Yaser Ostovari; shoja ghorbani; Hosseinali Bahrami; Mahdi Naderi; mozhgan abasi
Abstract
Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard ...
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Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard plots divided by erosivity factor. Since measuring the erodibility factor in the field especially watershed scale is time-consuming and costly, this factor is commonly estimated by pedotransfer functions (PTFs) using readily available soil properties. Wischmeier and Smith (1978) developed an equation using multiple linear regressions (MLR) to estimate erodibility factor of the USA using some readily available soil properties. This equation has been used to estimate K based on soil properties in many studies. As using PTFs in large sales is limited due to cost and time of collecting samples, recently soil spectroscopy technique has been widely used to predict certain soil properties using Point SpectroTransfer Functions (PSTFs). PSTFs use the correlation between soil spectra in Vis-NIR (350-2500 nm) and certain soil properties. The objective of this study was to develop PSTFs and PTFs for soil erodibility factor prediction in the Simakan watershed Fars, Iran.
Materials and Methods: The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06'-30°18'N and 53°05'-53°18'E (WGS′ 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, soil permeability, pH, EC were analyzed Spectra of the air-dried and sieved soil samples were recorded in the Vis-NIR-SWIR (350 to 2500 nm) range at 1.4- to 2-nm sampling intervals in a standard and controlled dark laboratory environment using a portable spectroradiometer apparatus (FieldSpec 3, Analytical Spectral Device, ASD Inc.). Some bands which had the highest correlation with K factor were chosen as input parameter for developing PSTFs. A stepwise multiple linear regression method was used for developing PTFs and SPTFs. R2, RMSE and ME were used for comparing PTFs and SPTFs.
Results and Discussion: The K values varied from 0.005 to 0.023 t h MJ−1 mm−1 with an average standard deviation of 0.014 and of 0.003 t h MJ−1 mm−1, respectively. The K estimated by Wischmeier and Smith (1978) equation varied from 0.015 to 0.045 t h MJ−1 mm−1 with an average of 0.030 t h MJ−1 mm−1. There was a significant difference (p
Mahboobeh Tayebi; Mahdi Naderi; jahangard mohammadi; Mahdieh Hosseinjani Zadeh
Abstract
Introduction: Soil texture is one of the majorphysical properties of soils thatplays important roles inwater holding capacity, soil fertility, environmental quality and agricultural developments. Measurement of soil texture elements in large scales is time consuming and costly due to the high volume ...
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Introduction: Soil texture is one of the majorphysical properties of soils thatplays important roles inwater holding capacity, soil fertility, environmental quality and agricultural developments. Measurement of soil texture elements in large scales is time consuming and costly due to the high volume of sampling and laboratory analysis. Therefore, assessing and using simple, quick, low-cost and advanced methods such as soil spectroscopy can be useful. The objectives of this study were to examine two statistical models of Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) to estimate soil texture elements using Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm).
Materials and Methods: A total of 120 composite soil samples (0-10 cm) were collected from the Kafemoor basin (55º 15' - 55º 25' E; 28º 51' - 29º 11' N), Sirjan, Iran. The samples were air dried and passed through a 2 mm sieve and soil texture components were determined by the hydrometer method (Miller and Keeny 1992). Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e.First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The cross‐validation method was used to evaluate calibration and validation sets in the first part (80%) and coefficient of determination (R2), Root Mean Square Error (RMSE) and Residual Prediction Deviation (RPD) were also calculated. For testing predictive models, the second part of data (20%) was used and R2 and RMSE of predictive accuracy were calculated.
