Geographic Data
Seyed Asadallah Hejazi; Fariba Karami; Saye Habibzadeh
Abstract
Extended AbstractIntroductionIn recent decades, cities have provided the prelude to widespread urban growth and development as the most important human settlements, due to the increasing degree of urbanization and the increase in urban population, which is one of the most important aspects of global ...
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Extended AbstractIntroductionIn recent decades, cities have provided the prelude to widespread urban growth and development as the most important human settlements, due to the increasing degree of urbanization and the increase in urban population, which is one of the most important aspects of global transformation. In recent decades, following the growing expansion of urbanization and urbanization, as well as the continuous increase in population, many cities in the country have faced significant physical development, which has left planners and city managers with the problem of determining the right axes. And the boundaries of future physical growth of cities have faced. Maku is one of the cities that experienced an annual growth of 3.7 percent between 1996 and 2016, with a population of 46,581. Given the forecast of the increase in the population of the city in the coming years, the identification of suitable land for its physical development is an inevitable necessity. Several factors, including geomorphological features, climatic conditions, geological features, are effective in choosing the location of cities. The study evaluated the role of geomorphology as one of the factors influencing the location and physical development of the city of Maku.Materials and MethodsThe research method is of a descriptive-analytical type with a functional purpose. In this study, raw data was collected through documented and field studies. This study examines the geomorphological factors influencing the physical-physical development of the city of Maku. To evaluate the optimal development of urban land, the components of lithology, soil, slope, distance from the river, direction of slope, height, land use, distance from fault and Road in the area of the surrounding city of Maku were used. To analyze data and select the optimal location, a combination of two phase - electro and Shannon entropy models has been used. To prepare the ground fit layer, the layers in question are standardized and phased in the ArcGis environment using the Phase model and by the calculator instrument and in the form of a raster in the form of a value of zero to one. Finally, the coating of layers using phase logic (gamma) to optimize the development of the city of Maku was determined, and then the development path of the city of Maku was classified into five groups: completely appropriate, relatively appropriate, appropriate, inappropriate and very inappropriate.DiscussionAfter determining the effective criteria in locating and detecting the weight of the criteria, the information layers should be combined with the appropriate method. The composition of the map is obtained by overlapping weighted maps. Merging and combining different spatial layers from different sources together is the main goal of GIS projects and its unique feature, so that the interactions are described and analyzed with the help of predictive models to support decision-makers. The final map of the development potential of the city of Maku was prepared by combining different layers of information and classified according to the Likert scale. In this classification, land was considered suitable for urban development in 5 groups of lands with very low, low, medium, high and very high development potential. According to the above map, most of the city's immediate land is located in the eastern and western parts of the city for Urban Development. The southern and northern lands of the kalbdi District of Maku are also small or very small for the future development of the city. The proximity to the epicenter of earthquakes, the short distance from the river and the location of the flood path are the main reasons for the inadequacy of the above land for the physical development of the city of Maku. The lands located east and West at the entrance of the city from the shout and merchant side are the only immediate areas of the city that are very suitable for the future development of the city.ConclusionAmong the seven geomorphological factors studied, the two factors "altitude" and "lithology" are the highest coefficients of importance, and the factors "slope direction" and "distance from the river" are also the least important. The results of comparative analysis of the eight geographical directions in terms of geomorphological factors also show that in terms of the litholysis factor, the east, west and northwest directions are more desirable compared to other options. In terms of the elevation factor, the Northeast and East Directions are more suitable, and in terms of the distance factor, the West and northwest directions are more preferred. Comparing options in terms of soil factor also indicates a greater favorability of the Northeast and northwest directions. Distance from the river was another component that, based on the analyses, the East and Southeast directions, identified more favorable areas for urban development in terms of this component; and finally, in terms of the slope direction criterion, the lands located in the southeast of the City face greater desirability. After determining the coefficient of importance of the criteria and the relative score of the options in terms of each of the factors studied the coefficients of importance of the criteria and the relative weight of the options were calculated within the framework of the method of the process of hierarchical analysis of the integration and score of each of the eight geographical directions as follows the East was calculated with a gradient of 5 West 5 southwest 1 northeast 2 North 0 south 0 Southeast 4 Northwest 0 thus in terms of geomorphological factors the study word in the ین research orientations east west and the southeast is proposed as a priority for the future development of the city of Maku.
Extraction, processing, production and display of geographic data
Khalil Valizadeh Kamran; Maryam Sadeghi; Asadollah Hejazi
Abstract
Extended Abstract:Introduction Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources. Also, taking care protecting natural resources requires awareness of the conditions and how to change different land uses. ...
