Journal of RS and GIS for Natural Resources
Year:2011 | Volume:2 | Issue:2|Papers Count:12

Physical development of cities requires studying a wide range of information to achieve sustainable development. In this study, which has been examined the potential of northern of Karaj city for urban development, Multi Criteria Decision Making (MCDM) and Analytical Hierarchy Process (AHP) have been used to pairwise comparisons of biophysical, environmental and social-economic criteria. Results have shown, despite the fact that social-economic criteria have allocated minimum weight than biophysical and environmental criteria had a significant impact in limiting the area for development. Furthermore, the results that have given by sensitivity analysis graphs and final maps shown basin area north of Karaj, has a limited ability to develop the city with power potential. Area in the region that can be grade 1 (suitable) have been identified, the 18.38 percent of total basin areas to 1855.50 hectares are allocated.

Land use mapping is one of the key factors in studies of environment and natural resources management. Mapping land use is often one of the most expensive parts of natural resources and environmental projects. Satellite data is one of the fastest and most cost-effective methods for mapping land use that is available for researchers. In recent years, researchers from the different methods of land use maps have been produced using this data. There is the different method to classify the images. Each method has advantages and disadvantages. This research to determine the best images nine supervised classification methods to extract land use map of the Noor city by ETM+ sensor. The results showed that the SVM classification by 0.9503 factor kappa coefficient and 90.94% overall accuracy is better than other methods. The accuracy of the order of priority 9 that is, SVM, Neural network, Mahalanobis distance, Maximum likelihood, Minimum distance from the mean, Spectral angle mapper, Spectral information divergence, parallel piped and binary code. All the research results of this study can be using the correct classification. Land use maps can be extracted with higher accuracy.

Decision making about choosing appropriate locations for groundwater dam construction by considering the various criteria that must be considered in the selection, is difficult. These criteria include technical, environmental, economic and social standards. Using Analytic Hierarchical Process (AHP) due to the possibility of considering various qualitative and quantitative criteria can be one way to select the appropriate location for constructing the underground dam. In This study, the spatial and non-spatial Analytic Hierarchical Process (AHP) was used and with regard to assessment criteria, five convenient locations for constructing an underground dam in the area of Pishkuh catchment's in Taft's township of Yazd province were prioritized. At the end with defining five scenarios these places were prioritized in which due to the property of some of the criteria, sub-criteria and evaluation indicators, the results of prioritizing two spatial and non-spatial methods were not the same.

The purpose of this study is to deliberate for human’s role in desertification. To achieve this purpose, we’ve used vegetation destruction and land management criterion, which land management criteria is including agriculture land management, rangeland management, mineral land management indices and vegetation destruction criteria is including plant cover percentage, range trend, resistance against drought and conservation against erosion indices. The data of each indicator imported to Arc GIS 9.3 after statistical analyzes and normality test with kolmogorov-smirnov method in SPSS15. These data interpolated with IDW and normal and discrete kriging methods to produce their maps. Methods with highest accuracy have been selected. Then, the maps scored between 100 in 200 by MEDALUS approach and final map of each criterion have been created from the geometric mean of its indicators. Finally current desertification status in Segzi plan of Isfahan has been created from the geometric mean of all six criteria. The results showed that the area of severe and very severe classes of desertification equaled to 74,169 and 37,998 hectares, respectively. The results also showed that vegetation cover percentage and rangeland management are the most important indicators of classification in Segzi region of Isfahan.

Soil erosion is one of the factors that threatened human life. Measuring soil erosion is always difficult. Lack of hydrometric stations in the majority of watersheds, to estimate soil erosion and deposition face with difficult. However, many researchers have tried to present empirical relationships in favor of weather conditions for the estimation of soil erosion and sedimentation. Among them, universal soil loss equation and its revised version are including models that have application worldwide. One of the main parameters of this model is a rainfall erosively factor that has the direct relationship with rainfall intensity. In this study, 22-year periods related to 16 climatology stations in the Ilam dam watershed were used for preparing rainfall erosivity factor map. After calculating a rainfall erosively factor (R factor) for all stations using kriging-based geostatistic techniques and geostatistic extension in ARC GIS 9.3 environment, the map of R for the whole watershed was prepared. Here, we have compared three kriging techniques: ordinary, simple, and universal Kriging. The obtained results show that simple kriging with a 67.92 Root Mean Square Error (RMSE) is the most proper interpolation technique. Furthermore, in compare to the RMSE, the Standard Error (SE) for calculating the amount of expectations, the simple, ordinary and universal kriging had underestimated than the expectation.

