In recent years, China’s economy has experienced rapid development, and its cities have undergone rapid expansion; however, the development of cities in the northwest region has been relatively slow due to various geographical and economic constraints. Studying the urban expansion in these regions is of significant importance for regional planning and development. This study selected the provincial capitals of five underdeveloped provinces in northwestern China as the research sample and used Landsat TM/OLI remote-sensing imagery as the primary data, supplemented by Digital Elevation Model (DEM), meteorological, and socio-economic data, the study extracted urban impervious surfaces using the ENDISI and MNDWI indices. It analyzed the spatial and temporal characteristics of urban impervious surfaces from 1990 to 2020 using indicators such as urban expansion intensity, compactness and fractal dimension, centroid migration, and standard deviation ellipse. Furthermore, the study quantified the influencing factors using Geodetectors. The findings reveal the following: (1) From 1990 to 2020, impervious surfaces in the five cities continued to expand, with Xi’an experiencing the largest expansion area at 549.94 km2 and Xining the smallest at only 132.83 km2, with an expansion intensity of merely 2.99%. However, significant disparities existed in expansion intensity and area across different periods. (2) Overall, the compactness of the cities decreased by 47.6% while the overall fractal dimension increased by 2.85%, indicating a trend towards more dispersed and complex urban forms. (3) Expansion directions varied among the cities, with Xi’an and Urumqi expanding towards the northwest, Lanzhou towards the north, Yinchuan primarily towards the east, and Xining mainly towards the west. (4) Economic, demographic, and investment factors were identified as the primary influencers of urban expansion, exhibiting changes over different periods. Analyzing the similarities and differences in city development can offer valuable insights into urban construction and sustainable development in underdeveloped areas.

Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.

A dry valley is a special landscape type that is formed by the combined effect of climate and topography. Accurately defining the scope of a dry valley and knowledge of its spatial distribution characteristics can provide data support for relevant studies in the region. Starting from natural ontological characteristics and formation mechanisms, we constructed a geographical ontological model of dry valleys through an analysis of concepts related to the dry valley and combined GIS technology and methods to accurately define the scope and analyze the spatial characteristics of the dry valleys in the Three Parallel Rivers Region (DVT). Our results show that: (1) The geographically and ontologically oriented method developed to define the scope of the dry valley has a high accuracy, with an overall accuracy of 92.3% and a kappa coefficient of 0.84, therefore it can provide a better mechanism for defining the scope of a dry valley on a large scale. (2) The total area and total length of the DVT are 6147.1 km2 and 2125.3 km, respectively. The dry valleys in this region are mainly located in the Tibet Autonomous Region and in the Sichuan and Yunnan provinces in China. (3) The terrain in the DVT is precipitous, and areas with slopes greater than 25° account for 70% of the total area of the dry valleys. The DVT area of sunny aspects (north, northeast, and northwest aspects) is larger than that of shady aspects (south, southeast, and southwest aspects), and the land cover is mainly grassland with a desert substrate. The result of our study can provide data support for further in-depth research in related fields of dry valleys.

BACKGROUND:This study aims to determine how atherosclerotic plaque prevalence and characteristics vary between individuals residing year-round at middle and high altitudes who have intracranial atherosclerotic disease. METHODS:We conducted a retrospective analysis of patient data from our hospital, focusing on individuals with cerebrovascular symptoms who underwent high-resolution vessel wall imaging (HR-VWI). Patients who had lived at an altitude of <2500 meters for an extended period were classified in group A (n = 91), while those residing at an altitude of ≥2500 meters were placed in group B (n = 75). We examined the differences in plaque prevalence and characteristics between these two groups. RESULTS:The detection rate of basilar artery plaque was higher in group A compared to group B (16% vs. 7.6%, p = 0.036). Conversely, the detection rate of anterior cerebral artery plaque was significantly lower in group A than in group B (4% vs. 11.8%, p = 0.016). The eccentricity index (EI) was greater in group B than in group A (0.72 ± 0.11 vs. 0.68 ± 0.12, p = 0.012). The prevalence of intraplaque hemorrhage (IPH) was lower in group B than in group A (39.5% vs. 58.7%, p = 0.002). CONCLUSIONS:IPH prevalence was lower in patients residing at high altitudes than in those residing at middle altitudes. However, patients living at high altitudes had a higher EI compared to those residing at middle altitudes. These findings underscore the presence of disparities in the prevalence and characteristics of intracranial atherosclerotic plaques between individuals residing at medium and high altitudes. It is essential to account for these distinctions when diagnosing plaques.