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Extended Abstract:Introduction Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources. Also, taking care protecting natural resources requires awareness of the conditions and how to change different land uses. Therefore, the purpose of this research is to evaluate the change of forest use in the forest area of Fandoqlu from 2010 to 2019 by using Landsat 5, 8 images and integrating them with Sentinel 2, Ester images. After preparing images from the years 2010, 2015 and 2019, geometrical, radiometric and atmospheric corrections of the images were done and the classification accuracy using kappa accuracy was% 93, %83, 91% respectively. The land use of Fandoqlu forest area was To model the change of use for 2025 with the Geomod model, it is necessary to prepare a suitability map of the area, which is prepared using the Fuzzy ANP method and the incompatibility coefficient is less than 0.06. In order to prepare a suitability map of four general factors: human, biological, topographic and climatic, and 12 sub-criteria were obtained with Boolean functions, and Boolean land use maps (forest and non-forest) 2010 and 2015 were modeled for 2019 and for modeling Land use for 2025 was done from the base map of 2019 and the transition matrix of Markov chain of land use in 2025 with the CA-Markov model And the result of location changes for 2025 was obtained. To evaluate the accuracy of the model, the agreement and non-agreement of pixels with Klocation and Standard were done with 98 and 95 accuracy, respectively. Modeling results for the year 2025 changes in a decreasing manner; The increase of non-utilized covers and the reduction of forest use, which will decrease from 3204.18 hectares in 2010 to 3070.55 hectares in 2019; According to the results of the human criterion and the sub-criteria of land use and distance from the road, the tourism potential of this area and the attraction of tourists as well as the interference of local residents can have a direct effect on this forest reduction process.Materials & Methods: organizations, people and local, is the only way to protect the forests of this region. In this study, remote sensing data such as satellite images of Landsat8,5, ASTER and Sentinel 2A were used to prepare the baseline map. Climatic data of all parameters up to 1396 were received from the synoptic station of Ardabil province. The digital model of 12.5 altitude was prepared from NASA website to prepare slope maps, slope direction, border layer of the study area and vegetation layer from Ardabil Natural Resources Organization. The research used Arc GIS, ENVI 5.3, TerrSet, eCognition 9 Google Earth pro and SUPER DECITION software. then based on the value and purpose of Reclassify and layer fuzzy. to predict the future conditions of forest cover changes by GEOMOD method, a time map of the start of the modeling process and a map of change appropriateness are needed. Geomod is used to model spatial patterns, forecast and probability of change. GEOMOD is used to simulate patterns of spatial change of use or change between two categories of use (forest and non-forest).Results and Discussion: In order to implement the GEOMOD model, a fit map prepared from the study area is required. Fuzzy ANP method was used to prepare the appropriateness map of the study area, which has four criteria: human (distance from the road, distance from the village, population), topography (slope, direction, height) and biological (land use, lithology, soil), criteria. And the following criteria are used in the map. Climatic parameter (average annual rainfall, temperature, altitude, slope, direction of slope, waterway) was used. 2025 user is required, so using 2015 and 2019 user with CA Markov model for 2025 was modeled. Decreased accuracy was associated. The results of predicting forest spatial changes for 2025 were used from the 2019 Boolean user map and the CA Markov modeled user map. Conclusion: To implement the GEOMOD model, we need a fit map for spatial modeling of changes. In this study, four criteria and 12 sub-criteria discussed in Chapters 3 and 4 were used to prepare a fit map of the region. They have acquired Super Decision software.Conclusion: Using the Boolean forest and non-forest boards and the 2015 and 2019 land use maps with the CA Markov model for 2025, it was modeled. Human, climate and biological have weights of 0.358, 0.258, 0.203 and 0.165, respectively, which the topography achieved the highest weight in Super Decision software. Among the sub-criteria, the type of land use has a high impact on changes in the region. The final output of the fit map was prepared by applying the OR function after applying the weights, which had a better result than the other functions. Finally, using the 2015 and 2025 user maps for 2025, forest spatial changes were made. To evaluate the accuracy of the model, the agreement and non-agreement of spatial pixels were used, which was modeled with Kappa 98% for 2019. The results of spatial change modeling show the high accuracy of the model in predicting spatial changes. GEOMOD results for 2025 will reach 3085 thousand hectares from 3151.9 hectares. Research conducted in different places. the country indicates a decline in forest areas in the coming decades.
