ارزیابی تأثیر ادغام تصاویر ماهواره‌های لندست-8 و سنتینل-2 در برآورد پهنه‌های سیلابی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی و مدیریت منابع آب، دانشکده مهندسی عمران، دانشگاه صنعتی شاهرود، ایران.

10.22059/jwim.2024.368243.1116

چکیده

پایش دقیق آب­های سطحی یکی از کاربردهای مهم و ضروری در استفاده از سیستم­های سنجش از راه دور است. برآوردن نیازهای مطرح‌شده در استفاده از داده‌های سنجش از دور برداشت‌شده از سطح زمین در بسیاری از کاربردها، تنها با استفاده از یک محصول و الگوریتم طبقه­بندی‌کننده کافی و ممکن نیست و برای درک دقیق‌تر، ادغام داده­ها می‌تواند گزینه بهتری باشد. لذا در این پژوهش از رویکردهای مختلفی همچون به‌کارگیری تصاویر دو سنجنده، شاخص­های استخراج آب و الگوریتم­های طبقه­بندی جهت شناسایی پهنه‌های آبی استفاده گردید. در این راستا ابتدا تصاویر سنجندهای نوری لندست-8 و سنتینل-2 با یکدیگر ادغام شدند که در نتیجه آن وضوح مکانی این سنجنده­ها با حفظ اطلاعات طیفی، از 30 به 10 متر ارتقا یافت. سپس شاخص­های استخراج آب همچون (NDWI, MNDWI, AWEI_sh, AWEI_nsh, WI) بر تصاویر ادغام­شده اعمال شد و پس از ترکیب آن با تصاویر اصلی ماهواره­های منتخب، با استفاده از الگوریتم­های طبقه­بندی (SVM, Maximum Likelihood, Minimum Distance, Neural Network, Random Forest) محدوده مطالعاتی به دو دسته پهنه‌های آبی و غیرآبی طبقه­بندی شد و در نهایت با استفاده از روش حداکثر رأی­گیری که از رویکردهای ادغام در سطح تصمیم­گیری محسوب می­شود نتایج حاصل از تمام الگوریتم­های طبقه­بندی برای تصاویر قبل و بعد از سیلاب استان مازندران در واقعه سیلاب سال 1398 شمسی با یکدیگر ادغام شدند. الگوریتم طبقه­بندی جنگل تصادفی با دقت کلی 76/97 و 12/94 و ضریب کاپا 49/94 و 41/91 برای تصاویر قبل و پس از سیلاب بهترین عملکرد طبقه­بندی در بین الگوریتم­های مورداستفاده در این پژوهش را داشت. ادغام الگوریتم­های طبقه­بندی نشان از بهبود عملکرد تفکیک پهنه‌های آبی و غیرآبی با افزایش دقت کلی تفکیک به 41/98 و 24/95 و ضریب کاپا 12/96 و 81/92 برای تصاویر قبل و پس از سیلاب داشت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of the Impact of Image Fusion of Landsat 8 and Sentinel 2 Satellites on Flood Zone Estimation

نویسندگان [English]

  • ashkan banikhedmat
  • Behnaz Bigdeli
  • seyed fazlollah seyed fazlollah
Water Resources Engineering and Management, Faculty of Civil Engineering, Shahrood university of technology, Shahrood, Iran.
چکیده [English]

