A Systematic Review of Remote Sensing for Forest Fire Monitoring

One hundred fifty peer-reviewed studies from 2016 to 2026 were analyzed using a novel automation-assisted methodology. This dataset compiles the state-of-the-art in advanced remote sensing and intelligent monitoring for forest fire prevention. The review employed a custom Python script to search six databases, following PRISMA guidelines.

Use Cases

• Benchmarking new remote sensing algorithms against published literature from the last decade.
• Identifying trends in automation and deep learning applications for fire detection.
• Conducting meta-analyses on the efficacy of different monitoring technologies.
• Informing policy or system design based on a compiled evidence base.

Strengths

• Covers a defined set of 150 peer-reviewed studies, providing a substantial evidence base.
• Employs a rigorous, PRISMA-compliant systematic review methodology with automated search.
• Has a clear temporal focus on recent advancements (2016-2026).

Limitations

• Column names and specific data structure are not provided, limiting immediate analysis.
• The dataset size (5.8 MB) and described content suggest it may contain summary findings rather than raw extracted data.
• Relies on a single author's compilation and automation script without detailed documentation of the extraction process.

Provenance

Source

Gabriel Osei Forkuo

Collection Method

Automation-assisted systematic review using a custom Python script to search six academic databases.

Time Range

2016-2026

Freshness

2026-03-25

Geography

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