Authors :
Ogechukwu Blessing Okereke; Adeyemi Abejoye; Prince Alex Ekhorutomwen; Amina Catherine Peter-Anyebe
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/j85fjap4
Scribd :
https://tinyurl.com/2fxu556m
DOI :
https://doi.org/10.38124/ijisrt/25apr1627
Google Scholar
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Abstract :
Wetland ecosystems play a vital role in maintaining global biodiversity, hydrological stability, and carbon
sequestration. However, these ecologically sensitive areas are increasingly threatened by climate-induced flooding,
anthropogenic disturbances, and habitat degradation. Synthetic Aperture Radar (SAR) technology has emerged as a
powerful remote sensing tool for real-time, all-weather flood detection, offering high-resolution imagery critical for wetland
monitoring and adaptive ecosystem management. This review explores the application of SAR-driven flood detection
systems in tracking water level fluctuations and inundation patterns within wetlands and evaluates their implications for
migratory bird habitat conservation. Emphasis is placed on SAR’s capability to penetrate cloud cover and detect changes
in surface moisture, which enhances early flood warning systems and informs decision-making for habitat protection. The
paper also investigates case studies where SAR data have been integrated into conservation planning, emphasizing
spatiotemporal analysis for managing seasonal wetlands that serve as critical stopover or breeding sites for migratory birds.
By highlighting technological advancements, methodological approaches, and interdisciplinary frameworks, the review
highlights the potential of SAR to support resilient wetland management strategies that align with global conservation goals.
Keywords :
Synthetic Aperture Radar (SAR), Flood Detection, Wetland Ecosystems, Migratory Birds, Habitat Management.
References :
- Adeli, S., Salehi, B., Mahdianpari, M., Quackenbush, L. J., Brisco, B., Tamiminia, H., & Shaw, S. (2020). Wetland monitoring using SAR data: A meta-analysis and comprehensive review. Remote Sensing, 12(14), 2190. https://doi.org/10.3390/rs12142190MDPI
- Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirmazloumi, S. M., ... & Gill, E. (2020). Wetland monitoring using synthetic aperture radar data: A review. Remote Sensing, 12(14), 2190. https://doi.org/10.3390/rs12142190
- Ayat, H., Evans, J. P., & Behrangi, A. (2021). How do different sensors impact IMERG precipitation estimates during hurricane days?. Remote Sensing of Environment, 259, 112417.
- Bamler, R., & Hartl, P. (1998). Synthetic aperture radar interferometry. Inverse Problems, 14(4), R1–R54. https://doi.org/10.1088/0266-5611/14/4/001:contentReference[oaicite:1]{index=1}
- Curlander, J. C., & McDonough, R. N. (1991). Synthetic aperture radar: Systems and signal processing. John Wiley & Sons.
- Dabboor, M. & Brisco, B. (2018). Wetland Monitoring and Mapping Using Synthetic Aperture Radar. Retrieved from: https://www.intechopen.com/chapters/63701
- Danielsen, F., Burgess, N. D., Jensen, P. M., & Pirhofer-Walzl, K. (2010). Environmental monitoring: The scale and speed of implementation varies according to the degree of people’s involvement. Journal of Applied Ecology, 47(6), 1166–1168. https://doi.org/10.1111/j.1365-2664.2010.01874.x
- Davidson, N. C. (2014). How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research, 65(10), 934–941. https://doi.org/10.1071/MF14173
- Deng, Q., Zhang, X., Zhang, L., Shao, X., & Gu, T. (2024). The impact mechanism of human activities on the evolution of coastal wetlands in the Liaohe River Delta. Frontiers in Ecology and Evolution, 12, 1423234. https://doi.org/10.3389/fevo.2024.1423234Frontiers
- Derksen, C., Xu, X., Dunbar, R. S., Colliander, A., Kim, Y., Kimball, J. S., ... & Stephens, J. (2017). Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements. Remote Sensing of Environment, 194, 48-62.
