechanisms is of great significance for promoting the coordinated development of the national economy and natural ecology. In this study, the northern slope of the Tianshan Mountains (NSTM) in Xinjiang was selected as the study area. Based on remote sensing data from 1990 to 2020, the temporal and spatial evolution characteristics of reservoirs on the NSTM in the past 30 years were analyzed using the grid cells method and the nuclear density analysis method, and the mechanisms by which natural geographical conditions and socio-economic development influence the temporal and spatial evolution of reservoirs in the NSTM were analyzed using GeoDetector. The results indicate the following: (1) In terms of temporal change, the area of reservoirs in the NSTM showed an overall expansion trend from 1990 to 2020, with a growth rate of 42.77%, and the number of reservoirs increased from 123 to 238 during the same period, with a growth rate of 93.50%. (2) In terms of spatial change, the distribution of reservoir accumulation areas on the NSTM gradually developed horizontally from the middle section with rapid economic development to the western and eastern sections. (3) The GeoDetector results showed that the main driving factors affecting the spatial differentiation of reservoir area in the NSTM from 1990 to 2020 were the average elevation (0.201), cultivated land area (0.112), and GDP (0.070), while the main driving factors combinations were the average elevation and cultivated land area (0.374), the average elevation and GDP (0.325), and the average elevation and average population (0.319). The research results can provide a theoretical basis and a decision-making reference for water conservancy project planning and water resources management in the NSTM. Full article