Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine

Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine

The frequency of wildfires is increasing worldwide, contributing to a third of forest loss over the last two decades. Tracking burned area progression using traditional optical remote sensing is hindered by cloud and smoke coverage. Therefore, this research employs multitemporal synthetic aperture r...

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Bibliographic Details
Main Authors: Daniel Paluba, Lorenzo G. Papale, Josef Lastovicka, Triantafyllia-M. Perivolioti, Panagiotis Kalaitzis, Antonios Mouratidis, Georgia Karadimou, Premysl Stych
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Burned area
Google Earth Engine (GEE)
Greece
synthetic aperture radar (SAR)
Sentinel-1
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/10598322/

Internet

https://ieeexplore.ieee.org/document/10598322/

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