پیش‌بینی تغییرات کاربری اراضی برای سال 2030 با استفاده از روش CA مارکوف (مطالعۀ موردی: شهر قوچان) مقالة

میزان گسترش و تخریب منابع را میتوان با پیشبینی تغییرات کاربری مشخص کرد و برنامهریزیهای آینده را به مسیر مناسبی سوق داد. هدف از این پژوهش ارزیابی کاربری اراضی شهرستان قوچان با استفاده از طبقه‌بندی شیءگرا و پیکسل پایه و پیش‌بینی تغییرات این کاربریها با استفاده از مدل CA مارکوف تا سال 2030 می‌باشد. در این تحقیق از تصاویر ماهواره لندست سنجندههای ETM و OLI مربوط به سال‌های 2000 و 2018 (ماه آگوست) استفاده شد. ﭘـﺲ از ﺗﻬﻴـﺔ ﺗـﺼاﻮﻳﺮ، تصحیحات رادﻳﻮﻣﺘﺮﻳﻚ بر روی ﺗﺼاﻮﻳﺮ اعمال گردید و سپس ﺑﺎ اﺳﺘﻔﺎده از دو روش پیکسل پایه و شیءگرا ﻧﻘﺸﺔ ﻛﺎرﺑﺮی اراﺿﻲ اﺳﺘﺨﺮاج ﮔﺮدﻳـﺪ. با استفاده از مدل‌سازی CA مارکوف و با توجه به دو نقشه کاربری اراضی به دست آمده، ماتریس احتمال تبدیل کاربریها به یکدیگر محاسبه شد و نقشه پیش‌بینی تغییرات CA مارکوف برای 12 سال بعد یعنی سال 2030 به دست آمد و مساحت و درصد هر کدام از کاربری‌ها به طور جداگانه محاسبه شد. ﺑـﺮای ارزﻳﺎﺑﻲ دﻗﺖ ﻃﺒﻘﻪ-ﺑﻨﺪی از ﺷﺎﺧﺺﻫﺎی دﻗﺖ ﻛلی و ﺿﺮﻳﺐ ﻛﺎﭘﺎ استفاده شد. نتایج به دست آمده در طبقه‌بندی شیءگرا در هر دو شاخص صحت کلی و ضریب کاپا به ترتیب 94 درصد و 97/0 درصد بود که دقیقتر از روش پیکسل پایه است. بیش‌ترین مساحت در منطقه با استفاده از طبقه‌بندی شیءگرا در سال 2000 مربوط به کاربری دیمزار و کاربری مناطق کوهستانی و با استفاده از روش پیکسل پایه مربوط به کاربری مرتع ضعیف و کاربری دیمزار میباشد. با توجه به طبقه‌بندی صورت گرفته برای سال 2018 با استفاده از طبقه‌بندی شیءگرا بیش‌ترین میزان مساحت را کاربری مرتع ضعیف و کاربری دیمزار داشتهاند. نتایج نشان داد که بیش‌ترین میزان افزایش تغییرات در بین کاربریها را کاربری مرتع ضعیف در سال 2030 خواهد داشت که نسبت به سال 2018 نیز 07/24491 هکتار افزایش یافته است. بیش‌ترین میزان کاهش مساحت را کاربری مرتع متراکم با 23/26615 هکتار خواهد داشت. کاربری انسان ساخت نیز در طی این بازه 12 ساله 62/530 هکتار رشد خواهد داشت. با پیش‌بینی تغییرات کاربری اراضی نه تنها می‌توان میزان گسترش یا تخریب منابع را مشخص کرد بلکه می‌توان این تغییرات را در مسیر برنامهریزیهای مناسب هدایت کرد.

