The assessment of spatial distribution of soil salinity risk using neural network

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km²) results were used to upscale soil salinity to a district area (∼300 km²). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m⁻¹). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated.     

Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques

The Harran Plain is located in the southeastern part of Turkey and has recently been developed for irrigation agriculture. It already faces soil salinity problems causing major yield losses. Management of the problem is hindered by the lack of information on the extent and geography of the salinization problem. A survey was carried out to delineate the spatial distribution of salt-affected areas by randomly selecting 140 locations that were sampled at two depths (0 to 30 and 30 to 60 cm) and analyzed for soil salinity variables: soil electrical conductivity (EC), soluble cations (Ca^2+, Mg²⁺, Na⁺, and K⁺), soluble anions (SO ₄ ^2? , Cl^?), exchangeable Na⁺ (me 100 g^?1) and exchangeable sodium percentage. Terrain attributes (slope, topographical wetness index) were extracted from the digital elevation model of the study area. Variogram analyses after log transformation and ordinary kriging (OK) were applied to map spatial patterns of soil salinity variables. Multivariate geostatistical methods—regression kriging (RK) and kriging with external drift (KED)—were used using elevation and soil electrical conductivity data as covariates. Performances of the three estimation methods (OK, RK, and KED) were compared using independent validation samples randomly selected from the main dataset. Soils were categorized into salinity classes using disjunctive kriging (DK) and ArcGIS, and classification accuracy was tested using the kappa statistic. Results showed that soil salinity variables all have skewed distribution and are poorly correlated with terrain indices but have strong correlations among each other. Up to 65 % improvement was obtained in the estimations of soil salinity variables using hybrid methods over OK with the best estimations obtained with RK using EC_0–30 as covariate. DK–ArcGIS successfully classified soil samples into different salinity groups with overall accuracy of 75 % and kappa of 0.55 (p?<?0.001).     

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta,China

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0–15, 15–30, 30–45 and 45–60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.     

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.     

Spatial variability of soil heavy metals in the three gorges area: multivariate and geostatistical analyses

There is a growing concern about environmental contamination in the three gorges area. The objectives of this study were to investigate the spatial variability and the possible influence factors of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in the center of this area based on multivariate and geostatistical approaches. All analyzed heavy metals were below their background levels, except Cd. The average concentrations of the analyzed elements in topsoil (0-20 cm) were 5.83 mg As kg(-1), 0.21 mg Cd kg(-1), 78.79 mg Cr kg(-1), 21.53 mg Cu kg (-1), 0.049 mg Hg kg(-1), 24.12 mg Pb kg(-1), and 68.5 mg Zn kg(-1). The concentration of As was mostly due to parent materials, whereas the source of Pb was mainly due to vehicle exhaust. The high concentration of Cd was resulted from agricultural practices and parent materials. The concentrations of Cr, Cu, Hg, and Zn were associated with parent materials and human activities.     

National assessment of sea level rise using topographic and census data for Turkish coastal zone

Turkish coastal zone elevation to sea level rise was illustrated by using digital elevation model and Geographical information systems methods. It was intended to determine several parameters such as population, settlements, land use, wetlands, contribution to national agricultural production and taxes at risk by using high resolution SRTM topographic, orthorectified Landsat Thematic Mapper Mosaics and census data with GIS methods within 0-10 m elevation of national level. All parameters were examined for coastal cities, coastal districts, settlements and villages' status. As a result of the analysis of data set, it was found that approximately 7,319 km(2) of land area lies below 10 m contour line in Turkey, and is hence highly vulnerable to sea-level rise. 28 coastal cities, 191 districts and 181 villages or towns are located below 10 m contour line in study area. In the short term, for the struggle of negative impact of sea level rise, the findings suggest that the Ministry of Environment should declare new areas as protection areas and develop special environmental programs for national level.     

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0–30cm, 30–60cm, 60–90cm and 90–120cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.     

Monitoring and assessment of toxic metals in Gulf War oil spill contaminated soil using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was applied for the detection of toxic metals in oil spill contaminated soil (OSCS). The OSCS samples were collected from Khursania Saudi Arabia along the coast of Persian Gulf exposed to oil spills in 1991 Gulf war. Environmentally important elements like Aluminum Magnesium, Calcium, Chromium, Titanium, Strontium, Iron, Barium, Sodium, potassium, Zirconium and Vanadium from the contaminated soil have been detected. Optimal experimental conditions for analysis were investigated. The LIBS system was calibrated using standard samples containing these trace elements. The results obtained using Laser-Induced Breakdown Spectroscopy (LIBS) were compared with the results obtained using Inductively Coupled Plasma Emission Spectroscopy (ICP). The concentrations of some elements (Ba and Cr) were found higher than permissible safe limits. Health risks associated with exposure to such toxic elements are also discussed.     

Insecticide residues in soil and water in coastal areas of vegetable production in Togo

Some common organochlorine, organophosphorus and pyrethroid insecticides were analysed in agricultural soil samples (n?=?35) and surface water and groundwater samples (n?=?25) collected from coastal areas of vegetable production in Togo. Analytical methods included solvent extraction of the insecticide residues and their subsequent quantification using GC-ECD. δ-HCH, heptachlor epoxide, 4,4-DDE, endosulphan (α, β and sulphate), lambda-cyalothrin and chlorpyrifos were found in the soil samples with concentrations that varied from non-detectable (ND) to 26.93 μg kg^?1 dry weight. For water samples, heptachlor epoxide, 2,4-DDD, 4,4-DDD, 4,4-DDE and endosulphan (α, β, and sulphate) were found at contamination levels that varied from ND to 0.116 μg L^?1. The concentration of insecticide residues detected in the water samples was below the limits set by the World Health Organization (WHO) and also by the European Union (EU), with the exception of the concentration of endosulphan sulphate at the Aného site, which was 0.116 μg L^?1.