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Our work focuses on the assessment of trends, erosion states and causal risk factors for soil erosion of the Asfalou watershed through the use of the Priority Actions Program/Regional Activity Center (PAP/RAC). This qualitative study model for water erosion makes it possible to assess susceptibility and determine potential fragile areas in order to diagnose the state of soil degradation. We adopted the PAP/RAC crossed matrices, the geographic information system (GIS) and remote sensing (RS) to develop the classical modelling. This method is based on three main approaches: predictive, descriptive and integration. Introducing soil types, slope length (LS) and climatic factors into our model, including rainfall erosivity (R), slope exposure, soil moisture index (SMI) and land surface temperature (LST), improved the reliability of our model. The correlation analysis identified these factors that explain erosion states and the risk of soil erosion. The coefficients of determination (R2) of the various erosive states resulting from the modified PAP/RAC approach explain respectively 98.30%, 77% and 49.3% of the observed variability of the erosive states. These factors provide information on the current state of soil degradation depending on the degree of influence of the different factors that control erosion. The descriptive approach has shown that soil loss manifests itself in different forms, whether for sheet erosion (L) and ravines (C1) successively affecting 79.95% and 17.84% of the land. The integration approach identifies factors and areas requiring intervention to counter the effects of soil erosion in the Asfalou watershed effectively and sustainably.

In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline–rise–decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type’s RSEI values behave differently at different times. As the UANST is the core area of Xinjiang’s urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.

This study aims to analyze the pattern of bank erosion and simulate the physical aspects of vulnerability in the lower Meghna River, Bangladesh using remote sensing (RS) and geographic information systems (GIS). The physical factors of vulnerability were analyzed using GIS-based Structured Query Language (SQL). A questionnaire survey, GPS survey and field observation survey were conducted for collecting the primary data in the study area. The secondary data were mainly satellite image collected from the United States Geological Survey (USGS) website. Using time series Landsat images (MSS, TM and OLI-TIRS), this study analyzed 36 years of erosion and accretion process in the Mehendiganj Upazila region from 1980 to 2016. The result indicates that an enormous amount of land (4470.47 ha) was submerged by the river and average land loss rate was 124.18 ha/year. The study quantifies the number of vulnerable households beneath the present condition and how much it will be altered after a positive/negative change with the factors of vulnerability related to the households. Simulation data reveals that under the present physical condition, 43.88% of households were identified as severely vulnerable. The output of this study can be used in the classification of vulnerable households and for the improvement of the physical infrastructure development process near the erosion prone areas, also helps to mitigate environmental disaster in the developing countries.

Good surface water quality is critical to human health and ecology. Land use determines the surface water heat and material balance, which cause climate change and affect water quality. There are many factors affecting water quality degradation, and the process of influence is complex. As rivers, lakes, and other water bodies are used as environmental receiving carriers, evaluating and quantifying how impacts occur between land use types and surface water quality is extremely important. Based on the summary of published studies, we can see that (1) land use for agricultural and construction has a negative impact on surface water quality, while woodland use has a certain degree of improvement on surface water quality; (2) statistical methods used in relevant research mainly include correlation analysis, regression analysis, redundancy analysis, etc. Different methods have their own advantages and limitations; (3) in recent years, remote sensing monitoring technology has developed rapidly, and has developed into an effective tool for comprehensive water quality assessment and management. However, the increase in spatial resolution of remote sensing data has been accompanied by a surge in data volume, which has caused difficulties in information interpretation and other aspects.

Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000, (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths, (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline, and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development, with the management of saline soil crucial in arid and semi-arid areas. The salt-affected soil is predominant in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the Northwestern China. It influences the development of agricultural economy. Rapid and accurate measurement of the soil salt content (SSC) is significant for the soil salinization control. However, the traditional method of obtaining soil salt is time-consuming and laborious. Nowadays, it is an unprecedented perspective to monitor soil salinity through Sentinel-2A multispectral remote sensing image to construct three-dimensional spectral index. In this study, through soil salt data of 97 ground-truth measurements, Sentinel-2A data-derived spectral indices, based on particle swarm optimization support vector machine (PSO-SVM), gray wolf optimization support vector machine (GWO-SVM) and differential evolution support vector machine (DE-SVM) algorithm to construct a best soil salt inversion models. The results show that three-band (3D) spectral index has better correlation with soil salinity than single band and two-band (2D) spectral index, among TBI5 and TBI7 has a high correlation with the salinity of the soil, and the points are concentrated on the 1:1 line. Therefore, this approach could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the Machine learning algorithm. The DE-SVM model performed the best by the three Model of accuracy with R2, Bias, and SEP2C of 0.56, − 2.03, and 8.62, respectively. Therefore through the soil salinity value predicted by the modeling constructs a linear relationship with the indexes TBI5 and TBI7, and draw the soil salt inversion map, soil salinity around the lake is relatively high, and decreases outward along the lake, which is consistent with the field. The result from this model will be useful for soil salinization monitoring in the study area and can provide theoretical support for the estimation of SSC in arid and semi-arid areas.

The eco-environment of the arid area is constantly fragile global-wide. The study on the spatial and temporal variation of its landscape pattern can quickly identify the driving factors, which is an effective analysis method of ecological conservation in the arid area. In this study, Jinghe County, Xinjiang, in Northwestern China, is chosen as a case study of understanding the major driving force of the oasis landscape. Landsat satellite images of 1994, 2007, and 2017 are used as the basic data sources for analysis. The moving window method, transition matrix, and factor analysis are selected as the main technical means for this paper to reveal the temporal and spatial variation of landscape pattern in Jinghe County, and to quantitatively describe its driving factors. The results of the analysis show that: (1) The landscape pattern of Jinghe County has changed dramatically, especially the construction land and farmland. Both show an increase of 351.29% and 269.01% respectively between 1994 and 2017. Farmland increased the most, with the proportion of 6.45% in Jinghe County, and expanded along with the “triangle” three sides. The area of forest land increased slightly, the area of grassland and unused land decreased, and the area of water did not change significantly. (2) The dominant obvious regional differences of landscape types in Jinghe County are mainly manifested in farmland and forest land. Taking the unused land as the boundary, the biodiversity of farmland decreased, and the aggregation degree deepened, while the fragmentation degree of forest land increased. (3) The spatial–temporal variation of landscape patterns in Jinghe County is the result of the combination of natural and human factors, but the correlation between human factors and the change of landscape area is significantly greater than that of natural factors. Thus, human factors are the main driving force of landscape pattern change in Jinghe County. The scientific management and planning of the intensity of human activities in Jinghe County should be the focus of ecological environment restoration and protection.

The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to dev...

This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content (SOMC) in the bare topsoil of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. The results indicated that the correlation of coefficients (R) between SOMCs and hyperspectral data processed by fractional derivative were significant at the 0.01 level; the number of wave bands increased initially and then decreased when the order increased. The correlation of coefficient peak appeared at the 1.2 order with a value of 0.52. The correlation of coefficients (R) between SOMCs and the optimal remote sensing indexes (the ratio index, RI; difference index, DI; and the normalized difference index, NDI) of peaked at the 1.2 order, with correlation of coefficients (R) values of 0.81, 0.86 and 0.82, respectively. Six SOMC estimation models were created by means of a single band and optimal remote sensing indexes using Gray Relational Analysis-BP Neural Network (GRA-BPNN). This study found that the optimal model was a 1.2 order derivative model, where the lowest root mean square error (RMSE) was 3.26 g/kg, the highest was 0.92, and the residual prediction deviation (RPD) was 2.26. To complete the high accuracy retrieval of SOMCs, based on Landsat OLI operational land images data, more ‘hidden’ information from the Landsat OLI images were obtained by employing the subsection of spectral band method and the fractional derivative algorithm. Accuracy of the SOMC map was attained by the optimal model of the ground hyperspectral data and the Landsat OLI data, which had low RMSE values of 4.21 g/kg and 4.16 g/kg, respectively. Therefore, we conclude that the SOMC can be estimated and retrieved using a fractional derivative algorithm, the subsection of spectral band method, and the optimal remote sensing index.

Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0–10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.

