Comprehensive database of diameter-based biomass regressions for North American tree species

Jenkins, Jennifer C.; Chojnacky, David C.; Heath, Linda S.; Birdsey, Richard A.

doi:10.2737/ne-gtr-319

Cited by 391 publications

(364 citation statements)

References 88 publications

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“…In the absence of direct allometric measurements above 2 m, we estimated branch thickness assuming the total branch biomass decreased linearly with height and that the number of branches doubled for each 2 m increase in height. The leaf biomass was estimated to be 2% of total woody (trunk and branches) biomass for mixed hardwood plant type (Jenkins et al 2003). We found total tree biomass of 7.24 kg m −2 , comprised of 2.89 kg m −2 of trunk, 4.21 kg m −2 of branches and 0.142 kg m −2 of leaf biomass.…”

Section: Biomass Quantity and Thermal Propertiesmentioning

confidence: 91%

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

Garai

Kleissl

Smith

2010

Boundary-Layer Meteorol

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A new method to estimate tree biomass heat storage from thermal infrared (TIR) imaging of biomass surface temperature is presented. TIR images of the canopy are classified into trunk, branches, and leaves. The one-dimensional heat equation in cylindrical coordinates is forced with trunk and branch surface temperatures to simulate the temperature distribution and heat storage in tree trunks and branches. Assuming uniform leaf temperatures, heat storage in leaves is computed from the surface temperature of the leaves separately for the sunlit upper and shaded lower canopy. The sum of trunk, branches, leaf, and air heat storage gives the canopy heat storage. Measurements in a walnut orchard near Davis, California, in early June 2007 showed that biomass heat storage was of the same order as air heat storage and about 1% of daytime and 9% of nighttime net radiation.

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Section: Biomass Quantity and Thermal Propertiesmentioning

confidence: 91%

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

Garai

Kleissl

Smith

2010

Boundary-Layer Meteorol

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“…The statistical models needed to estimate the tree biomass and its litter production are also available (e.g. [21,43,61,65,78]). Models of ground vegetation are less available however, and more research is needed to develop them for different forests.…”

Section: Methods For Calculating Forest Carbon Balance Based On Forestmentioning

confidence: 99%

“…In addition, the Kyoto Protocol states that some of these changes will be accounted for in the GHG emissions of error because the measurements are taken at thousands of forest sites [28,70]. In addition, it is a fairly straightforward matter to estimate the carbon balance of tree biomass based on the inventory data on stem volume, using conversion factors available for many tree species and geographical regions [21,30,32,62,65,74,75,78].…”

Section: Introductionmentioning

confidence: 99%

Carbon accumulation in Finland's forests 1922–2004 – an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil

Lehtonen²,

et al. 2006

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-Comparable regional scale estimates for the carbon balance of forests are needed for scientific and political purposes. We developed a method for deriving these estimates from readily available forest inventory data by using statistical biomass models and dynamic modelling of litter and soil. Here, we demonstrate this method and apply it to Finland's forests between 1922 and 2004. The method was reliable, since the results obtained were comparable to independent data. The amount of carbon stored in the forests increased by 29%, 79% of which was found in the biomass and 21% in the litter and soil. The carbon balance varied annually, depending on the climate and level of harvesting, with each of these factors having effects on the biomass differing from those on the litter and soil. Our results demonstrate the importance of accounting for all forest carbon pools to avoid misleading pictures of short-and long-term forest carbon balance.carbon inventory / forest biomass / greenhouse gas inventory / litter / soil modelling Résumé -Accumulation de carbone dans les forêts finlandaises entre 1922 et 2004, une estimation obtenue en combinant les données de l'inventaire forestier avec une modélisation de la biomasse de la litière et du sol. Une estimation comparable à l'échelle régionale du bilan de carbone des forêts était nécessaire pour des objectifs scientifiques et politiques. Nous avons développé une méthode pour déduire ces estimations de données facilement disponibles de l'inventaire forestier en utilisant des modèles statistique de la biomasse et une modélisation dynamique de la litière et du sol. Ici nous présentons cette méthode et l'appliquons aux forêts de Finlande entre 1922 et 2004. La méthode a été fiable, puisque les résultats obtenus ont été comparables à des données indépendantes. La quantité de carbone accumulée dans les forêts s'est accrue de 29 %,79 % de ce qui a été trouvé dans la biomasse et 21 % dans la litière et le sol. Le bilan de carbone varie annuellement, selon le climat et l'importance de la récolte, chacun de ces facteurs ayant des effets sur la biomasse différents de ceux qui agissent sur la litière et sur le sol. Nos résultats démontrent l'importance de comptabiliser tous les réservoirs de carbone en forêt pour éviter des images trompeuses du bilan de carbone des forêts à court et moyen terme.inventaire du carbone / biomasse forestière / inventaire des gaz à effet de serre / litière / sol

