JWSS - Journal of Water and Soil Science

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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.