Human activities and environmental degradation have resulted in landscape structure changes and can eventually affect the ecosystem service value (ESV) of its region. Nevertheless, research on the spatial correlations between ESVs and landscape pattern changes is lacking. Thus, 13 landscape metrics and nine ESV types in Ebinur Lake Basin were chosen and used to analyse their spatial correlations using multiple linear regression models in this study. The results revealed that eight out of the 13 landscape metrics showed direct spatial correlations with ESV type, and there were landscape metrics that were positively and negatively correlated with the different ESV types. The interspersion and juxtaposition index (IJI), patch richness (PR), and patch richness density (PRD) had no effects on the provision of ESVs. The results also showed that the land-use/land-cover classification types play a linking role, as changes in land-use/land-cover affect the provision of ESVs and the fragmentation of landscape patterns. At the same time, the total ESV of Ebinur Lake Basin was 21.21 × 109 CNY in 2014. Wood and grassland contributed the highest ESV in Ebinur Lake Basin, i.e., 16.29 × 109 CNY, followed by water bodies and farmland, i.e., 1.785 × 109 CNY and 1.239 × 109 CNY, respectively. The regression models that were obtained quantitatively assessed how the changes in landscape patterns have affected the provision of ESVs. These models greatly contribute to the application of the ecosystem service approach in research as well as in practice and provide a better understanding of landscape planning in Ebinur Lake Basin.

This study aims to analyze the pollution characteristics and sources of heavy metal elements for the first time in the Zhundong mining area in Xinjiang using the linear regression model. Additionaly, the health risks with their probability and infleuencing factors on different groups of people's were also evaluated using Monte Carlo (MC) simulation approach. The results shows that 89.28% of Hg was from coal combustion, 40.28% of Pb was from transportation, and 19.54% of As was from atmospheric dust. The main source of Cu and Cr was coal dust, Hg has the greatest impact on potential ecological risks. which accounted for 60.2% and 81.46% of the Cu and Cr content in soil, respectively. The all samples taken from Pb have been Extremely polluted (100%). 93.3% samples taken from As have been Extremely polluted. The overall potential ecological risk was moderate. Adults experienced higher non-carcinogenic risks of heavy metals from their diets than children. Interestingly, body weight was the main factor affecting the adult's health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas.

Gross Primary Production (GPP) is the sum of ecosystem photosynthetic production and is an important variable in the study of the carbon cycle. Remote sensing methods were used to calculate GPP and compare the GPP of different regions with various land cover types (LCTs). We used a remote sensing-based vegetation-solar radiation (VSr) GPP estimation model to simulate changes in short-term GPP where vegetation ecosystem measurements are lacking. The study was conducted in 2011 at a typical arid and semi-arid oasis site in Jinghe County, Xinjiang, North China. The VSr model was appraised using 254 global radiation and MODIS GPP data points, including 84 grassland vegetation points (GP), 86 forest vegetation points (FP), and 84 cultivated land vegetation points (CP). The model was developed using the enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and solar radiation was obtained by calculation. Our results indicated that the updated VSr model improved the GPP accuracy compared with the standard MODIS GPP product by decreasing the root mean square errors (RMSEs) by 5.083, 4.802 and 3.076 for the GP, FP and CP sites, respectively. A comparison of test samples of GPP MODIS products and the VSr GPP model calculation showed that the VSr model had higher accuracy and stability. The VSr model can be used to simulate changes in short-term GPP where vegetation ecosystem measurements are lacking.

Development of tools for real-time monitoring of water quality is crucial for management of stream water pollution. In this study, fluorescence excitation-emission matrix (EEM) with parallel factor analysis (PARAFAC) were used to estimate five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended sediment (SS), total nitrogen (TN), pH, total phosphorus (TP), ammonia nitrogen (NH3+-N), turbidity (NTU), and dissolved oxygen (DO) concentrations in inland freshwater streams. The study area was located in Ebinur Lake Watershed, Xinjiang Uygur Autonomous Region, China. Our results showed that the four fluorescence components were successfully extrapolated by the PARAFAC factor analysis modeling from the fluorescence EEM data including microbial humic-like (C1), terrestrial humic-like organic substances (C2, C4), and protein-like organic substances (C3). The four PARAFAC components were selected as the water quality monitoring index of river water samples. Three-dimensional fluorescence index analysis showed that terrigenous organic pollution was the main organic pollution type in the watershed. This indicates that the watershed is subject to human disturbance, given the large variation of organic pollution in the water body. There was a significant correlation between the three-dimensional fluorescence index, the fluorescence components, and surface water quality index. In addition, the application of three-dimensional fluorescence index and fluorescence component to establish water quality index produced a model fitting coefficient greater than 0.5, which demonstrated that the accuracy of the model was in line with monitoring requirements in practice. This implies that the use of three-dimensional fluorescence index and three-dimensional fluorescence component to monitor Ebinur Lake Watershed water quality has a great potential.

