The Use of Remote Sensing Data in Mitigating Forest Fire Threats and Its Impact on National Security

Authors

  • Aji Setyo Wibowo Sekolah Tinggi Intelijen Negara

DOI:

https://doi.org/10.70710/sitj.v3i1.91

Keywords:

Forest Fire Mitigation, National Security, Remote Sensing

Abstract

Forest fires have emerged as a recurring environmental and national security threat, exacerbated by climate change, deforestation, and unsustainable human activities such as slash-and-burn agriculture. These fires result in ecosystem degradation, biodiversity loss, greenhouse gas emissions, and disruption to health, food security, and local economies. Remote sensing technology, particularly through satellite sensors and drones, offers real-time data for detecting hotspots, tracking smoke dispersion, and assessing post-fire impacts. Tools like MODIS, VIIRS, and Sentinel have proven effective in fire monitoring and mitigation planning. However, limitations in data resolution, weather interference, and policy integration hinder optimal implementation. In Indonesia, the integration of remote sensing into disaster management systems remains fragmented, often resulting in delayed responses and ineffective coordination. Strengthening human resource capabilities, regulatory frameworks, and multi-sector collaboration is essential to leverage this technology fully. Remote sensing thus stands as a critical solution to reduce wildfire risks and their cascading effects on national security, environmental resilience, and public health.

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References

Baltzer, J. L., Day, N. J., Walker, X. J., Greene, D., Mack, M. C., Alexander, H. D., ... Johnstone, J. F. (2021). Increasing fire and the decline of fire adapted black spruce in the boreal forest. Proceedings of the National Academy of Sciences of the United States of America, 118(45). https://doi.org/10.1073/pnas.2024872118

Berndt, E., Smith, N., Burks, J., White, K., Esmaili, R., Kuciauskas, A., ... Szkodzinski, J. (2020). Gridded satellite sounding retrievals in operational weather forecasting: Product description and emerging applications. Remote Sensing, 12(20), 1-30. https://doi.org/10.3390/rs12203311

Bryan, K., Ward, S., Roberts, L., White, M. P., Landeg, O., Taylor, T., & McEwen, L. (2020). The health and well-being effects of drought: assessing multi-stakeholder perspectives through narratives from the UK. Climatic Change, 163(4), 2073-2095. https://doi.org/10.1007/s10584-020-02916-x

Chuvieco, E., Yebra, M., Martino, S., Thonicke, K., Gómez-Giménez, M., San-Miguel, J., ... Viegas, D. (2023). Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn. Fire, 6(5). https://doi.org/10.3390/fire6050215

Cleland, S. E., Serre, M. L., Rappold, A. G., & West, J. J. (2021). Estimating the Acute Health Impacts of Fire-Originated PM2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs. GeoHealth, 5(7). https://doi.org/10.1029/2021GH000414

Coughlan, R., Di Giuseppe, F., Vitolo, C., Barnard, C., Lopez, P., & Drusch, M. (2021). Using machine learning to predict fire-ignition occurrences from lightning forecasts. Meteorological Applications, 28(1). https://doi.org/10.1002/met.1973

De Freitas, A., Ferreira, J., Escada, M., Reis, J., Leite, C., Andrade, D., ... Anderson, L. (2022). Fire exposure index as a tool for guiding prevention and management. Frontiers in Physics, 10. https://doi.org/10.3389/fphy.2022.1064162

de Lima, R. P., & Marfurt, K. (2020). Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sensing, 12(1). https://doi.org/10.3390/rs12010086

Di Napoli, M., Marsiglia, P., Di Martire, D., Ramondini, M., Ullo, S. L., & Calcaterra, D. (2020). Landslide susceptibility assessment of wildfire burnt areas through earth-observation techniques and a machine learning-based approach. Remote Sensing, 12(15). https://doi.org/10.3390/RS12152505

Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138. https://doi.org/10.1016/j.tre.2020.101967