Results and Discussion: The results of applying two statistical models for estimatingLogClay (%) showed that R2of calibration (R2CV) and validation (R2VAL) datasetranged from 0.22 to 0.72 and 0.12 to 0.54, respectively. The lowest RMSE was computed for PLSR model with SD pre-processing. The highest RPD of calibration (RPDCV) and validation (RPDVAL) were obtained for PLSR with SD pre-processing technique which was classified as a very good and good model, respectively. The results indicated possible prediction of soil clay content by using PCR model with SD pre-processing techniques. In addition, the PCR predicted soil texture elements poorly according to RPD values while the PLSR model with SD pre-processing was the best model for predicting soil clay content. The R2CV and R2VAL of PLSR models for LogSilt (%) varied from 0.34 to 0.73 and 0.27 to 0.58, respectively. The RMSECV varied from 0.14 for FD pre-processing to 0.23 for no-preprocessing and the RMSEVAL rangedbetween 0.18 and0.24. The highest RPDCV (2.07) and RPDVAL (1.59) were obtained for PLSR with FD pre-processing which were classified as very good and good models, respectively. The results of PCR model developments for estimating LogSilt (%) indicated that the highest RPDCV and RPDVAL were, respectively, 1.31 and 1.25 for MSC pre-processing techniques which were rated as poor models. On the contrary to PLSR models, PCR models were not reliable for predicting LogSilt (%).Theresultsof PLSR models for estimatingLogSand (%) revealedthat the highest R2CV and R2VAL were 0.56 and 0.47, respectively and the lowest RMSECV and RMSEVAL were 0.14 and 0.16, respectively which were obtained for SD pre-processing. The RPDCV and RPDVAL values for SD pre-processing in PLSR model were 1.59 and 1.39 which were rated as good and poor performance of predictions, respectively. The highest RPDCV and RPDVALfor PCR models were obtained with the MSC pre-processing indicating poor model. Therefore, PLSR model with SD pre-processing techniques was superior model for estimation of LogSand(%).Overall, PLSR model with SD pre-processing techniques performed better in estimatingclay and sand and PLSR model with FD pre-processing gave better estimate of silt content.
Conclusions: Our finding indicated thatclay and silt contentcan be estimated by using electromagnetic spectrum between VNIR-SWIR region. Further, spectroscopy could be considered as a simple, fast and low cost method in predicting soil texture and PLSR model with SD and FD pre-processing seems to be more robust algorithm to estimateLogClay and LogSilt, respectively.
sara kalbali; Shoja Ghorbani-Dashtaki; Mahdi Naderi; Salman Mirzaee
Abstract
Introduction: Rock fragments on soil surfaces can also have several contrasting effects on the hydraulics of overland flow and soil erosion processes. Many investigators have found that a cover of rock fragments on a soil surface can decrease its erosion potential compared to bare soil surface (1, 12 ...
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Introduction: Rock fragments on soil surfaces can also have several contrasting effects on the hydraulics of overland flow and soil erosion processes. Many investigators have found that a cover of rock fragments on a soil surface can decrease its erosion potential compared to bare soil surface (1, 12 and 18). This has mainly been attributed to the protection of the soil surface by rock fragments against the beating action of rain. This leads to a decrease in the intensity of surface sealing, an increase in the infiltration rate, a decrease in the runoff volume and rate, and, hence, a decrease in sediment generation and production for soils covered by rock fragments. Parameters that have been reported to be important for explaining the degree of runoff or soil loss from soils containing rock fragments include the position and size (15), geometry (18), and percentage cover (11 and 12) of rock fragments and the structure of fine earth (16). Surface rock fragment cover is a more important factor for hydroulic properties of surface flows such as flow depth, flow velocity, Manning’s roughness coefficient (n parameter) and flow shear stress and geometrics properties of formed rill such as time, location, number, length, width and depth of rill. Surface rock fragment cover is directly affected soil erosion processes in dry area specially in areas that plant can not grow because of sever dryness and salinity. Also, Surface rock fragment prevent the contact of rain drops to aggregates, decreasing physical degradation by decreasing flow velocity. The objective of this study was to investigate the effect of different surface rock fragment cover on hydraulic properties of surface flows and geometrics properties of formed rill.
Materials and Methods: For this purpose, 36 field plots of 20 meter length and 0.5 meter width with 3% slope were established in research field of agricultural faculty, Shahrekord University. Before each erosion event, topsoil was tilled and smoothed with hand tools to remove soil irregularities and soil sealing, update aggregates which come from deeper soil. Then, for beginning the experiment, surface rock fragment cover is scattered randomly on plot surface. Experiment equipment such as collecting the runoff systems installed at the end of plots. In each experiment after setting the surface flow, surface runoff inter to soil surface and testing continued for 60 minutes after starting runoff. Flow velocity was measured using a dye-tracing technique (potassium permanganate) and depth, width and length of rill were measured using a ruler. Treatments were including four level rock fragment cover (0, 10, 20 and 30%) and three rate runoff (2.5, 5 and 7.5 L min-1) with three replications that experiments were done in a factorial with randomized complete block design. Surface runoff samples were oven-dried and weighed to determine sediment loads. Sediment concentration was determined as the ratio of dry sediment mass to runoff volume, while the erosion rate was calculated as the sediment yield per unit area per period of time.
Results and Discussion: The results of this study showed that surface rock fragment cover plays an important role in water distribution. Based on the results, the positive effects of rock fragment cover on Manning’s n and the negative effect on flow velocity. Increasing surface rock fragment cover increased hydroulic properties such as flow depth, Manning’s n and flow shear stress significantly (p
parvane mohaghegh; Mahdi Naderi; jahangard mohammadi
Abstract
Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to ...