In a regional economy, the central city of a metropolitan area has a radiative effect and an accumulative effect on its surrounding cities. Considering the limitations of traditional data sources (e.g., its subjectivity) and the advantages of nighttime light data, including its objectivity, availability and cyclicity, this paper proposes a likelihood spatial statistical transformation model (LBSSTM) to invert for the gross domestic product (GDP) of the surrounding cities, using time series of Sum of Lights (SOL) data covering the central city and taking advantage of the economic and spatial association between the central city and the surrounding cities within a metropolitan area and the correlation between SOL and GDP. The Wuhan Metropolitan Area is chosen to verify the model using time series analysis and exploratory spatial data analysis (ESDA). The experimental results show the feasibility of the proposed LBSSTM. The prediction accuracy of our model is verified by cross-validation using data from 1998, 2004 and 2011, based on the 3 σ rule. This model can quantitatively express the agglomeration and diffusion effect of the central city and reveal the spatial pattern of this effect. The results of this work are potentially useful in making spatio-temporal economic projections and filling in missing data from some regions, as well as gaining a deeper quantitative and spatio-temporal understanding of the laws underlying regional economic development.

For effective monitoring and protection, researchers need to analyze the trends and causes of landscape pattern change in wetlands. The present study used Geographic Information System and remote sensing technology to analyze temporal and spatial dynamics of landscape pattern and related driving forces in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in China. We used Landsat images from 1972, 1998, 2007 and 2013, and classified the landscape into six types of land cover: water body, wetland, vegetation, salinized land, desert, and other objects (i.e., Gobi and Mountain). Landscape transition matrices and change pattern analysis were used understand wetland dynamics. Our results showed that land cover had changed dramatically during 1972–2013. The spatial extent of much of the wetland areas in the ELWNNR had reduced gradually from 1972 to 2007, and wetland loss occurred more rapidly after 2007. From 1972 to 2013, 415.8 km2 of wetland area had been lost and only 250.1 km2 wetland area had expanded, which represents a 22% decrease in wetland area in the ELWNNR. At the same time, the landscape pattern experienced diverse changes in the Nature Reserve. The high-density human population of the region has an intensive effect on the region. Wetlands in the nature reserve have also changed with recent rapid development in the region. This study may provide scientific information for developing effective and sustainable conservation plans for the ELWNNR.

Wetlands are sensitive indicators of climate change and have a profound impact on the supply of water resources in surrounding areas. In this study, the hydrochemical, isotopic characteristics (&delta, 18O and &delta, 2H) of groundwater and surface water (lake, reservoir, and river) in the Ebinur Lake Watershed, northwestern China, were investigated to reveal the relationships between various water bodies. The results suggest that the groundwater is alkaline and has pH and total dissolved solids (TDS) values less than those of surface water. Ca2+ and SO42&minus, are the major ions in the groundwater and river water, whereas lake water and reservoir water are enriched in Na+ and SO42&minus, With the decrease in elevation, both groundwater and river water are affected by carbonate dissolution at high elevation and by evaporitic rock dissolution at low elevation, thus, the water surrounding Ebinur Lake is subjected to runoff affected by intense evaporation&ndash, dissolution of evaporitic rocks. The stable isotope compositions suggested that the upstream part of the river is recharged by glacial meltwater from high mountains, whereas the middle&ndash, downstream parts of the river are recharged by low-elevation precipitation. Shallow groundwater and reservoir water are mainly recharged by river water and are more enriched in the downstream part of river. Water samples were also classified according to different indices, such as chemical oxygen demand (COD), NH3-N, volatile phenol, sulfate, Zn, Co, Cu, total hardness, and Cr6+, and results showed that most groundwater is suitable for drinking and irrigation purposes. Except for Cr6+, the metal concentrations are within permissible limits. However, both groundwater and reservoir water are affected to some extent by nearby rivers from anthropogenic activity.

