پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,099,001 |
| تعداد دریافت فایل اصل مقاله | 97,206,558 |
پهنه‌‌بندی و پایش خطر سیل بهار 1398 خوزستان با استفاده از داده های لندست-8 | ||
| اکوهیدرولوژی | ||
| مقاله 8، دوره 7، شماره 3، مهر 1399، صفحه 647-662 اصل مقاله (1.21 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2020.302703.1333 | ||
| نویسندگان | ||
| کریم سلیمانی* 1؛ شادمان درویشی2 | ||
| 1استاد گروه مهندسی علوم آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| 2دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، مؤسسۀ آموزش عالی آبان هراز آمل | ||
| چکیده | ||
| پایش و پهنه‌‌بندی سیلاب کارکرد زیادی در کاهش خسارت‌های ناشی از آن دارد. هدف از مقالۀ حاضر، بررسی خطر سیل فروردین 1398 خوزستان با استفاده از داده‌های لندست-8 است. ابتدا پیشپردازش تصاویر در نرمافزار ENVI 5.3 انجام شد. سپس، برای پایش سیل فروردین 1398 از شاخصهای MNDWI و NDWI استفاده شد. پس از آن، نقشۀ پهنۀ خطر سیل در نرمافزار ArcGIS10.4 تهیه شد. نتایج نشان می‌دهد بخشهای جنوب و جنوب غربی از وضعیت خیلی شدید و بخشهای مرکزی و جنوب شرقی از وضعیت شدید خطر سیل برخوردارند که از مستعدترین نواحی سیلگیر در استان هستند. همچنین، پایش نقشههای سیل در استان خوزستان نشان می‌دهد که یک انطباق کامل بین نقشۀ پهنه‌‌بندی سیل و سیل اخیر وجود دارد. بهطوری که با بررسی نقشهها مشخص شد که سیل اخیر بیشتر در بخشهای غرب، جنوب و جنوب غربی اتفاق افتاده است. بررسی مکانی نواحی سیلابی نشان می‌دهد شهرهای هویزه، دشت آزادگان، اهواز، خرمشهر، بندر ماهشهر، آبادان و به‌خصوص شادگان بیشتر از دیگر شهرها دچار سیل شدهاند. در این میان، شهر شادگان بر اساس شاخصهای MNDWI و NDWI به‌ترتیب 191349 و 174813 هکتار از اراضی آن تحت تأثیر سیل بوده است که بیشترین میزان نسبت به دیگر شهرهای استان را نشان می‌دهد. بهطور کلی، با توجه به نتایج، استفاده از داده‌های سنجشازدور و شاخصهای MNDWI و NDWI برای پایش سیل و همچنین، استفاده از سیستم اطلاعات جغرافیایی به منظور پهنه‌‌بندی نواحی خطر سیلاب در مطالعات مرتبط پیشنهاد میشود. | ||
| کلیدواژه‌ها | ||
| سنجش از دور؛ MNDWI؛ NDWI؛ GIS | ||
| مراجع | ||
|
[1]. Abedini M, Fathi M. Flood hazard zoning using network analysis process. Journal of Hydrogeomorphology.2015; 1(3): 99-120. [Persian] [2]. Akgun A, Türk N. Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environmental Earth Sciences.2010; 61(3): 595- 611. [3]. Chormanski j, Okruszko T, Ignar S, Batelaan O, Rebel KT, Wassen MJ. Flood mapping with remote sensing and hydrochemistry a new method to distinguish the origin of flood water during floods. Ecological Engineering. 2011; 37(9): 1334-1349. [4]. Ashouri M, Rezaeimoghaddam MH, Piry Z. Morphologic Change Assessment of Riverbed Before and after Dam Construction Using HEC RAS Model and GIS in Downstream of Satarkhan Dam. Physical Geography Research Quarterly. 2013; 45(1): 87-100. [Persian] [5]. Van der Sande CJ, De Jong S M, De Roo A P J A. segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of applied earth observation and geoinformation. 2003; 4(3):217-229. [6]. Ahamed A, Bolten JD. A MODIS-based automated flood monitoring system for southeast asia. International Journal of Applied Earth Observation and Geoinformation.2017; 61: 104-117.
[7]. Ashcroft L, Karoly DJ, Dowdy AJ. Historical extreme rainfall events in southeastern Australia. Weather and Climate Extremes. 2019; 25: 1-12.
