Tashpolat Tiyip

Study on Soil Salinization Information in Arid Region Using Remote Sensing Technique

Extracting information about saline soils from remote sensing data is useful, particularly given the environmental significance and changing nature of these areas in arid environments. One interesting case study to consider is the delta oasis of the Weigan and Kuqa rivers, China, which was studied using a Landsat Enhanced Thematic Mapper Plus (ETM+) image collected in August 2001. In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Principal component analysis (PCA) is a popular data reduction technique used to help build a decision tree; it reduces complexity and can help the classification precision of a decision tree to be improved. A decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area. According to the research, the third principal component (PC3) is an effective variable in the decision tree classification for salinized soil information extraction. The research demonstrated that the PC3 was the best band to identify areas of severely salinized soil; the blue spectral band from the ETM+ sensor (TM1) was the best band to identify salinized soil with the salt-tolerant vegetation of tamarisk (Tamarix chinensis Lour); and areas comprising mixed water bodies and vegetation can be identified using the spectral indices MNDWI (modified normalized difference water index) and NDVI (normalized difference vegetation index). Based upon this analysis, a decision tree classifier was applied to classify landcover types with different levels of soil saline. The results were checked using a statistical accuracy assessment. The overall accuracy of the classification was 94.80%, which suggested that the decision tree model is a simple and effective method with relatively high precision.

Vegetation dynamics and its response to climate change in Central Asia

The plant ecosystems are particularly sensitive to climate change in arid and semi-arid regions. However, the responses of vegetation dynamics to climate change in Central Asia are still unclear. In this study, we used the normalized difference vegetation index (NDVI) data to analyze the spatial-temporal changes of vegetation and the correlation of vegetation and climatic variables over the period of 1982–2012 in Central Asia by using the empirical orthogonal function and least square methods. The results showed that the annual NDVI in Central Asia experienced a weak increasing trend overall during the study period. Specifically, the annual NDVI showed a significant increasing trend between1982 and 1994, and exhibited a decreasing trend since 1994. The regions where the annual NDVI decreased were mainly distributed in western Central Asia, which may be caused by the decreased precipitation. The NDVI exhibited a larger increasing trend in spring than in the other three seasons. In mountainous areas, the NDVI had a significant increasing trend at the annual and seasonal scales; further, the largest increasing trend of NDVI mainly appeared in the middle mountain belt (1,700–2,650 m asl). The annual NDVI was positively correlated with annual precipitation in Central Asia, and there was a weak negative correlation between annual NDVI and temperature. Moreover, a one-month time lag was found in the response of NDVI to temperature from June to September in Central Asia during 1982–2012.

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

Estimating soil sand content using thermal infrared spectra in arid lands

a b s t r a c t Sand content is a textural property of soils closely related to soil quality. A fast determination of sand content at large scales is paramount importance for monitoring soil degradation to improve agricultural practices. The main objective of this study is to evaluate the ability of the thermal infrared region (TIR) to estimate sand content of soils. Thermal infrared spectra obtained in the field from a Fourier Transform Spectrometer are used to develop a partial least square regression model (PLSR) that translates thermal emittance to soil texture properties. Our results show that the 9.435-9.473 m wavelength regions hold a great promise for prediction of sand content. Coefficient of determination R2 is 0.87 and standard error (SE) is 2.79. We also show that second derivative of thermal spectral profiles is very useful to detect kaolinite in sand dominated soils. The results of this study provide further insights for developing future thermal sensors aimed at predicting soil quality as indicated by the sand content and other textural properties.

The Effects of the Chemical Components of Soil Salinity on Electrical Conductivity in the Region of the Delta Oasis of Weigan and Kuqa Rivers, China

In order to assess the effects of chemical properties of soil salinity on electrical conductivity of 1:5 soil/water extract (EC1:5), the study focused on revealing the main chemical factors contributing to EC of soil extracts and their relative importance. The relationship between EC1:5 and the chemical properties of soil salinity in the delta oasis of Weigan and Kuqa rivers, China, were studied using path coefficient analysis, a path analysis method. We studied each key element affecting EC1:5 either directly or indirectly. The results obtained show that the salt content, total dissolved solids (TDS), and the sum of the sodium ion concentration and the kalium ion concentration are the most influential factors on 1:5 soil/water extract (EC1:5) in the 0–10 cm and the 30–50 cm soil layer. The results show that the sequence of direct path coefficients in the 0–10 cm and the 30–50 cm soil layers on soil conductivity is TDS→Na+ +K+→Salt content→Ca2+→Cl−→the sodium dianion ratio (SDR)→pH→SO42−→HCO3−→Mg2+ the soluble sodium percentage (SSP)→sodium absorption ratio (SAR) and TDS→Salt content→Na+ +K+→Ca2+→SDR→Mg2+→HCO3−→SSP→pH→SO42−→SAR→Cl−. The salt content, chlorine ion, and SAR are the main factors affecting 1:5 soil/water extract (EC1:5) in the 10–30 centimeter soil layer. The order of direct path coefficients result is as follows: Salt content→Cl−→SAR→SSP→TDS→Ca2+→Mg2+ =SO42−→HCO3−→pH→SDR→Na+ +K+. Moreover, the effects of HCO3−, pH were very weak. Though the direct path coefficients between EC1:5 and SAR, SO42− and Ca2+ were not high, influence of other chemical factors caused the coefficients to increase, making the summation of their direct and indirect path coefficients relatively high. The models of the different soil layers were structured separately. Evidences showed that multiple regression relations between EC1:5 and most of the primary factors had sound reliability and very good accuracy. The research results can serve as a reference to the scientific management amelioration and utilization of saline in the Delta Oasis of Weigan and Kuqa rivers.

