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Predicting forest biodiversity from spaceborne GEDI LiDAR and machine learning: structural drivers and regional transferability across Pacific Northwest forests Cover

Predicting forest biodiversity from spaceborne GEDI LiDAR and machine learning: structural drivers and regional transferability across Pacific Northwest forests

By:   
Open Access
|Sep 2026
DOI: https://doi.org/10.2478/foecol-2026-0018 | Journal eISSN: 1338-7014 | Journal ISSN: 1336-5266
Language: English
Page range: 213 - 226
Submitted on: Jan 26, 2026
Accepted on: Aug 5, 2026
Published online: Sep 30, 2026
Published in print: Sep 1, 2026
Published by: Slovak Academy of Sciences, Institute of Forest Ecology
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2026 Satyam Shah, published by Slovak Academy of Sciences, Institute of Forest Ecology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.