[8]. Feng LH, Lu J. The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications. 2010; 37(4):2974-2977.
[9]. Samanta S, Kumarpal D, Palsamanta B. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science. 2018; 8(66): 1-14.
[10]. Motevalli A, Vafakhah M. Flood hazard mapping using synthesis hydraulic and geomorphic properties at watershed scale. Stoch Environ Res Risk Assess. 2016; 30:1889-1900.
[11]. Yang Y C E, Ray PA, Brown C M, Khalil A F, Yu W H. Estimation of Flood Damage Functions for River Basin Planning: a Case Study in Bangladesh. Natural Hazards. 2015; 75(3): 2773-279.
[12]. Wu Y, Zhong P, Zhang Y, Xu B, Ma B, Yan K., Integrated flood risk assessment and zonation method: a case study in Huaihe River basin, China, Natural Hazards. 2015; 78(1): 635-651.
[13]. Arvind CS, Vanjare A, Omkar SN. Senthilnath J, Mani V. Flood Assessment using Multi-temporal Modis Satellite Images. Procedia Computer Science. 2016; 89: 575-586.
[14]. Das S. Geographic information system and AHP-based flood hazard zonation of Vaitarna basin in Maharashtra India. Arabian Journal of Geosciences. 2018; 11(576): 1-13.
[15]. Mishra K, Sinha R. Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach, Geomorphology. 2020; 350:1-13.
[16]. Tudose NC, Ungurean C, Davidescu CI, Marin M, Nita M D, Adorjani A, et al. Torrential flood risk assessment and environmentally friendly solutions for small catchments located in the Romania Natura 2000 sites Ciucas, Postavaru and Piatra Mare, Science of The Total Environment. 2020; 698: 1-16.
[17]. Ogato GS, Bantider A, Abebe K, Geneletti D. Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. Journal of Hydrology: Regional Studies. 2020; 27: 1-18.
[18]. Zhang D, Shi X, Xu H, Jing Q, Pan X, Liu T, et al. A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin, China. Environmental Impact Assessment Review. 2020; 83:1-13.
[19]. Wang Y, Fang Z, Hong H, Peng L. Flood susceptibility mapping using convolutional neural network frameworks, Journal of Hydrology. 2020; 582: 1-48.
[20]. Solaimani K, Habibnejad M. The role of hydro-climatic factors in flood occurrence of Nika watershed. Journal of Natural Resources. 2002; 55(1): 23-35. [Persian]
[21]. Solaimani K, Bashirgonbad M, Mousavi R, Khalighi Sh. Investigating the potential of flood production in watershed using HEC_HMS in Kasilian basin. Physical Geography Research. 2008; 65:51-60. [Persian]
[22]. Shaikhalishahi N, Jamali A, Hasanzadehnfoti M. Flood zoning using hydraulic model of river analysis (Case study: Manshad watershed - Yazd province). Geographical Space. 2016; 16(53): 77-96. [Persian]
[23]. Rostaimosavi R, Alizadehgorji R. Prepare a flood Nika watershed zoning map using the SCS-CN and GIS / RS models. Journal of Quantitative Geomorphological Research. 2017; 6(1): 108-118. [Persian]
[24]. Mahmudzadeh H, Bakoii M. Flood zoning using fuzzy logic (Case study: Sari city). Journal of Natural Environmental Hazards. 2018; 7(18): 51-68. [Persian]
[25]. Rad M, Vafakhah M, Qolamailfard M. Flood zoning using HEC-RAS hydrological model at the bottom of Khorramabad watershed. Journal of Natural Environmental Hazards. 2018; 7(6): 211-226 [Persian].
[26]. Rajabizaheh E, Aubzadeh A, Qamshi M. Investigation of floods in Khuzestan province during the water year 1397-1398 and providing strategies for controlling and managing it in the future. Journal of Eco hydrology. 2019; 6(4): 1069-1084. [Persian]
[27]. Jahangir M H, Mousavi Reineh M, Abolghasemi M. Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes. 2019; 25: 1-11.
[28]. Smith C, Scyphers S. Past hurricane damage and flood zone outweigh shoreline hardening for predicting residential-scale impacts of Hurricane Matthew. Environmental Science & Policy. 2019; 101: 46-53.
