Estimation of Tree Biomass, Carbon Stocks, and Error Propagation in Mecrusse Woodlands

Abstract

We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees; in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg•ha −1 , respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among individual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Almost none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attributed to plot selection and 10% to the allometric biomass model. The total error of our measurements was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.

Live above- and belowground biomass of a Mozambican evergreen forest: a comparison of estimates based on regression equations and biomass expansion factors

“…The errors of regression-based biomass estimates are the same as those obtained by Magalhães and Seifert (2015b) for the relevant tree components. However, the errors of the BEF-based estimates were slightly different from those obtained by Magalhães and Seifert (2015c); these differences might be attributed to the different approaches used to compute the errors.…”

“…In fact, according to Cunia (1986a) and McRoberts and Westfall (2015), when the statistical model used fits reasonably well the sample data, the statistical model error is generally small and can be ignored. The second source of error is quantified by Magalhães and Seifert (2015b). The third source of error is expressed by the parameter variancecovariance matrix, S bb .…”

“…Therefore, the precision and bias associated with those estimates were critically analysed. This study is a follow up of the study by Magalhães and Seifert (2015b). However, unlike the study by those authors, that considered only five tree components, the current study is extended to 11 components (taproot, lateral roots, root system, stem wood, stem bark, stem, branches, foliage, crown, shoot system, and whole tree), and to bias analyses not considered by Magalhães and Seifert (2015b, c) for either method of estimating biomass.…”

Biomass conversion and expansion factors in Douglas-fir stands of different planting density: variation according to individual growth and prediction equations

“…However, BCEF values can vary according to vegetation type, precipitation regime, mean annual temperature and tree age and size (e.g. Lehtonen et al, 2004, Tobin & Nieuwenhuis, 2007, Petersson et al, 2012, thus, use of default values for national-or regional-scale estimates might result in unreliable assessments of biomass, and carbon (Magalhães & Seifert, 2015). This is recognized by LULUCF guidance, since the higher tier methods call for greater specificity, such as country-level factors and factors specific to species.…”

Aboveground Biomass and Carbon in a South African Mistbelt Forest and the Relationships with Tree Species Diversity and Forest Structures

“…From previous studies, the quantification of forest biomass relies on different methods, from remote sensing techniques to tree-based allometric approaches [11,[21][22][23][24]. Multispecies allometric equations have extensively been studied and offer possibilities to accurately estimate forest biomass at smaller scales, and to elucidate the relationships of forest biomass with stand variables.…”