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Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran.
Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods.
Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively.
Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.
Ali Morshedi; Seyed Hassan Tabatabaei; Mahdi Naderi
Abstract
Introduction: Evapotranspiration (ET) is an important component of the hydrological cycle, energy equations at the surface and water balance. ET estimation is needed in various fields of science, such as hydrology, agriculture, forestry and pasture, and water resources management. Conventional methods ...
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Introduction: Evapotranspiration (ET) is an important component of the hydrological cycle, energy equations at the surface and water balance. ET estimation is needed in various fields of science, such as hydrology, agriculture, forestry and pasture, and water resources management. Conventional methods used to estimate evapotranspiration from point measurements. Remote sensing models have the capability to estimate ET using surface albedo, surface temperature and vegetation indices in larger scales. Surface Energy Balance Algorithm for Land (SEBAL) estimate ET at the moment of satellite path as a residual of energy balance equation for each pixel. In this study Hargreaves-Samani (HS) and SEBAL models ET compared to an alfalfa lysimeter data’s, located in Shahrekord plain within the Karun basin. Satellite imageries were based on Landsat 7 ETM+ sensor data’s in seven satellite passes for path 164 and row 38 in the World Reference System, similar to lysimeter sampling data period, from April to October 2011. SEBAL uses the energy balance equation to estimate evapotranspiration. Equation No. 1 shows the energy balance equation for an evaporative surface:
λET=Rn–G–H [1]
In this equation Rn, H, G and λET represent the net radiation flux input to the surface (W/m2), Sensible heat flux (W/m2), soil heat flux (W/m2), and latent heat of vaporization (W/m2), respectively. In this equation the vertical flux considered and the horizontal fluxes of energy are neglected. The above equation must be used for large surfaces and uniformly full cover plant area. SEBAL is provided for estimating ET, using the minimum data measured by ground equipment. This model is applied and tested in more than 30 countries with an accuracy of about 85% at field scale, and 95 percent in the daily and seasonal scales. In Borkhar watershed (East of Isfahan, IRAN) ASTER and MODIS satellite imageries were used for SEBAL to compare Penman-Monteith model. Results showed that estimated ET of SEBAL were about 20% less than sugar beet ET and about 15% more for maize ET by Penman-Monteith. He concluded the differences may be due to the limited number of satellite imageries which extrapolated ET through the entire growth period and the data obtained from the weather station far from 24 km in the studied area. In another study at Zayanderud Basin, the different irrigation networks were examined using Landsat 7 imageries to increase the spatial resolution of NOAA satellite to determine the energy balance components and actual evapotranspiration. In this study, data from a lysimeter to a depth of 2.5 m and a diameter of 3 meters planted with alfalfa in the Chahar-Takhteh agricultural research station (Agricultural and natural resources research center of Shahrekord, IRAN) was used. The lysimeter (LYS_REF) located in the in the middle of 25 × 40 m (1000 square meter) alfalfa cultivated farm, surrounded by other planted area. The lysimeter used to measure the reference evapotranspiration (ETr) and around alfalfa was used as cold pixels.
Materials and Methods: This study was conducted to evaluate SEBAL and Hargreaves-Samani estimated ET models against evapotranspiration measured by lysimeter within the Shahrekord plain. Meteorological data required for a period of 185 days (according to the lysimeter data period) includes minimum and maximum relative humidity (RHmax and RHmin), maximum and minimum air temperature (Tmax and Tmin), wind speed at two meters (U2), precipitation, evaporation rate, sunshine hours, air pressure and dew point temperature obtained from a weather station nearby lysimeter. In order to assess reference evapotranspiration (ETr) models, statistical indices such as the coefficient of determination (R2), mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE) and index of agreement (d) were used.
Results and Discussion: The results showed that RMSE, MAE and MBE for SEBAL model over the lysimeter data were 1.782, 1.275 and -0.272 mm/day and 0.700 for the d index, respectively. Similar indices for the Hargreaves-Samani model were 1.003, 0.580 and 0.290 mm/day and 0.917 for the d index. For HS model results show that RMSE, MAE and MBE values were 0.813, 0.477 and 0.206 mm/day, and 0.930 for the index of d, during the entire growing period (185 days).
Conclusion: However, results showed that the efficiency and reliability of the SEBAL model by processing satellite visible, near infrared and thermal infrared bands. The need for irrigation water requirements and ET estimation are noteworthy, during the growth of various plants, which vary and thus the complete time series of satellite imageries is required to estimate the total and annual evapotranspiration.
