Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear...

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Bibliographic Details
Main Authors: Giandomenico De Luca, Giuseppe Modica, João M. N. Silva, Salvatore Praticò, José M.C. Pereira
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Digital Earth
Subjects:
post-fire assessment
fire severity
post-fire vegetation recovery
random forest (rf)
scikit-learn
fraction image extraction
spectral unmixing
endmembers
crown fire damage mapping
fully constrained least squares
pixel purity index
Online Access:http://dx.doi.org/10.1080/17538947.2023.2243900

Internet

http://dx.doi.org/10.1080/17538947.2023.2243900

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