پایش و تحلیل ماندگاری سیلاب مورخ اسفند 1397 تا اردیبهشت 1398 شهر آققلا با استفاده از تصاویر راداری
نویسندگان
1
دانشآموخته مقطع کارشناسیارشد، گروه مهندسی نقشه برداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران.
2
گروه مهندسی نقشهبرداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران
10.22034/wmji.2026.2090221.1138
چکیده
پایش سریع و دقیق پهنههای سیلابی برای مدیریت بحران و کاهش خسارات اقتصادی، بهویژه در مناطق جلگهای با ماندگاری بالای آب، اهمیت دارد. هدف این پژوهش، تحلیل پویای زمانی-مکانی سیلاب مورخ اسفند 1397 تا اردیبهشت 1398 در شهر آققلا با استفاده از تصاویر راداری سنتینل-۱ و بستر گوگل ارث انجین است. در این مطالعه، شش بازه زمانی از اسفند ۱۳۹۷ تا اردیبهشت ۱۳۹۸ با پلاریزاسیونهای VV و VH بررسی شد و نقشه کاربری اراضی با تصاویر سنتینل-۲ و الگوریتم SVM تهیه گردید نتایج اعتبارسنجی ضریب کاپا 0/85 و صحت کلی 91 درصد را نشان میدهد. نتایج نشان داد منطقه چرخه کامل سیلاب ناگهانی تا تخلیه تدریجی را تجربه کرده و بیشینه آبگرفتگی در تاریخ چهارم با 94/27 کیلومترمربع رخداده است. اراضی کشاورزی با 19/60 کیلومترمربع بیشترین پهنه سیل را داشتند. همچنین 1/36 کیلومترمربع هسته پایدار آبگرفتگی تا تاریخ ششم (20 تا 30 اردیبهشت 1398) باقی ماند که نشاندهنده ضعف زهکشی است. در مقابل، اراضی شهری با حداکثر درگیری 0/04 کیلومترمربع، زهکش سریع بیشتری نشان دادند. نتایج، کارایی دادههای SAR را در پایش سیلاب و مدیریت بحران تأیید میکند.
کلیدواژهها
عنوان مقاله English
Monitoring and Analysis of Flood Duration from March to May 2019 in Aqqala Using Synthetic Aperture Radar (SAR)
نویسندگان English
pouya safaei anaraki
1
Nikroz Mostofi
2
1
Department of Surveying Engineering, ST.C., Islamic Azad University, Tehran, Iran.
2
Department of Surveying Engineering, ST.C., Islamic Azad University, Tehran, Iran
چکیده English
Rapid and accurate monitoring of flood extent and duration is essential for effective disaster management and reducing economic losses, particularly in lowland areas where floodwaters may persist for extended periods. This study aimed to monitor and analyze flood duration in Aqqala, Iran, from March to May 2019 using Sentinel-1 Synthetic Aperture Radar (SAR) imagery processed within the Google Earth Engine (GEE) platform. Six temporal periods spanning March to May 2019 were analyzed using both VV and VH polarizations. In addition, a land use/land cover (LULC) map was generated from Sentinel-2 imagery using the Support Vector Machine (SVM) classification algorithm. The classification achieved an overall accuracy of 91% and a Kappa coefficient of 0.85. The results revealed the complete flood cycle, from rapid inundation to gradual drainage, across the study area. The maximum flood extent occurred during the fourth observation period, reaching 27.94 km². Agricultural lands experienced the greatest inundation, with 19.60 km² affected, indicating their high vulnerability to prolonged flooding. Furthermore, 1.36 km² of persistent flooded areas remained until the sixth observation period (20–30 May 2019), suggesting inadequate drainage conditions. In contrast, urban areas exhibited a maximum inundation extent of only 0.04 km², reflecting more efficient drainage. Overall, the findings demonstrate the effectiveness of Sentinel-1 SAR imagery and the Google Earth Engine platform for monitoring flood duration and supporting flood hazard assessment and disaster management.
کلیدواژهها English
Flood Duration
Google Earth Engine (GEE)
Land Use/Land Cover (LULC)
Sentinel-1
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