Earth Sciences, Ruhuna Valley College of Applied Studies, Matara, Sri Lanka
JournalPIJST
Volume / Issue1 / 12
Pages32–45
Published31 Dec 2024
Paper IDPIJST112D24003
Views / Downloads1 / 0
Article summary
Abstract
Natural disasters such as earthquakes, floods, landslides, hurricanes, and wildfires pose serious threats to human lives, infrastructure, and the environment. Effective monitoring is essential for disaster preparedness, risk reduction, and recovery planning. Remote sensing has emerged as a vital tool in this context due to its ability to provide timely, wide-scale, and multi-sensor observations. Satellite-based techniques, particularly Synthetic Aperture Radar (SAR), enable detection of seismic ground deformation, rapid damage mapping, and flood extent delineation under all-weather conditions. Aerial platforms, including UAVs, complement satellite data by supplying ultra-high-resolution imagery for detailed post-disaster assessment, while ground-based systems such as LiDAR and photogrammetry deliver precise local measurements. The integration of these approaches enhances disaster monitoring capabilities and mitigates limitations inherent to individual platforms. Applications extend beyond earthquake monitoring to include flood forecasting, wildfire mapping, landslide susceptibility analysis, and hurricane track prediction. Emerging technologies such as machine learning and artificial intelligence further improve the interpretation of remote sensing datasets, supporting early warning systems and real-time decision-making. This paper highlights the importance of combining satellite, aerial, and ground-based remote sensing techniques to strengthen disaster management strategies and foster global resilience against natural hazards.
Disastersand Earthquakes, N. Perera (2024). Remote Sensing for Monitoring Natural. Procedure International Journal of Science and Technology, 1(12), 32–45. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural
Disastersand Earthquakes, N. Perera. “Remote Sensing for Monitoring Natural.” Procedure International Journal of Science and Technology, vol. 1, no. 12, 2024, pp. 32–45. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural
Disastersand Earthquakes, N. Perera. “Remote Sensing for Monitoring Natural.” Procedure International Journal of Science and Technology 1, no. 12 (2024): 32–45. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural
Disastersand Earthquakes, N. Perera (2024) ‘Remote Sensing for Monitoring Natural’, Procedure International Journal of Science and Technology, 1(12), pp. 32–45. Available at: https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural.
Disastersand Earthquakes, N. Perera, “Remote Sensing for Monitoring Natural,” Procedure International Journal of Science and Technology, vol. 1, no. 12, pp. 32–45, 2024. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural.
Disastersand Earthquakes, N. Perera. Remote Sensing for Monitoring Natural. Procedure International Journal of Science and Technology. 2024;1(12):32–45. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural.
Disastersand Earthquakes, N. Perera. Remote Sensing for Monitoring Natural. Procedure International Journal of Science and Technology 2024, 1 (12), 32–45. https://www.pijst.com/article/pijst112d24003/remote-sensing-for-monitoring-natural.
No separate funding declaration was available in the verified source record; the journal policy applies.
Conflict of Interest
No separate conflict-of-interest declaration was available in the verified source record; the journal policy applies.
Ethical Approval
No separate ethical approval statement was available in the verified source record; the article and journal policies apply.
Data Availability
No separate data-availability statement was available in the verified source record; contact the author(s) or editorial office where appropriate.
Author Contributions
No separate author-contribution statement was available in the verified source record; authorship follows the published article record.
AI-use Declaration
No separate AI-use declaration was available in the verified source record; the journal AI-use policy applies.
Editorial record
Publisher's Note
The views, opinions and conclusions expressed in this article are those of the author(s). Publication does not imply endorsement by the journal, editorial board or publisher. Responsibility for accuracy, originality and integrity remains with the author(s). Readers should independently evaluate and verify information before application or citation.