The rapid and effective identification of the spatial distribution of rock deserts in ice–snow melting areas can provide useful information for the prevention of natural disasters. In this study, Landsat TM/OLI were used to track the evolution of snow lines in the snow melting areas of Yushu Tibetan Autonomous Prefecture of the Qinghai-Tibet Plateau (hereinafter referred to as Yushu Prefecture) during the last 30 years. A total of seven extraction schemes were used to classify the rock desert in the ice–snow melting area by remote sensing. Our results show that: (1) The accuracy of the multi-index factor compound analysis and object-oriented classification compound method was the highest. This method can provide a rapid and efficient reference scheme for rock desert extraction in the plateau ice–snow melting area. (2) The combinations of two single methods with higher accuracy can further improve the total accuracy. If a single method with lower accuracy is involved in the multi-method fusion, the accuracy of the method with lower accuracy can be improved. (3) In the past 30 years, there has been a large amount of ice–snow melting in Yushu Prefecture. The ice–snow melting area accounts for 53.78% (1451.04 km2) of the ice–snow area, and the bare rock and bare stone in the rock desert account for 63.77% of the total area of the ice–snow melting area. Bare sand and bare soil area account for 30.27% of the total area of ice and snow melting area.

Global climate change, mainly characterized by warming, has resulted in significant migration of temperature-sensitive crops from traditional planting areas, making crops more vulnerable to climate change and natural disasters, increasing yield losses caused by disasters. Based on the MaxEnt model, combining Representative Concentration Pathways 4.5 and 8.5, the potential suitable areas for highland barley planting on the Qinghai-Tibet Plateau were estimated, and the results showed that: (1) Over 30% of the Qinghai-Tibet Plateau is unsuitable for highland barley cultivation, the area of moderately suitable area for highland barley planting is close to 50%, and the marginally suitable and highly suitable area is less than 20%; (2) From the past distribution to the near and medium-term distributions, the unsuitable area for highland barley planting is gradually shrinking. In the moderately suitable area for highland barley planting, some of the area with relatively low suitability was transformed from unsuitable area, and some of the area with relatively high suitability was transformed into marginally suitable area, so that the total area remained basically unchanged. A small part of the marginally suitable area was converted into high-suitability area, which increased the highly suitable area; (3) From the perspective of different scenarios, in the near and medium term, the area with a slight decrease or no change in suitability under RCP 8.5 was smaller than under RCP 4.5, but the area with a significant increase was larger than under RCP 4.5. The areas with a small decrease or no change in suitability accounted for 23.66–33.77% of the total plateau area and were concentrated in the northwestern Qinghai-Tibet Plateau and the Qaidam Basin. Areas with a large increase in suitability accounted for 3.47–15.64% of the total area and were located in the southern, central, and eastern parts of the Qinghai-Tibet Plateau, this area increased significantly with time; (4) Judging from the average altitude change in highland barley planting, the average altitude of the highly suitable area will rise from 3759 m to 3937 m (RCP 4.5) and 3959 m (RCP 8.5) in the near term. By the medium term, the average elevation of the highly suitable area will increase from 3759 m to 4017 m (RCP 4.5) and 4090 m (RCP 8.5). The trend of rising average altitude continues to strengthen.

Monitoring rock desert formation caused by two different origins (ice-snow melting and drying) through remote sensing is crucial to our understanding of the interaction between the underlying surface of different rock desert and land-atmosphere types, as well as the relationship between bare land and soil erosion. A number of achievements have been made in remote sensing monitoring of desert areas, but there is a lack of accurate classification and remote sensing identification of rock desert types based on formation mechanism. In this study, the north and south sides of the eastern Kunlun Mountains in the northern part of the Qinghai-Tibet Plateau of China were taken as the study areas. Landsat operational landscape imager, digital elevation model, and precipitation and temperature grid data were used as data sources. By identifying the bare areas based on the normalized difference vegetation index (NDVI), we used the multi-element fusion method of contours, isotherms, and isohyets to identify the rock desert types in the ice-snow melting and dry areas. The results showed that: (1) the rock desert areas identified by remote sensing based on topographic and meteorological elements were highly accurate, with an overall accuracy of 88.45% and kappa coefficient of 0.77. The multi-element fusion method of contours, isotherms, and isohyets could effectively identify the rock desert types in the ice-snow melting and dry areas; (2) the optimal segmentation range of the ice-snow melting and dry areas was 3600 m contour, −2°C–2°C isotherms, and 100–130 mm isohyets. The areas with elevation less than 3600 m, annual average temperature higher than 2°C, and average annual precipitation less than 100 mm were rock desert in the dry areas. The range of −2°C–2°C isotherms and 100–130 mm isohyets was the transition area between the ice-snow melting and dry areas. The areas with elevation higher than 3600 m, annual average temperature less than −2°C, and average annual precipitation higher than 130 mm were rock desert in the ice-snow melting areas; and (3) the identification accuracy of the bare areas based on the NDVI method was better, specifically, the identification accuracy of plain bare areas was generally better than that of mountain bare areas. The remote sensing identification method considers not only the topographic factors that have great influence on the spatial distribution of the two types of rock desert areas, but also the meteorological factors, which can provide a scientific reference for the effective identification of the two types of rock desert areas.

