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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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Automatic Methodology for Forest Fire Mapping with SuperDove Imagery.

Dionisio Rodríguez-Esparragón¹, Paolo Gamba², Javier Marcello¹

¹Instituto de Oceanografía y Cambio Global, IOCAG, Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Spain.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

This study shows SuperDove satellite imagery can accurately map wildfire burned areas and severity. The developed method uses vegetation indices and clustering for precise fire assessments, aiding wildfire management.

Keywords:

PlanetScope SuperDove burned-area mapping climate change global warning k-means severity mapping vegetation index wildfire

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Area of Science:

Remote Sensing
Geospatial Analysis
Climate Change Impact

Background:

Climate change is increasing wildfire frequency and intensity globally.
Accurate wildfire mapping is crucial for effective disaster response and land management.
Existing remote sensing methods require validation with new data sources.

Purpose of the Study:

To assess the utility of SuperDove imagery for wildfire mapping.
To develop and test an automated methodology for burned area and severity assessment.
To compare the methodology's performance against established data sources like CEMS.

Main Methods:

Utilized SuperDove (PlanetScope) satellite imagery.
Applied a combination of vegetation indices and clustering algorithms (bisecting k-means, k-means).
Analyzed pre- and post-fire imagery to delineate burned areas and classify severity.

Main Results:

The methodology successfully delineated burned areas with high precision.
Burned areas were effectively classified by severity level.
Results showed good agreement with Copernicus Emergency Management Service (CEMS) data.

Conclusions:

SuperDove imagery shows significant potential for wildfire monitoring.
The automated methodology provides accurate burned area and severity assessments.
This approach can enhance wildfire management strategies despite sensor limitations (e.g., lack of SWIR).