JWSS - Journal of Water and Soil Science

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Landscape ecology as a modern interdisciplinary science offers new concepts, theories, and methods for land evaluation and management. One main part of landscape ecology is describing patterns in the landscape and interpreting the ecological effects of these patterns on flora, fauna, flow of energy and materials. Landscape studies require methods to identify and quantify spatial patterns of landscape. Quantification of spatial patterns is essential to understand landscape functions and processes. Landscape indices as diversity and naturalness can provide quantitative information about landscape pattern. Remote sensing and GIS techniques have high ability for landscape researchers to specify, map and analyze landscape patterns. The objectives of the research include mapping and quantifing diversity and naturalness indices for Mooteh wildlife refuge by land use/land cover map derived from remote sensing images. Finally, diversity and naturalness were classified in 4 and 6 classes, respectively. Results showed that the intermediate and high diversity classes (class 1 & 2) have occupied the largest area in the study area. Among naturalness classes, class 1 which represents the high level of naturalness has taken the largest area in Mooteh W.R.

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Monitoring Land Use and Land Cover Changes have a significant role in environmental programming and management. Satellite data is an essential tool for detecting and analyzing environmental changes. Many change detection techniques have been developed which have advantages or disadvantages. Change Vector Analysis (CVA) technique is one such a method. This method is based on radiometric changes between two dates of satellite imagery. Main advantage of this method is that it provides direction and magnitude image of change. The aim of this study was to describe change vector analysis technique and it applies to detect land cover change in Isfahan area during an 11-year period. The data used for this study were two images Landsat: TM 05 June 1987 and 03 June 1998. Correction radiometric was not carried out because of the similar sensor and acquisition time of the remote sensing data. After geometric correction, the study area was selected from Landsat images. Change vector technique was applied to analyze magnitude and direction of change. The change map showed Kappa and overall accuracy coefficient of 63.19% and 74.4%, respectively. The results showed that the changed land cover was 3340 ha during this period. Overall, the results show that 1325 hectares (especially agricultural lands) have been converted into urban areas, agricultural areas were increased up to1385 hectares, and 435 hectares of agricultural areas were converted to other land use over the period of study. This study showed that CVA is a robust approach for detecting and characterizing radiometric change in multi-spectral remote sensing data sets.

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Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land cover type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.

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Among the environmental pollutants, heavy metals according to their irresolvable and physiological effects on living organisms at low concentrations, are of special importance These elements due to low mobility are gradually accumulated in soil Being accumulated in soil, they eventually enter the food chains and threaten human health and other creatures Therefore, studying concentration distribution of heavy metals for soil pollution monitoring and maintaining environmental quality is essential In this study we investigated the effect of agricultural land use and geology on the concentration of heavy metals contamination of soil and spatial distribution map, using collected data, GIS and GeostatisticsUsing systematic stratified random sampling, 135 surface soil samples( 0-20 cm) from an area of 7262 sq km area and we measured total concentration of elements Nickel, Chromium and Cobalt and soil characteristics including pH, organic matter and texture. The mean value of elements concentrations turned out to be Cr: 88.9+22.7 Co: 17.6+3.5 Ni 63.1+17.7 mg per kg and the mean acidity is 7.8 which in the area is an indication …… property. Formetal concentrations interpolation procedures, Geostatistics was used. By the aid of spatial correlation analysis, appropriate interpolation method using functions mean absolute error and bias average error were selected. Interpolation map concentrations of heavy metals Chromium, Cobalt and Nickel with ordinary kriging method and the exponential model were developed Interpolation map analysis of heavy metals by the aid of geological and land use maps show that the distribution of the elements Chromium, Cobalt and Nickel are consistent with the geology classes However, they did not match the agriculture pattern Findings of this study in the area give us appropriate information about the concentration distribution of heavy metals Chromium, Cobalt and Nickel which can be used in monitoring and evaluation processes of heavy metals pollution in agricultural lands area. But on the other hand sampling in the areas far away from human effects, showed that the heavy metals concentration is naturally high.

