The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen’s slope, Spearman’s rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Z_c = −0.784) and July (Z_c = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Z_c > Z_0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for “high” monthly precipitation. According to the “low” monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Z_c. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as “high” and “low” values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods.

Populus euphratica is an important tree species in desert ecosystems. The protection and restoration of natural Populus euphratica forests requires accurate positioning information. The use of Sentinel-2 images to map the Populus euphratica distribution at a large scale faces challenges associated with discriminating between Populus euphratica and Tamarix chinensis. To address this problem, this study selected the Daliyabuyi Oasis in the hinterland of the Taklimakan Desert as the study site and sought to distinguish Populus euphratica from Tamarix chinensis. First, we determined the peak spectral difference period (optimal time window) between Populus euphratica and Tamarix chinensis within monthly Sentinel-2 time-series images. Then, an appropriate vegetation index was selected to represent the spectral difference between Populus euphratica and Tamarix chinensis within the key phenological stage. Finally, the maximum entropy method was used to automatically determine the threshold to map the Populus euphratica distribution. The results indicated that the period from 22 April to 1 May was the optimal time window for mapping the Populus euphratica distribution in the Daliyabuyi Oasis. The combination of the inverted red-edge chlorophyll index (IRECI) and the maximum entropy method can effectively distinguish Populus euphratica from Tamarix chinensis. The user’s accuracy of the Populus euphratica distribution extraction from single-data Sentinel-2 images acquired within the optimal time window was 0.83, the producer’s accuracy was 0.72, and the F1-score was 0.77. This study verified the feasibility of mapping Populus euphratica distribution based on Sentinel-2 images, and analyzed the validity of exploiting spectral differences within the key phenological stage from a single-data image to distinguish between the two species. The results can be used to extract the distribution of Populus euphratica and serve as an auxiliary variable for other plant classification methods, providing a reference for the extraction and classification of desert plants.

Surface water is an important factor affecting vegetation change in desert areas. However, little research has been conducted on the effects of surface water on vegetation expansion. In this study, the annual spatial distribution range of vegetation and surface water in the Daliyabuyi Oasis from 1990 to 2020 was extracted using Landsat time-series images. Based on multi-temporal and multi-scale remote sensing images, several plots were selected to demonstrate the process of landform change and vegetation expansion, and the influence of surface water on vegetation expansion was analyzed. The results show that the vegetation distribution and surface water coverage have increased from 1990 to 2020; and surface water is a critical factor that drives the expansion of vegetation. On the one hand, surface water in the study area was essential for reshaping the riparian landform, driving the transformation of dunes into floodplains, and increasing the potential colonization sites for vegetation. However, landform changes ultimately changed the redistribution of surface water, ensuring that enough water and nutrients provided by sediment were available for plant growth. Our study provides a critical reference for the restoration of desert vegetation and the sustainable development of oases.

Here, unmanned aerial vehicle (UAV) remote sensing and machine vision were used to automatically, accurately, and efficiently count Tianshan spruce and improve the efficiency of scientific forest management, focusing on a typical Tianshan spruce forest on Tianshan Mountain, middle Asia. First, the UAV in the sampling area was cropped from the image, and a target-labeling tool was used. The Tianshan spruce trees were annotated to construct a data set, and four models were used to identify and verify them in three different areas (low, medium, and high canopy closures). Finally, the combined number of trees was calculated. The average accuracy of the detection frame, mean accuracy and precision (mAP), was used to determine the target detection accuracy. The Faster Region Convolutional Neural Network (Faster-RCNN) model achieved the highest accuracies (96.36%, 96.32%, and 95.54% under low, medium, and high canopy closures, respectively) and the highest mAP (85%). Canopy closure affected the detection and recognition accuracy; YOLOv3, YOLOv4, and Faster-RCNN all showed varying spruce recognition accuracies at different densities. The accuracy of the Faster-RCNN model decreased by at least 0.82%. Combining UAV remote sensing with target detection networks can identify and quantify statistics regarding Tianshan spruce. This solves the shortcomings of traditional monitoring methods and is significant for understanding and monitoring forest ecosystems.

Intensive mining activities in large-scale opencast coal mines have had a significant impact on the local environment. Elements that are potentially harmful to the environment are brought to the surface from deep underground, altering the geochemical conditions for their transport and redistributing them to the surface, causing serious local pollution. However, in-depth studies of toxic metal contamination in soils of arid coal mining areas have not yet received the attention they deserve. Although previous studies have conducted a great deal of research on heavy metal elements in surface coal mine soils, there are few studies related to the more seriously polluted surface coal mines in the arid regions of Northwest China, and there are no in-depth studies on the ecology of soil heavy metal contamination, health risks and source analysis according to the authors’ knowledge. To make up for this shortcoming, the present study takes Zn, Cu, Cr, Pb, Hg, and As in the surroundings of the tertiary coal mines in the Hongsachuan Mining Area (northern Xinjiang, China) as an example. The health, ecological risks, and pollution sources of heavy metal elements in surface coal mine soils were comprehensively analyzed. The results showed that the average concentrations of Cr, Hg, and As in the soils of open-pit coal mines greatly exceeded the corresponding provincial background values, with the Cr content exceeding China’s soil environmental quality standard Ⅰ and the As content even higher than standard Ⅱ (GB15618-1995). Geostatistical and multivariate statistical results showed that the six metals analyzed in this study can be divided into four groups, as follows. Group 1 included Zn and Cu and was mainly controlled by natural sources related to soil parent materials. Group 2 consisted of Cr and Hg and was associated with industrial practices. Group 3 was explained by As and was mainly from coal combustion during the mining activities. Group 4 was Pb and was dominantly from natural sources, together with vehicular emission during the mining activities, indicating a mixed source. Potential ecological risk index (PER) values exhibited low ecological risk in contaminated soils with Zn, Cu, Pb, and Cr, and only 10% of As samples exhibited moderate risks, while 77% of Hg samples posed ecological risks at different level, implying that Hg was the main contributor for comprehensive risk index (RI). Regardless of non-carcinogenic and carcinogenic health risk assessment, As was the primary risk element followed by Cr, and children tended to have a higher health risk than adults. In this paper, statistical methods, pollution assessment methods, and potential ecological risk models are skillfully combined, and relevant conclusions are drawn based on the human and economic geographical background information of the study area. The results can provide references for the investigation and evaluation of soil heavy metals and quantitative analysis of pollution sources in the same type of areas. In order to grasp the pollution level of potential toxic elements in the soil of large open-pit coal mines in arid areas, effective source-cutting measures are taken to provide data support the sustainable management of coal mines and local soil safety utilization measures.