Accurate monitoring of surface water is one of the important and necessary applications in the use of remote sensing systems. Meeting the needs raised in the use of remote sensing data collected from the earth's surface in many applications, using only one product and classification algorithm is not sufficient and possible, and for a more accurate understanding, data fusion can be a better option. In this system, various approaches such as water extraction indices or classification algorithms are used to identify water areas. In this research, an fusion approach of Landsat-8 and Sentinel-2 optical sensor images was used. Firstly, the spatial resolution of these sensors was enhanced from 30 to 10 meters by Pansharpening them and preserving spectral information. Then, water extraction indices such as NDWI, MNDWI, AWEI_sh, AWEI_nsh, and WI were applied to the integrated images. Subsequently, using classification algorithms such as SVM, Maximum Likelihood, Minimum Distance, Neural Network, and Random Forest, the study area was classified into two categories of water and non-water areas. Finally, the results obtained from all classification algorithms for pre and post-flood images of Mazandaran province in the 2019 flood event were merged using the majority voting method, which is considered an integration approach at the decision-making level. Random forest classification algorithm with overall accuracy of 97.76 and 94.12 and Kappa coefficient 94.49 and 91.41 for images before and after flood had the best classification performance among the algorithms used in this research. The fusion of classification algorithms showed an improvement in the separation performance of water and non-water areas with an increase in the overall accuracy of separation to 98.41 and 95.24 and Kappa coefficient 96.12 and 92.81 for the images before and after the flood.

کلیدواژه‌ها [English]

  • Classification algorithms
  • Image fusion
  • Majority voting method
  • Optical sensor
  • Water extraction indices

