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Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning.

Yifang Ban¹, Puzhao Zhang², Andrea Nascetti³

¹Division of Geoinformatics, KTH Royal Institute of Technology, 10044, Stockholm, Sweden. yifang@kth.se.

Scientific Reports

|January 30, 2020

Summary

This summary is machine-generated.

This study shows that Sentinel-1 Synthetic Aperture Radar (SAR) time series data, combined with a deep learning framework, can effectively monitor wildfire progression in near real-time. The CNN-based approach offers higher accuracy in detecting burnt areas compared to traditional methods.

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Area of Science:

Earth Observation
Artificial Intelligence
Environmental Monitoring

Background:

Devastating wildfires necessitate effective near real-time monitoring for emergency response and mitigation.
Synthetic Aperture Radar (SAR) offers cloud and smoke penetration capabilities for day and night imaging, crucial for wildfire monitoring.

Purpose of the Study:

To investigate and demonstrate the potential of Sentinel-1 SAR time series data for near real-time wildfire progression monitoring.
To develop and apply a deep learning framework for automated burnt area detection using SAR data.

Main Methods:

Utilized Sentinel-1 SAR time series data.
Developed a Convolutional Neural Network (CNN) based deep learning framework.
Exploited pre-fire SAR time series to characterize temporal backscatter variations for burnt area detection.

Main Results:

Sentinel-1 SAR backscatter successfully detected wildfires and captured temporal progression for three major fires (Elephant Hill, Camp Fire, Chuckegg Creek).
The CNN-based deep learning framework demonstrated higher accuracy in distinguishing burnt areas compared to the traditional log-ratio operator.

Conclusions:

Spaceborne SAR time series data, integrated with deep learning, shows significant potential for near real-time wildfire monitoring.
Future advancements with constellations like RADARSAT and SAR CubeSats will enhance the frequency and utility of SAR data for wildfire management.