جستجوی مقالات مرتبط با کلیدواژه « مدل مارکوف » در نشریات گروه « جغرافیا »

Identification of geomorphological landforms is very important in terms of the origin of their formation and the process of their changes in the past, present and future. Therefore, natural obstacles and the extent of the study areas and field studies are challenging. To solve such problems, the use of remote sensing technologies for environmental management and natural hazards is essential. One of the newest methods is to use the capabilities of satellite images to extract and study changes in landforms at different time intervals and predict their changes in the future. The main goal of this research is to classify, reveal and simulate the changes in the main and important landforms of the Sejasrud watershed using Landsat satellite images and the maximum likelihood supervised classification algorithm between 1986 and 2018, as well as predicting the changes using the Markov method for The year is 2050.

The research method in this research is applied in terms of purpose and in terms of survey-analytical nature. Field and library methods were used to perform and collect information. LandsatTM and OLI satellite images from 1986 and 2018 were used to collect preliminary data .In this regard, in addition to extensive field studies, Google Earth images and topographic maps of the area were used to better identify land forms. After preparing the data required for better detection and detection of geomorphological landforms according to, different stages of image processing were performed. Then, for classification, educational samples that had previously been radiomatically and atmospherically corrected were used. Using trial and error process, the best method for educational examples was obtained by generating a vector layer on images and converting them into educational examples in ENVI5.3 software environment. In the next step, using the supervised classification method, the maximum probability of land classification of the surface forms of the studied area was studied. Finally, using the functions related to the study of changes in landforms, the rate of change and simulation until 2050 was done. In the process of this research, ENVI 5.3, ArcGIS10.1 EXCEL2013 TERRSE IDRISI software were used.

What can be deduced from Tables (5, 4, 3) and Figure (7) is the extensive changes in the surface area of important geomorphological landforms of Sajasrood catchment, which shows the dynamics of effective processes in landforms and strong performance of tectonic forces, especially external dynamics and human activities. In the evolution of geomorphological forms. According to statistics and information obtained over 32 years, alluvial fans and mountain and river garrisons had the most decreasing change. Instead, the area of alluvial plains and vegetation (Figures 7 and 8) show a significant increase. The area of mountainous areas, hills and recent alluviums also show slight decreasing changes. The Markov model according to Table (4) showed that new alluviums, alluvial barracks and alluvial fans have the highest pixel transfer to the alluvial plain landform, respectively. Also, according to Table (5), the highest area displacement is related to alluvial fans and alluvial plain. The reasons for these reductions and increases are mostly due to the extensive industrial and agricultural activities in the last three decades and climate change. The proliferation of alluvial fans, alluvial garrisons, and new alluviums is closely related to the amount of precipitation, especially snowfall, which has unfortunately declined over the past three decades. On the other hand, agricultural activities have experienced tremendous growth in the last three decades, which has led to changes in land use in various geoforms such as alluvial fans, mountain slopes, hills, barracks and recent alluvial beds, as well as the physical, chemical and biological properties of land surface. Has changed and transformed. Because in the process of baseline pixel classification, the smallest change in ground surface effects in different spectral bands shows its satellite images. Therefore, spatial changes in terms of area and shape over a period of 30 years are quite natural and logical. Activities related to cryoclasts, thermoclasts, and snow and rain constantly cause erosion of mountainous and hilly areas. On the other hand, agricultural activities in the foothills and hills affect the reflection of satellite electromagnetic waves. As a result, parts of the landforms mentioned in the vegetation class may be included in the image classification. However, due to the active tectonic activity in the basin, the effective role of this factor should not be overlooked. Figure (8) shows the process of landform changes in terms of the intensity or severity of the changes. An increasing or decreasing trend means more intensity and speed of change.

