Research Article
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Year 2023, , 64 - 76, 26.12.2023
https://doi.org/10.30897/ijegeo.1348753

Abstract

References

  • Akinyemi, F. O. (2005). Mapping land use dynamics at a regional scale in southwestern Nigeria.
  • Ali, M. Z., Qazi, W., Aslam, N. (2018). A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. The Egyptian Journal of Remote Sensing and Space Science, 21, S29-S35.
  • Alvan Romero, N., Cigna, F., Tapete, D. (2020). ERS-1/2 and Sentinel-1 SAR data mining for flood hazard and risk assessment in Lima, Peru. Applied Sciences, 10(18), 6598.
  • Bangira, T., Iannini, L., Menenti, M., Van Niekerk, A., Vekerdy, Z. (2021). Flood extent mapping in the Caprivi floodplain using sentinel-1 time series. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 5667-5683.
  • Benzougagh, B., Frison, P. L., Meshram, S. G., Boudad, L., Dridri, A., Sadkaoui, D., ..., Khedher, K. M. (2021). Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-10.
  • Carreño Conde, F., De Mata Muñoz, M. (2019). Flood monitoring based on the study of Sentinel-1 SAR images: The Ebro River case study. Water, 11(12), 2454.
  • Clement, M. A., Kilsby, C. G., Moore, P. (2018). Multi‐temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152-168.
  • Cloke, H., Pappenberger, F. (2009). Ensemble flood forecasting: A review. Journal of Hydrology, 375(34), 613- 626.
  • Dumitru, C. O., Cui, S., Faur, D., Datcu, M. (2014). Data analytics for rapid mapping: Case study of a flooding event in Germany and the tsunami in Japan using very high resolution SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 114-129.
  • Farina, G., Bernini, A., Alvisi, S., Franchini, M. (2018). Preliminary GIS elaborations to apply rapid flood spreading models. EPiC Series in Engineering, 3, 684-691.
  • Ganji, K., Gharachelou, S., Ahmadi, A. (2019). Urban’s river flood analysing using Sentinel-1 data case study:(Gorganrood, Aq’qala). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 415-419.
  • Gebeyehu, A. (1989). Regional Flood Frequency Analysis. Hydraulics Laboratory, The Royal Institute of Technology, Stockholm. Bulletin No. TRITA-AVI-148.
  • Henry, J. B., Chastanet, P., Fellah, K., Desnos, Y. L. (2006). Envisat multi‐polarized ASAR data for flood mapping. International Journal of Remote Sensing, 27(10), 1921-1929.
  • Isiaka, I. O., Gafar, S., Ajadi, S. A., Mukaila, I., Ndukwe, K. O., Mustapha, S. O. (2023). Flood Susceptibility Assessment of Lagos State, Nigeria using Geographical Information System (GIS)-based Frequency Ratio Model. International Journal of Environment and Geoinformatics, 10(1), 76-89.
  • Li, Y., Martinis, S., Plank, S., Ludwig, R. (2018). An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. International Journal Of Applied Earth Observation And Geoinformation, 73, 123-135.
  • Mason, D. C., Schumann, G. P., Neal, J. C., Garcia-Pintado, J., Bates, P. D. (2012). Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study. Remote Sensing of Environment, 124, 705-716.
  • Mata, C. B., Balderama, O. F., Alejo, L. A., Bareng, J. L. R., Kantoush, S. A. (2022). Satellite-based flood inundation and damage assessment.
  • Menteş, E. N., Şinasi, K. A. Y. A., Tanik, A., Gazioğlu, C. (2019). Calculation of flood risk index for Yesilirmak Basin-Turkey. International Journal of Environment and Geoinformatics, 6(3), 288-299.
  • Moazzam, M. F. U., Vansarochana, A., Rahman, A. U. (2018). Analysis of flood susceptibility and zonation for risk management using frequency ratio model in District Charsadda, Pakistan. International Journal of Environment and Geoinformatics, 5(2), 140-153.
  • Mohammad Muqtada Ali Khan., Nor Ashikin Shaari., Arham Muchtar Achmad Nahar., Md. Azizul Baten., Dony Adriansyah Nazaruddin. (2014). Flood impact assessment in Kota Bharu, Malaysia: a statistical analysis. World Applied Sciences Journal, 32(4), 626-634.
  • Moharrami, M., Javanbakht, M., Attarchi, S. (2021). Automatic flood detection using sentinel- 1 images on the google earth engine. Environmental Monitoring and Assessment, 193, 1-17.
  • Nguyen, T. H., Ricci, S., Piacentini, A., Fatras, C., Kettig, P., Blanchet, G., ..., Baillarin, S. (2023). Assimilation of SAR-derived flood extent observations for improving fluvial flood forecast–A proof-of-concept. In IOP Conference Series: Earth and Environmental Science (Vol. 1136, No. 1, p. 012018). IOP Publishing.
  • Norovsuren, B., Tseveen, B., Batomunkuev, V., Renchin, T., Natsagdorj, E., Yangiv, A., Mart, Z. (2019, November). Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia. In IOP Conference Series: Earth and Environmental Science (Vol. 381, No. 1, p. 012054). IOP Publishing.
  • Ojigi, L. M. (2006). Analysis of spatial variations of Abuja land use and land cover from image classification algorithms. In Symposium Remote Sensing: From Pixel to Processes, Enschede, Netherlands (p. 6).
  • Osayomi, T., Jnr, P. O., Ogunwumi, T., Fatayo, O. C., Akpoterai, L. E., Mshelia, Z. H., Abatcha, I. U. (2022). “I lost all I had to the flood…”: A Post-Disaster Assessment of the 2018 Kogi State Flood in Nigeria. Ife Social Sciences Review, 30(2), 1-20.
  • Oyedele, P., Kola, E., Olorunfemi, F., Walz, Y. (2022). Understanding flood vulnerability in local communities of Kogi State, Nigeria, using an index-based approach. Water, 14(17), 2746.
  • Ozulu, G., Essien, G. P., Akudo, E. O. (2021). Geological and Geospatial Mapping of Vulnerability Areas for Proper Flood Mitigation: Ganaja, Lokoja Metropolis, North-Central Nigeria. International Journal of Environment and Geoinformatics, 8(3), 267-275.
  • Perrou, T., Garioud, A., Parcharidis, I. (2018). Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin. Frontiers of Earth Science, 12, 506-520.
  • Psomiadis, E. (2016, October). Flash flood area mapping utilising SENTINEL-1 radar data. In Earth resources and environmental remote sensing/GIS applications VII (Vol. 10005, pp. 382-392). SPIE.
  • Qiu, J., Cao, B., Park, E., Yang, X., Zhang, W., Tarolli, P. (2021). Flood monitoring in rural areas of the Pearl River Basin (China) using Sentinel-1 SAR. Remote Sensing, 13(7), 1384.
  • Richards, J. A., Jia, X. (2006). Image classification methodologies. Remote sensing digital image analysis: An introduction, 295-332.
  • Scheffran, J., Link, P. M., Schilling, J. (2019). Climate and conflict in Africa. In Oxford Research Encyclopedia of Climate Science.
  • Twele, A., Cao, W., Plank, S., Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990-3004.
  • Vishnu, C. L., Sajinkumar, K. S., Oommen, T., Coffman, R. A., Thrivikramji, K. P., Rani, V. R., Keerthy, S. (2019). Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomatics, Natural Hazards and Risk, 10(1), 758-767.

