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.
Gholm Reaza Rahi; A Kaviyan; K Soleimani; H Pourghasemi
Abstract
Creating a gully is a reaction to the geomorphologic conditions, this type of erosion extends over a wide range of environments, and the threshold of topography is related to the slope and drainage level and Controls the position and expansion of gullies in different uses. The slop-area equation as a ...
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Creating a gully is a reaction to the geomorphologic conditions, this type of erosion extends over a wide range of environments, and the threshold of topography is related to the slope and drainage level and Controls the position and expansion of gullies in different uses. The slop-area equation as a relationship between the slope and the upstream area of the watershed in any area will in part help predict gully erosion. The above relationship has been influenced by several factors including environmental conditions (climate, lithology, soil type), the type of land use, and the type of effective mechanisms for the creation and expansion of the gully (surface flow, sub-surface flow, dissolation, and piping), and the type of method used for extraction of slope - area is located. And this is the simplest formula used by two parameters to predict gully erosion. The results show that the value of the relationship between -0.233 and -0.205 was obtained. The equation for agricultural use was equal to Y = 5.7426X-0.205and for the pasture use equal to Y = 10.653X-0.233. In this equation, the power can be close but the coefficient of the equation is different. Gully erosion of the whole hinterland (farmland, agriculture) is expanding. The threshold of topography indicates a negative relationship between land area and slop in different kind of agricultural and rangeland. By reducing the slope, more area is needed for the development and expansion of the gully, and most of the gully is due to surface runoff.
D. Askarizadeh; Gh. A. Heshmati
Abstract
Abiotic factors, as topographic and physicochemical properties of soil, are the most important effective factor on vegetation in rangeland ecosystems which have the most important performances to forming and succession of plant vegetation. Ecologic management of rangelands can be desired by better understanding ...
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Abiotic factors, as topographic and physicochemical properties of soil, are the most important effective factor on vegetation in rangeland ecosystems which have the most important performances to forming and succession of plant vegetation. Ecologic management of rangelands can be desired by better understanding of these effective factors. Then, rangeland of Javaherdeh (Ramsar) in the northern Alborz Mountains ranging 2000-3200 m a.s.l. was selected in this study and altitudinal classes of 300 meter were selected to obtain field records on the basis of field monitoring and plants structures. About 15 plots (1 m2) in each altitudinal class were considered in order to obtain the field data, e.g. percentage of life-form covers. It was also chosen five plots to gather soil samples. Statistical analyses, using cluster analysis, DCA and CCA, were done by PC-Ord V.5.1 software. The results showed that life forms of plant under 183 species and 33 families have been divided into five sub-associations so that their segregation is done based upon elevation, aspect, and soil properties. Multivariate analysis (CCA) also can as well divide the life forms of plants based on their ecological requirements into subgroups include annual and perennial grasses with perennial forbs, annual forbs, shrubs, and bushy trees. These life forms are also found different ecologic niches funded upon influence of the topographic factors and physicochemical properties of soils. Hence, ecologic management of terrestrial ecosystems needs to knowing and understanding of vegetation structures under different environmental factors.