Rege Cambrin, D., & Garza, P. (2024). QuakeSet: A dataset and low-resource models (Online) to monitor earthquakes through Sentinel-1. [PDF]. Source
Novellino, A., Jordan, C., Ager, G., Bateson, L., Fleming, C., & Confuorto, P. (2018). Remote sensing for natural or man-made disasters and environmental changes. [PDF]. Source
Zhang, Q., Zhang, Y., Yang, X., & Su, B. (2014). Automatic recognition of seismic intensity based on RS and GIS: A case study in Wenchuan Ms8.0 earthquake of China. Scientific World Journal. Source
Lacava, T., Ciancia, E., Faruolo, M., Pergola, N., Satriano, V., & Tramutoli, V. (2018). Analyzing the December 2013 Metaponto Plain (Southern Italy) flood event by integrating optical sensors satellite data. [PDF]. Source
Bolten, J. D., & Ahamed, A. (2017). A MODIS-based automated flood monitoring system for Southeast Asia. [PDF]. Source
Fordham, A. (2002). Band selection and algorithm development for remote sensing of wildfires. [PDF]. Source
Domenikiotis, C., Loukas, A., & Dalezios, N. R. (2003). The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods. [PDF]. https:/ /core.ac.uk/download/pdf/148311285.pdf.
Casagli, N., Cigna, F., Bianchini, S., Hölbling, D., Füreder, P., Righini, G., Del Conte, S., Friedl, B., Schneiderbauer, S., Iasio, C., Vlcko, J., Greif, V., Proske, H., Granica, K., Lozzi, S., Mora, O., Arnaud, A., Novali, F., & Bianchi, M. (2016). Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the ECFP7 project SAFER. Remote Sensing Applications: Society and Environment. [PDF]. Source
Mostafiz, C. (2017). Assessing interactions between estuary water quality and terrestrial land cover in hurricane events with multi-sensor remote sensing. [PDF]. Source
Xiong, P., Tong, L., Zhang, K., Shen, X., Battiston, R., Ouzounov, D., Iuppa, R., Crookes, D., Long, C., & Zhou, H. (2021). Towards advancing earthquake forecasting by machine learning of satellite data. [PDF]. Source
Mohammadi, M. E., & Wood, R. L. (2018). Damage assessment of built-up areas via UAS-SfM derived point cloud data. [PDF]. Source
Alatza, S., Papoutsis, I., Paradissis, D., Kontoes, C., & Papadopoulos, G. A. (2020). Multi-temporal InSAR analysis for monitoring ground deformation in Amorgos Island, Greece. Remote Sensing. Source
Wieland, M., & Martinis, S. (2019). A modular processing chain for automated flood monitoring from multi-spectral satellite data. [PDF]. Source
Tiwari, V., Kumar, V., Matin, M. A., Thapa, A., Ellenburg, W. L., Gupta, N., & Thapa, S. (2020). Flood inundation mapping—Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. Remote Sensing. Source
Oxoli, D., Boccardo, P., Brovelli, M. A., Molinari, M. E., & Monti Guarnieri, A. (2018). Coherent change detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings. [PDF]. Source
Killough, B. D. (2003). A semi-empirical cellular automata model for wildfire monitoring from a geosynchronous space platform. [PDF]. Source
Golovko, D., Roessner, S., Behling, R., Wetzel, H. U., & Kleinschmit, B. (2017). Evaluation of remote-sensing-based landslide inventories for hazard assessment in (Online) Southern Kyrgyzstan. [PDF]. Source
Lin, Y. B., Lin, Y. P., Deng, D. P., & Chen, K. W. (2008). Integrating remote sensing data with directional two-dimensional wavelet analysis and open geospatial techniques for efficient disaster monitoring and management. Sensors. Source
Hamlington, B. D., Leben, R. R., Godin, O. A., Gica, E., Titov, V. V., Haines, B. J., & Desai, S. D. (2012). Could satellite altimetry have improved early detection and warning of the 2011 Tohoku tsunami? [PDF]. Source
Adams, E., Munroe, T., Weigel, A., Cherrington, E., Pulla, S., Lucey, R., Anderson, E., Tondapu, G., Schultz, L., Jones, M., Molthan, A., Herndon, K., Markert, K., Muench, R., Layne, G., Flores, A., & Bell, J. (2017). Collaborative, rapid mapping of water extents during Hurricane Harvey using optical and radar satellite sensors. [PDF]. Source
Harb, M., & Dell’Acqua, F. (2013). Radar-based damage assessment: Near-real-time spotlight acquisition on single building collapses from informal online news scanning. [PDF]. Source
