Document Type : Original Research Paper
Authors
گروه مهندسی نقشهبرداری، دانشکده عمران، آب و محیطزیست، دانشگاه شهید بهشتی، تهران، ایران
Abstract
Background and Objectives: The issue of urbanization and monitoring of urban expansion and land use changes using satellite images has become a basic focus in the society. Easy and stable access to satellite data has made it possible to monitor and monitor land changes more accurately; But for optimal use of these images, it is necessary to collect samples of images and then classify their pixels based on regional features and characteristics. This process faces challenges such as data dispersion, which can be solved by using appropriate classification methods. In this study, in order to evaluate the area of land uses in cities, various methods of machine learning have been used. Instead of using a fixed and absolute method for classifying pixels, four different machine learning methods are investigated separately for each image. These diverse methods of machine learning provide the possibility of choosing the best and most efficient method for each image, thus improving the ability to detect and classify pixels for land use areas in cities and increasing accuracy and efficiency.
Methods: In this research, the Landsat 9 satellite image has been used to study and analyze different areas of Tehran in 2023. First, the desired image was subjected to the necessary corrections and then four appropriate machine learning algorithms (which included K-nearest neighbor, support vector machine, random forest and maximum likelihood) were used to classify Landsat 9 satellite images related to four different areas of Tehran (including 2, 5 , 21, 22) were used. To evaluate the accuracy of the results, more than 200 check points were created on the image using the Stratified Random method, and then Google Earth Pro was used to check the check points. The overall classification accuracy and kappa coefficient were evaluated as evaluation criteria for the best classification method of image pixels. In the next step, the studied area was divided into equal blocks in order to better understand the area of land uses in that area. Then, using Zonal Statistics, the amount of land use area in each block was investigated.
Findings: Based on the methods used, the performance of the SVM method in this study achieved the highest possible accuracy, which is equal to 95%, and the Kappa coefficient, which is 89%. These results may be justified due to the non-uniformity of pixel areas in dense urban environments. In addition, different areas of land, including green areas with an area of 12 square kilometers, barren lands with an area of 64 square kilometers, and built-up areas with an area of 137 square kilometers were also examined in this analysis.
Conclusion: Through this approach, we have presented a highly accurate classification method for the analysis of satellite images related to the Landsat 9 satellite. This method enables a more accurate assessment of the area of land uses and provides urban decision makers and policy makers with a direct link with valuable insights for sustainable development in cities. This can play an effective role in the process of facilitating development plans to improve cities and citizens' lives, because it provides accurate and reliable information that helps strategic decisions in the field of urban development and enables more effective and targeted changes in urban policies and programs.
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COPYRIGHTS
© 2024 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)
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