20th-Century hurricanes leave long-lasting legacies on tropical forest height and the abundance of a dominant wind-resistant palm.

canopy height machine learning tropical cyclones

Journal

Ecology and evolution
ISSN: 2045-7758
Titre abrégé: Ecol Evol
Pays: England
ID NLM: 101566408

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 04 05 2023
revised: 08 11 2023
accepted: 13 11 2023
medline: 29 11 2023
pubmed: 29 11 2023
entrez: 29 11 2023
Statut: epublish

Résumé

Projected increases in hurricane intensity under a warming climate will have profound effects on many forest ecosystems. One key challenge is to disentangle the effects of wind damage from the myriad factors that influence forest structure and species distributions over large spatial scales. Here, we employ a novel machine learning framework with high-resolution aerial photos, and LiDAR collected over 115 km

Identifiants

pubmed: 38020686
doi: 10.1002/ece3.10776
pii: ECE310776
pmc: PMC10680431
doi:

Banques de données

Dryad
['10.5061/dryad.7h44j1011I']

Types de publication

Journal Article

Langues

eng

Pagination

e10776

Informations de copyright

© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Déclaration de conflit d'intérêts

The authors declare no competing interests.

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Auteurs

María Uriarte (M)

Department of Ecology Evolution & Environmental Biology Columbia University New York New York USA.

Chengliang Tang (C)

Department of Statistics Columbia University New York New York USA.

Douglas C Morton (DC)

Biospheric Sciences Laboratory NASA Goddard Space Flight Center Greenbelt Maryland USA.

Jess K Zimmerman (JK)

Department of Environmental Sciences Universidad de Puerto Rico San Juan Puerto Rico USA.

Tian Zheng (T)

Department of Statistics Columbia University New York New York USA.

Classifications MeSH