D. Baharlooi; S. Ghorbani Dashtaki; B. Khalil Moghadam; Mahdi Naderi; P. Tahmasebi
Abstract
Introduction: The detachment process can be conceptually divided in two sub-processes included aggregate breakdown (Le Bissonnais, 1996) and movement initiation of breakdown products(Kinnell, 2005). soil detachment depends on raindrop size and mass(Elison, 1944; Bisal, 1960), drop velocity(Elison, 1944; ...
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Introduction: The detachment process can be conceptually divided in two sub-processes included aggregate breakdown (Le Bissonnais, 1996) and movement initiation of breakdown products(Kinnell, 2005). soil detachment depends on raindrop size and mass(Elison, 1944; Bisal, 1960), drop velocity(Elison, 1944; Bisal, 1960), intensity rainfall (Ting et al, 2008), kinetic energy (Kinnell, 2003; Fernandez- Raga et al, 2010), runoff depth(Torri et al, 1987; Kinnell,1991 and 2005), crop covers(Bancy, 1994; Ghahremani et al, 2011), wind speed( Erpul et al, 2000) and experimental area (cup size) (Leguedois et al, 2005; Luk, 1979; Torri and poesen, 1988). Many of studies have been conducted to evaluate the relationship between splash and slope (Bryan, 1979; Torri and Poesen, 1992; Wan et al, 1996).Torri and Poesen (1992) expressed that in steep slope the gravity force adds to the drop detaching force and decreases of soil resistance, consequently increases splash erosion rate with increasing slope. Soil splash erosion is also strongly influenced by soil properties including soil particles size distribution (Mazurak and Mosher, 1968; Legout et al, 2005; fan and li, 1993), soil shear strength(Cruse and Larson, 1977; Al-Durrah and Bradford,1981; Ekwue and ohi; 1990 ), soil cohesion(Torri et al, 1987), soil organic matter content and aggregate size (Ekwue and Maiduguri, 1991; Qinjuan et al, 2008), soil aggregates stability(Qinjuan et al, 2008), surface crust (Qinjuan et al, 2008).
Fire, play an important role in splash erosion. The absence of vegetation cover in disturbed lands accelerates splash erosion rates by as much as several folds compared to undisturbed sites (Lal, 2001; Thomaz and luiz, 2012).The detachment of soil particles by splash depends on several raindrop characteristics, including raindrop size and mass, drop velocity, kinetic energy, and water drop impact angle (Sharma et al., 1993; Singer and Le Bissonnais, 1998; Cruse et al., 2000, Bhattacharyya et al., 2010). Detachment rate is strongly influenced by soil properties, including soil texture and thickness of the water layer at the soil surface (De Ploey and Savat, 1968; Moss and Green, 1983; Sharma et al., 1991; Kinnell, 1991, Jomaa et al., 2010), soil strength, bulk density, cohesion, soil organic matter content, moisture content, infiltration capacity (Nearing et al., 1988; Owoputi, 1994; Morgan et al., 1998, Planchon et al., 2000, Ghahramani et al., 2011), soil initial water content, surface compaction and roughness (Planchon et al., 2000), the nature of soil aggregates and crust, porosity, capacity of ionic interchange, and clay content (Poesen and Torri, 1988). Several studies have shown that splash detachment rate is mainly related to surface rock fragments in soils with sparse vegetation cover (Jomaa et al., 2012). The present study was conducted to investigate the effects of fire on splash erosion and some erosion depended properties in semi-steppe rangeland of Karsanak region in Chaharmahal and Bakhtiari province which affected by man-made fire during 2008, 2009, 2010 and 2011.
Materials and Methods: Soil samples were obtained on 2012 from the mentioned regions (8 samplesfrom the burned area and 8 samples as a control (unburned) in the adjacent burned area) from 0-7 cm depth. Splash erosion under simulated rainfall intensity of 2 mm per minute was measured using multivariate splash cup apparatus considering the slope of 5 and 25 degree. Soil pH, soil electrical conductivity, equivalent calcium carbonate, soil organic matter, sand size fraction particulate organic matter (SSF POM), mean weight diameter and, geometric mean diameter of aggregates, percent of macro and micro-aggregates, percent of clay, silt, sand, water dispersible clay and soil bulk density were measured. Statistical data analysis was performed by t-test at 5% level.