Graham, A. M., Pringle, K. J., Pope, R. J., Arnold, S. R., Conibear, L. A., Burns, H., ... McQuaid, J. B. (2021). Impact of the 2019/2020 Australian Megafires on Air Quality and Health. GeoHealth, 5(10). https://doi.org/10.1029/2021GH000454

Hagmann, R. K., Hessburg, P. F., Prichard, S. J., Povak, N. A., Brown, P. M., Fulé, P. Z., ... Waltz, A. E. M. (2021). Evidence for widespread changes in the structure, composition, and fire regimes of western North American forests. Ecological Applications, 31(8). https://doi.org/10.1002/eap.2431

Harris, F., Amarnath, G., Joy, E. J., Dangour, A. D., & Green, R. F. (2022). Climate-related hazards and Indian food supply: Assessing the risk using recent historical data. Global Food Security, 33. https://doi.org/10.1016/j.gfs.2022.100625

Kean, J. W., & Staley, D. M. (2021). Forecasting the Frequency and Magnitude of Postfire Debris Flows Across Southern California. Earth's Future, 9(3). https://doi.org/10.1029/2020EF001735

Kim, D., Ha, K. J., & Yeo, J. H. (2021). New Drought Projections Over East Asia Using Evapotranspiration Deficits From the CMIP6 Warming Scenarios. Earth's Future, 9(6). https://doi.org/10.1029/2020EF001697

Kyrkou, C., & Theocharides, T. (2020). EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1687-1699. https://doi.org/10.1109/JSTARS.2020.2969809

Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., & Chanussot, J. (2022). Deep learning in multimodal remote sensing data fusion: A comprehensive review. International Journal of Applied Earth Observation and Geoinformation, 112. https://doi.org/10.1016/j.jag.2022.102926

McColl-Gausden, S. C., Bennett, L. T., Duff, T. J., Cawson, J. G., & Penman, T. D. (2020). Climatic and edaphic gradients predict variation in wildland fuel hazard in south-eastern Australia. Ecography, 43(3), 443-455. https://doi.org/10.1111/ecog.04714

Molotoks, A., Smith, P., & Dawson, T. P. (2021). Impacts of land use, population, and climate change on global food security. Food and Energy Security, 10(1). https://doi.org/10.1002/fes3.261

Monteiro, A., Basart, S., Kazadzis, S., Gkikas, A., Vandenbussche, S., Tobias, A., ... Meinander, O. (2022). Multi-sectoral impact assessment of an extreme African dust episode in the Eastern Mediterranean in March 2018. Science of the Total Environment, 843. https://doi.org/10.1016/j.scitotenv.2022.156861

Nazarova, T., Martin, P., & Giuliani, G. (2020). Monitoring vegetation change in the presence of high cloud cover with sentinel-2 in a lowland tropical forest region in Brazil. Remote Sensing, 12(11). https://doi.org/10.3390/rs12111829

Neris, J., Santin, C., Lew, R., Robichaud, P. R., Elliot, W. J., Lewis, S. A., ... Doerr, S. H. (2021). Designing tools to predict and mitigate impacts on water quality following the Australian 2019/2020 wildfires: Insights from Sydney's largest water supply catchment. Integrated Environmental Assessment and Management, 17(6), 1151-1161. https://doi.org/10.1002/ieam.4406

Oliveira, M., Delerue-Matos, C., Pereira, M. C., & Morais, S. (2020). Environmental particulate matter levels during 2017 large forest fires and megafires in the centre region of Portugal: A public health concern? International Journal of Environmental Research and Public Health, 17(3). https://doi.org/10.3390/ijerph17031032

Pastick, N. J., Dahal, D., Wylie, B. K., Parajuli, S., Boyte, S. P., & Wu, Z. (2020). Characterising land surface phenology and exotic annual grasses in dryland ecosystems using landsat and sentinel-2 data in harmony. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040725

Peek, L., Tobin, J., Adams, R. M., Wu, H., & Mathews, M. C. (2020). A Framework for Convergence Research in the Hazards and Disasters Field: The Natural Hazards Engineering Research Infrastructure CONVERGE Facility. Frontiers in Built Environment, 6. https://doi.org/10.3389/fbuil.2020.00110

