Sapwood biomass carbon in northern boreal and temperate forests

Abstract

Aim Information on the amount of carbon stored in the living tissue of tree stems (sapwood) is crucial for carbon and water cycle applications. Here, we aim to investigate sapwood‐to‐stem proportions and differences therein between tree genera and derive a sapwood biomass map. Location Northern Hemisphere boreal and temperate forests. Time period 2010. Major taxa studied Twenty‐five common tree genera. Methods First, we develop a theoretical framework to estimate sapwood biomass for a given stem biomass by applying relationships between sapwood cross‐sectional area (CSA) and stem CSA and between stem CSA and stem biomass. These measurements are extracted from a biomass and allometry database (BAAD), an extensive literature review and our own studies. The established allometric relationships are applied to a remote sensing‐based stem biomass product in order to derive a spatially continuous sapwood biomass map. The application of new products on the distribution of stand density and tree genera facilitates the synergy of satellite and forest inventory data. Results Sapwood‐to‐stem CSA relationships can be modelled with moderate to very high modelling efficiency for different genera. The total estimated sapwood biomass equals 12.87 ± 6.56 petagrams of carbon (PgC) in boreal (mean carbon density: 1.13 ± 0.58 kgC m−2) and 15.80 ± 9.10 PgC in temperate (2.03 ± 1.17 kgC m−2) forests. Spatial patterns of sapwood‐to‐stem biomass proportions are crucially driven by the distribution of genera (spanning from 20–30% in Larix to > 70% in Pinus and Betula forests). Main conclusions The presented sapwood biomass map will be the basis for large‐scale estimates of plant respiration and transpiration. The enormous spatial differences in sapwood biomass proportions reveal the need to consider the functionally more important sapwood instead of the entire stem biomass in global carbon and water cycle studies. Alterations in tree species distribution, induced by forest management or climate change, can strongly affect the available sapwood biomass even if stem biomass remains unchanged.

Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations

“…As we were using datasets that are representative for both photosynthetic carbon uptake (FAPAR and SIF) and vegetation carbon turnover (AGB, land cover and burned area), we were also able to constrain many model parameters that control carbon stocks and different processes of carbon turnover such as phenology, mortality and bioclimatic limits. Novel datasets on leaf and sapwood biomass 40 could further help to constrain parameters that control different biomass compartments. However, the largest uncertainty in the size of land carbon stocks is largely caused by different data-based estimates for soil carbon 21,41 .…”

Scaling Approach for Estimating Stand Sapwood Area from Leaf Area Index in Five Boreal species

“…Since the accuracy of water balance components is crucial in this area of work, the authors believe that determining SA, SA plot , LA and LA plot propagated errors are fundamental to any such research and modelling. A number of studies have already highlighted the relevance of creating reliable allometric models, and have analyzed the error associated with their allometric correlations in order to support their reliability [26,[49][50][51].…”

Tropical forests are a net carbon source based on aboveground measurements of gain and loss

“…Reducing the uncertainty of these estimates is not only fundamental to advancing carbon cycle science, but is also of increasing relevance in the context of climate change mitigation policies designed to reduce atmospheric CO 2 emissions from deforestation and forest degradation (e.g., REDD+). Conventional approaches to estimating the net carbon balance of tropical forests rely on satellite-based estimates of forest area change between two time periods combined with information on biomass density (1,7,(9)(10)(11)(12)(13). Alternative strategies based on a range of active (14)(15)(16)(17)(18)(19)(20) and passive (21) remote-sensing techniques have also been advanced; however, the majority of these are limited in terms of geographic scope, spatial resolution and/or data availability.…”