Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery

Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery

Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive and often lack the spatial detail required for contemporary decision-making. This study ad...

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Bibliographic Details
Main Authors: Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Remote Sensing
Subjects:
forest attributes
spatial resolution
machine learning
remotely sensed data
prediction
performance
Online Access:https://www.mdpi.com/2072-4292/17/4/715
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