Rapid urbanization in China in recent decades has given rise to increasingly serious ecological and environmental problems. Specifically, the provinces along the Silk Road Economic Belt (SREB) in China have experienced rapid urbanization, and need to take pro-active action in balancing urbanization development with eco-environment conservation. This paper aims to develop comprehensive urbanization and eco-environment (UAEE) evaluation models to explore UAEE development of SREB in China from 2004 to 2018. In terms of methodology, these evaluation models are subsequently combined with CCDM (Coupling coordination degree model) and GTWR (Geographically and Temporally Weighted Regression) models to measure and analyze coupling degree and spatio-temporal heterogeneity of UAEE. The results show that: Firstly, from 2004 to 2018 in all regions of the SREB, the urbanization showed a gradual upward trend. Among them, comprehensive urbanization index (CUI) of Guangxi increased by 3.067%. Secondly, the comprehensive eco-environment index (CEI) of Chongqing, Sichuan, Guangxi, Shaanxi and Gansu has improved; while the eco-environment quality of Yunnan, Qinghai, Ningxia and Xinjiang has deteriorated during this period, with the rate of decrease in Xinjiang reaching a significant 2%; Thirdly, in 2018, Chongqing, Sichuan, Guangxi, Shaanxi and Gansu are in a state of high coordination level with the eco-environment under the rapid urbanization process, while Yunnan, Qinghai, Ningxia and Xinjiang are at a moderate coordination level. Since 2011, these areas are in a state of lagging development of the eco-environment. Finally, the interactive relationship between UAEE tends to converge. There is heterogeneity in time and space among SREB regions. The negative interaction effect of UAEE is mainly focused on the northwest regions. In conclusion, this research will provide a basis of scientific evaluation for the coordinated degree of UAEE, promote regional urbanization vis-a-vis the improvement in environmental policy making.

Abstract Soil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.

Water is the most common and important resources on earth. In this paper, we tested and analyzed a variety of water indices for water surface extraction using Landsat TM/ETM+ images, and evaluated extraction accuracies over Ebinur Lake area in Xinjiang Uyghur Autonomous Region of China. Eleven algorithms found in literature on land surface water extraction, including normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automatic water extraction index with no shadow (AWEI nsh ), automatic water extraction with shadow (AWEI sh ), vegetation index 1 (VI-1), vegetation index 2 (VI-2), vegetation index 3 (VI-3), water index (LBV_B), national wetland inventory (NWI), enhanced water index (EWI), and revised normalized different water index (RNDWI) were used. The results were validated by a maximum likelihood classification method, edge extraction accuracy assessment and extracted the area of Ebinur Lake from higher-resolution Landsat ETM+ panchromatic band. The results showed that these algorithms have different accuracies in extracting water information. We proposed VI-2 for KT 3 + TM 4 >TM 2 + TM 7 and VI-3 for KT 3 + TM 2 >TM 4 + TM 3 as an optimized and comprehensive method for land surface water extraction from Landsat TM/ETM+ Images (KT 3 , i.e. Wetness index from the Kauth-Thomas Transformation). The proposed VI-2 and VI-3 models demonstrated its potential in water body extraction with 92.95% and 85.04% accuracies, outperforming other algorithms. Using the optimal mask water model, the two models achieved up to 93.80% accuracy while minimizing the disturbance from vegetation and non-exploited land. Through comparison with other commonly used methods, it shows that the performance of the proposed method is superior to the others. Therefore VI-2 and VI-3 are the best indicators for water mapping using Landsat TM/ETM+ images. This study provided its great potential for quantitative evaluating of temporal changes of Ebinur Lake in Xinjiang Uyghur Autonomous Region of China.

