Yayah Parvizi; Zahra Gerami; Mahmood Arabkhedri
Abstract
The degradation of soil structure and reduced water permeability are indicators of soil destruction, contributing to diminished stability, compromised production quality, and environmental issues. To counteract soil degradation, soil conservation methods are widely employed to modify soil and water processes, ...
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The degradation of soil structure and reduced water permeability are indicators of soil destruction, contributing to diminished stability, compromised production quality, and environmental issues. To counteract soil degradation, soil conservation methods are widely employed to modify soil and water processes, enhancing properties like structure and permeability. However, limited research has evaluated the effectiveness of these methods. This study focuses on assessing the impact of soil management and protection measures on soil structure and permeability in the Rezin watershed of Kermanshah province. Eight restoration and protection techniques were chosen and examined in the study area. Following profile excavation and soil sampling, indices such as MWD, GMD, and WSA >0.25 were measured and computed. The final penetration speed was also assessed. Comparative analysis of MWD, GMD, WSA >0.25, and final infiltration rates between soil protection operations and control areas was conducted using a T-test for independent samples via SPSS software. Results indicated that the lowest MWD indices, at 0.15 and 0.35 mm, were associated with land leveling and planting, while the highest, at 1.9, 1.8, and 1.6 mm, were linked to 20- and 10-year gardens and forest areas, respectively. Notably, fodder operations and 10-year garden construction exhibited the highest WSA >0.25 index, indicating the formation of coarse and stable soil aggregates due to protection operations. Final infiltration rate results demonstrated changes in drylands to seedling cultivation, with 10-year gardens showing the highest increase at 21.8% compared to the control.
Mohsen Zabihi; Seyed Hamidreza Sadeghi; Mehdi Vafakhah
Abstract
Soil erosion as a threatening phenomenon for the world population is mostly the result of the combined effects of unsuitable land use and climatic factors. Among climatic factors, rainfall is considered as one of the main causes of soil erosion and therefore detailed study of the different properties ...
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Soil erosion as a threatening phenomenon for the world population is mostly the result of the combined effects of unsuitable land use and climatic factors. Among climatic factors, rainfall is considered as one of the main causes of soil erosion and therefore detailed study of the different properties of rainfall such as rainfall erosivity is necessary. However, investigation of spatial variability of rainfall erosivity factor at different scales at national level has been less considered. Therefore, the present study aimed to investigate the spatial variability of rainfall erosivity factor in Universal Soil Loss Equation (USLE) for monthly, seasonal and annual scales in Iran. Towards this attempt, the amounts of rainfall erosivity factor were calculated through calculation of kinetic energy and maximum 30-minute intensity over 12,000 showers occurred at 70 stations in the study period of 20 years (1984-2004) in Iran. The spatial patterns of temporal variation were also in different time scales. According to the results, existing stations in the south west and north of the country had the first priority of annual rainfall erosivity factor hazard. The west and south west stations and south east stations had also the highest and the lowest seasonal and monthly risk rainfall erosivity factor, respectively. Also, results showed Tangpich in Khuzistan, Anzali in Giulan and Poleshalo in Khuzistan had the maximum rainfall erosivity factor whereas Bande Enherafi in Semnan, Tabas in South Khorasan and Bam in Kerman Provinces had the minimum annual rainfall erosivity factor hazard country wide. The average annual rainfall erosivity factor in the country was ultimately obtained 14.13 tm.ha-1.cm.h-1.
Maral Khodadadi; Mohammad Sadegh Askari; Fereydoon Sarmadian; Hossein Gholi Refahi; Ali Akbar Norouzi; Ahmad Heidari; Hamid Reza Matinfar
Volume 1, Issue 2 , July 2009, , Pages 99-110
Abstract
Salinity is the major factors of soil degradation in semi arid and arid regions. The main aim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinity mapping in the selected part of the Qazvin plain, an area of arid environment. In this study spectral classes carried out on ...
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Salinity is the major factors of soil degradation in semi arid and arid regions. The main aim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinity mapping in the selected part of the Qazvin plain, an area of arid environment. In this study spectral classes carried out on remotely sensed data and with the help of field observation and soil analysis were regrouped to soil salinity classes to prepare soil salinity map.. Soil sampling was implemented using stratified random sampling method, depending on landscape complexity and homogeneity as well as on the representativeness of Landsat ETM+ data. Also in each soil map unit at least one profile was studied for subsoil salinity variations. Field samples taken by using augur and profiles were analyzed in laboratory for Na+ , Ca2+ , Mg2+ cations, as well as soil texture, ECe and pH. We have analyzed the effectiveness of additional data such as digital elevation model to improve the accuracy of classification. Also NDVI, SRVI, PVI, SAVI, SI, BI and NDSI indices, PCA and Tasseled cap were analyzed. Soil salinity map of each selected bands produced and with ground truth map crossed. The results indicated that combination of DEM with ETM+ bands has highest accuracy. This study addressed that thermal band of ETM+ can increase the classification accuracy which illustrated its effective role to classify the soil salinity. Tasseled cap and other indices had almost high accuracy among studied image processing techniques. The SI and BI indices had the highest correlation with EC and could distinguish the saline and non saline soils while the optimum index factor had overall low accuracy.
