تحلیل عملکرد شاخص های پوشش برف در منطقه کوهستانی سهند (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
این پژوهش باهدف بررسی و مقایسه شاخص های استخراج پوشش برف در سامانه گوگل ارث انجین (GEE) برای توده کوهستان سهند انجام شده است. در این مطالعه، پس از اعمال عملیات بهبود تصویر و افزایش وضوح تصاویر ماهواره ای لندست 8 از 30 متر به 15 متر، شاخص های مختلف برف شامل NDSI، NDSII، NDSall، NBSIMS، SWI و S3 محاسبه و به کار گرفته شدند. نتایج حاصل از تحلیل تصاویر نشان داد که تمامی شاخص های مورد استفاده توانایی بالایی در شناسایی و تفکیک مناطق برفی به ویژه در نواحی با آلبیدوی پایین، سایه ها و توپوگرافی خشن دارند. شاخص های NDSInw و S3 با دقت کلی 100 درصد و ضریب کاپای 1 به عنوان بهترین شاخص ها برای تفکیک پوشش برف از سایر ویژگی های سطح زمین در این منطقه معرفی شدند. همچنین، شاخص های NBSIMS و SWI نیز به شکل مناسبی در شناسایی ویژگی های آبی و مرطوب در مناطق برفی عمل کردند و نشان دادند که می توانند در تحلیل های هیدرولوژیکی و مدیریت منابع آب به کار روند. این مطالعه نشان می دهد که داده های ماهواره ای با شاخص های طیفی مناسب می تواند به شناسایی دقیق تر پوشش برف، تخمین میزان آب موجود در برف و بررسی تغییرات فصلی برف کمک کند. این نتایج اطلاعات ارزشمندی برای مدیریت منابع آب، پیش بینی سیلاب ها و تحلیل تأثیرات تغییرات اقلیمی فراهم می کند. در این پژوهش مشخص شد که شاخص های NDSInw و S3 با دقت کلی ۱۰۰ درصد و ضریب کاپای ۱، بهترین عملکرد را در تفکیک پوشش برف از سایر عوارض سطح زمین در منطقه کوهستان سهند دارند. همچنین شاخص های NBSIMS و SWI در شناسایی نواحی مرطوب و برف آب مؤثر ظاهر شدند. این یافته ها اهمیت استفاده از فناوری های سنجش از دور را در پایش پوشش برف و مدیریت منابع طبیعی در مناطق کوهستانی به خوبی نشان می دهند.Performance Analysis of Snow Cover Indices in the Sahand Mountain Region
Extended Abstract Introduction Snow cover is one of the essential components of Earth's biological, climatic, and hydrological cycles. It plays a crucial role in reflecting sunlight and reducing the Earth's heat effects, while also having a direct impact on global atmospheric circulation. Snow, particularly in mountainous and cold regions, serves as the primary source of surface water, and its melting contributes significantly to freshwater supply in many areas. However, snow cover is highly affected by climate change and human activities, leading to its reduction in some regions and extreme changes in its distribution patterns. Therefore, the use of modern technologies like remote sensing is essential for monitoring snow cover changes on large scales. This study aims to evaluate and compare various indices for extracting snow cover in the Sahand Mountain Massif, one of the most important volcanic mountains in Iran. Methodology For this study, satellite data from Landsat 8 for the year 2024 was used. This data includes multispectral images with various bands that, after the pan-sharpening process, were converted from 30-meter to 15-meter resolution to improve spatial accuracy. Various indices such as NDSI (Normalized Difference Snow Index), NDSII (Normalized Difference Ice and Snow Index), NDSall, NBSIMS (Multicomponent Snow Index), SWI (Snow Water Index), and S3 were used to identify and distinguish snow-covered areas from other land surfaces. These indices are based on spectral reflectance differences between snow, ice, and other surface elements in various satellite image bands. The data was processed and analyzed using Google Earth Engine (GEE), a cloud-based platform for remote sensing data analysis. This system is highly useful in land cover studies due to its high processing speed and ability to handle large datasets. Results and Discussion The results demonstrated that the indices used are highly effective in detecting snow cover. Especially in areas with low albedo (such as shadows or low-reflectance regions), the indices NDSInw and S3 were identified as the best indicators, with an overall accuracy of 100% and a Kappa coefficient of 1. These indices were able to effectively separate snow-covered areas from other phenomena such as soil, rocks, and vegetation. Additionally, the NBSIMS index performed well in identifying water-related features in snow-covered areas, such as meltwater, and could be useful in hydrological analyses. Another notable result was the SWI index's ability to identify the amount of water within the snow cover, which can be effectively used in water resource management and flood forecasting. The findings of this research confirm that various spectral indices can serve as powerful tools for monitoring snow cover and distinguishing it from other surface elements. Particularly in mountainous areas where rapid climatic and hydrological changes occur, indices like NDSI and S3 are highly useful due to their accuracy in snow detection. These results can be beneficial in natural resource management, improving flood forecasting methods, and designing management programs to mitigate the impacts of climate change. Moreover, this research demonstrated that combining remote sensing data with new computational methods, such as cloud-based processing, can enhance the accuracy and speed of environmental analyses. The results of this research show that the use of Landsat satellite images and various indicators to identify and distinguish snow cover, especially in areas with low albedo, can lead to accurate and reliable results. Analyzes using NDSI, NDSall, NDSII, NDSInw, S3, SWI and NBSIMS indices showed that each of these indices has its own strengths and limitations. Conclusion This research demonstrated that using Landsat 8 satellite data and various spectral indices is an effective tool for identifying and analyzing snow cover. Indices such as NDSI and S3, due to their high accuracy and efficiency in distinguishing snow cover in mountainous regions, can be employed in water resource management and flood forecasting. Additionally, this study showed that utilizing the GEE system can significantly improve the speed and accuracy of analyses and provide a foundation for future research on monitoring climate and environmental changes. The findings of this research can serve as a basis for further studies in similar regions and improving management and conservation methods in the face of climate change. Using a combination of different indices can help identify snow cover and its geographical distribution more accurately. This information is very useful and effective for water resource management, flood forecasting, climate change analysis, agricultural planning, and natural resource protection. Especially in areas where snow plays a key role in feeding water resources and determining hydrological patterns, the use of these indicators can be very useful in improving long-term planning and crisis management related to climate change.
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