The mountainous region of Sajasrood catchment and its various geomorphological phenomena are changing due to different geomorphological processes. Landsat satellite imagery was used to study the geomorphological changes of the surface forms of this catchment and to discover its 32-year changes. After preprocessing, processing and post-processing steps, the geoforms were extracted using the supervised classification method using the maximum similarity method. Then, using two functions, Change Detection Statistics and Image Change Workflow in ENVI software environment, which are used in the field of change detection, changes were extracted statistically and map.The results of the research showed that the biggest incremental changes are related to alluvial plains and mountains and hills, and the biggest decreasing changes are related to alluvial cones. The results of table (6), which is a comparison between the 2018 map and the 2050 forecast map, indicate an increase in the area of new vegetation and alluvium and a decrease in the area of other landforms. This is completely logical because with the expansion of the physical development of cities and villages, the expansion of industries and the cultivation of areas related to the levels of alluvial cones and alluvial plains, foothills and hilly lands on the extent of covered areas.A plant will be added and other side effects will be reduced.

Speedy land-use changes in the suburbs of Iran, in present decades, have posed many problems, including the destruction of natural resources, environmental pollution, and inadequate urban growth. In most cases, these effects were due to unprincipled land-use change. Considering the rate of change and destruction of resources in previous years and feasibility, forecasting of these changes, and the growth pattern of the city in the coming years can be a significant step towards planning and optimal use of resources in the future. In this research, using Landsat multi-time satellite images related to 1986, 2002, and 2019, side information and Markov chain model of land-use and land cover changes that have occurred during 33 years in Khoy city to expose the extent of the changes. Convert for each period and forecast the desired uses. To conduct research, first, the images used in terms of the best bands for band composition were reviewed and selected. Then, the neural network method was used to classify and evaluate the accuracy of classification with the kappa coefficient and overall accuracy. Finally, land-use changes for 2036 were predicted using the Markov forecast model. The results show the uncontrolled expansion of built-up areas and severe destruction of agricultural lands from 2002 to 2019 and the sharp decline of barren lands and their conversion into built-up lands within the city limits and suburbs. Modeling the changes also shows that from 2019 to 2036, the city will change to a Mountainous area and gardens for growth.

In recent decades due to the rapid and unplanned growth of cities, urban management has been faced with several issues, including models and patterns of urban development and the selection of optimal options for location. Neyshabour is known as the second most populous city in Khorasan Razavi province. Due to reduced rainfall and drought, agriculture has lost its prosperity and most of the rural population has migrated to the city. Urban development research has been done by the ABM model using the factors affecting Neyshabour urban development. These factors include: the dominant position of desirable socio-Access-quality road access to green space and privacy watercourse Services and usage is taken into account. The ABM model is a set of independent factors with goals and capabilities that interact with each other. To validate the model, the Markov-cellular method based on Landsat 7 and 8 satellite images has been used for 17 years. Based on the results of urban development simulation using the ABM, the area of the urban area has increased from 2018 to 2035 with a growth rate of 19.8%, and in the Markov-cellular model, the area of the urban area from 2018 to 2035 with a growth rate of / Will increase by 23%. Accordingly, these results do not differ much from each other. The results show that in the implementation of these two models, The ABM model suggests that social influence is dominant class and carefully detailed model compared with Markov-cellular automation is acceptable

In order to study urban development and land use changes in subsequent periods, we also obtained land use maps and land survey data from Landsat satellite imagery and land use studies in Shahmirzad city to achieve this. Important software is ENVI 5.3, ARC.GIS10.5 and Terrset. . The results show that over the years studied, the area of horticultural, waste land has declined, and residential and human-made land use has increased. It was 2855094 square meters in 2009 and 2429144 square meters in 2019, following a downward trend. Residential and man-made land in 1999 was 360623 square meters, in 2009 it was 1264976 square meters and in 2019 it was 2495357 square meters, indicating a significant increase. . The change detection revealed that most land use conversions in 1999-2009 were related to conversion of arable land to wastewater by about 20% and from 2009 to 2019 related to conversion of arable land into residential land. With about 16%. Survey results show that in the first 10 years, about 20% of the horticultural land has become waste land and in the second 10 years about 7% of the land has become residential and human-made. Validation of the model with a kappa coefficient of 0.76 indicates that the model may have weaknesses but has acceptable ability to predict changes in the region.