Advancing Disaster Management through Remote Sensing: Applications, Challenges, and Future Prospects
DOI:
https://doi.org/10.61173/ye1me637Keywords:
Remote Sensing, Natural Disasters, Satellite Technology, Early Warning Systems, Real-Time MonitoringAbstract
The increasing occurrence and intensity of natural disasters, driven by factors such as climate change and urbanization, emphasize the urgent requirement for advanced monitoring and management approaches. This study investigates the critical function of remote sensing technologies in disaster management, concentrating on their application across various natural disasters, including earthquakes, floods, landslides, tropical cyclones, wildfires, and droughts. By utilizing satellite, aerial, and ground-based remote sensing techniques, this research illustrates how these technologies enhance real-time monitoring, early warning systems, and recovery efforts following disasters. The results demonstrate that remote sensing provides considerable benefits, such as extensive coverage and the capability to access otherwise unreachable areas, although challenges persist regarding data resolution and integration. Additionally, the study explores future potential, particularly the integration of remote sensing with artificial intelligence and big data analytics. These advancements highlight the essential role of remote sensing in global disaster risk reduction, offering critical insights for policymakers and disaster management professionals.
References
[1] Abdulwahid W M, Pradhan B. Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides, 2017, 14: 1057-1076.
[2] Adams S M, Levitan M L, Friedland C J. High resolution imagery collection for post-disaster studies utilizing unmanned aircraft systems (UAS). Photogrammetric Engineering & Remote Sensing, 2014, 80(12): 1161-1168.
[3] Allison R S, Johnston J M, Craig G, et al. Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 2016, 16(8): 1310.
[4] Aydöner C, Maktav D, Alparslana E. Ground deformation mapping using InSAR[C]//ISPRS Congress Technical Commission I. 2004: 120-123. Dean&Francis
[5] Banholzer S, Kossin J, Donner S. The impact of climate change on natural disasters[C]//Reducing disaster: Early warning systems for climate change. 2014: 21-49.
[6] Brown M E. Famine early warning systems and remote sensing data[M]. Springer Science & Business Media, 2008.
[7] Chen C H, Yen K W. Developing International Collaboration Indicators in Fisheries Remote Sensing Research to Achieve SDG 14 and 17. Sustainability, 2023, 15(18): 14031.
[8] Chi M, Plaza A, Benediktsson J A, et al. Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 2016, 104(11): 2207-2219.
[9] Cohen C J. Early history of remote sensing. Proceedings 29th Applied Imagery Pattern Recognition Workshop, 2000: 3-3.
[10] De Leeuw J, Georgiadou Y, Kerle N, et al. The function of remote sensing in support of environmental policy. Remote Sensing, 2010, 2(7): 1731-1750.
[11] Gutter K, Vega R, Silva G C. Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management. Journal of Sensor and Actuator Networks, 2024, 13(4): 39.
[12] Huntley D, Rotheram-Clarke D, Pon A, et al. Benchmarked RADARSAT-2, SENTINEL-1 and RADARSAT Constellation Mission change-detection monitoring at North Slide, Thompson River Valley, British Columbia: ensuring a landslide-resilient national railway network. Canadian Journal of Remote Sensing, 2021, 47(4): 635-656.
[13] Hua L, Shao G. The progress of operational forest fire monitoring with infrared remote sensing. Journal of Forestry Research, 2017, 28(2): 215-229.
[14] Irmak A, Allen R G, Kjaersgaard J, et al. Operational remote sensing of ET and challenges[C]//Evapotranspiration— Remote Sensing and Modeling. 2012: 467-492.
[15] Joyce K E, Samsonov S V, Levick S R, et al. Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data. Natural Hazards, 2014, 73: 137-163.
[16] Jha M K, Chowdary V M. Challenges of using remote sensing and GIS in developing nations. Hydrogeology Journal, 2007, 15: 197-200.
[17] Kaku K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. International Journal of Disaster Risk Reduction, 2019, 33: 417-432.
[18] Kemper H, Kemper G. Sensor fusion, GIS and AI technologies for disaster management. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 43: 1677-1683.
[19] Khorram S, van der Wiele C F, Koch F H, et al. Future trends in remote sensing. Principles of Applied Remote Sensing, 2016: 277-285.
[20] Lei T, Wang J, Li X, et al. Flood disaster monitoring and emergency assessment based on multi-source remote sensing observations. Water, 2022, 14(14): 2207.
[21] Liu Q, Zhang J, Zhang H, et al. Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Science of the Total Environment, 2021, 789: 147803.
[22] Slonecker E T, Shaw D M, Lillesand T M. Emerging legal and ethical issues in advanced remote sensing technology. Photogrammetric Engineering and Remote Sensing, 1998, 64(6): 589-595.
[23] Tan J, Yang Q, Hu J, et al. Tropical cyclone intensity estimation using Himawari-8 satellite cloud products and deep learning. Remote Sensing, 2022, 14(4): 812.
[24] Ticehurst C J, Dyce P, Guerschman J P. Using passive microwave and optical remote sensing to monitor flood inundation in support of hydrologic modelling. Interfacing Modelling and Simulation with Mathematical and Computational Sciences, 2009: 13-17.
[25] Upadhyay V, Kumar A. Hyperspectral remote sensing of forests: technological advancements, opportunities and challenges. Earth Science Informatics, 2018, 11(4): 487-524.
[26] Van Westen C J. Remote sensing for natural disaster management. International Archives of Photogrammetry and Remote Sensing, 2000, 33(B7/4; PART 7): 1609-1617.
[27] Wieland M, Martinis S, Li Y. Semantic segmentation of water bodies in multi-spectral satellite images for situational awareness in emergency response. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, 42: 273-277.
[28] Wolski P, Murray-Hudson M, Thito K, Cassidy L. Keeping it simple: Monitoring flood extent in large data-poor wetlands using MODIS SWIR data. International Journal of Applied Earth Observation and Geoinformation, 2017, 57: 224-234.
[29] Yamazaki F, Matsuoka M. Remote sensing technologies in post-disaster damage assessment. Journal of Earth Tsunami, 2007, 1(03): 193-210.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