Flood Impact Assessment in Koton Karfe Using Sentinel-1 Synthetic Aperture Radar (SAR) Data

Year 2023, , 64 - 76, 26.12.2023
https://doi.org/10.30897/ijegeo.1348753

Abstract

Flood has proven to be an incessant menace in Nigeria more threatening to riverine areas. The most recent flood ensued in 2022 as a result of heavy rainfall and the release of water from Lagdo Dam in Cameroon which became very devastating in many areas notably the Koton Karfe area in Kogi State, causing business shutdowns and the loss of lives and properties. In this work, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was used for flood inundation mapping, and the accompanying damages were investigated using Landsat derived Land cover maps of Koton Karfe during the 2022 devastating flood. Overall, the results obtained in this study show that the regions that felt the impact of the flood the most were the southern and western areas, which must have experienced such an impact due to their proximity to the rivers Niger and Benue and also the water coming from the upper stream part of Cameroon. Further findings revealed that during the flood period on October 13, 2022, the total inundated area in Koton Karfe was estimated to be 198.255 sq. km. In terms of damage assessment, the urban areas had reduced from 220.902 sq. km in May 2022 to 87.473 sq. km in November 2022. This shows that over 133 sq. km of the urban settlement have been lost, indicating that lives must have been lost, properties too, and humans must have been displaced. This research will assist in the space of flood emergency response and disaster management.