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Precision farming aims to optimize field-level management by providing information on production rate, crop needs, nutrients, pest/disease control, environmental contamination, timing of field practices, soil organic matter and irrigation. Remote sensing and GIS have made huge impacts on agricultural industry by monitoring and managing agricultural lands. Using vegetation indices have been widely used for quantifying net annual production on different scales. The aim of this study was to find a rapid method with acceptable precision for the identification and classification of agricultural lands under cultivation (wheat and barley, alfalfa and potatoes). We used multi-temporal AWiFS data and applied Boolean logic and unsupervised classification. Results indicated that Boolean logic approach had a higher accuracy and precision in comparison to unsupervised classification, although it is more complicated and time consuming.

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LISS IV sensor's data from IRS-P6 satellite was used to produce land use map of eastern region of Isfahan, the studied part of which has an area of 22121 hectares. Its three band data, namely band 2 (Green), band 3 (Red) and band 4 (Near infra red) of LISS-IV sensor images with 5.8 m ground resolution were georeferenced by nearest neighbor method and first-order polynomial model to the DEM map of 1:25000, where the RMSE was equal to 0.3 pixel. To analyze the satellite data, various image processing methods such as supervised and unsupervised classification methods, principal component analysis, NDVI vegetation index and filtering were applied to the satellite data. Finally, the land use map was produced with hybrid method. The final map detected 6 land uses very clearly, which are: Agricultural lands, barren lands, disturbed lands, cultivated Haloxylon amodendron, roads, residential areas and industrial locations. The kappa of land use map is 0.89 and the overall precision is 0.92. The barren lands have a very poor natural vegetation and are considered as natural deserts. Disturbed lands have been formed because of brick kiln activities, and the vegetation cover of these areas has disappeared completely The LISS IV data has a high ability to detect the various studied land-uses especially to digitize the roads. They can be used to update the 1:25000 topographic maps, as well.

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Physicochemical characteristics of soil, land cover/use and human activities have effects on heavy metals distribution. In this study, we applied Classification and Regression Tree model (CART) to predict the spatial distribution of zinc in surface soil of Hamadan province under Geographic Information System environment. Two approaches were used to build the model. In the first approach, 10% of total data were randomly selected as test data and residual data were used for building model. In the second approach, all data were used to build and evaluate the CART model. Determination coefficient (R2) and Mean Square Error (MSE) were applied to estimate the accuracy of model. Final model included 51 nodes and 26 terminal nodes (leaf). Calcium carbonate, slope, sand, silt and land use/cover were determined by the CART model to predict spatial distribution of Zn as the most important independent variables. The regions of western Hamadan province had the highest concentration of Zn whereas the lowest concentration of Zn occurred in the regions of northern Hamadan province. The results indicate good accuracy of CART model using R2 and MSE indices.

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Drought is a one of the most important natural disasters that have high socio-economic and environmental impacts. However, drought is more than a physical phenomenon or natural event. Its impact results from the relation between a natural event and demands on the water supply, and it is often exacerbated by human activities. The traditional approach to drought management has been reactive, relying on crisis management. Due to the drawbacks of crisis management, employing proper risk management techniques has been suggested. In order to move from crisis management to risk management, in this study, risk of drought in Isfahan province was evaluated. Drought hazard index and vulnerability index are components of the drought risk management. Standardized Precipitation Index (SPI) was used as the index of drought hazard. For the calculation of SPI, the monthly rainfall data in 47 meteorological stations during the period of 1975-2007 were collected. The time series of rainfall data were prepared and for calculation of the standardized precipitation index in a 12 month timescale they were imported to SPI program. Percentage of drought occurrence in each severity was calculated and then the drought index map was obtained. Vulnerability index was calculated through socio-economic indicators (population density and percentage of people involved in agriculture), and physical indicators (available water capacity of soil and land use). Weighted Linear Combination (WLC) technique was applied for combination of vulnerability indicators. To assign weights to the criteria, an Analytical Hierarchy Process (AHP) was used. After providing the maps, fuzzy membership functions for every criterion were used for their standardization. For the weighting of the criteria, a questionnaire was prepared and criteria comparison was done using the participatory approach by a group of experts. Finally, the drought risk index was calculated by multiplying the drought hazard index and vulnerability index. The results showed that hazard of very severe drought is mainly concentrated in the central part of province. The North and North East of Isfahan province could experience condition of severe drought. South West of Isfahan province is under moderate drought condition compared to the other parts of the province. Map of drought vulnerability index showed that the most vulnerability is in the West, South and North-East of province. Map of drought risk index showed that the Northern Province demonstrated high risk. To reduce the drought risk in Isfahan province, improving monitoring, early warning, increasing environmental awareness, and promoting water resource management practices should be considered.

