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.
afshin sadeghirad; Negar Eini; Atefeh Fatahi; Harir Sohrabi
Abstract
Any plant according to its needs selects an optimal location of the environment as habitat. The aim of this study was to evaluate the relationship between plant species composition and edaphotopopographic factors in the steppe rangelands of Marvdasht in Fars province. Soil and vegetation sampling was ...
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Any plant according to its needs selects an optimal location of the environment as habitat. The aim of this study was to evaluate the relationship between plant species composition and edaphotopopographic factors in the steppe rangelands of Marvdasht in Fars province. Soil and vegetation sampling was carried out by random sampling and transect method, respectively. In this study, transects with a length of 30m were used. After sampling the soil samples were transported to the laboratory. In the laboratory content of OM, EC, pH, sand, silt and clay, Na, K and P were measured. The relationship between environmental factors and species composition was determined using DCA and CCA multivariate analyzes. The most important factors in the distribution of vegetation, were soil EC and Na. Altitude and slope were identified as effective topographic factors the composition of plant species.
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.
D Talebpoor Asl; S Khezry
Volume 63, Issue 3 , December 2010, , Pages 341-358
Abstract
The Mahabad River Watershed has been exposed to severe erosion as a result of landuse change. The aim of this study is to quantify the amount of sediment yield and the relationship between land use change and slope. Different sources such as geologic and topographic maps, satellite images, hydrometric ...
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The Mahabad River Watershed has been exposed to severe erosion as a result of landuse change. The aim of this study is to quantify the amount of sediment yield and the relationship between land use change and slope. Different sources such as geologic and topographic maps, satellite images, hydrometric as well as meteorological data were used to extract and gather the information needed for this research. Six factors including altitude, slope, precipitation, rock erodibility, time of concentration and land use were specified as the most effective factors. The overlapping of the six factors maps resulted in the preparation of sediment delivery potential map. The distribution maps prepared on each of these parameters and their overlaps have shown that areas with more severe slopes are categorized as highly susceptible to erosion. However, in areas with resistant rocks, change of land use has been the determining factor. Comparison of water and sediment discharge data of 1996-97 with 2001-02(similar annual water volume) showed that the sediment yield has increased. Studying land use map prepared using satellite imagery of 1987 and 1998 showed that land use of the area has severely changed from pasture to dryland farming. This factor has also caused the intensification of mass movement recently occurred in the area. Landuse is the only parameter modified by human, and it is the only one which can be quickly and effectively changed. Hence, it seems that the upstream areas in the south and southwest of the basin with low concentration time, steep slope, high erodibility, high amount of precipitation, and the landuse of forest and pasture mixture as well as alluvial terraces with fine and granular sediments are the most sensitive areas which need protecting and control measures.
