Published: 2026-05-19

AI-Enhanced Remote Sensing for Forest Fire Mitigation: Integrating UAV and Satellite Imagery to Strengthen Indonesia's National Security

DOI: 10.35870/ijmsit.v6i1.6981

Anggi Pradikti Purna Dewi, Asep Adang Supriyadi
  • Anggi Pradikti Purna Dewi: Sekolah Tinggi Intelijen Negara
  • Asep Adang Supriyadi: Universitas Pertahanan Republik Indonesia
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Abstract

Forest fires pose a significant ecological and national security threat, with increasing frequency and intensity driven by both natural and anthropogenic factors. Remote sensing technology has emerged as a strategic solution for early detection and mitigation of forest fires in high-risk areas. This study employs a qualitative literature review method, analyzing more than 15 recent scholarly articles focusing on the use of satellite imagery, UAVs, and machine learning integration in fire monitoring systems. The data were examined to assess the effectiveness of remote sensing in prediction, active detection, and post-fire management. Findings indicate that remote sensing technology, especially those utilizing multispectral imagery and surface temperature sensors, can improve fire detection accuracy to over 90%. AI-based segmentation systems and UAV imagery have proven effective in accelerating real-time wildfire response. Additionally, spatial data contributes to mapping fire-prone zones and planning strategic mitigation efforts. In conclusion, the integrative use of remote sensing strengthens early warning systems and decision-making processes in forest fire management, while safeguarding ecosystem stability and national security.

Keywords

Remote sensing; Forest fire; Early detection; UAV; AI; National security

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How to Cite

Purna Dewi, A. P., & Supriyadi, A. A. (2026). AI-Enhanced Remote Sensing for Forest Fire Mitigation: Integrating UAV and Satellite Imagery to Strengthen Indonesia’s National Security. International Journal of Management Science and Information Technology, 6(1), 568-577. https://doi.org/10.35870/ijmsit.v6i1.6981
Article Information

This article has been peer-reviewed and published in the International Journal of Management Science and Information Technology. The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 6 No. 1 (2026)

  • Section: Articles

  • Published: 2026-05-19

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Author Biographies
Anggi Pradikti Purna Dewi, Sekolah Tinggi Intelijen Negara
Google Scholar ResearchGate ORCID

Intelligence Studies, Sekolah Tinggi Intelijen Negara, Bogor Regency, West Java Province, Indonesia

Asep Adang Supriyadi, Universitas Pertahanan Republik Indonesia
Google Scholar ResearchGate ORCID

Sensing Technology Study program, Faculty of Science and Technology, Universitas Pertahanan Republik Indonesia, Bogor Regency, West Java Province, Indonesia

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