پایش آب‌های سطحی سیلاب با استفاده از قطبش‌های مختلف و شاخص‌های طیفی آب مبتنی بر سنجش از دور در آبخیز کرخه

نوع مقاله : پژوهشی

نویسندگان

1 دانشیار گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، پژوهشکده مدیریت آب، دانشگاه محقق اردبیلی، اردبیل، ایران

2 دانش‌آموخته کارشناسی ارشد بیابان‌زدایی، گروه مرتع و آبخیزداری، دانشکده کویرشناسی سمنان، سمنان، ایران

10.22092/wmrj.2025.370814.1635

چکیده

مقدمه و هدف
بر اساس آمار جهانی، سیلاب بر بیش از ۴۰% از جمعیت جهان اثرگذار است و سبب خسارت جانی و مالی قابل توجهی می‌شود. در ایران، بیش از ۸۰% از شهرها در معرض خطر سیلاب هستند. ازاین‌رو، سرمایه‌گذاری برای انجام اقدامات پیشگیرانه و فناوری‌های پیشرفته برای مبارزه با سیلاب بیش از هر زمان دیگری ضروری است. آمادگی داشتن و کاهش خطر پیش از رویداد بلایا سبب کاهش معنادار هزینه بازیابی پس از آن می‌شود. نقشه‌برداری دقیق و پایش بلند‌مدت سیلاب نقش مهمی در برنامه‌ریزی و پیشگیری دارد. یکی از ابزارهای کارآمد برای پایش و نقشه‌برداری سیلاب در مدیریت بهینه بلایا، استفاده از تصویرهای ماهواره‌ای است. اگرچه تصویرهای راداری با دیافراگم مصنوعی (SAR) سنتینل-1 در شرایط ابری برای شناسایی مناطق سیلابی مفید هستند، اما این روش نیز محدودیت‌هایی دارد. حساسیت داده‌های راداری به پوشش گیاهی متراکم و سازه‌های عمودی ممکن است باعث برآورد کم یا زیاد مناطق آب‌گرفته شود. هم‌چنین، شاخص آب تفاضلی بهنجار‌شده (NDWI)، شاخص آب تفاضلی بهنجار‌شده اصلاح‌شده (MNDWI) و شاخص استخراج خودکار آب (AWEI) به‌دست آمده از داده‌های نوری در شرایط ابری یا سایه گیاهان ممکن است خطا داشته باشند و باعث شناسایی ناقص یا اشتباه مناطق سیلابی شوند. ازاین‌رو، نتایج به‌دست آمده از هر دو روش باید با دقت تفسیر شوند و با ترکیب داده‌های راداری و نوری می‌توان این محدودیت‌ها را کاهش داد، ولی آن‌ها را به‌طور کامل از بین نمی‌برد. ازاین‌رو، در این پژوهش داده‌های رادار و نوری با استفاده از شاخص‌های سنجش از دور مرتبط با طیف آب، تحلیل و مقایسه شدند و مناطق سیلابی در آبخیز کرخه در استان خوزستان شناسایی شد.
مواد و روش‌ها
در آبخیز کرخه در ماه‌های فروردین و اردیبهشت 1398 سیلاب گسترده و غیرقابل پیش‌بینی رخ می‌‌دهد که منجر به خسارت قابل توجهی می‌شود. بر این اساس، منطقه‌ای به مساحت 3/3838 کیلومترمربع انتخاب شد. در این پژوهش، از سه روش مختلف برای شناسایی و تحلیل آب‌های سطحی ناشی از سیلاب استفاده شد: (1) در راستای آشکارسازی تغییرات، از داده‌های سه بازه زمانی شامل پیش از فصل کشاورزی (6 مهر تا 3 آبان 1397)، پس از فصل کشاورزی (9 اسفند 1397 تا 5 فروردین 1398) و هنگام سیلاب (8 فروردین تا 5 اردیبهشت 1398) استفاده شد. (2) تصویرهای SAR به‌دست آمده از ماهواره سنتینل-1 با قطبش‌های VV و VH با استفاده از فیلتر Refined Lee و الگوریتم آستانه Otsu به‌منظور شناسایی پهنه‌های سیلابی، پردازش شدند. (3) برای استخراج دقیق‌تر آب‌های سطحی از تصویرهای نوری، شاخص‌های طیفی آب شامل NDWI، MNDWI و AWEI به‌دست آمده از ماهواره سنتینل-2 استفاده شد. شایان ذکر است که AWEI در دو نسخه بدون سایه برای مناطق باز و با سایه برای کاهش اثر سایه در نواحی شهری و کوهستانی وجود داشت که در این پژوهش از نسخه بدون سایه استفاده شد. در پایان، با تلفیق نتایج به‌دست آمده از روش‌های راداری و نوری و تحلیل تغییرات چندزمانه، نقشه‌های جامعی از گسترش سیلاب تولید و اعتبارسنجی شد.