Usage of modern technologies such as GIS and RS in plant ecosystems studies and especially land cover mapping is needed to recognition of these instruments efficiency and also identification of the best methods for applying them. This study aimed to compare the efficiency of three common supervised classification methods of satellite data (Minimum to Distance, Parallelepiped and Maximum Likelihood) to identification of plant groups in Goloul-via-Sarani protected area, Northern Khorasan Province, Iran. In order to this, 143 training samples (>30m2) were collected from areas that shown a homogenous plant species composition. These data recorded by GPS device and so transported to a GIS database. Satellite data included Landsat ETM+, and IRS-P6 LISS III that were prepared and analyzed by ENVI 4.2 software. Amount of efficiency for each method was evaluated by measurement of overall accuracy (OA) and Kappa coefficient (KC) criteria. Results showed that ML method makes the highest accuracy for two data series (OA=90.35, 82.19 and KC=0.878, 0.772 for Landsat and IRS data respectively). In the face, PP method showed the worst results (OA=67.09, 58.76 and KC=0.593, 0.478). These results suggested that collection of sufficient training samples from natural classes and surveying probability of image pixel's dependency on these classes can be useful for classification of plant groups.

Regarding the role of vegetation cover the analysis of the spatial extent and temporal changes of land cover is a fundamental component in urban and sub-urban environment studies. In this study, to estimate the changes of Vegetation cover, the NDVI Index was applied on TM and IRS images of 1990 and 2006 and Vegetation cover maps of District 4 of Tehran municipality by underlining the different greenness levels were generated. The comparison between the areas of greenness classes of vegetation cover maps revealed that non-vegetated and moderate greenness level vegetated area increased by 1779.917 and 272.7938 ha respectively, decreased poor greenness level vegetated area by 2052.7108 ha was noticed. Further more, Post-classification change detection technique was conducted to investigate the procedure of changes of the study area's vegetation cover based on the conversion of classes. It showed that although the vegetated area has been increased by 108.4416 ha due to the conversion of non-vegetated to the vegetated area, according to the conversion of the vegetated area to non-vegetated one by 1888.3586 ha, it has been decreased by 1779.917, finally. The vegetation changes were also projected for the next nineteen years when IRAN envisages achieving Vision 1404 using Markov chain analysis. A statistic Results shows that maximum probability of vegetation destruction belongs to poor greenness level vegetated area by 0.0167.

The purpose of this study to determine the spatial distribution of heavy-metal cadmium, copper and lead, are using GIS and Geo-statistics, and Geological structure and Land use effects on the concentration of these metals in the soil. First, using 39 surface soils (0-20 cm) random systematic sampling in Nahavand city in Hamadan province 1623 square kilometers were collected and total element concentrations, soil characteristics, including the pH, EC and organic matters were measured. Interpolation for heavy metals concentrations were used kriging methods, and assisting location correlation analysis, interpolation suitable method was chosen using RMSE and RMSS values and MAE and MBE function. For heavy metal's concentration maps, cadmium, we used Disjunctive Kriging and exponential model, for Copper, Ordinary Kriging and exponential models and Zinc, Disjunctive Kriging and Spherical model. Furthermore, for analyzing the metal's concentration distribution maps from land use and geology maps were used. Land use map using the hybrid method (The combination of supervised and unsupervised classification) on the image AWIFS 1387 with Kappa 0.91 and accuracy 95% was obtained. Interpolation map's analysis showed that the metals cadmium, Copper and Zinc have geological and agricultural origins. These metals are present naturally in soil, but human activity has caused the accumulation of these metals in the soil.

The aim of this study was evaluation and classification of salinity with the FAO-UNEP method in Qomroud watershed with area 1614176 ha. Current and potential salinity was evaluated and classified with different parameters by remote sensing and GIS techniques. In first, Layers were created and combined and finally by classified layer maps, the current and potential maps were prepared in GIS environment. Classification of the current situation showed that 86.6 percent of watershed areas are not prone to salinity, 6.9 percent are moderately, 3.7 percent severely and 2.8 percent very severely prone to salinity. Potentially, 68.6% 31% and 0.1% are in low, moderate and server classes in terms of their readiness to salinity. The results also showed that the method FAO-UNEP, in spite of appropriate methods for evaluation and classification of salinity, the defects among which is finally a high reliance on that information and statistics on the lack of information, done this way is difficult.