Carbon stock and density of northern boreal and temperate forests

Abstract

Aim To infer a forest carbon density map at 0.01°resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests. LocationThe study area extends from 30°N to 80°N, covering three forest biomes -temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) -over three continents (North America, Europe and Asia). MethodsThis study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventorybased biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons.Results Our derived carbon density map agrees well with inventory data at regional scales (r 2 = 0.70-0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m −2 retained in TCF and 5.80 ± 2.21 kg C m −2 in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m −2 . Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant. Main conclusionsThe presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.

Generation of a global fuel data set using the Fuel Characteristic Classification System

“…-Northern boreal and temperate above-ground biomass (AGB) from the carbon stock and density map developed by Thurner et al (2014). This map is based on the growing stock volume (GSV) estimates obtained with the Biomasar algorithm using EN-VISAT ASAR images.…”

“…Many studies have shown continental differences within biogeographical regions, including species richness (Barthlott et al, 2007;Kreft and Jetz, 2007), total biomass Baccini et al, 2012;Banin et al, 2014;Thurner et al, 2014), and fire behavior (Lehmann et al, 2014;Rogers et al, 2015). Some of the most evident differences regarding vegetation behavior to fire were addressed with the inclusion of the regional GlobCover map to account for needle-leaved deciduous trees (Larix) in Asia (fuelbeds with land covers 92, 102, 112 and 122), and with the creation of specific fuelbeds for Australia with Eucalyptus vegetation (land covers 43, 113, 123 and 133).…”

Towards an advanced atmospheric chemistry-enabled ESM with dynamic land surface processes: Part I - Linking LPJ-GUESS (v4.0) with EMAC modelling system (v2.53)

“…Instead we performed basic evaluation using an expertderived Potential Natural Vegetation (PNV) map and using remotely-sensed data sets of tree cover (Dimiceli et al, 2015), canopy height (Simard et al, 2011) and biomass (Avitabile et al, 2016;Thurner et al, 2014) to consider how LPJ-GUESS responds when EMAC climate is used as the forcing data. To provide an overall summary metric of data-model agreement across the relevant spatial domain, the Normalised Mean Error (NME) is presented following the prescription and recommendations…”

“…We combined two biomass datasets, one tropical (Avitabile et al, 2016) and one northern temperate and boreal (Thurner et al, 2014), The coupled model simulates well the global patterns of biomass (Fig 3), but it does not capture the very high biomass observed in the tropical forests. This underestimation may be due to the use of pre-industrial nitrogen deposition data.…”

An overview of existing and promising technologies for national forest monitoring

“…For instance, a C-band SAR signal is known to saturate rapidly with forest biomass because its wavelength is small (5-6 cm). However, using a hypertemporal series of C-band images, Santoro et al (2011) and Thurner et al(2014) were able to estimate the biomass of boreal and temperate forests. As part of the Copernicus mission from the European Space Agency (ESA), Sentinel-1A with C-band radar was successfully launched in April 2014 and will soon provide C-band imagery.…”