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Abstract

Climate change, geological activity, and environmental degradation have increased the number of natural disasters in recent years. To mitigate the social, economic, and infrastructure impacts, early disaster detection and mitigation are crucial. Real-time monitoring of geospatial phenomena such as forest fires, floods, and landslides is aided by satellite technology. Combining remote sensing technology with big data and artificial intelligence improves prediction accuracy and enhances rapid disaster response. In addition to disaster mitigation, satellite technology contributes to national resilience by monitoring geospatial threats and regional security, including border surveillance and cyber threat detection. The development of satellite technology in Indonesia faces constraints such as limited budget and human resources, but through strategic investment and collaboration, there is significant potential to increase its independence and resilience. Satellite technology is expected to strengthen national resilience and support sustainable development and public welfare in the face of global threats.

Keywords

satelit bencana alam Resiliensi Nasional National Resilience satellite disaster

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Copyright (c) 2025 Ire Pratiwi

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How to Cite
Pratiwi, I., Supriyadi, A. A., & Prihanto, Y. (2026). Application of Satellite Technology in Disaster Management: A Geospatial Science and National Resilience Perspective. PENDIPA Journal of Science Education, 10(1), 1–12. https://doi.org/10.33369/pendipa.10.1.1-12
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References

  1. Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M., Homod, R. Z., Albahri, O. S., Alamoodi, A. H., & Alzubaidi, L. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, 118(PB), 109409. https://doi.org/10.1016/j.compeleceng.2024.109409
  2. Algarni, A., Acarer, T., & Ahmad, Z. (2024). An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications. IEEE Access, 12, 53646–53663. https://doi.org/10.1109/ACCESS.2024.3387529
  3. Aloscious, A. A., Artuso, M., & Torabi Moghadam, S. (2025). Nature-Based Solutions for Flood Mitigation: The Case Study of Kochi. Sustainability (Switzerland), 17(5). https://doi.org/10.3390/su17051983
  4. Aravidou, K., Triantari, S., & Zervas, I. (2025). Sustainable Leadership and Conflict Management: Insights from Greece’s Public Sector. Sustainability (Switzerland), 17(5), 1–25. https://doi.org/10.3390/su17052248
  5. Ballada, C. J. A., Aruta, J. J. B. R., Callueng, C. M., Antazo, B. G., Kimhi, S., Reinert, M., Eshel, Y., Marciano, H., Adini, B., da Silva, J. D., & Verdu, F. C. (2022). Bouncing back from COVID-19: Individual and ecological factors influence national resilience in adults from Israel, the Philippines, and Brazil. Journal of Community and Applied Social Psychology, 32(3), 452–475. https://doi.org/10.1002/casp.2569
  6. Bello, M., Singh, S., Singh, S. K., Pandey, V., Kumar, P., Meraj, G., Kanga, S., & Sajan, B. (2024). Geospatial Analysis of Flood Susceptibility in Nigeria’s Vulnerable Coastal States: A Detailed Assessment and Mitigation Strategy Proposal. Climate, 12(7). https://doi.org/10.3390/cli12070093
  7. Belu, M. G., & Marinoiu, A. M. (2025). AI-Enabled Supply Chain Management: A Bibliometric Analysis Using VOSviewer and RStudio Bibliometrix Software Tools. Sustainability (Switzerland), 17(5). https://doi.org/10.3390/su17052092
