Sahereh Safarlaki; Azadeh Safadoust; Mahmood Rostaminia; Seyedeh Bahareh Azimi
Abstract
Accurate spatial data on soil property distribution is crucial for monitoring of land resources, informed management practices, and robust environmental modeling, especially in arid and semi-arid regions. This study aimed to develop a spatial prediction model for soil salinity in the Meymeh Plain, Dehloran ...
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Accurate spatial data on soil property distribution is crucial for monitoring of land resources, informed management practices, and robust environmental modeling, especially in arid and semi-arid regions. This study aimed to develop a spatial prediction model for soil salinity in the Meymeh Plain, Dehloran Province. The Random Forest (RF) algorithm was employed to investigate spatial variations in soil salinity within the surface (0–30 cm) and subsurface (30–60 cm) soil layers. Soil samples were collected from 100 sites, analyzed for electrical conductivity (EC), and the spatial variability of soil salinity was modeled using random forest (RF) analysis. Seven environmental variables of Greenery, Diffuse Radiation, Valley Bottom Flatness Index, Normalized Difference Vegetation Index, Salinity Index, Wind Direction Index, and Brightness were selected based on the Variance Inflation Factor, including parameters from a digital elevation model and Sentinel-2 satellite reflectance data. The model used 80% of the data for calibration and 20% for validation, with performance assessed through root mean square error (RMSE), coefficient of determination (R²), and concordance correlation coefficient (CCC). The RF model showed high prediction accuracy for surface EC and relatively acceptable results for subsurface layers. The R² for the surface layer was 0.92, and for the subsurface layer was 0.37; the RMSE for the surface and subsurface layers was 0.22; and the CCC for the surface layer was 0.82 and for the subsurface layer was 0.97. Overall, topographic derivatives demonstrated a greater influence on predicting soil salinity in both surface and subsurface layers compared to remote sensing data. The multi-resolution valley bottom flatness index with high spatial resolution was identified as the most important predictor of soil salinity, highlighting the impact of topographic factors in the study area.
Mohammad Jafari; ِAli Tavili; Yahya Esmaeilpour; Hossein Azarnivand; Mohammad Zare Chahouki; Hamid Reza Asghari
Abstract
Abstract In this study the effects and costs of application of two kinds of super absorbent polymer on the establishment and surviving of seedlings was investigated. Experiments were done in the field and on two kind of soil with different salinity (EC: 4.5 and 8 dsm-1). The results showed: the establishment ...
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Abstract In this study the effects and costs of application of two kinds of super absorbent polymer on the establishment and surviving of seedlings was investigated. Experiments were done in the field and on two kind of soil with different salinity (EC: 4.5 and 8 dsm-1). The results showed: the establishment rate of seedling in control was about 40 percent. The percentage of seedling establishment in the hydrogel treatments significantly increased and statistically is equal to the optimal surviving of the seedling (80%). Soil salinity on seedling establishment in the same treatments did not produce significant differences. The results were compared with a similar project to determine the costs and benefits. Based on the results a 30 percent reduction in water usage per round and 33 to 50 percent reduction in the number of irrigation replicate were recorded. The costs in compare to the normal project varied from -2 to 29 percent respectively for treatment Herbosorb® 0.5% and Aquasorb 1%. The treatments Herbosorb® and Aquasorb with usage level of 0.5% caused about 2.5 percent for each one percent increasing in the cost. There is no significant increase in the percentage of seedling survival by using 1% super-absorbent compared to 0.5 percent.
Hossein Bashari; Mostafa Tarkesh; Tayebeh Shahabadi; Mohammad Reza Mosadeghi
Abstract
Abstract
This study aimed to investigate the effects of urban wastewater on some soil physical and chemical properties in Segzi, East Isfahan. Six sites were selected considering the planted species (Haloxylon persicum and Nitraria schoberi) and irrigation types (control or no irrigation, water logged ...