نتایج و بحث
نتایج این پژوهش نشان داد عملکرد قطبش VH با اندازة 254 کیلومترمربع در تشخیص مناطق سیلابی کمی بهتر از قطبش VV با اندازة 252 کیلومترمربع بود. با یک روش ترکیبی و استفاده از هر دو قطبش و فقط با انتخاب مناطقی با پراکندگی بازگشتی کم در هر دو قطبش، 239 کیلومترمربع از مناطق سیلابی، شناسایی شد. ترکیب قطبش‌ها دقت و قابلیت اطمینان نقشه‌برداری سیلاب را بهبود بخشید و به‌دلیل حساس نبودن رادار به پوشش ابر و شرایط روشنایی، نسبت به داده‌های نوری، برتری داشت. نتایج تجزیه و تحلیل زمانی آب‌های دائمی و موقت در روش ترکیبی (VV+VH) در سه دوره نشان داد که پوشش آب پیش از فعالیت‌های کشاورزی زیاد (1560 کیلومترمربع) بود، در اواخر زمستان به اندازة 847 کیلومترمربع کاهش یافت. سپس، در طول رویداد سیلاب به تقریباً 974 کیلومترمربع افزایش یافت. با وجود آب کمتر در مقایسه با دوره پیش از فعالیت‌های کشاورزی، سیلاب گسترده‌ای به‌دلیل بارندگی شدید، ناگهانی و ظرفیت محدود زمین و زیرساخت‌ها رخ داد. با استفاده از MNDWI با اندازة 227 کیلومترمربع بیشترین مساحت پوشیده از آب، شناسایی شد. در حالی‌که با استفاده از AWEI، با اندازة  126 کیلومترمربع با رویکردی محافظه‌کارانه‌تر، فقط مناطق با احتمال بسیارزیاد وجود آب‌های سطحی، شناسایی شد. با ترکیب هر سه شاخص طیفی، مساحتی معادل ۶۲ کیلومترمربع به‌عنوان مناطق مستعد سیلاب، شناسایی شد. تغییرات میان شاخص‌ها به‌دلیل تفاوت در باندهای طیفی و حساسیت به وجود آب است. نتایج تحلیل NDWI، MNDWI و AWEI، نشان داد تغییرات گستردگی آب دائمی و موقت در طول سیلاب قابل توجه بود به‌طوری که از 98 کیلومترمربع به 324 کیلومترمربع افزایش یافت. روش آستانه‌گذاری Otsu به‌طور مؤثر برای استانداردسازی و طبقه‌بندی مناطق آبی در میان شاخص‌ها به‌کار گرفته شد.
نتیجه‌گیری و پیشنهادها
بر اساس نتایج این پژوهش می‌توان نتیجه گرفت که تلفیق داده‌های راداری و نوری منجر به شناسایی ۵۸ کیلومترمربع از مناطق سیلابی شد و میان نتایج دو ماهواره 39% هم‌پوشانی بود. استفاده از این تلفیق، سبب افزایش چشم‌گیر دقت و قابلیت اطمینان شناسایی مناطق سیلابی شد. از آنجایی‌که تغییرات در گسترة آب فقط نشان‌دهنده رویداد یا شدت سیلاب نیست و بیانگر آن است که عامل‌های آب‌شناختی و مدیریتی نقش مهمتری در رویداد سیلاب دارند. ازاین‌رو، پیشنهاد می‌شود به‌منظور توسعه سامانه‌های هشدار اولیه و برنامه‌ریزی مدیریت بحران، از داده‌های ترکیبی ‌همراه با تجزیه و تحلیل عامل‌های محیطی، استفاده شود. افزون بر این، با بهره‌گیری از این روش امکان پهنه‌بندی دوره‌ای از سیلاب فراهم‌شده است که پیشنهاد می‌شود در توسعه اقدام های پیشگیرانه، مدیریت بهینه سیلاب و برنامه‌ریزی کشاورزی از آن استفاده شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Monitoring Flood-induced Surface Waters Using Different Remote Sensing-based Polarizations and Spectral Water Indices in the Karkheh Watershed

نویسندگان [English]

  • Zeinab Hazbavi 1
  • Marzieh Ghashamshami 2
1 Associate Professor, Department of Range and Watershed Management, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran
2 Former M.Sc. Student, Department of Range and Watershed Management, Faculty of Desert Studies, Semnan University, Semnan, Iran
چکیده [English]

