چکیده:
دادههای سنجش از دور و الگوریتمهای مختلف طبقهبندی تصاویر ماهوارهای، ارزیابی روند تغییرات محیطی را در مقایسة چندزمانه امکانپذیر میکنند. هدف پژوهش حاضر، ارزیابی روند تغییرات کاربری اراضی محدوده و حریم شهر زنجان طی دو دهة گذشته با استفاده از الگوریتمهای شیگرا و پیکسل پایه است. در این پژوهش، از تصاویر ماهوارهای لندست 5 سنجندة TM سالهای 1999 و 2009 و سنجندة OLI/TRIS لندست 8 سال 2019 استفاده شد؛ همچنین از قابلیتهای سامانة گوگلارث انجین بهمنظور اخذ تصاویر تصحیحشده و طبقهبندی کاربری اراضی استفاده شد. بهمنظور تهیة نقشة کاربری اراضی، الگوریتمهای طبقهبندی ماشین بردار پشتیبان، حداقل فاصله و جنگل تصادفی در بستر گوگلارث انجین با روش نزدیکترین همسایة الگوریتم طبقهبندی شیگرا در نرمافزار eCognition مقایسه شدند. براساس نتایج ارزیابی صحت، ضرایب کاپا و صحت کلی الگوریتم طبقهبندی شیگرا برای سال 2019 و الگوریتم طبقهبندی ماشین بردار پشتیبان برای سالهای 1999 و 2009، بهترین نتیجه را نسبت به سایر الگوریتمها نشان دادند و مبنای ارزیابی تغییرات کاربری قرار گرفتند. نتایج ارزیابی تغییرات طی سالهای گذشته (1999- 2019) نشان میدهد اراضی دیمی 1264 هکتار، مراتع 648 هکتار، زراعت آبی و فضای سبز 142 هکتار و شبکة دسترسی راهها 122 هکتار به کاربری اراضی ساختهشده تغییر کاربری دادند و مناطق حومهای جدید مانند شهرک الهیه، گلشهر، کاظمیه، کارمندان، کوی سایان، کوی فرهنگ، کوی فاطمیه و شهر آرا نیز در این دوره توسعه یافتهاند؛ این امر ضرورت توجه به موضوع گسترش شهری و پیامدهای آن را در شهر و پیرامون آن نشان میدهد.
To assess environmental changes, monitoring systems and remote sensing satellites provide powerful tools that make the assessment of environmental change trends easier by multi-temporal comparisons. In recent decades, remote sensing data and GIS techniques for various aspects of urban spatial expansion and urban dispersal such as mapping (for expansion pattern), control (for process pattern recognition), measurement and evaluation (for analysis), and modeling (for Expansion simulation) are used. The object-based analysis is one of the emerging advanced techniques in the classification of satellite images. The object-oriented classification uses a segmentation process and a learning algorithm to analyze the spectral, spatial, and textural properties of the pixels. Along with the object-oriented classification method, Google Earth Engine, with extensive support for free satellite data and images, enables the classification and processing of high-speed satellite imagery that can be used in the monitoring and mapping land use. Methodology: In the present study, the digital data of the Landsat satellite provided by GEE are used. The data do not require pre-processing and initial correction (geometric, radiometric, etc.) and are readily available for processing. Landsat image types (1 to 8) can be summons with any processing level in GEE. In this study, atmospheric correction images of the Surface Reflectance Tier1 are used. This dataset is modified for atmospheric errors and includes OLI / TIRS sensors for Landsat 8. With simple coding patterns in GEE, the images of 1999, 2009, and 2019 are corrected for the processing step. GEE has provided a modern set of pixel-based classification that can be used for monitoring and mapping. By analyzing the corrected image of 1999, 2009, and 2019 and capturing the training samples, the images are classified with the support vector machine algorithms, random forest, and minimum distance. To perform object-oriented analysis and classification, images are segmented using the multiresolution segmentation algorithm in specialized recognition software. Geometric properties of land use classes (including shape, size, texture) are used for segmentation. By analyzing the results of the segmentation of images with different scale parameters, the optimal values of scale, shape, and compression for the images used are obtained. In this study, based on spatial resolution and image quality, four land use and land cover classes were considered in Zanjan urban areas. These classes include built-up, irrigated and urban green areas, dry farming, and rangelands. By selecting the above classes, training samples for multi-temporal images (1999, 2009, and 2019) are prepared. The nearest neighbor algorithm is used to classify images based on the object-oriented method. In this process, the maximum difference index of mean and NDVI vegetation index are also applied for each of the classes to reduce class mixing and improve the classification accuracy of influential parameters such as normalized difference built-up index (ndbi), mean and standard deviation of each band, area, the ratio of length to width, compaction, and brightness. Statistical parameters of kappa coefficients and overall accuracy are used for the accurate assessment of the classified images. To understand the changes in the area, after producing the land use maps and assessing them, the classification methods are used to evaluate the land use changes that occurred in the period 1999 to 2019. Discussion: After the classification of Landsat 5 and 8 satellite images, land use maps of 1999, 2009, and 2019 are prepared using object-oriented and pixel-based methods. Since in this study the parameters and characteristics of mean and standard deviation of bands, NDBI, NDVI indices, etc. are used to improve the results of the algorithm nearest neighbor object-oriented method, the results of image classification accuracy assessment show that the object-oriented