  8. Emrich, C. T., Zhou, Y., Aksha, S. K., & Longenecker, H. E. (2022). Creating a Nationwide Composite Hazard Index Using Empirically Based Threat Assessment Approaches Applied to Open Geospatial Data. Sustainability (Switzerland), 14(5). https://doi.org/10.3390/su14052685
  9. Handwerger, A. L., Huang, M. H., Jones, S. Y., Amatya, P., Kerner, H. R., & Kirschbaum, D. B. (2022). Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Natural Hazards and Earth System Sciences, 22(3), 753–773. https://doi.org/10.5194/nhess-22-753-2022
  10. Helled, A., & Pala, C. (2024). When Nations Adapt: National Resilience between State(s) and Identity(ies). Political Studies Review, 22(1), 93–107. https://doi.org/10.1177/14789299221144620
  11. Hollifield, M., Toolson, E. C., Verbillis-Kolp, S., Farmer, B., Yamazaki, J., Woldehaimanot, T., & Holland, A. (2021). Distress and resilience in resettled refugees of war: Implications for screening. International Journal of Environmental Research and Public Health, 18(3), 1–15. https://doi.org/10.3390/ijerph18031238
  12. Kim, D. J., Sur, J. M., & Cho, H. U. (2023). A long term expected risk estimation of maritime accidents through Markov chain approach and probabilistic risk matrix. Asian Journal of Shipping and Logistics, 39(3), 1–12. https://doi.org/10.1016/j.ajsl.2023.04.002
  13. Kimhi, S., Eshel, Y., Marciano, H., & Adini, B. (2021). Fluctuations in national resilience during the covid-19 pandemic. International Journal of Environmental Research and Public Health, 18(8), 1–11. https://doi.org/10.3390/ijerph18083876
  14. Li, H., Yang, D., Zhu, Z., Wei, Y., Zhou, Y., Ishidaira, H., Commey, N. A., & Cheng, H. (2024). Flood Risk Analysis of Urban Agglomerations in the Yangtze River Basin Under Extreme Precipitation Based on Remote Sensing Technology. Remote Sensing, 16(22), 4289. https://doi.org/10.3390/rs16224289
  15. Mahar, A. S., Zhang, Y., Sadiq, B., & Gul, R. F. (2025). Sustainability Transformation Through Green Supply Chain Management Practices and Green Innovations in Pakistan’s Manufacturing and Service Industries. Sustainability (Switzerland), 17(5), 1–28. https://doi.org/10.3390/su17052204
  16. Marlier, M. E., Resetar, S. A., Lachman, B. E., Anania, K., & Adams, K. (2022). Remote sensing for natural disaster recovery: Lessons learned from Hurricanes Irma and Maria in Puerto Rico. Environmental Science and Policy, 132(February), 153–159. https://doi.org/10.1016/j.envsci.2022.02.023
  17. Massaro, L., Forte, G., De Falco, M., Rauseo, F., & Santo, A. (2024). Rockfall source identification and trajectory analysis from UAV-based data in volcano-tectonic areas: a case study from Ischia Island, Southern Italy. Bulletin of Engineering Geology and the Environment, 83(3), 75. https://doi.org/10.1007/s10064-024-03569-1
  18. Mendelson, T., Turner, A. K., & Tandon, S. D. (2010). Social Class As Moderator of the Relationship Between (Dis)Empowering Processes and Psychological Empowerment. Journal of Community Psychology, 38(5), 607–621. https://doi.org/10.1002/jcop
  19. Mijbas, H. A., Islam, M. K., & Khudari, M. (2025). The Role of IT Flexibility in Enhancing Supply Chain Resilience in the Oil Products Distribution Sector. Sustainability (Switzerland), 17(5). https://doi.org/10.3390/su17052295
  20. Nirwansyah, A. W., Mandili, A., Inez-Pedro, B., Aini, J., Sriyanto, S., & Sadeli, E. H. (2024). Using Spatial Literacy for Disaster Management in Coastal Communities of Small Island Developing States (SIDS): A Case Study from Lavongai, Papua New Guinea. Sustainability, 16(21), 9152. https://doi.org/10.3390/su16219152