- Dralle, D. N., Rossi, G., Georgakakos, P., Hahm, W. J., Rempe, D. M., Blanchard, M. R., Power, M., Dietrich, W. E., Carison, S. M. (2023). The salmonid and the subsurface: Hillslope storage capacity determines the quality and distribution of fish habitat. Retrieved from: https://www.researchgate.net/figure/Seasonal-hydrological-dynamics-between-hillslopes-representing-two-dominant-geologies-in_fig2_368692308
- Enyejo, J. O., Adeyemi, A. F., Olola, T. M., Igba, E & Obani, O. Q. (2024). Resilience in supply chains: How technology is helping USA companies navigate disruptions. Magna Scientia Advanced Research and Reviews, 2024, 11(02), 261–277. https://doi.org/10.30574/msarr.2024.11.2.0129
- Enyejo, J. O., Balogun, T. K., Klu, E. Ahmadu, E. O., & Olola, T. M. (2024). The Intersection of Traumatic Brain Injury, Substance Abuse, and Mental Health Disorders in Incarcerated Women Addressing Intergenerational Trauma through Neuropsychological Rehabilitation. American Journal of Human Psychology (AJHP). Volume 2 Issue 1, Year 2024 ISSN: 2994-8878 (Online). https://journals.e-palli.com/home/index.php/ajhp/article/view/383
- Ficetola, G. F., Taberlet, P., & Coissac, E. (2016). How to limit false positives in environmental DNA and metabarcoding? Molecular Ecology Resources, 16(3), 604–607. https://doi.org/10.1111/1755-0998.12401Wikipedia
- Finlayson, C. M., & Spiers, A. G. (2018). Global review of wetland resources and priorities for wetland inventory. Marine and Freshwater Research, 69(10), 1521–1534. https://doi.org/10.1071/MF18055
- Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer, S., & Matgen, P. (2016). Probabilistic flood mapping using synthetic aperture radar data. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 6958-6969.
- Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
- Hauduc, H., Neumann, M. B., Muschalla, D., Gamerith, V., Gillot, S., & Vanrolleghem, P. A. (2015). Efficiency criteria for environmental model quality assessment: A review and its application to wastewater treatment. Environmental Modelling & Software, 68, 196-204.
- Hess, L. L., Melack, J. M., & Simonett, D. S. (1990). Radar detection of flooding beneath the forest canopy: A review. International Journal of Remote Sensing, 11(7), 1313–1325. https://doi.org/10.1080/01431169008955090
- Huang, Z., Zhang, X., Tang, Z., Xu, F., Datcu, M., & Han, J. (2024). Generative artificial intelligence meets synthetic aperture radar: A survey. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–25. https://doi.org/10.1109/TGRS.2024.1234567
- Idowu, O. S., Ayoola, V. O., Adegbola, F. & Adeyeye, Y. (2024). Air, Water, and Soil Microbiomes as Catalysts for Smart Agriculture, Urban Ecosystem Revitalization, Climate Adaptation, and Public Health Advancements. International Journal of Scientific Research and Modern Technology (IJSRMT) Volume 3, Issue 5, 2024 DOI:https://doi.org/10.5281/zenodo.1487445
- Igba E., Ihimoyan, M. K., Awotinwo, B., & Apampa, A. K. (2024). Integrating BERT, GPT, Prophet Algorithm, and Finance Investment Strategies for Enhanced Predictive Modeling and Trend Analysis in Blockchain Technology. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., November-December-2024, 10 (6) : 1620-1645.https://doi.org/10.32628/CSEIT241061214
- 2Igba, E., Abiodun, K. & Ali, E. O. (2025). Building the Backbone of the Digital Economy and Financial Innovation through Strategic Investments in Data Centers. International Journal of Innovative Science and Research Technology, ISSN No:-2456-2165. https://doi.org/10.5281/zenodo.14651210
- Ijiga, A. C., Aboi, E. J., Idoko, P. I., Enyejo, L. A., & Odeyemi, M. O. (2024). Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking. Global Journal of Engineering and Technology Advances, 2024,18(03), 106-123. https://gjeta.com/sites/default/files/GJETA-2024-0046.pdf
- Ijiga, A. C., Abutu E. P., Idoko, P. I., Ezebuka, C. I., Harry, K. D., Ukatu, I. E., & Agbo, D. O. (2024). Technological innovations in mitigating winter health challenges in New York City, USA. International Journal of Science and Research Archive, 2024, 11(01), 535–551.· https://ijsra.net/sites/default/files/IJSRA-2024-0078.pdf
- Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 2024, 07(01), 048–063. https://ijbpsa.com/sites/default/files/IJBPSA-2024-0015.pdf
- Ijiga, A. C., Balogun, T. K., Ahmadu, E. O., Klu, E., Olola, T. M., & Addo, G. (2024). The role of the United States in shaping youth mental health advocacy and suicide prevention through foreign policy and media in conflict zones. Magna Scientia Advanced Research and Reviews, 2024, 12(01), 202–218. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0174.pdf
- Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA. Open Access Research Journal of Biology and Pharmacy, 2024, 10(02), 081–104. https://oarjbp.com/content/integrating-community-based-partnerships-enhanced-health-outcomes-collaborative-model
- Ijiga, A. C., Igbede, M. A., Ukaegbu, C., Olatunde, T. I., Olajide, F. I. & Enyejo, L. A. (2024). Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction. World Journal of Biology Pharmacy and Health Sciences, 2024, 18(01), 336–354. https://wjbphs.com/sites/default/files/WJBPHS-2024-0214.pdf
- Ijiga, A. C., Igbede, M. A., Ukaegbu, C., Olatunde, T. I., Olajide, F. I. & Enyejo, L. A. (2024). Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction. World Journal of Biology Pharmacy and Health Sciences, 2024, 18(01), 336–354. https://wjbphs.com/sites/default/files/WJBPHS-2024-0214.pdf
- Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 2024, 11(01), 267–286. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0091.pdf.
- Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. Open Access Research Journals. Volume 13, Issue. https://doi.org/10.53022/oarjst.2024.11.1.0060I
- Ijiga. A. C., Eguagie, M. O. & Tokowa, A. (2025). Mineralization Potential of the Lithium-Bearing Micas in the St Austell Granite, SW England. International Journal of Innovative Science and Research Technology. ISSN No:-2456-2165, https://doi.org/10.5281/zenodo.14709730
- Jung, M., Henkel, K., Herold, M., & Chini, L. (2020). Mapping wetland dynamics across the globe using high-resolution Synthetic Aperture Radar (SAR) data. Remote Sensing of Environment, 239, 111629. https://doi.org/10.1016/j.rse.2019.111629
- Junk, W. J., & de Cunha, C. N. (2005). Pantanal: a large South American wetland at a crossroads. Ecological Engineering, 24(4), 391-401.
- Kays, R., Crofoot, M. C., Jetz, W., & Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348(6240), aaa2478. https://doi.org/10.1126/science.aaa2478
- Lahsaini, M., Albano, F., Albano, R., Mazzariello, A., & Lacava, T. (2024). A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods. Remote Sensing, 16(12), 2193. https://doi.org/10.3390/rs16122193
- Mantyka-Pringle, C. S., Martin, T. G., & Rhodes, J. R. (2012). Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global Change Biology, 18(4), 1239–1252. https://doi.org/10.1111/j.1365-2486.2011.02593.x
- Martínez, B., Sastre, P., Palacín, C., Arévalo, J. R., & Moreno, C. (2022). Integrating spatial modeling and satellite imagery for migratory bird habitat prioritization under climate change scenarios. Diversity and Distributions, 28(2), 284–298. https://doi.org/10.1111/ddi.13465
- Martinis, S., Kersten, J., & Twele, A. (2015). A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 203–212. https://doi.org/10.1016/j.isprsjprs.2015.02.014
- McNie, E. C. (2007). Reconciling the supply of scientific information with user demands: An analysis of the problem and review of the literature. Environmental Science & Policy, 10(1), 17–38. https://doi.org/10.1016/j.envsci.2006.10.004
- Mitsch, W. J., & Gosselink, J. G. (2015). Wetlands and climate change: The role of wetland restoration in a changing world. Wetlands Ecology and Management, 23(3), 351–363. https://doi.org/10.1007/s11273-015-9469-7
- Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.
- Ntiamoa-Baidu, Y., Piersma, T., Wiersma, P., Poot, M., Battley, P. F., & Gordon, C. (2008). Water depth selection, daily feeding routines and diets of waterbirds in coastal lagoons in Ghana. Ibis, 150(1), 155–166. https://doi.org/10.1111/j.1474-919X.2007.00756.x
- Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B., & Matthews, E. (2010). Interannual variability of surface water extent at the global scale, 1993–2004. Journal of Geophysical Research: Atmospheres, 115(D12). https://doi.org/10.1029/2009JD012674
- Polk, M. (2015). Transdisciplinary co-production: Designing and testing a transdisciplinary research framework for societal problem solving. Futures, 65, 110-122.