References

  • Akinyemi, F. O. (2005). Mapping land use dynamics at a regional scale in southwestern Nigeria.
  • Ali, M. Z., Qazi, W., Aslam, N. (2018). A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. The Egyptian Journal of Remote Sensing and Space Science, 21, S29-S35.
  • Alvan Romero, N., Cigna, F., Tapete, D. (2020). ERS-1/2 and Sentinel-1 SAR data mining for flood hazard and risk assessment in Lima, Peru. Applied Sciences, 10(18), 6598.
  • Bangira, T., Iannini, L., Menenti, M., Van Niekerk, A., Vekerdy, Z. (2021). Flood extent mapping in the Caprivi floodplain using sentinel-1 time series. IEEE Journal of selected topics in applied earth observations and remote sensing, 14, 5667-5683.
  • Benzougagh, B., Frison, P. L., Meshram, S. G., Boudad, L., Dridri, A., Sadkaoui, D., ..., Khedher, K. M. (2021). Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-10.
  • Carreño Conde, F., De Mata Muñoz, M. (2019). Flood monitoring based on the study of Sentinel-1 SAR images: The Ebro River case study. Water, 11(12), 2454.
  • Clement, M. A., Kilsby, C. G., Moore, P. (2018). Multi‐temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152-168.
  • Cloke, H., Pappenberger, F. (2009). Ensemble flood forecasting: A review. Journal of Hydrology, 375(34), 613- 626.
  • Dumitru, C. O., Cui, S., Faur, D., Datcu, M. (2014). Data analytics for rapid mapping: Case study of a flooding event in Germany and the tsunami in Japan using very high resolution SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 114-129.
  • Farina, G., Bernini, A., Alvisi, S., Franchini, M. (2018). Preliminary GIS elaborations to apply rapid flood spreading models. EPiC Series in Engineering, 3, 684-691.
  • Ganji, K., Gharachelou, S., Ahmadi, A. (2019). Urban’s river flood analysing using Sentinel-1 data case study:(Gorganrood, Aq’qala). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 415-419.
  • Gebeyehu, A. (1989). Regional Flood Frequency Analysis. Hydraulics Laboratory, The Royal Institute of Technology, Stockholm. Bulletin No. TRITA-AVI-148.
  • Henry, J. B., Chastanet, P., Fellah, K., Desnos, Y. L. (2006). Envisat multi‐polarized ASAR data for flood mapping. International Journal of Remote Sensing, 27(10), 1921-1929.
  • Isiaka, I. O., Gafar, S., Ajadi, S. A., Mukaila, I., Ndukwe, K. O., Mustapha, S. O. (2023). Flood Susceptibility Assessment of Lagos State, Nigeria using Geographical Information System (GIS)-based Frequency Ratio Model. International Journal of Environment and Geoinformatics, 10(1), 76-89.
  • Li, Y., Martinis, S., Plank, S., Ludwig, R. (2018). An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. International Journal Of Applied Earth Observation And Geoinformation, 73, 123-135.
  • Mason, D. C., Schumann, G. P., Neal, J. C., Garcia-Pintado, J., Bates, P. D. (2012). Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study. Remote Sensing of Environment, 124, 705-716.
  • Mata, C. B., Balderama, O. F., Alejo, L. A., Bareng, J. L. R., Kantoush, S. A. (2022). Satellite-based flood inundation and damage assessment.
  • Menteş, E. N., Şinasi, K. A. Y. A., Tanik, A., Gazioğlu, C. (2019). Calculation of flood risk index for Yesilirmak Basin-Turkey. International Journal of Environment and Geoinformatics, 6(3), 288-299.
  • Moazzam, M. F. U., Vansarochana, A., Rahman, A. U. (2018). Analysis of flood susceptibility and zonation for risk management using frequency ratio model in District Charsadda, Pakistan. International Journal of Environment and Geoinformatics, 5(2), 140-153.
  • Mohammad Muqtada Ali Khan., Nor Ashikin Shaari., Arham Muchtar Achmad Nahar., Md. Azizul Baten., Dony Adriansyah Nazaruddin. (2014). Flood impact assessment in Kota Bharu, Malaysia: a statistical analysis. World Applied Sciences Journal, 32(4), 626-634.
