Assessment of land-cover/land-use change and landscape patterns in the two national nature reserves of Ebinur Lake Watershed, Xinjiang, China

Land-cover and land-use change (LCLUC) alters landscape patterns and affects regional ecosystems. The objective of this study was to examine LCLUC and landscape patterns in Ebinur LakeWetland National Nature Reserve (ELWNNR) and Ganjia Lake Haloxylon Forest National Nature Reserve (GLHFNNR), two biodiversity-rich national nature reserves in the Ebinur LakeWatershed (ELW), Xinjiang, China. Landsat satellite images from 1972, 1998, 2007 and 2013 were used to calculate the dynamics of a land-cover and land-use (LCLU) transition matrix and landscape pattern index using ENVI 5.1 and FRAGSTATS 3.3. The results showed drastic land use modifications have occurred in ELWNNR during the past four decades. Between 1972 and 1998, 1998 and 2007, and 2007 and 2013, approximately 251.50 km2 (7.93%), 122.70 km2 (3.87%), and 195.40 km2 (6.16%) of wetland were turned into salinized land. In GLHFNNR both low and medium density Haloxylon forest area declined while high density Haloxylon forest area increased. This contribution presents a method for characterizing LCLUC using one or more cross-tabulation matrices based on Sankey diagrams, demonstrating the depiction of flows of energy or materials through ecosystem network. The ecological landscape index displayed that a unique landscape patches have shrunk in size, scattered, and fragmented. It becomes a more diverse landscape. Human activities like farming were negatively correlated with the landscape diversity of wetlands. Furthermore, evidence of degraded wetlands caused by air temperature and annual precipitation, was also observed. We conclude that national and regional policies related to agriculture and water use have significantly contributed to the extensive changes; the ELWNNR and GLHFNNR are highly susceptible to LCLUC in the surrounding Ebinur Lake Watershed

New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China

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

Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model

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 develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (κ) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions

Soil salinity inversion based on novel spectral index

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

Spatio-temporal variation of oasis landscape pattern in arid area: Human or natural driving?

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

Change Detection of Land Surface Temperature (LST) and some Related Parameters Using Landsat Image: a Case Study of the Ebinur Lake Watershed, Xinjiang, China

This study assesses and detects land use/cover (LUC) and land surface temperature (LST) change using multi-temporal Landsat TM satellite data. NDVI, albedo and MNDWI were used to analyze the LST qualitatively. The results revealed that the accuracy of LST measurements in watershed is within 1.5 °C. Then, temperature changes between 1998 and 2011 were analyzed. The classifications of land surface temperatures lie in five categories as follows: lower (1.9–8.9 °C), low (8.9–15.9 °C), middle (15.9–22.9 °C), high (22.9–29.9 °C), higher (29.9–36.9 °C), and highest (36.9–43.9 °C). Second, east-west profiles of the characteristics of the distribution of LUC types were made based on 1998 and 2011 images. By comparing LSTs in these two years, one can conclude woodland-grassland has a very strong influence on temperature. Third, LST increased with the increases in the density of salinized and desert lands, but decreased with the increase in vegetation cover. The relationship between MNDWI and LST was significantly negatively correlated. Multiple regression analyses between LST and each index as well as elevation were created to evaluate the watershed thermal environment. This regression showed that NDVI, albedo, MNDWI and a digital elevation model were effective indicators for quantifying the effects of land use/cover change (LUCC) on LST, and the correlation coefficient R was 0.806. Finally, natural and human factors were important factors affecting temperature change. Generally, the temperature of the oasis was lower than the surroundings, which results in a ‘cold island effect’

Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China

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 (AWEInsh), automatic water extraction with shadow (AWEIsh), 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 KT3+ TM4\u3eTM2+ TM7 and VI-3 for KT3+ TM2\u3eTM4+ TM3 as an optimized and comprehensive method for land surface water extraction from Landsat TM/ETM+ Images (KT3, 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

Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China

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\u27s I showed an increased trend first and then decreased. The value of Moran\u27s 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

Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China

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\u27s 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\u27s health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas

Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis, Xinjiang, China

Land use and land cover (LULC) changes caused by human activities have strong influences on regional environment. Land surface temperate plays an important role in studying the impact of LULC changes on regional environment. In this paper, remotely sensed thermal infrared data were used to assess land surface temperature (LST) in the Weigan and Kuqa river oasis, Xingjiang, one of the important agricultural areas in the northwestern China. The present study deals with the extraction of LST and the relationship between LULC changes using Landsat 5 TM acquired on September 25, 1989, and September 6, 2011. The results indicate that the surface temperature of water body, bare land, and desert changed significantly between 1989 and 2011. In general, the LST was lower in 1989 than in 2011. There were no lower, higher, and highest temperature zones in 1989. However, the minimum temperature was 10.7 °C in 1989 and 15.8 °C in 2011. The maximum temperature was 29.3 °C in 1989 and 41.8 °C in 2011. Regarding the LULC types, the desert features in the Gobi Desert warmed more quickly than the oasis. So, the temperature of the oasis was lower than the surrounded areas, resulting in a so-called “cold island” phenomenon. Oasis cold island effect index (OCIEI) shows that stability of oasis had rising trend from 1989 to 2011. In addition, the impact of LULC changes on LST was analyzed and the driving forces were also analyzed from 1977 to 2011. This study is significant for further understanding of the energy exchange status of soil-plant-atmospheric system and the regional heat distribution in arid and semi-arid areas of the northwest China