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“…This biomass model is of the form Aboveground Biomass (kg) ¼ CF$a$[DBH (cm) b , where a and b are parameters, CF is bias correction CF ¼ exp (Mean Squared Error/2) and DBH is diameter at breast height (cm). is the one most commonly used in the scientific literature [24,25].…”

Section: Methodsmentioning

confidence: 99%

Productivity and cost of biomass harvesting for energy production in coppice natural stands of Quercus pyrenaica Willd. in central Spain

Laina

Tolosana

Ambrosio

2013

Biomass and Bioenergy

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a b s t r a c tBiomass growing stocks, harvesting productivity and costs of energy production, were studied in thinnings of Quercus pyrenaica.Eleven forest sites were selected in coppices located in Castilla y Leo´ n (Spain). Three systems were employed for harvesting: a mech-anised whole tree harvesting (WTH) system, which included chipping at landing; a fully mechanised cut-to-length (CTL) harvesting system for firewood; and a semi-mechanised CTL system with manual felling and bunching. Three WTH trials had a hauling off 34.7 e44.1 oven-dried tonnes (odt) ha À1 . In contrast, only 11.6e30.0 odt ha À1 were obtained in the other eight CTL trials.Biomass weight equations for stems and whole trees were fitted. Worker operations were time-studied using software designed by the authors. The WTH system reached maximum productivity rates of 3.9 oven-dried tonnes per productive hour (odt h À1 ) for the felling and bunching operation and 6.9 odt h À1 for the forwarding opera-tion. The mechanised CTL harvesting method achieved a productivity range of 1.3 e0.5 odt h À1 for the harvesting operation. Furthermore, the average forwarding produc-tivity was 7.3 odt h À1 . Motor-manual felling and crosscutting in the CTL system accom-plished a productivity range of 0.7e1.9 odt h À1 . Manual bunching resulted in similar values. The lowest firewood unit cost for trees with 10 cm of diameter at breast height (DBH) was 62.0 V odt À1 using a semi-mechanised CTL system. Under similar conditions, this cost was 85.2 V odt À1 for the mechanised CTL system. Finally, the cost corresponding to chips at landing from WTH sites (average DBH ¼ 10 cm) was 65.3 V odt À1 . IntroductionThe Spanish Renewable Energy Plan (2005e2010) [1] has finished. This plan tried to increase renewable energy (RE) production rate of total primary energy production (4.77 Â 10 12 MJ) from 6.3% in 2005 to 30% in 2010. Bioenergy was an important part of the plan's goal. Currently, there are only 648 MW generating capacity installed [2] in biomass power plants; however, the plan had aimed to reach 1317 MW by 2010. The objective remained unaccomplished despite government subsidies for electricity production from forest residuals (0.11 V kWh À1 ) and from forest woody crops (0.16 V kWh À1 ) [3]. According to the RE National Action Plan 2011e2020 [4], renewable energies accounted for 9.4% of primary energy consumption in 2010, 132,000 Mtep, whereas the goal for 2020 is 20.1%. Regarding electricity production, RE

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Comprehensive database of diameter-based biomass regressions for North American tree species

Cited by 391 publications

References 88 publications

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

Carbon accumulation in Finland's forests 1922–2004 – an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil

Productivity and cost of biomass harvesting for energy production in coppice natural stands of Quercus pyrenaica Willd. in central Spain

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