Hamed Eskandari Damaneh; Gholamreza Zehtabian; Hassan Khosravi; Hosein Azarnivand; Aliakbar Barati
Volume 28, Issue 3 , October 2021, , Pages 520-536
Abstract
In the present study, the existing land uses in the Minab plain were simulated using the CA-Markov combined method. For this purpose, land use maps for the years 2000, 2010 and 2020 were generated using Landsat satellite images using the maximum probability classification method and after evaluating ...
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In the present study, the existing land uses in the Minab plain were simulated using the CA-Markov combined method. For this purpose, land use maps for the years 2000, 2010 and 2020 were generated using Landsat satellite images using the maximum probability classification method and after evaluating the model, the land use map for 2030 and 2040 was predicted using the combined CA-Markov method. Analysis of land use change patterns in Minab plain showed that during the statistical period 2000-2020 in the level of land uses in this area has changed significantly so that during this 20-year period the area of agricultural land use, urban and man-made areas, saline lands and rangelands and barren lands respectively from 38.91, 25.99, 20.09 and 15 % in 2000 to 40.75, 40.02, 12.44 and 6.80 percent in 2020. Evaluation of the model using kappa index above 90% indicates the high accuracy of the model for predicting land uses. Prediction of changes in 2030 and 2040 show that the use of agricultural lands and urban areas and man-made are increasing at a rate of 0.05 and 0.39 %, respectively, which are advancing from the east of the plain to the west; Meanwhile, the uses of saline areas, rangelands and barren lands are decreasing at a rate of 0.44%, which is more evident in the west and northwest of this plain. Finally, one of the most important executive strategies of planners and officials to prevent land use change and ultimately land degradation in this area, can be to improve the cultivation pattern, new irrigation methods, nourish the bed of this plain and maintain and restore native vegetation.
Zahedeh Heidarizadi; Ali Mohammadian Behbahani
Volume 26, Issue 3 , September 2019, , Pages 660-674
Abstract
Protecting an ecosystem and conserving natural resources requires knowledge of the conditions and land-use changes. The purpose of this study was to monitor land-use changes in the past and evaluate the performance of GEOMOD and LCM models in simulating land-use changes in order ...
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Protecting an ecosystem and conserving natural resources requires knowledge of the conditions and land-use changes. The purpose of this study was to monitor land-use changes in the past and evaluate the performance of GEOMOD and LCM models in simulating land-use changes in order to select a more appropriate model for predicting land-use changes in the future. Landsat satellite images were used during the periods of 1990, 2003, and 2016 and land-use changes were monitored by using these images. The simulation of land use status by the LCM model for 2016 was done using the maps of the years 1990 and 2003. Using MLP and Markov chain, the land use map was simulated for 2016. To run the GEOMOD model, the image of the single-user map of 2003 and 2016 were used and the map of "appropriateness of changes" was made by the use of variables affecting land-use change and they were introduced into the model. The results of the accuracy of the simulation map of 2016 showed that LCM and GEOMOD had Kappa coefficients of 81% and 71%, respectively. Therefore, the LCM model was chosen as the most appropriate model and the map of the year 2029 was predicted and prepared.
Mohammad reza Jamalizadeh Tajabadi; Ali reza Moghadam nia; Jamshid piri; Mohammad reza Ekhtesasi
Volume 17, Issue 2 , September 2010, , Pages 205-220
Abstract
Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial ...
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Dust storms are common climatic events in arid, semi arid and desert regions of the world. These events impact human resources by foundation losses, every year. Accurate prediction of these events can be effective for decision support in environmental, health, army, and other related fields. An artificial neural network is a method which can predict nonlinear problems. In this study we attempted to predict dust storms and low visibility in Zabol city using synoptic data. Result indicates that this method is somewhat successful and appears that via identification of much more dust storm occurrence process, we can do more accurate prediction.