Soil salinization is one of the environmental threats affecting the sustainable development of arid oases in the northwest of China. Thus, it is necessary to assess the risk of soil salinity and analyze spatial and temporal changes. The objective of this paper is to develop a temporal and spatial soil salinity risk assessment method based on an integrated scoring method by combining the advantages of remote sensing and GIS technology. Based on correlation coefficient analysis to determine the weights of risk evaluation factors, a comprehensive scoring system for the risk of salinity in the dry and wet seasons was constructed for the Ebinur Lake Wetland National Nature Reserve (ELWNNR), and the risk of spatial variation of soil salinity in the study area was analyzed in the dry and wet seasons. The results show the following: (1) The risk of soil salinity during the wet season is mainly influenced by the plant senescence reflectance index (PSRI), deep soil water content (D_wat), and the effect of shallow soil salinity (SH_sal). The risk of soil salinity during the dry season is mainly influenced by shallow soil salinity (SH_sal), land use and land cover change (LUCC), and deep soil moisture content (D_wat). (2) The wet season was found to have a high risk of salinization, which is mainly characterized by moderate, high, and very high risks. However, in the dry season, the risk of salinity is mainly characterized by low and moderate risk of salinity. (3) In the ELWNNR, as the wet season changes to dry season (from May to August), moderate-risk area in the wet season easily shifts to low risk and risk-free, and the area of high risk in the wet season easily shifts to moderate risk. In general, the overall change in salinity risk of the ELWNNR showed a significant relationship with changes in lake water volume, indicating that changes in water volume play an important role in the risk of soil salinity occurrence. Ideally, the quantitative analysis of salinity risk proposed in this study, which takes into account temporal and spatial variations, can help decision makers to propose more targeted soil management options.

Remote sensing technology can objectively and quantitatively evaluate spatial-temporal change of ecological environmental quality. This paper uses images from Landsat5 Thematic Mapper (TM) in 2007 and Landsat8 operational land imager (OLI) in 2013 and 2016 to extract indicators such as greenness, wetness, heat, and dryness that reflect the ecological environment quality of arid area. The principal component analysis (PCA) used the evaluating ecological environment quality of the Ebinur Lake Wetland National Nature Reserve to explore the characteristics of spatial differentiation for ecological quality in environment as indicated in the remote sensing ecological index (RSEI). The results showed that the greenness and wetness have positive effects on the ecological environment while the heat and dryness have negative effects. Wetness has a greater impact on the ecological environment than the other three indicators. The proportion of RSEI of degeneration, invariability, and improvement were 20.57%, 42.98%, 36.44%. Thus, the ecological environment had improved. The improved area in 2016 was once located in bad level in 2007. The ecological environment had degraded in the southern part of the lake. The value of Moran's I showed an increased trend first and then decreased. The value of Moran's I increased in 2013, in comparison with 2007, indicating a strong positive correlation and certain internal connection between the ecological environment qualities in the study area. The spatial distribution was clustered rather than random. In comparison with 2013, the auto-correlation in 2016 was relatively weak, an indication that the spatial distribution was random and disperse. The area of High-High (HH) mostly reached to the significant level of 0.05 in 2007 and then to 0.01 in 2013 and 2016. This study offered important results and information for ecological environment protection and region planning.

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R2) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.

National nature reserves in China protect ecosystem and well-being in its region while benefiting humans by providing ecosystem services. Land-use/cover changes result in changes in the landscape pattern and affect the provision of ecosystem service value (ESV). The monetary evaluation of spatial–temporal changes in landscape patterns and ecosystem services in the arid region of China has been inadequately studied. Therefore, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the arid region of China was chosen as a study object to evaluate the spatial–temporal variation in ecosystem service values (ESVs) under changes in land use/cover. The research analysed land-use/cover change in ELWNNR using a Sankey diagram, the spatial–temporal variation in ELWNNR ESVs using ArcGIS. Meanwhile, it also analysed the landscape patterns and total ESVs of the core area, buffer area and experimental area of ELWNNR at a regional scale. Four Landsat data sets from 1972, 1998, 2007 and 2016 were chosen to estimate the changes in land-use/cover types and ESV in ELWNNR. Meanwhile, the sensitivity of ESV was also analysed to validate the estimation of ESV in ELWNNR. The results showed that the total ESV of ELWNNR was 3.12 × 109 CNY (USD$ 4.88 × 108) in 1972 and dropped to 2.71 × 109 CNY (USD$ 4.24 × 108) in 2016. The provision of total ESV in ELWNNR decreased by approximately 0.41 × 109 CNY (USD$ 6.42 × 107) from 1972 to 2016, although there were fluctuations in 1998 and 2007. The core area possessed the highest ESV among all the areas, showing an increase in the provision of ESV in ELWNNR. All the areas of ELWNNR became highly fragmented, and the landscape patterns became more complex during this time period. The results of the study provide sufficient information about the ecosystem services in the region in the absence of complete data sets and contribute to conservation planning of national nature reserves.