Results Discussion: The results showed that soil splashing increased significantly in treatment 1 year after the fire in both slope 5 and 25 degree and in treatment 2 year after the fire in slope 25 degree. The amounts of increase in soil splashing compared to control treatment were 22, 24 and 15 percent in treatment 1 year after fire in slope 5 and 25 degree and in treatment 2 years after the fire in slope of 25 degree respectively. Comparison of the total soil splash on slopes of 25 degree at 1, 2, 3 and 4 years after the fire, showed a significant increase in the level of five percent relative to the slope of 5 degree at 1, 2, 3 and 4 years after the fire. The other measured soil properties (except equivalent calcium carbonate) was affected by fire. Also, the differences between many of the mentioned properties in the first 2 years after the fire was significant compared with the control area, but they have been reached to the initial values in the third and fourth years after the fire.
Conclusion: Time was shown to be effective factor inrecovering soil propertiesin Karsanak region of Chaharmahal and Bakhtiari province which affected by man-made fire during 2008, 2009, 2010 and 2011. Fire accelerates splash erosion rates by as much as several folds compared to control in this area.
M. Dayani; M. Naderi; J. Mohammadi
Abstract
Abstract
Mine excavation, concentration and transportation of minerals make Southern Esfahan municipality and suburb of Sepahanshahr vulnerable to pollution and endanger their people by heavy metals. Nowadays, spectrophotometers and spectral reflectance of soils in different parts of spectrum are applied ...
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Abstract
Mine excavation, concentration and transportation of minerals make Southern Esfahan municipality and suburb of Sepahanshahr vulnerable to pollution and endanger their people by heavy metals. Nowadays, spectrophotometers and spectral reflectance of soils in different parts of spectrum are applied for estimating soil characteristics and pollutants. This research was conducted to study the potential of Landsat ETM+ data for estimating and mapping spatial distribution of heavy elements in the Southern Isfahan municipality. During a field survey 100 surface soil samples were collected randomly. Samples were air dried and fine earth was treated by 4 M HNO3 (at 80 °C) and total Pb, Zn and Cd concentration was measured by Atomic Absorption Spectrophotometer and ICP. Statistical analysis reveals negative and significant correlation coefficient between concentration of heavy metals and visible and NIR data and consequently, possible delineation of heavy metals. Spatial distribution of Pb, Zn and Cd concentration mapped using several stepwise multiple regression equations. Results indicate that concentration of Pb, and Zn are above the thresholds and that of Cd is not serious at the moment in proximity of Sepahanshahr.
Keywords: Soil pollution, Heavy metal, Landsat ETM+, Mapping
M. Dayani; J. Mohammadi; M. Naderi
Abstract
Generally heavy metals exist in all soils, but soil pollution is rising by time due to human activities. Soils in the proximity of mines are more pruning to pollution of heavy metals due to mine exploring and excavation. This research was carried out to evaluate the soil pollution of Sepahanshahr Suburb ...
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Generally heavy metals exist in all soils, but soil pollution is rising by time due to human activities. Soils in the proximity of mines are more pruning to pollution of heavy metals due to mine exploring and excavation. This research was carried out to evaluate the soil pollution of Sepahanshahr Suburb with Pb, Zn and Cd. During a field work campaign 100 soil samples were selected randomly from 9000 ha area. The soil samples were treated with 4 M HNO3. Total amounts of Pb, Zn and Cd were measured using Atomic Absorption Spectrometer. The results indicated that concentration of Pb and Zn were beyond the defined soil pollution thresholds (
M. Naderi; A. Karimi
Abstract
Abstract
In case the performance of irrigation and drainage systems could be monitored by using satellite data, which are taken in short intervals, the problems concerning these systems could be corrected. Roodasht region which is located in the lower part of the Zayanderood River Basin was considered ...
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Abstract
In case the performance of irrigation and drainage systems could be monitored by using satellite data, which are taken in short intervals, the problems concerning these systems could be corrected. Roodasht region which is located in the lower part of the Zayanderood River Basin was considered as a pilot plain. The basin is struggling with salinity and waterlogging which started by construction of the Zayanderood Dam and consequently, doubling the share of irrigation water of the area. For this purpose the satellite images of Landsat MSS and TM of 1976 through 1990 were used. Modifications was performed after field works, reviewing the available reports and maps from the area, and then, the satellite data were classified. Temporal analysis of the satellite images showed that by doubling the irrigation water share during 14 years, soils with severe and no salinity risk were decreased by 5 and 16%, respectively, while 20% was added to the land with moderately salinity risk. During this time the area of waterlogged lands has been doubled. The images of 1990 showed that new waterlogged lands were developed in the vicinity of the drainage and irrigation canals.
Key words: Satellite images, Landsat MSS and TM, Temporal analysis, salinity, Waterlogging