  • Moharrami, M., Javanbakht, M., Attarchi, S. (2021). Automatic flood detection using sentinel- 1 images on the google earth engine. Environmental Monitoring and Assessment, 193, 1-17.
  • Nguyen, T. H., Ricci, S., Piacentini, A., Fatras, C., Kettig, P., Blanchet, G., ..., Baillarin, S. (2023). Assimilation of SAR-derived flood extent observations for improving fluvial flood forecast–A proof-of-concept. In IOP Conference Series: Earth and Environmental Science (Vol. 1136, No. 1, p. 012018). IOP Publishing.
  • Norovsuren, B., Tseveen, B., Batomunkuev, V., Renchin, T., Natsagdorj, E., Yangiv, A., Mart, Z. (2019, November). Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia. In IOP Conference Series: Earth and Environmental Science (Vol. 381, No. 1, p. 012054). IOP Publishing.
  • Ojigi, L. M. (2006). Analysis of spatial variations of Abuja land use and land cover from image classification algorithms. In Symposium Remote Sensing: From Pixel to Processes, Enschede, Netherlands (p. 6).
  • Osayomi, T., Jnr, P. O., Ogunwumi, T., Fatayo, O. C., Akpoterai, L. E., Mshelia, Z. H., Abatcha, I. U. (2022). “I lost all I had to the flood…”: A Post-Disaster Assessment of the 2018 Kogi State Flood in Nigeria. Ife Social Sciences Review, 30(2), 1-20.
  • Oyedele, P., Kola, E., Olorunfemi, F., Walz, Y. (2022). Understanding flood vulnerability in local communities of Kogi State, Nigeria, using an index-based approach. Water, 14(17), 2746.
  • Ozulu, G., Essien, G. P., Akudo, E. O. (2021). Geological and Geospatial Mapping of Vulnerability Areas for Proper Flood Mitigation: Ganaja, Lokoja Metropolis, North-Central Nigeria. International Journal of Environment and Geoinformatics, 8(3), 267-275.
  • Perrou, T., Garioud, A., Parcharidis, I. (2018). Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin. Frontiers of Earth Science, 12, 506-520.
  • Psomiadis, E. (2016, October). Flash flood area mapping utilising SENTINEL-1 radar data. In Earth resources and environmental remote sensing/GIS applications VII (Vol. 10005, pp. 382-392). SPIE.
  • Qiu, J., Cao, B., Park, E., Yang, X., Zhang, W., Tarolli, P. (2021). Flood monitoring in rural areas of the Pearl River Basin (China) using Sentinel-1 SAR. Remote Sensing, 13(7), 1384.
  • Richards, J. A., Jia, X. (2006). Image classification methodologies. Remote sensing digital image analysis: An introduction, 295-332.
  • Scheffran, J., Link, P. M., Schilling, J. (2019). Climate and conflict in Africa. In Oxford Research Encyclopedia of Climate Science.
  • Twele, A., Cao, W., Plank, S., Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990-3004.
  • Vishnu, C. L., Sajinkumar, K. S., Oommen, T., Coffman, R. A., Thrivikramji, K. P., Rani, V. R., Keerthy, S. (2019). Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomatics, Natural Hazards and Risk, 10(1), 758-767.
There are 34 citations in total.

Details

Primary Language English
Subjects Water Resources and Water Structures, Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Ibrahim Opeyemi Isiaka 0000-0002-4420-0261

Sodiq Abayomi Ajadi 0000-0003-3275-1100

Sodiq Ayobami Arowolo 0009-0003-3804-923X

Suebat Oluwakemi Mustapha 0000-0002-1060-4209

Kingsley Odinakachukwu Ndukwe 0000-0002-8703-1357

Christian Chibuike Oluoma 0009-0003-8382-3263

Early Pub Date December 1, 2023
Publication Date December 26, 2023
Published in Issue Year 2023

Cite

APA Isiaka, I. O., Ajadi, S. A., Arowolo, S. A., Mustapha, S. O., et al. (2023). Flood Impact Assessment in Koton Karfe Using Sentinel-1 Synthetic Aperture Radar (SAR) Data. International Journal of Environment and Geoinformatics, 10(4), 64-76. https://doi.org/10.30897/ijegeo.1348753