Introduction and Goal
According to global statistics, floods affect more than 40 % of the world's population and cause significant loss of life and property. In Iran, more than 80 % of cities are at risk of flooding. Therefore, investing in preventive measures and advanced technologies to combat floods is more necessary than ever. Preparedness and risk reduction before disasters could significantly reduce the cost of post-disaster recovery. Accurate mapping and long-term monitoring of floods play a key role in planning and prevention. One of the efficient tools for monitoring and mapping floods in optimal disaster management is the use of satellite imagery. Although Sentinel-1 synthetic aperture radar (SAR) images are useful for identifying flood areas in cloudy conditions, this method also has limitations. The sensitivity of radar data to dense vegetation and vertical structures may lead to under- or over-estimation of flooded areas. Besides, the Normalized Differential Water Index (NDWI), Modified Normalized Differential Water Index (MNDWI), and Automatic Water Extraction Index (AWEI) obtained from optical data in cloudy or plant shadows may have errors and cause incomplete or incorrect identification of flood areas. Therefore, the results obtained from both methods should be interpreted carefully, and combining radar and optical data can reduce these limitations, but does not completely eliminate them. Therefore, in this study, radar and optical data were analyzed and compared using remote sensing indices related to the water spectrum, and flooded areas were identified in the Karkheh watershed in Khuzestan Province.
Materials and Methods
In the Karkheh watershed, widespread and unpredictable flood occur in the months of March and April 2019, which resulted in significant damage. Accordingly, an area of ​​3838.3 km2 was selected. In this study, three different methods were used to identify and analyze surface water caused by flooding: (1) to detect changes, data from three time periods were used, including pre-agriculture season (September 28 to October 25, 2018), post-agriculture season (February 28 to March 25, 2019), and during the flood (March 28 to April 25, 2019). (2) SAR images obtained from the Sentinel-1 satellite with VV and VH polarizations were processed using the Refined Lee filter and the Otsu threshold algorithm to identify flooded areas. (3) To more accurately extract surface water from optical images, spectral water indices including NDWI, MNDWI, and AWEI obtained from the Sentinel-2 satellite were used. It is worth noting that AWEI was available in two versions: shadowless for open areas and shaded to reduce the effect of shadow in urban and mountainous areas, and the shadowless version was used in this study. Finally, by combining the results from radar and optical methods and analyzing multi-temporal variations, comprehensive maps of flood spread were produced and validated.
Results and Discussion
The results of this study showed that the performance of the VH polarization with a value of 254 km2 was slightly better in detecting flood areas than the VV polarization with a value of 252 km2. With a combined method using both polarizations and only selecting areas with low backscatter in both polarizations, 239 km² of flooded regions were identified. The Combination of polarizations improved the accuracy and reliability of flood mapping and was superior to optical data because the radar was insensitive to cloud cover and lighting conditions. The results of temporal analysis of permanent and temporary water in the combined method (VV+VH) in three periods revealed that water coverage was high (about 1560 km²) pre-agricultural activities, decreased to 847 km² in late winter. Then increased to approximately 974 km² during the flood event. Despite lower water compared to the pre-agriculture period, a widespread flood occurred due to intense, sudden rainfall and the limited land and infrastructure capacity. The largest area covered by water was identified using MNDWI with an area 227 km2. While the AWEI, with a value of 126 km2, with a more conservative approach, only areas with a very high probability of surface water were identified. By combining all three spectral indices, an area of ​​62 km2 was identified as flood-prone areas. The variations between the indices are due to the differences in spectral bands and sensitivity to water. Analysis of the results of the NDWI, MNDWI and AWEI showed that the changes in permanent and temporary water extent during the flood were significant, increasing from 98 km2 to 324 km2. The Otsu thresholding method was effectively applied to standardize and classify water areas across indices.
Conclusion and Suggestions
Based on the results of this study, it concluded that combining radar and optical data led to the identification of 58 km2 of flood areas, and there was a 39% overlap between the results of the two satellites. Using this combination significantly increased the accuracy and reliability of identifying flood-affected areas. Since changes in water extent do not only indicate the occurrence or severity of floods, it indicates that hydrological and management factors play a more important role in flood occurrence. Therefore, it is suggested that combined data, along with environmental factor analysis, be used to develop early warning systems and crisis management planning. In addition, using this method allows for periodic zoning of floods, which is recommended for use in developing preventive measures, optimal flood management, and agricultural planning.

کلیدواژه‌ها [English]

  • Flood management
  • flood mapping
  • Otsu algorithm
  • remote sensing indices
  • SAR images

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