Remote Sensing and GIS Applications in Environmental Sciences

Detection of the Snow-Cover Changes on the Urmia Lake western Basins Using Object-Based Image Analysis (OBIA) Methods

Document Type : Original Article

Authors

1 Narges Samadi,Ph.D.student,Department of Hydrology and Meteorology,Marand Branch,Islamic Azad University,Marand,Iran

2 Professor,Department of Meteorology,Faculty of Planning and Environmental Sciences,Tabriz University,Iran

3 Professor of Geography Department,Faculty of Planning and Environmental Sciences,Tabriz University,Iran

4 Professor of Remote Sensing and GIS Department,Faculty of Planning and Environmental Sciences,Tabriz University,Iran

10.22034/rsgi.2024.62524.1089

Abstract

This article focuses on analyzing snow cover changes in the western watersheds of Lake Urmia (Zola Chay, Nazlou Chay, Shahr Chay, and Barandoz Chay) using object-based image analysis methods applied to satellite imagery. The study utilizes Sentinel-2 and Landsat-8 satellite images to extract the extent of snow cover for days with maximum snowfall. This data was then processed using object-based methods, and the future state of snow cover in these four watersheds for the year 2029 was simulated with 80% accuracy using the CA-Markov model.
In addition, the depth of snow was examined using FLDAS_NOAH data and analyzed through the normalized residual mass curve method in Excel. The study reveals fluctuations in snow cover and depth across all four sub-watersheds between 2016 and 2023, showing an overall decreasing trend. The highest snow cover occurred in 2018, 2019, and 2021. In terms of snow depth, the maximum and average values reached their peak in 2020, with figures of 2.28 meters and 0.68 meters, respectively. Conversely, the lowest maximum snow depth was recorded in 2016 and 2018, at 1.07 meters and 1.1 meters. Moreover, the CA-Markov snow cover map for 2029 indicates a relatively increasing trend in snow cover. Despite the region's water scarcity issues, the findings of this study suggest that other factors are contributing to the intensification of water shortages and the reduction of Lake Urmia's water level. Identifying and examining these factors is crucial for effective water management in the region.

Keywords

Main Subjects

هدف این مقاله، تشخیص تغییرات پوشش برفی با استفاده از روش‌های تحلیل شی‌ءگرای تصاویر ماهواره‌ای در حوضه‌های آبریز‌غربی دریاچه ارومیه (زولاچای، نازلوچای، شهرچای و باراندوزچای) است. در این راستا، وسعت پوشش برف به کمک تصاویر ماهواره‎ای Sentinel-2 و Landsat-8 برای روزهای با بیشترین برف به روش شی­ءگرا استخراج و در ادامه به کمک روش CA-Markov وضعیت آتی پوشش برفی برفی هر 4 حوضه در سال 2029 با دقت بالا شبیه‌سازی شد. سپس، عمق برف به کمک داده‎های FLDAS_NOAH و با استفاده از روش منحنی جرم باقیمانده نرمال شده در محیط اکسل مورد بررسی قرار گرفت. تغییرات پوشش و عمق برف در هر 4 زیرحوضه طی سال‎های 2016 تا 2023، دارای افت و خیزهایی بوده است و یک روند کاهشی داشته‎اند. بیشترین پوشش برفی مربوط به سال‏های 2018، 2019 و 2021 می‎باشد. بیشینه و میانگین مقادیر عمق برف در سال‏ 2020 به ترتیب به 28/2 و 68/0 و در سال‏های 2016 و 2018 نیز بیشینه عمق برف به کمترین میزان خود یعنی 07/1 و 1/1 رسید. همچنین بررسی نقشه پوشش برفی CA_Markov برای سال 2029 نشان دهنده روند افزایشی نسبی برف است. نتایج نهایی مطالعه حاضر نشان دهنده این واقعیت هستند که روش‌های بهینه‌سازی و همجوشی سنجنده‌های لندست و سنتینل می‌توانند در فرآیند استخراج و شناسایی پوشش برف با دقت بالا بسیار موثر باشند. بنابراین علارغم وجود مشکلات کم‌آبی در منطقه، بررسی نتایج حاصل از این تحقیق نشان می‎دهد که عوامل دیگری در تشدید کم‏آبی منطقه و کاهش آب دریاچه ارومیه دارد که یافتن و بررسی این، برای مدیریت مشکلات آبی منطقه بسیار حائز اهمیت است.

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Remote Sensing and GIS Applications in Environmental Sciences
Volume 4, Issue 11
August 2024
Pages 50-23
  • Receive Date: 16 July 2024
  • Revise Date: 09 August 2024
  • Accept Date: 17 October 2024