Allometric relationships of stand level carbon stocks to basal area, tree height and wood density of nine tree species in Bangladesh

• Published in: Global ecology and conservation Vol. 22; p. e01025
• Main Authors: Khan, Md. Nabiul Islam, Islam, Md. Rafikul, Rahman, Atikur, Azad, Md. Salim, Mollick, Abdus Subhan, Kamruzzaman, Md, Sadath, Md. Nazmus, Feroz, S.M., Rakkibu, Md. Golam, Knohl, Alexander
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020; Elsevier
• Subjects: Aboveground biomass; allometry; Bangladesh; Belowground biomass; biomass; carbon; climate change; deforestation; forestry; forests; prediction; reducing emissions from deforestation and forest degradation; spatial data; stand basal area; Stand variables; Three-variable allometric model; tree height; trees; Wood density; Bangladesh; Wood density; Stand variables; Three-variable allometric model; Belowground biomass; Aboveground biomass
• ISSN: 2351-9894; 2351-9894
• DOI: 10.1016/j.gecco.2020.e01025

Forest carbon stocks can be estimated using some easily measureable stand variables, such as basal area, mean or maximum tree height and wood density. Estimation of stand level carbon stocks per unit area of forests or woodlands is of great importance for forestry related mitigation options of future climate change such as United Nations-led “Reducing Emissions from Deforestation and Forest Degradation (REDD+)” Program. Although tree level common biomass models are available, allometric models to predict stand level carbon stocks are not well represented. The study aims to develop allometric relationships of stand level carbon stocks to basal area, tree height and wood density of nine tree species found in plantations and natural patches in Bangladesh. The stand level allometric models were built based on extensive field data and published tree level biomass models using easily measurable stand variables, e.g. basal area, mean or maximum tree height and species wood density. The allometric models having three variables, such as stand basal area, mean or maximum tree height and wood density of tree species yield high precision estimates of stand level carbon stocks having less than 2% mean prediction errors (MPEs). Although the three-variable model having basal area, wood density and mean tree height was found appropriate for high precision estimates of stand level carbon stocks, the mean tree height can be replaced by stand maximum/dominant tree height depending on availability of spatial data. This study provides a multi-species stand level carbon stocks model as a useful alternative of individual level assessment for estimating the forest carbon stocks from easily measurable stand variables. •Stand level carbon stocks in pure stands can be obtained through stand basal area, mean or maximum tree height and wood density of tree species•In the model, stand maximum/dominant tree height can be used instead when mean tree height is unavailable.•A combined-variable model having product of BA, wood density and maximum/dominant tree height would be a suitable alternative.