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Related Experiment Video

Updated: May 26, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Automatic forest-fire measuring using ground stations and Unmanned Aerial Systems.

José Ramiro Martínez-de Dios1, Luis Merino, Fernando Caballero

  • 1Robotics, Computer Vision and Control Group, Universidad de Sevilla, Seville, Spain. jdedios@cartuja.us.es

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary

This study introduces an automated forest fire measurement system using ground and Unmanned Aerial System (UAS) cameras. It accurately determines fire characteristics like spread rate and flame height in real-time.

Keywords:
Unmanned Aerial Systemsforest firesperception systems

Related Experiment Videos

Last Updated: May 26, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Forestry Science
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Accurate real-time forest fire measurement is crucial for effective management and response.
  • Existing methods face challenges due to environmental factors and limited data acquisition.
  • The need for integrated systems combining ground and aerial perspectives is evident.

Purpose of the Study:

  • To develop and validate a novel, automated system for real-time forest fire geometrical measurements.
  • To address inherent errors in fire measurement by leveraging complementary sensor data.
  • To integrate data from multiple sources for enhanced measurement accuracy.

Main Methods:

  • Utilized a network of ground-based infrared and visual cameras, supplemented by Unmanned Aerial Systems (UAS) cameras.
  • Applied advanced image processing and geo-location techniques for individual camera-based measurements.
  • Employed statistical data fusion to integrate data from all sensors for robust fire characterization.

Main Results:

  • The system successfully obtained real-time geometrical measurements, including fire front location, shape, flame height, and rate of spread.
  • Integration of data from diverse camera types significantly improved measurement accuracy and reliability.
  • Validation in near-operational conditions in Portugal and Spain demonstrated system efficacy.

Conclusions:

  • The proposed system offers a significant advancement in automated forest fire monitoring and measurement.
  • The fusion of ground and aerial sensor data provides a comprehensive and accurate approach to fire characterization.
  • This technology holds potential for improved wildfire management and safety.