JWSS - Journal of Water and Soil Science

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Remote sensing is the main technology for assessing expansion and rate of land cover changes. Knowing the different kinds of land cover changes and human activities in different parts of lands, as the base information for different planning is especially important. In this study, the land cover changes of Isfahan city that is consist of Isfahan and its` surrounded area was studied for the past 4 decades. For researching the study objectives, the aerial photos with scale of 1:50000 taken in 1955, MSS, TM and ETM+ images from Landsat satellite taken respectively in 1972, 1990 and 2001 and the topography maps of Isfahan city and its` surrounding were used. All of the aerial photos and satellite images with the nearest neighbor sampling were georegistered with the RMSe less than one pixel. For image processing, the best false colored composite image was first produced according to OIF index. Then land cover maps of the studied area were produced in 5 classes by using the combination of supervised and unsupervised classification and NDVI index. At the end, the produced maps compared with post-classification method. The results showed that the most urban area sprawl was occurred between 1972-1990 with the mean of 571 ha in a year and the least growth was come about between 1955-1972 with approximately 324 ha in a year. However, by declining the annual mean of green cover 1263 ha during 1955-1972, the most green cover demolition occurred in study area.

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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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The present study aimed to compare the effect of land cover on runoff and sediment with different coverage levels in Mehrian watershed. The study was carried out in a plot with the dimensions 3 × 2 meters during one year, in three different treatments (including without coverage, grass treatment and integrated treatment having brush and grass coverage) and in three replications. At the end of each plot, runoff and sediment collection tanks were installed. Sampling was performed during a year. The monthly rainfall, and runoff and sediment after harvest were determined. Then, runoff and sediment samples were transferred to the laboratory and calculated through decantation method. Also, the amount of plant cover with the plots of 60 × 25 was determined. Statistical analysis using SPSS was performed. Results indicated that the minimum and maximum runoffs in covers without plots and shrub cover and integrated cover were equal to 38 and 162, , 15 and 74, 15 and 96 liters, respectively. The minimum and maximum sediments were equal to 8.3 and 21, 8.1 and 11, 9.1 and 13 gr.l. Statistical analysis in the Spilt plot design showed significant differences between treatments in runoff and sediment (P <0.01). Also, the results showed that the amount of runoff in a bush cover is 2.1 times more than the cover without treatment, 8.1 times more than the integrated treatment, and in the integrated treatment 1.1 times more than bush cover. In contrast, the rates of sedimentation in the above treatments were 4.2, 6.1 and 5.1, respectively.

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