The purpose of this research is to evaluate the land use of Ghouchan city by using object-oriented and pixel-based classification as well as predicting these changes using the CA Markov model until 2030. In this research, Landsat satellite images of ETM and OLI sensors for the years 2000 and 2018 (August) were used. After the images were taken, radiometric corrections were applied to the images, and then using the ground-object and object-oriented pixel methods, a land use map was extracted. In order to evaluate the classification accuracy, general accuracy and Kappa coefficients were used. The results obtained in the object-oriented classification in both of the general accuracy and kappa coefficients were 94% and 97%, respectively, which is more accurate than the pixel-based method. Most of the area in the region, using Object Oriented Classification in 2000, is related to land use and mountainous land use, and to base land use and land use, respectively. According to the Classification for 2018 using Object Oriented Classification, most of the area had the weakest land use and dry land use. Using the CA Markov modeling and considering the two land use maps, the probability matrix was calculated, and the CA mapping prediction map for the next 12 years, 2030, was obtained, and the area and percentage of each Uses were calculated separately. The results showed that the greatest increase in the variation among the users would be for poor pasture users in 2030, which is also increased by 2019-27449 hectarrs. The largest reduction in area will be for dense pasture users with a total area of 236666 hectares. Man-made human consumption will grow at 62.530 hectares during this 12-year period. By predicting user variations, the extent to which resources can be expanded or degraded can be guided, and this can be done by directing these changes to appropriate paths. Introduction Urban sprawl due to widespread changes in land use and land cover has had a negative impact on global environmental quality. Land use changes, urban and agricultural development, and deforestation have led to a change in local and regional temperature regimes. The surface temperature of the earth as an indication of the intensity of heat is of a particular nature in understanding the climate. Awareness of the surface temperature is important in helping to address a wide range of issues related to earth sciences, such as the urban climate, global environmental changes, and the study of human and environmental interactions. What is considered as a major disadvantage in land surface temperature monitoring is the lack of sufficient meteorological stations to know the temperature values at stations without stations. Due to the information constraints that are encountered in data systems especially in large volumes with many problems and obstacles and real-time grasping is difficult or impossible, the need for using remote sensing technology with time conditions, coupled with the connectivity and exploitation feature in Extensive range can be very effective. Nowadays, remote sensing technique is a variable method for estimating surface temperature in any topographic conditions and weather conditions in the region and is used to estimate the surface temperature of the thermal bands. Earth surface temperature is one of the most important components in global studies, which is used in important factors in controlling biological, chemical and physical processes of the earth. Earth's temperature is related to the temperature of the earth's surface and the temperature of the Earth's atmosphere. Since the area studied is the city of Meshkinshahr and its marginal lands, it can be said that the city is located in the northwest of the country and is one of the major centers of the population. So, given the growing trend of the city of Ardabil, considering that the city has long been a tourist destination and climber for the weather especially in the summer, then its climate is essential. To be discussed. Method and Materials The city of Meshkinshahr was selected as the provincial capital of Ardabil province in February of 1993 with the separation of Ardabil province and has a growing trend with its superior facilities including economic, cultural, scientific and artistic attractions in comparison with other cities of the province. The data used in this study includes Landsat 8 satellite imagery from the Sensor (OLI) from the American Geological Survey. To extract the land use map using visible and infrared bands and extracting surface temperature using thermal bands in the years 1987-2015 and the months of June, July, August. In order to prepare images, geometric and radiometric corrections were performed on images using ENVI 5.3 software, and then land use classification using object-oriented method and using the nearest neighboring algorithm by eCognition software. Then, in order to extract the spatial data of the urban thermal islands, the data space pattern was used to determine the quantity as well as the spatial structure test of the observed parameters from the global Moran statistic. Moran spatial dependence explores spatial dependence based on the distribution of two values and analyzes the desired attribute of the geographical component in that location. To calculate the Moran Index or Index, the standard score is calculated first, Z and P-Value. In the next step, the significance of the index was evaluated and analyzed using ArcGIS 10.5 software. ArcGis10.5 software was used to extract