Global forest management data for 2015 at a 100 m resolution.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
10 05 2022
10 05 2022
Historique:
received:
27
08
2021
accepted:
11
04
2022
entrez:
10
5
2022
pubmed:
11
5
2022
medline:
14
5
2022
Statut:
epublish
Résumé
Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.
Identifiants
pubmed: 35538078
doi: 10.1038/s41597-022-01332-3
pii: 10.1038/s41597-022-01332-3
pmc: PMC9091236
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
199Subventions
Organisme : Russian Science Foundation (RSF)
ID : 21-46-07002
Informations de copyright
© 2022. The Author(s).
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