Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from light detection and ranging (lidar) has proven...

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Bibliographic Details
Main Authors: Eric L Bullock, Sean P Healey, Zhiqiang Yang, Regino Acosta, Hermelinda Villalba, Katherin Patricia Insfrán, Joana B Melo, Sylvia Wilson, Laura Duncanson, Erik Næsset, John Armston, Svetlana Saarela, Göran Ståhl, Paul L Patterson, Ralph Dubayah
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Environmental Research Letters
Subjects:
lidar
GEDI
biomass
carbon stocks
greenhouse gas inventories
hybrid inference
Online Access:https://doi.org/10.1088/1748-9326/acdf03

Internet

https://doi.org/10.1088/1748-9326/acdf03

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