- Pulvirenti, L., Chini, M., Pierdicca, N., Guerriero, L., & Ferrazzoli, P. (2011). Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sensing of Environment, 115(4), 990–1002. https://doi.org/10.1016/j.rse.2010.12.002
- Pulwarty, R. S., & Sivakumar, M. V. K. (2014). Information systems in a changing climate: Early warnings and drought risk management. Weather and Climate Extremes, 3, 14–21. https://doi.org/10.1016/j.wace.2014.03.005
- Rebelo, L. M., McCartney, M. P., & Finlayson, C. M. (2010). Wetlands of sub-Saharan Africa: Distribution and contribution of agriculture to livelihoods. Wetlands Ecology and Management, 18(5), 557–572. https://doi.org/10.1007/s11273-009-9142-x
- Reed, M. S. (2008). Stakeholder participation for environmental management: A literature review. Biological Conservation, 141(10), 2417–2431. https://doi.org/10.1016/j.biocon.2008.07.014
- Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Scambos, T. A. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
- Runge, C. A., Watson, J. E. M., Butchart, S. H. M., Hanson, J. O., Possingham, H. P., & Fuller, R. A. (2015). Protected areas and global conservation of migratory birds. Science, 350(6265), 1255–1258. https://doi.org/10.1126/science.aac9180
- Schimel, D., Keller, M., Berukoff, S., Kao, R., Loescher, H., & Landis, D. (2015). Observing terrestrial ecosystems and the carbon cycle from space. Global Change Biology, 21(5), 1762–1776. https://doi.org/10.1111/gcb.12822
- Stralberg, D., Herzog, M. P., Nur, N., Reynolds, M. D., & Gardali, T. (2020). Adapting wetland conservation to climate change: Flooding risks for breeding birds in California’s Central Valley. Ecological Applications, 30(6), e02114. https://doi.org/10.1002/eap.2114
- Uzoma, E., Enyejo, J. O. & Olola, T. M. (2025). A Comprehensive Review of Multi-Cloud Distributed Ledger Integration for Enhancing Data Integrity and Transactional Security, International Journal of Innovative Science and Research Technology Volume 10, Issue 3, March ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/25mar1970
- Wohlfart, C., Exbrayat, J. F., Schepaschenko, D., Shvidenko, A., & Obersteiner, M. (2018). Impact of climate extremes on the terrestrial carbon cycle: A review of recent findings. Global Change Biology, 24(1), 39–54. https://doi.org/10.1111/gcb.13868
- Woodburn, J. (2023). Record flooding in New South Wales wetlands triggers bird breeding bonanza. Retrieved from: https://www.abc.net.au/news/2023-02-11/record-flooding-in-nsw-triggeres-bird-breeding-bonanza-/101812042
- Xu, Y., Liu, G., Li, H., Liu, Q., & Liu, X. (2023). Remote sensing applications in wetland conservation: Progress, challenges, and future directions. Ecological Indicators, 148, 110123. https://doi.org/10.1016/j.ecolind.2023.110123
- Zhao, J., Li, M., Li, Y., Matgen, P., & Chini, M. (2024). Urban flood mapping using satellite synthetic aperture radar data: A review of characteristics, approaches and datasets. Remote Sensing of Environment, 295, 113626. https://doi.org/10.1016/j.rse.2023.113626arXiv
Wetland ecosystems play a vital role in maintaining global biodiversity, hydrological stability, and carbon
sequestration. However, these ecologically sensitive areas are increasingly threatened by climate-induced flooding,
anthropogenic disturbances, and habitat degradation. Synthetic Aperture Radar (SAR) technology has emerged as a
powerful remote sensing tool for real-time, all-weather flood detection, offering high-resolution imagery critical for wetland
monitoring and adaptive ecosystem management. This review explores the application of SAR-driven flood detection
systems in tracking water level fluctuations and inundation patterns within wetlands and evaluates their implications for
migratory bird habitat conservation. Emphasis is placed on SAR’s capability to penetrate cloud cover and detect changes
in surface moisture, which enhances early flood warning systems and informs decision-making for habitat protection. The
paper also investigates case studies where SAR data have been integrated into conservation planning, emphasizing
spatiotemporal analysis for managing seasonal wetlands that serve as critical stopover or breeding sites for migratory birds.
By highlighting technological advancements, methodological approaches, and interdisciplinary frameworks, the review
highlights the potential of SAR to support resilient wetland management strategies that align with global conservation goals.
Keywords :
Synthetic Aperture Radar (SAR), Flood Detection, Wetland Ecosystems, Migratory Birds, Habitat Management.