مراجع

  1. Acharya, T. D., Subedi, A., Yang, I. T., & Lee, D. H. (2018). Combining Water Indices for Water and Background Threshold in Landsat Image. Proceedings, 2, 143-149.
  2. Al-Juaidi, A. E. M., Nassar, A.M., & Al-Juaidi, O.E.M. (2018). Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci, 11, 765, 1-10.
  3. Atkinson, P. M., Jeganathan, C., Dash, J., & Atzberger, C. (2012). Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123, 400-417.
  4. Bhatt, C.M., Rao, G.S., Farooq, M., Manjusree, P., Shukla, A., & Sharma, S.V.S.P. (2017) Satellite-Based Assessment of the Catastrophic Jhelum Floods of September 2014, Jammu & Kashmir, India. Journal of Geomatics, Natural Hazards and Risk, 8, 309-327.
  5. Breiman, L. 1996. Bagging predictors. Machine Learning, 26, 123-140.
  6. Nielsen, M. A. (2015). Neural Networks and Deep Learning, Vol. 2018, Determination Press, San Francisco, California.
  7. Breiman, L. 2001. Random forests. Machine Learning, 45, 5-32.
  8. Drusch, M & et al. (2012). “Sentinel-2: ESA’s optical high-resolution mission for GMES operational services,” Remote Sensing of Environment, 120, 25-36.
  9. ERDAS, “Field Guide,” 5th Edition, ERDAS, Inc., Atlanta, 1999.
  10. Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment. 140, 23- 35.
  11. Fisher, A., Flood, N., & Danaher, T. (2016). Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment. 175, 167- 182.
  12. Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., & Yang, Y. (2015). Fusing Landsat and MODIS Data for Vegetation Monitoring. IEEE Geoscience and Remote Sensing Magazine. 3, 47- 60.
  13. Ghassemian, H. (2016). A Review of Remote Sensing Image Fusion Methods. Information Fusion, 32, 75-89.
  14. Guvel, S. P., Akgul, M. A., Aksu, H. (2022). Flood inundation maps using Sentinel-2: a case study in Berdan Plain. Water Supply, 22 (4), 4098–4108.
  15. Jiang, W., Ni, Y., Pang, Z., He, G., Fu, J., Lu, J., Yang, K., Long, T., & Lei, T. (2020). A new index for identifying water body from sentinel-2 satellite remote sensing imagery, ISPRS Annals. Photogramm. Remote Sensing, 3, 33-38.
  16. Jensen, V. (2014). Remote sensing of the environment: An earth resource perspective. Prentice-Hall, Inc. 2, 1-10.
  17. Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards 83, 947–987.
  18. Klemas, V. (2015). Remote Sensing of Floods and Flood-Prone Areas: An Overview. Journal of Coastal Research, 31, 1005-1013.
  19. Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons. publication, Hoboken, New jersey. canada.
  20. Kuncheva, L., & Whitaker, C. J. (2003). Measures of diversity in classifier ensemble and their relationship with the ensemble accuracy, Machine Learning, 51, 181- 207.
  21. Mather, P., & Tso, B. (2009). Classification Methods for Remotely Sensed Data. CRC Press, Boca Raton.
  22. Mcfeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425-1432.
  23. Nandi, I., Srivastava, P. K., & Shah, K. (2017). Floodplain Mapping through Support Vector Machine
  24. and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi. Water Resource Manage, 1568, 1-15.
  25. Ning, F. S., & Lee, Y. C. (2021). Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan. Water, 13, 2774-2791.
  26. Ogadhawara, I., Curtarelli, M. P., & Ferreira, C. M. (2013). The use of optical remote sensing for mapping flooded areas. Journsl of Engineering Research and Application, 3, 1956-1960.
  27. Otukei, J. R., & Blaschke, T. (2010). Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, 27-S31.
  28. Pandit, V., & Bhiwani, R. J. (2015). Image Fusion in Remote Sensing Applications: A Review.  International Journal of Computer Applications 120, 22-32.
  29. Richards, J. A. (2006). Remote Sensing Digital Image Analysis. Springer.
  30. Samadzadegan, F., Tabibmahmoudi, F., & Bigdeli, B. (2015). Data fusion in remote sensing: theory and methods: In Persian.
  31. Sanyal, J., & Lu, X.X. (2004). Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review. Natural Hazards, 33, 283-301.
  32. Schumann, G. J. P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, V83-84, 84-95.
  33. Sghaier, M. O., Hammami, I., Foucher, S., & Lepage, R. (2018). Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion. Remote Sens, 10, 237, 1-30.
  34. Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A., Bahrami, S., & et al. (2020). Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing, 12, 266, 1- 30.
  35. Sigurdsson, J., Armannsson, S.E., Ulfarsson, S.E., & Sveinsson, J.R. (2022). Fusing Sentinel-2 and Landsat 8 Satellite Images Using a Model-Based Method. Remote Sensing, 14(13), 3224.
  36. Tavus, B., Kocaman, S., Nefeslioghlu, H. A., & Gokceoglu, C. (2020). A Fusion approach for flood mapping using sentinel-1 and sentinel-2 datasets. Int. Arch. Photogramm. Remote Sensing. Spatial Inf. Sci., XLIII-B3, 641-648.
  37. Terry, A., Samuel, G., John, G., & Darrel, W. (2006). Landsat-7 Long-Term Acquisition Plan. Photogrammetric Engineering & Remote Sensing, 10, 1137-1146.
  38. Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V.P., Chapi, K., Shirzadi, A., Panahi, S., Chen, W., & et al. (2018). New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling. Water, 10, 1210, 1-28.
  39. Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A.M., Thai Pham, B., Pourghasemi, H.R., Mahmoudi, M., Bahrami, S., & et al. (2019). Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing, 11, 1589, 1-27.
  40. Vapnik, V. (1979). Estimation of dependences based on empirical data [in Russian]. Nauka, Moscow. (English translation: Springer-Verlag, New York).
  41. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
  42. Wang, Q., Blackburn, G. A., Onojeghuo, A. O., Dash, J., Zhou, L., Zhang, Y., & Atkinson, P.M. (2017). Fusion of Landsat 8 OLI and Sentinel-2 MSI Data. IEEE Trans. Geosci. Remote Sens, 55, 3885-3899.
  43. H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing. 27, 3025- 3033.
  44. Yu, J. J., Qin, X. S., & Larsen, O. (2012). Joint Monte Carlo and possibilistic simulation for flooddamage assessment. Stoch Environ Res Risk Assess, 27(3), 1-12.
  45. Zhang, H., Zhang, Y., Gao, T., Lan, Sh., Tong, F., & Li, M. (2023). Landsat-8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes. Remote Sensing, 15(11), 2951.
  46. Zhu, Z., Wang, S., & Woodcock, C. E. (2015). Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159, 269- 277.
مدیریت آب و آبیاری
دوره 14، شماره 2
مهر 1403
صفحه 421-438

فایل ها

هم رسانی

ارجاع به این مقاله

آمار