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Segzi, located in the east of Isfahan, is one of the most important centers of desertification crisis in Isfahan province. Human overtaking, land deformation and the presence of huge artificial topography in flat plain has created a very unpleasant landscape in the area. In this study, satellite images Cartosat-1 were used for mapping land degradation. By using DGPS, 9 points with appropriate distributions related to road junctions were selected. These points after Interior and exterior orientation determined as control points in Cartosat-1 pair images. To improve compliance, process of points development and production of 31 tie points was done. These points was coordinated in triangulation process and introduced as check points. Desirable RMSe, 0.3 pixel is obtained. Then DEM based on 40 points was prepared with 15×15m pixel size. The DEM, in GIS software was classified to 9 elavation classes by Natural Breaks method. The file of classified raster DEM convert to vector andcut and fill appeared as polygon that by encoding them, excavation map is produced in GIS with Kappa 0.95 and 0.97 overall accuracy. The Results of this study show that Cartosat-1 satellite images have ability for study of degraded lands and anthropogenic holes. The topographic changes caused the loss of natural vegetation and desertification in this area has developed.

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Heavy metals are known to have deleterious effects on human health. The main route of human exposure to heavy metals is the daily intake of food. This study was designed to investigate the heavy metal concentrations (Cu, Zn, Mn, Fe, Cr, Ni and Cd) in soil and major food crops (wheat, potato and corn) and estimate the health risks of metals to humans via soil and the crops consumed in Hamedan Province, using the total non-cancer hazard quotient. Daily metal intakes were estimated for three receptor groups and then compared with health guideline values. The non-cancer risk estimations showed that chromium, manganese, cadmium, zinc, Iron, Nickel and copper have oral Hazard Quotient values less than a value of one. The Hazard Index values were greater than 1 for all age groups, suggesting that adults and children in the study area may experience a potential non-cancer risk due to diet of heavy metal via wheat, corn and potato consumption and soil ingestion. Consumption of plant foods particularly wheat was found to be the major route of human exposure to heavy metal. The soil ingestion route is also important.

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Pollutants are considered the disturbing factors of environment, and among them the heavy metals are more important considering their non-degradability and physiological effects on organisms in low concentrations. The goal of this research was to investigate the effect of industrial landuse on Cd and Pb concentrations in surface soils of the southwest Isfahan. According to satellite images and topographic maps (1:50000) of the study area, soil samples (depth: 0–20 cm) were collected using random sampling. A total of 38 surface soil samples were obtained from industrial areas (lowest distance = 1480 m) in the area of 73481 ha. Total concentrations of Cd and Pb in the digested solution were measured by Atomic Absorption Spectrophotometry (AAS). Using Arc GIS, the spatial distribution patterns and Cd and Pb variography of samples were analysed and finally the best models of spatial distribution of heavy metals were achieved. The primary results showed that the mean concentrations of Cd, and Pb of surface soil samples in industrial areas were 1.8 to 31.5 mg Kg^-1 higher than the world’s mean values, respectively. Although the mean concentrations of Cd and Pb were respectively 8 to 700 mg Kg^-1 lower than the standard of Iranian Department of Environment for industrial landuse.

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Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R² was 0.63 when regression was carried out on LST and air temperature.
 

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