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Abstract
This study aimed to investigate the effects of urban wastewater on some soil physical and chemical properties in Segzi, East Isfahan. Six sites were selected considering the planted species (Haloxylon persicum and Nitraria schoberi) and irrigation types (control or no irrigation, water logged and furrow irrigation methods). The 4 to 12 replications were chosen in each site depending on its' size. Soil samples were collected in all replications from the 0‒10 and 10‒30 cm layers. Soil properties such as texture, organic matter, acidity, electrical conductivity, total soluble sodium, calcium and magnesium concentrations, and structural stability (dispersible clay) were measured for 94 collected soil samples. One-way ANOVA, Duncan's mean test and Principal Component Analysis were used to analyze and interpret the data. The results showed that wastewater affected some soil properties by leaching soluble salts into deeper soil horizons and also adding organic matter and some minerals into the soil. Electrical conductivities of the 0‒10 cm layer in control sites (no irrigation) and the 10‒30 cm layer in sites with furrow irrigation method were significantly greater than water-logged irrigated sites (α= 0.05). Applying wastewater increased soil organic matter content and aggregate stability significantly (α= 0.05). Irrigating the sites with wastewater decreased soluble sodium contents and dispersible clay of the soil surfaces; hence the soil structural stability was increased. Overall, applying wastewater to irrigate the planted seedlings in rangeland restoration projects has benefits as its use in these areas has lower environmental risk when compared with agricultural fields.
zahra giveiiashraf; Mohammad Ali Hakimzade; Mohammad Zare; Zohre Ebrahimi Khusfii; Kazem Dashtakian
Abstract
Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of ...
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Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of remote sensing to assess desertification in the Marvast plain, Yazd province. Two images of Terra satellite, ASTER synchronous to 2003 and 2010 are used. After preprocessing and analyzing of the images, relationship between parameters of soil salinity (i.e. SAR and EC) and spectral reflections were determined and, both two satellite images were classified using maximum likelihood method. Then, the surface area of each class and the amount of its changes were calculated. Results showed that during the period of 7 years (2003-2010), area of non-saline lands has decreased while, the area of saline land has increased, which leads to the salinization of agricultural lands, reduction of its yield and also extent of desertification in this region. Accuracy of EC map classification for 2003 and 2010 images are 87.5% and, 82.5%, respectively. Kappa coefficients for both images are 0.83 and 0.76. Accuracy of SAR map classification for 2003 and 2010images are 87.5% and 87.5%, respectively. Kappa coefficients for these two images are 0.81 and 0.77, respectively. Generally, it can be conclude that using of remote sensing data, especially ASTER images has high efficiency for change detection analysis in soil salinity and natural resources management.
TAYYEBEH MESBAHZADEH
Abstract
This research was done in order to submit a model for salinity map made with TM satellite data and salinity values in a Buienzahra. The necessary processings such as principal component analysis and producing of different indices was done on the main bands. The 38 soil samples using random sampling (with ...
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This research was done in order to submit a model for salinity map made with TM satellite data and salinity values in a Buienzahra. The necessary processings such as principal component analysis and producing of different indices was done on the main bands. The 38 soil samples using random sampling (with 10×10 km dimension) from different horizons were designed and performed on the study area. The position of each node was registered with global positioning system (GPS), and the surface electric conductivity of samples was measured using EC meter instrument in soil saturation extract. Correlation between spectral values (main bands, produced indices) with electrical conductivity values were investigated for 80% of the samples. The regression analysis of ECe showed that there is a significant correlation between ECe with spectral data in all of main bands and with BI, NDMI, SI1, SI2, SI3 indices in 99% levels. The accuracy assessment of estimations using validation 20% samples was done. Results showed the produced ECe model could predict the soil salinity with ME and RMSE of 0.08 and 2.53 dS/m respectively. At finally, Salinity map with different salinity classes ( 0-2, 2-4, 4-16, 16-32, 32< dS m-1) was produced.
Ruollah Taghizadeh Mehrjardi; Fereydoon Sarmadian; Gholem Reza Savaghebi; Mahmoud Omid; Nourayer Toomanian; Mohammad Javad Rousta; Mohammad Hasan Rahimiyan
Abstract
In recent years, alternative methods have been used for estimation of soil salinity. Therefore, at present research, 600 soil samples collected from Ardakan in central Iran. Then EM38 and terrain parameters such as wetness index, land index and curvature as readily measured properties and soil salinity ...
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In recent years, alternative methods have been used for estimation of soil salinity. Therefore, at present research, 600 soil samples collected from Ardakan in central Iran. Then EM38 and terrain parameters such as wetness index, land index and curvature as readily measured properties and soil salinity (0-30 and 0-100) as predicted variables were measured. After that, the data set was divided into two subsets for calibration (80%) and testing (20%) of the models. For predicting of mentioned parameters, ANFIS, GA, ANNs and MLR were applied. In order to evaluate models, some evaluation parameters such as root mean square, average error, average standard error and coefficient of determination were used. Results showed that the ANFIS model gives better estimation than the other techniques for all characteristics whereas this model increased accuracy of predictions about 17 and 11% for EC30 and EC100 respectability. After ANFIS model, GA and ANN had better accuracy than multivariate regression.
