Artificial intelligence and remote sensing frameworks for wildfire monitoring and risk analysis across multiple ecosystems: a review

Keval Jodhani; Lakshya Bhatiya; Lalit Sirvi; Abhishek Chanda; Sonal Thakkar; Upaka Rathnayake; Keval Jodhani; Lakshya Bhatiya; Lalit Sirvi; Abhishek Chanda; Sonal Thakkar; Upaka Rathnayake

doi:10.3934/environsci.2026004

AIMS Environmental Science

2026, Volume 13, Issue 1: 56-98. doi: 10.3934/environsci.2026004

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Review Special Issues

Artificial intelligence and remote sensing frameworks for wildfire monitoring and risk analysis across multiple ecosystems: a review

1.
Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382481, India
2.
Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo F91 YW50, Ireland

Received: 08 July 2025 Revised: 09 January 2026 Accepted: 28 January 2026 Published: 10 February 2026

In this review, we synthesized advancements in remote sensing and machine learning (ML) for wildfire risk assessment, early detection, and spread prediction across forests, grasslands, shrublands, and WUI ecosystems. We examined the capabilities and limitations of key satellite, UAV, and multisensor datasets, alongside the performance and transferability of ML and deep learning models. Major research gaps were identified in cross-ecosystem generalization, real-time data integration, model optimization, and uncertainty quantification. In the review, we contribute an ecosystem-inclusive comparison of RS–AI frameworks and outline future directions for operational, next-generation wildfire monitoring.
- artificial intelligence,
- climate change,
- fire risk assessments,
- machine learning,
- NBR,
- remote sensing & GIS,
- wildfire prediction,
- real time fire detection
Citation: Keval Jodhani, Lakshya Bhatiya, Lalit Sirvi, Abhishek Chanda, Sonal Thakkar, Upaka Rathnayake. Artificial intelligence and remote sensing frameworks for wildfire monitoring and risk analysis across multiple ecosystems: a review[J]. AIMS Environmental Science, 2026, 13(1): 56-98. doi: 10.3934/environsci.2026004

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Abstract

In this review, we synthesized advancements in remote sensing and machine learning (ML) for wildfire risk assessment, early detection, and spread prediction across forests, grasslands, shrublands, and WUI ecosystems. We examined the capabilities and limitations of key satellite, UAV, and multisensor datasets, alongside the performance and transferability of ML and deep learning models. Major research gaps were identified in cross-ecosystem generalization, real-time data integration, model optimization, and uncertainty quantification. In the review, we contribute an ecosystem-inclusive comparison of RS–AI frameworks and outline future directions for operational, next-generation wildfire monitoring.

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