In this paper, we use the extraction method of multi-factors fusion to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. The study results indicate that: (1) the method (extracting through multi-factors fusion) is efficient during the extracting process and is highly accurate in extraction results. This extraction scheme allows for not only the spatial heterogeneity of different physical geographic units, but also the impact of multi-factors on crop cultivation; (2) according to our research, the total Highland barley cultivation area on Qinghai–Tibet Plateau is about 2.74 × 105 ha. Based on the statistics, we draw the first distribution map of the Highland barley cultivation area on Qinghai–Tibet Plateau, which upgrades its spatial distribution pattern from administrative unit to patch unit; (3) Highland barley in various divisions has a distinct spatial heterogeneity in elevation. On the whole, the Highland barley on the plateau is planted at an elevation of 2500–4500 m, up to 5200 m. Due to the impact of topography diversity, temperature, moisture, light, arable land and irrigation conditions, its cultivation area at the same elevation varies in different divisions.

In this study, we explored the feasibility of estimating the soil heavy metal concentrations using the hyperspectral satellite image. The concentration of As, Pb, Zn and Cd elements in 48 topsoil samples collected from the field in Yushu County of the Sanjiangyuan regions was measured in the laboratory. We then extracted 176 vegetation spectral reflectance bands of 48 soil samples as well as five vegetation indices from two Hyperion images. Following that, the partial least squares regression (PLSR) method was employed to estimate the soil heavy metal concentrations using the above two independent sets of Hyperion-derived variables, separately constructed the estimation model between the 176 vegetation spectral reflectance bands and the soil heavy metal concentrations (called the vegetation spectral reflectance-based estimation model), and between the five vegetation indices being used as the independent variable and the soil heavy metal concentrations (called synthetic vegetation index-based estimation model). Using RPD (the ratio of standard deviation from the 4 heavy metals measured values of the validation samples to RMSE) as the validation criteria, the RPDs of As and Pb concentrations from the two models were both less than 1.4, which suggested that both models were incapable of roughly estimating As and Pb concentrations; whereas the RPDs of Zn and Cd were 1.53, 1.46 and 1.46, 1.42, respectively, which implied that both models had the ability for rough estimation of Zn and Cd concentrations. Based on those results, the vegetation spectral-based estimation model was selected to obtain the spatial distribution map of Zn concentration in combination with the Hyperion image. The estimated Zn map showed that the zones with high Zn concentrations were distributed near the provincial road 308, national road 214 and towns, which could be influenced by human activities. Our study proved that the spectral reflectance of Hyperion image was useful in estimating the soil concentrations of Zn and Cd.

The objective of this paper was to evaluate the prediction ability of visible and near-infrared reflectance spectroscopy(VNIRP) to estimate soil total nitrogen concentration (TN) in the Sanjiang Yuan regions, Qinghai province, China by using partial least squares regression (PLSR) method. A total of 149 soil samples (0-30cm) were collected respectively from Yushu county and Maduo county in August 2012. The soil total nitrogen concentrations were measured using Vario EL Ⅲ elemental analyzer (Germany Elementar Inc), and the soil spectral reflectance of with spectral range 350-2500nm was measured using ASD FieldSpec 4 in the laboratory. PLSR model was used to relate the reflectance spectra and its five different pre-processing transformations including the first order derivative reflectance (FDR), the second order derivative reflectance(SDR), the logarithm of the inverse of the reflectance (Log (1/R)), the depth of the band (Band depth) and the first order derivative of the logarithm of reflectance (( Log R)′) to the soil total nitrogen contents measured. The results showed that the combination of visible-near infrared reflectance spectroscopy and PLSR to estimate TN produced a good prediction ability with R2 < 0.76 and RPD < 2.01 in the Sanjiang Yuan regions. The best prediction ability was obtained by (Log R) ' with R2CV = 0.87, R2V =0.88 and RPD <2.67, followed by Log (1/R), Band Depth, SDR, FDR and Reflectance. Visible and near infrared spectroscopy provided a rapid and effective method for estimating soil TN from five different soil types in the Sanjiang Yuan Regions, Qinghai province.