Derivative spectroscopy is a powerful mathematical tool that provides more useful information of spectral data than untreated data. Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. The VIS-NIR spectrum is one of the most important data acquisition technologies for digital soil mapping, precision agriculture, and soil resource surveys. The objective of this study was to develop a soil salt content model using the fractional-order derivatives of field-measured spectral data paired with ground measurements. The results showed the following: There was a significant correlation between the single-band reflectance spectra and the soil salt content, and as the derivative order increased, reflectance values first increased and then decreased, with a peak value, R = 0.525, in the 1.2-order derivative. Strong correlations between the estimation of soil salt content and the derivative spectral data of the difference index (DI), ratio index (RI), and normalized difference index (NDI) were also produced by the 1.2-order derivative. R values for the DI, RI, and NDI were 0.818, 0.8624, and 0.8297, respectively. Selected bands with the highest correlation values from the 1.2-order derivative spectral data of the R1428, DI (R1426 / R2151), RI (R1429 − R2024), and NDI (R1526 − R2470)/(R1526 + R2470)] were used to build a model to estimate soil salt content, which showed an R2 value of 0.53 and low RMSE and SD values. The 15 samples of the data were then used to validate the single-band model, R1428, and the combination models, DI (R1426 / R2151), RI (R1429 − R2024), and NDI [(R1526 − R2470)/(R1526 + R2470)], which demonstrated that the R2 values were greater than 0.8. The RMSE and SD values were low, and the value of the RPD was greater than 1.4. This study demonstrated the potential of using fractional derivatives rather than integer derivatives for soil salt content estimation. By making full use of hyperspectral data, fractional derivatives could enrich data preprocessing methods and unearth information about spectral emissions that are lost by integer derivatives. Although this study is a straightforward application of the fractional derivative method, it provides a reference for the estimation of other parameters with hyperspectral technology.

Ebinur Lake Watershed, with its oases and deserts, is a typically arid and mountainous region on the northern slope of Tianshan Mountains. Along with ever increasing human activities, agricultural and domestic wastes have been directly discharged into river systems around the Watershed, which consequently poses a grave threat to the sustainable development of Xinjiang. Through statistical and spatial analysis, we have determined the relationships between land use/cover (LULC) and hydro-chemical characteristics during rainy and dry seasons in 2014. Spatial patterns of hydro-chemical characteristics as demonstrated by mineralization, pH, electrical conductivity (EC), and SO 4 2−, HCO 3 −, Cl−, Mg2+, Na+, Ca2+ and K+ concentrations were examined in 113 sites. Since hydro-chemical characteristics were affected by LULC patterns, this study delineated six zones to analyze the distribution characteristics of hydro-chemical parameters and its relationship with LULC patterns. The results showed that, except for the pH, all variables demonstrated significant spatial differences during dry and rainy seasons. In addition, the relationships between water quality and LULC patterns indicated that the farmland, forest–grassland, water body and salinized land all affected hydro-chemical characteristics during both rainy and dry seasons. Especially, decreased rainfall, irrigation, surface runoff, the area of lake, etc. largely led to the increase in ion content, which had great influence on hydro-chemical characteristics parameters in dry season. Furthermore, we established several stepwise linear multiple regressions models. The results showed that pH, mineralization and Ca2+ were defined by forest-grassland, while the Cl− and Mg2+ were defined by salinized land during the rainy season. The pH and Na+ were estimated based on farmland, whereas Cl− and Ca2+ were estimated based on forest-grassland during the dry season. In conclusion, this research on the relationships between the spatial distribution of hydro-chemical characteristics in Ebinur Lake Watershed and LULC patterns will be significant for the sustainable management of the arid regions in northwest China.

Combined biofilters system was developed to purify a micro-polluted urban shallow lake. The performance of the system was investigated from January 2007 to December 2011. The results showed that the effluent concentration of COD dropped below 20 mg /L, NH4-N dropped below 0.2 mg /L, TN decreased below 1.0 mg/L, and TP decreased below 0.2 mg/L, all of the water quality indexes reached the class III standard of “Surface Water Quality Standards” (GB 3838-2002) except TP. The combined system showed strong anti-shock load and provided an option for the treatment of micro-polluted urban lake.