[29]. Iranian meteorological organization. The climate of Khuzestan province and precipitation data. Access date (2019/09/01). http://www.irimo.ir. [Persian]
[30]. Meteorological Organization of Khuzestan Province. Access date (2019/09/01). khuzestanmet.ir. [Persian]
[31]. Campbell J, Wynne RH. Introduction to Remote Sensing, 5th ed. New York: The Guilford; 2011.
[32]. Richards JA, Xiuping J. Remote sensing Digital Image Analysis, An Introduction, 4th ed. Berlin: Springer; 2006.
[33]. Moahedi S, Hedarinaserabad B, Hashimiana K, Ranjbar F. Climate zoning of Khuzestan province. Journal of geographic space. 2012; 12(4): 64-73. [Persian]
[34]. Aminiparsa V, Yavari A, Nejadi A. Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve of Iran. Modeling Earth Systems and Environment. 2016; 2(4): 1-13.
[35]. Pal S, Ziaul S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science. 2017; 20(1): 125-145.
[36]. Dube T, Gumindoga W, Chawira M. Detection of land cover changes around LakeMutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. African Journal of Aquatic Science. 2014; 39(1): 89-95.
[37]. Mather P, Brandt T. Classification methods for remotely sensed Data. 2nd ed. London: Taylor& Francis; 2009.
[38]. Deng Y, Fan F, Chen R. Extraction and Analysis of Impervious Surfaces Based on a Spectral Un-Mixing Method Using Pearl River Delta of China Landsat TM/ETM+ Imagery from 1998 to 2008. journal of Sensors. 2012; 12: 1846-1862.
[39]. Sarp G, Ozcelik M. Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science. 2017; 11(3): 381-391.
[40]. Gautam VK, Gaurav KP, Murugan P, Annadurai M. Assessment of Surface Water Dynamicsin Bangalore using WRI NDWI MNDWI Supervised Classification and K-T Transformation. Aquatic Procedia. 2014; 4:739-746
[41]. Ceccato P, Flasse S, Tarantola S, Jacquemond S, Gregoire JM. Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment. 2001; 77: 22-33.
[42]. Xu H. Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing. 2006; 27:3025-3033.
[43]. Zhang FF, Li J, Shen Q, Zhang B, Ye H, Wang SH, et al. Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images, earth sciences. Environmental sciences. 2016; 1:1- 18.
[44]. Zhai K, Wu X, Yuanwei Q, Du P. Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. journal of Geo-spatial Information Science. 2015; 18: 32-42
[45]. Yang J, Du X. An enhanced water index in extracting water bodies from Landsat TM imagery. Annals of GIS. 2017; 23(3):141-148
[46]. Darvishi SH, Solaimani K, Rashidpour M. The effect of vegetation index and urban characteristics on land temperature changes in Sanandaj city. Journal of RS and GIS for Natural Resources. 2019: 10(1): 17-35. [Persian]
[47]. Smits PC, Dellepiane SG, Schowengerdt RA. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing. 1999; 20(8): 1461-1486.
[48]. Alavipanah K, Matenfar H, Rafieiemam A. Application of Information Technology in Earth Sciences (Digital Soil Science). 1th ed. Tehran: University of Tehran publications; 2008. [Persian]
[49]. Fatemi B, Rizaei E. Fundamentals of remote sensing. 2en ed. Tehran: Azadeh publications; 2010. [Persian]
[50]. Solaimani K, Darvishi Sh, Shokrian F. Analysis of agriculture drought using remote sensing index in Marivan city. Journal of RS and GIS for Natural Resources. 2019; 10(2): 15-33. [Persian]
[51]. Allam M, Bakr N, Elbably W. Multi-temporal assessment of land use/land cover change in arid region based on landsat satellite imagery in Fayoum Region of Egypt. Remote Sensing Applications Society and Environment. 2019;14: 8-19.
[52]. Manandhar R, Odeh IOA, Ancev T. Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote sensing. 2009;1(3): 330-344. [53]. Hishe S, Bewket W, Nyssen J, Lyimo J. Analyzing past land use land cover change and CA-Markov based future modeling in the Middle Suluh Valley in Northern Ethiopia. Geocarto International. 2020; 35(3): 225-255 | ||
|
آمار تعداد مشاهده مقاله: 1,122 تعداد دریافت فایل اصل مقاله: 792 |
||