From small-scale forest structure to Amazon-wide carbon estimates

Edna Rödig (), Nikolai Knapp, Rico Fischer, Friedrich J. Bohn, Ralph Dubayah, Hao Tang and Andreas Huth
Additional contact information
Edna Rödig: UFZ - Helmholtz Centre for Environmental Research
Nikolai Knapp: UFZ - Helmholtz Centre for Environmental Research
Rico Fischer: UFZ - Helmholtz Centre for Environmental Research
Friedrich J. Bohn: UFZ - Helmholtz Centre for Environmental Research
Ralph Dubayah: University of Maryland, College Park
Hao Tang: University of Maryland, College Park
Andreas Huth: UFZ - Helmholtz Centre for Environmental Research

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: Abstract Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-13063-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13063-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-13063-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().