Salis, M., Arca, B., Del Giudice, L., Palaiologou, P., Alcasena-Urdiroz, F., Ager, A., ... Duce, P. (2021). Application of simulation modelling for wildfire exposure and transmission assessment in Sardinia, Italy. International Journal of Disaster Risk Reduction, 58. https://doi.org/10.1016/j.ijdrr.2021.102189

Sambasivam, G., & Opiyo, G. D. (2021). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Informatics Journal, 22(1), 27-34. https://doi.org/10.1016/j.eij.2020.02.007

Seydi, S. T., Saeidi, V., Kalantar, B., Ueda, N., & Halin, A. A. (2022). Fire-Net: A Deep Learning Framework for Active Forest Fire Detection. Journal of Sensors, 2022. https://doi.org/10.1155/2022/8044390

Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 1-31. https://doi.org/10.3390/rs12193136

Soshenskyi, O., Zibtsev, S., Gumeniuk, V., Goldammer, J.G., Vasylyshyn, R., & Blyshchyk, V. (2021). Current land fire management in Ukraine and strategies for improvement. Environmental and Socioeconomic Studies, 9(2), 39-51. https://doi.org/10.2478/environ-2021-0009

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

Thi Ngo, P. T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Kariminejad, N., Cerda, A., & Lee, S. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505-519. https://doi.org/10.1016/j.gsf.2020.06.013

Tiranti, D., Cremonini, R., & Sanmartino, D. (2021). Wildfires effect on debris flow occurrence in italian western alps: Preliminary considerations to refine debris flow early warnings system criteria. Geosciences (Switzerland), 11(10). https://doi.org/10.3390/geosciences11100422

Tymstra, C., Stocks, B. J., Cai, X., & Flannigan, M. D. (2020). Wildfire management in Canada: Review, challenges and opportunities. Progress in Disaster Science, 5. https://doi.org/10.1016/j.pdisas.2019.100045

Ullah, K., & Zhang, J. (2020). GIS-based flood hazard mapping using relative frequency ratio method: A case study of panjkora river basin, eastern Hindu Kush, Pakistan. PLoS ONE, 15(3). https://doi.org/10.1371/journal.pone.0229153

Ward, P. J., de Ruiter, M. C., Mård, J., Schröter, K., Van Loon, A., Veldkamp, T., ... Wens, M. (2020). The need to integrate flood and drought disaster risk reduction strategies. Water Security, 11. https://doi.org/10.1016/j.wasec.2020.100070

Wu, C., Venevsky, S., Sitch, S., Mercado, L. M., Huntingford, C., & Staver, A. C. (2021). Historical and future global burned area with changing climate and human demography. One Earth, 4(4), 517-530. https://doi.org/10.1016/j.oneear.2021.03.002

Xu, Z., Zhang, W., Zhang, T., Yang, Z., & Li, J. (2021). Efficient transformer for remote sensing image segmentation. Remote Sensing, 13(18). https://doi.org/10.3390/rs13183585

Yang, X., Zhang, M., Oliveira, L., Ollivier, Q. R., Faulkner, S., & Roff, A. (2020). Rapid assessment of hillslope erosion risk after the 2019-2020 wildfires and storm events in sydney drinking water catchment. Remote Sensing, 12(22), 1-20. https://doi.org/10.3390/rs12223805

Zhou, F., Pan, H., Gao, Z., Huang, X., Qian, G., Zhu, Y., & Xiao, F. (2021). Fire Prediction Based on CatBoost Algorithm. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/1929137

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Published

2026-03-14

How to Cite

Wibowo, A. S. (2026). The Use of Remote Sensing Data in Mitigating Forest Fire Threats and Its Impact on National Security. Security Intelligence Terrorism Journal (SITJ), 3(1), 76–83. https://doi.org/10.70710/sitj.v3i1.91

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