method is weaker in separating rangelands and built-up in 1999 and 2009 than the support vector machine classification method. However, the object-oriented classification results for 2019 show the best performance of all the utilized classification algorithms. Due to the better results of the support vector machine classification method for 1999 and 2009 and the object-oriented method for 2019, the results of these methods are used in the assessment of land use changes in the study area. According to the results, significant changes have occurred in the region from 1999 to 2019. During this period, the land (mainly Zanjan) showed an increase of 5036 hectares. Also, the results show that Zanjan has grown and expanded into rangelands and dry farming in the suburbs over the period 1999 to 2019. Conclusion: Comparing the results of classifier accuracy assessment, the nearest neighbor object-oriented classification algorithm for 2019 showed better performance in terms of kappa coefficient and overall accuracy than other algorithms. Also, by comparing the results of the assessment of the classification maps of 1999 and 2009, the support vector machine algorithm showed the best performance compared to other classification algorithms in the study area. The support vector machine was the basis for the assessment of changes. Based on the results of land use changes assessment, in recent years, significant land use changes have occurred around Zanjan city. The reason for the increased land area in Zanjan in 2019 (26.12%) is the increase of population and the development of new settlements in the suburbs, and consequently, the reduction of agricultural rangelands. Keywords: Google Earth Engine, Object-Oriented, Support Vector Machine, Zanjan City. References: - Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study of Simly Watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 251-259. - Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing. Guilford Press. - De Oliveira Silveira, E. M., De Menezes, M. D., Junior, F. W. A., Terra, M. C. N. S., & De Mello, J. M. (2017). Assessment of Geostatistical Features for Object-Based Image Classification of Contrasted Landscape Vegetation Cover. Journal of Applied Remote Sensing, 11(3), 036004. - Dewan, A. M., & Yamaguchi, Y. (2009). Land Use and Land Cover Change in Greater Dhaka, Bangladesh: Using Remote Sensing to Promote Sustainable Urbanization. Journal of Applied Geography, 29(3), 390-401. - Dingle Robertson, L., & King, D. J. (2011). Comparison of Pixel-and Object-Based Classification in Land Cover Change Mapping. International Journal of Remote Sensing, 32(6), 1505-1529. - El-Asmar, H. M., Hereher, M. E., & El Kafrawy, S. B. (2013). Surface Area Change Detection of the Burullus Lagoon, North of the Nile Delta, Egypt, Using Water Indices: A Remote Sensing Approach. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 119-123. - Esam, I., Abdalla, F., & Erich, N. (2012). Land Use and Land Cover Changes of West Tahta Region, Sohag Governorate, Upper Egypt. Journal of Geographic Information System, 4(06), 483. - Feizizadeh, B., Blaschke, T., Nazmfar, H., Akbari, E., & Kohbanani, H. R. (2013). Monitoring Land Surface Temperature Relationship to Land Use/Land Cover from Satellite Imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9), 1290-1315. - Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X., & Khan, M. (2016). Extracting and Analyzing Forest and Woodland Cover Change in Eritrea Based on Landsat Data Using Supervised Classification. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 37-47. - Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Journal of Remote Sensing of Environment, 202, 18-27. - Huo, L. Z., Boschetti, L., & Sparks, A. M. (2019). Object-Based Classification of Forest Disturbance Types in the Conterminous United States. Journal of Remote Sensing, 11(5), 477. - Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object‐Based Change Detection Using Correlation Image Analysis and Image Segmentation. 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خلاصه ماشینی:
از ديگر منابع اطلاعاتي و نرم افزارهاي استفاده شده در پـايش زمـاني تغييـرات کـاربري اراضـي، سـامانۀ تخصصـي سنجش از دور تحت وب مانند سامانۀ گوگل ارث انجين است ؛ اين سامانه بـا پشـتيباني گسـترده از داده هـا و تصـاوير ماهواره اي رايگان ، امکان طبقه بندي و پردازش تصاوير ماهواره اي را با سرعت بسيار زياد و آسـان فـراهم کـرده اسـت (احراري، ١٣٩٨: ١ ;١٧ :٢٠١٧ ‚.
در اين پژوهش از قابليت هاي سامانۀ گوگل ارث انجين براي اخذ و طبقه بنـدي تصـاوير مـاهواره اي در محـدوده و حريم شهر زنجان استفاده شده است ؛ همچنين نتايج طبقه بندي شي گرا و طبقه بندي پيکسل پايه در بستر GEE٤ مقايسه مي شود تا از بين روش ها، روشي بهينه به منظور تهيۀ نقشۀ کاربري اراضي و ارزيابي تغييرات انتخاب شـود.
مشخصات تصاوير استفاده شده در تهيۀ نقشه هاي کاربري اراضي در سال هاي مختلف (منبع : نويسندگان ، (رجوع شود به تصویر صفحه) ارزيابي تغييرات کاربري اراضي شهر زنجان در بازة زماني ١٩٩٩- ٢٠١٩ شکل ٢.
مقادير بهينۀ پارامترهاي سگمنت بندي تصاوير استفاده شده (منبع : نويسندگان ، ١٣٩٩) (رجوع شود به تصویر صفحه) با انتخاب کلاس هاي کاربري اراضي، نمونه هاي تعليمي براي تصاوير چندزمانۀ ١٩٩٩، ٢٠٠٩ و ٢٠١٩ تهيه شد.
(رجوع شود به تصویر صفحه) طبقه بندي پيکسل پايۀ تصاوير در اين پژوهش ، از طبقه بندي پيکسل پايه در بستر GEE استفاده شده است که مجموعه اي مدرن از الگـوريتم هـاي طبقه بندي را شامل ميشود و قابليت پايش و تهيۀ نقشۀ کاربري را دارد (١٧ :٢٠١٧ ‚.