Vegetation fractional coverage change in a typical oasis region in Tarim River Watershed based on remote sensing

Vegetation fractional coverage (VFC) is an important index to describe and evaluate the ecological system. The vegetation index is widely used to monitor vegetation coverage in the field of remote sensing (RS). In this paper, the author conducted a case study of the delta oasis of Weigan and Kuqa rivers, which is a typical saline area in the Tarim River Watershed. The current study was based on the TM/ETM+ images of 1989, 2001, and 2006, and supported by Geographic Information System (GIS) spatial analysis, vegetation index, and dimidiate pixel model. In addition, VBSI (vegetation, bare soil and shadow indices) suitable for TM/ETM+ images, constructed with FCD (forest canopy density) model principle and put forward by ITTO (International Tropical Timber Organization), was used, and it was applied to estimate the VFC. The estimation accuracy was later proven to be up to 83.52%. Further, the study analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of the study area by producing the map of VFC levels in the delta oasis. Forest, grassland, and farmland were the three main land-use types with high and extremely-high coverage, and they played an important role in maintaining the vegetation. The forest area determined the changes of the coverage area, whereas the other two land types affected the directions of change. Therefore, planting trees, protecting grasslands, reclaiming farmlands, and controlling unused lands should be included in a long-term program because of their importance in keeping regional vegetation coverage. Finally, the dynamic variation of VFC in the study area was evaluated according to the quantity and spatial distribution rendered by plant cover digital images to deeply analyze the reason behind the variation.

Studies on the reflectance spectral features of saline soil along the middle reaches of Tarim River: a case study in Xinjiang Autonomous Region, China

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, 53 soil samples were collected from the oasis in the Weigan and Kuqa River delta along the middle reaches of Tarim River to investigate the level of soil chemical components in relation to soil spectral. An approach combining spectral technology and multi-variant statistical analysis was used to determine the reflectance spectral features of saline soil. The spectral data was first pretreated to remove noises and absorption bands from water, which eliminated influence from instrument errors and other external background factors. Several spectral absorption features were calculated for several saline soil samples to confirm that soil at the same salinity level had similar absorption spectral properties. Secondly, a correlation relationship between reflectance spectra and salinity factors was estimated by bivariate correlation method. Fourteen salinity factors including eight major ions and soil electrical conductivity (EC), soil salt content (SSC), pH, and total dissolved solid (TDS) in the saline soil were evaluated. Datasets of the salinity factors that correlated significantly with field data measurements of reflectance rate and the corresponding spectrum data were used to construct quantitative regression models. According to the multiple linear regression analysis, SSC, SO42−, TDS, and EC had a correlation coefficient at 0.746, 0.908, 0.798, and 0.933 with the raw spectral data, respectively, which confirmed strong correlation between salinity factors and soil reflectance spectrum. Findings from this study will have significant impact on characterization of spectral features of saline soil in oasis in arid land.

Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2) and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/ had excellent capacity of estimating soil salt content in the study area ( g/kg,  g/kg, , , and RPD = 2.080). This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

Modeling of the equivalent permeability for an underground coal fire zone, Xinjiang region, China

Underground coal fires (UCFs) constitute severe disasters, yet the quantification of the process of coal fire propagation, the first step in knowing how to extinguish a UCF, remains a great challenge even after decades of research. An important feature for understanding oxygen supply to a coal fire is the permeability of the UCF zone. Here, we propose a model for a typical UCF zone based on an analysis of the deformation of the overlying rocks caused by the UCF. We delineate the physical boundaries of the UCF and show how the zone includes both porous media and fractured zones. We then attempted to quantify the permeability of these porous and fractured zones and use the quantitative model to design, build, and employ an elementary UCF air/smoke flow experimental apparatus to run a permeability experiment. Subsequent to the experiments, we used simulation software to build a numerical model of the experimental apparatus. Calculated results from the numerical model agreed reasonably well with results from the physical experiments, though for this model to be applied in practice to quantify UCF propagation, further research on problems such as the size and distribution of the fractures and the relationship between the fractures and stress in the rock matrix will be required.

Optimal root system strategies for desert phreatophytic seedlings in the search for groundwater

Desert phreatophytes are greatly dependent on groundwater, but how their root systems adapt to different groundwater depths is poorly understood. In the present study, shoot and root growths of Alhagi sparsifolia Shap. seedlings were studied across a gradient of groundwater depths. Leaves, stems and roots of different orders were measured after 120 days of different groundwater treatments. Results indicated that the depth of soil wetting front and the vertical distribution of soil water contents were highly controlled by groundwater depths. The shoot growth and biomass of A. sparsifolia decreased, but the root growth and rooting depth increased under deeper groundwater conditions. The higher ratios of root biomass, root/shoot and root length/leaf area under deeper groundwater conditions implied that seedlings of A. sparsifolia economized carbon cost on their shoot growths. The roots of A. sparsifolia distributed evenly around the soil wetting fronts under deeper groundwater conditions. Root diameters and root lengths of all orders were correlated with soil water availabilities both within and among treatments. Seedlings of A. sparsifolia produced finer first- and second-order roots but larger third- and fourth-order roots in dry soils. The results demonstrated that the root systems of desert phreatophytes can be optimized to acquire groundwater resources and maximize seedling growth by balancing the costs of carbon gain.