the relevant maps. Result and Discussion In this research, in order to control the surface temperature and land use with surface temperature and spatial correlation of the city of Meshkinshahr, using OLI imaging images, we used this method. Firstly, in order to study the land use change, land use map of the city was prepared for the years 1987-2015, and after obtaining land use maps each year, land use map of the area was extracted. The highest temperature is recorded for use in urban areas in 1987, and the lowest temperature is recorded for aquatic areas. The results obtained from the images show that the city of Ardabil was experiencing Thermal Island phenomena in 1987, with human-made structures that are mostly heat absorbent having the largest share in this phenomenon. Also, fuel from machinery and factories is also effective in this phenomenon. Considering the land use map and the land surface temperature map for 2015, the above analysis also shows that the highest temperature in this year belongs to urban use, along with rangelands, respectively, with an average of 45 ° C and 42 ° C, respectively, and the lowest recorded temperature In both years, it is also related to water use, which is an average of 33 ° C. In order to evaluate the spatial correlation values, surface temperature data of Ardabil city with a spatial scale of 30 meters was used. The value of the Morgan Index for the two study periods was above 0.99 and the highest Moran World Index with a value of 996725/0 for 2015. The z statistic for the two study periods is 1161. If the surface temperature for the studied courses were to be distributed normally in Ardabil, Moran World Index would be 0.000001-0. The surface temperature of Ardabil city in all studied years has a positive spatial self-correlation. The combined evaluation of the obtained values with a significant threshold showed that all values obtained for the studied years were significant (0.01α). So, we conclude that the surface temperature data of the city of Ardabil has a spatial structure or, in other words, the surface temperature of the city of Ardabil is distributed in cluster form, that is, high and low temperature cells tend to be concentrated or clustered in space. Conclusion The results from the 1987-2015 time series show that 2015 with an average total accuracy of 94% and a Kappa coefficient of 0.91 and in 2018, the overall accuracy is 90% and the Kappa coefficient is 0.88, and according to the accuracy In general, maps that have accepted overall accuracy coefficients of more than 85% are acceptable, the results from the classification of uses for both individual uses and for the overall accuracy and statistics of the kappa have an acceptable accuracy in relation to the generated data. Then, the area and percentage of applications were investigated individually and the average area in each two years was allocated to the agricultural area and the lowest area to the water areas. The largest changes were in human-made areas and agricultural areas, indicating that agricultural areas were destroyed in urban areas, but overall, due to the low level of this period, there were no significant changes for 4 years. Due to the results obtained from the surface temperature, the earth is affected by surface factors and its characteristics. With these interpretations, the surface temperature obtained results in the conclusion that the highest temperatures in the years 2015 and 2018 are related to human areas on average, due to the thermal attractiveness of this user and indicates the concentration of heat in these areas is urban. The use of Hot Spot Analysis was conducted with the aim of studying the formation and clustering of urban thermal islands in Ardebil. The results of the global moron spatial correlation obtained from the rejection of the hypothesis of the lack of spatial relationship between the surface temperatures of Meshkinshahr city indicate that Ardabil surface temperature data are spatially or clustered.

هدف از این پژوهش ارزیابی کاربری اراضی شهرستان قوچان با استفاده از طبقهبندی شیءگرا و پیکسل پایه و پیشبینی تغییرات این کاربریها با استفاده از مدل CA مارکوف تا سال ٢٠٣٠ میباشد. در این مطالعه با استفاده از دادههای به دست آمده از تصاویر ماهوارهای مربوط به سالهای ١٩٩٥ و ٢٠٠٠ مدل تغییرات کاربری اراضی تالب برای سال ٢٠٠٦ ساخته شد. در این مدل از نقشه پوشش اراضی سالهای ٢٠٠٠ و ٢٠١٨ استفاده شد تا نقشه تغییرات کاربری اراضی برای سال ٢٠٣٠ پیشبینی شود. 5. Arkhi, Saleh (1394): "Detecting Land Use Changes by Object-Oriented Processing of Satellite Images Using EDRISI SELVA Software Case Study: Abadan Area", Sepehr Geographic Information System Research Quarterly, No. 24, pp. 6. Aslami, Farnoush, Ghorbani, Ardavan, Sobhani, Behrouz and Mohsen Panahandeh (1394): "Comparison of Neural Network, Vector Machine and Object Oriented Methods in Land Use Extraction and Landscape Vegetation Extraction", Journal of Remote Sensing and Geographic Information System In Natural Resources Science, No. 3, pp. 8. Bell, EJ, 1974, Markov analysis of land use change - an application of stochastic processes to remotely sensed data, Socio-Economic Planning Sciences, 8, 6, 311-316. "Classifying urban land use by integrating remote sensing and social media data", International Journal of Geographical Information Science 31(8), Pp 1675- 1696. Omidipour, Reza, Moradi, Hamid Reza and Saleh Arkhi (1392): "Teaching Using Basic and Object Oriented Pixel Books in Land Use Stores Using Satellite Data", Iranian Journal of Remote Sensing and GIS , No. 3, pp.