  21. Noy, I., & Yonson, R. (2018). Economic vulnerability and resilience to natural hazards: A survey of concepts and measurements. Sustainability (Switzerland), 10(8). https://doi.org/10.3390/su10082850
  22. Odiji, C., James, G., Oyewumi, A., Karau, S., Odia, B., Idris, H., Aderoju, O., & Taminu, A. (2024). Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine. Journal of Water and Climate Change, 15(2), 348–369. https://doi.org/10.2166/wcc.2024.166
  23. Opryshko, O., Kiktev, N., Shvorov, S., Hluhan, F., Polishchuk, R., Murakhovskiy, M., Hutsol, T., Glowacki, S., Nurek, T., & Sojak, M. (2025). Evaluation of Promising Areas for Biogas Production by Indirect Assessment of Raw Materials Using Satellite Monitoring. Sustainability (Switzerland), 17(5), 1–23. https://doi.org/10.3390/su17052098
  24. Park, J. W. (2025). An Analysis of the Capacity of Outdoor Earthquake Evacuation Sites in Daegu, South Korea: Assessing De Facto Population Dynamics and Accessibility Through the Geographic Information System (GIS). Sustainability (Switzerland), 17(5). https://doi.org/10.3390/su17052129
  25. Paul, S., Sharma, P. J., & Teegavarapu, R. S. V. (2024). Spatial assessment of the reproducibility of Indian summer monsoon rainfall regimes in multiple gridded rainfall products. Scientific Reports, 14(1), 1–13. https://doi.org/10.1038/s41598-024-75320-5
  26. Puspitawati, D., Prakoso, L. Y., Kusumaningrum, A., & Harahab, N. (2023). Reconstruction of State Territorial Management to Optimize National Resilience in Indonesia. Legality: Jurnal Ilmiah Hukum, 31(1), 21–41. https://doi.org/10.22219/ljih.v31i1.23636
  27. Shaddick, G., Topping, D., Hales, T. C., Kadri, U., Patterson, J., Pickett, J., Petri, I., Taylor, S., Li, P., Sharma, A., Venkatkrishnan, V., Wadhwa, A., Ding, J., Bowyer, R., & Rana, O. (2025). Data Science and AI for Sustainable Futures: Opportunities and Challenges. Sustainability (Switzerland), 17(5), 1–20. https://doi.org/10.3390/su17052019
  28. Song, T., Yang, K., Li, X., Peng, S., & Meng, F. (2024). Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images. Remote Sensing, 16(4), 1–19. https://doi.org/10.3390/rs16040606
  29. Taherizadeh, M., Niknam, A., Nguyen-Huy, T., Mezősi, G., & Sarli, R. (2023). Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process and satellite-derived information. In Natural Hazards (Vol. 118, Issue 3, pp. 2309–2335). https://doi.org/10.1007/s11069-023-06089-5
  30. Thangavel, K., Spiller, D., Sabatini, R., Amici, S., Sasidharan, S. T., Fayek, H., & Marzocca, P. (2023). Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire. Remote Sensing, 15(3). https://doi.org/10.3390/rs15030720
  31. Wolniak, R., & Grebski, W. W. (2025). Sustainable Trends and Determinants of Wheat Cultivation in Poland (2004–2023): A Spatiotemporal Analysis of Productivity, Resilience, and Climate Adaptation. Sustainability (Switzerland), 17(5), 1–35. https://doi.org/10.3390/su17052225
  32. Wu, Z., Ye, R., Huang, J., Fu, X., & Chen, Y. (2025). Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation. Remote Sensing, 17(2). https://doi.org/10.3390/rs17020339
  33. Yao, M., Zhang, D., Chen, Y., Liu, Y., & Elsadek, M. (2024). Urban Fire Risk Dynamics and Mitigation Strategies in Shanghai: Integrating Spatial Analysis and Game Theory. Land, 13(8). https://doi.org/10.3390/land13081125
  34. Zhou, X., Zhang, S., Zhang, Q., Liu, Q., Ma, Z., Wang, T., Tian, J., & Li, X. (2022). Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS. Remote Sensing, 14(22). https://doi.org/10.3390/rs14225687