Aboveground biomass estimation at different scales for subtropical forests in China

Abstract

BackgroundThe accurate estimation of forest biomass at different scales is the critical step in the assessment of forest carbon stocks. We used three models at increasing scales: allometric model at ecoregional scale (model 1), dummy variable allometric model at both ecoregion and regional scales (model 2), and allometric model at regional scale (model 3) to estimate the aboveground biomass of six subtropical forests in China. Furthermore, we also tested whether wood density can improve the accuracy of the allometric model at regional scale.ResultsAboveground biomass estimates for six subtropical forests were significantly affected by the ecoregions (p < 0.05). Model 1 and model 2 had good fitness with higher values of R 2, lower RSE (residual standard error) and MPSE (mean percent standard error) than model 3. The values of MPSE for model 1, model 2, and model 3 ranged from 2.79 to 30.40%, 5.15 to 40.94%, and 13.25 to 80.81% at ecoregion scale, respectively. At regional scale, MPSE of model 2 was very similar to that of model 1, and was less than model 3. New allometric models with wood density had greater R 2, lower RSE and MPSE than the traditional allometric models without wood density variable for six subtropical forests at regional scale.ConclusionThe dummy variable allometric models have better performances to estimate aboveground biomass for six subtropical forests in China, which provided an effective approach to improve the compatibility of forest biomass estimations from different scales. New allometric models with wood density substantially improved accuracies of aboveground biomass estimation for subtropical forests at regional scale.Electronic supplementary materialThe online version of this article (10.1186/s40529-017-0199-1) contains supplementary material, which is available to authorized users.

Site-Specific Allometric Models for Prediction of Above-and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China

“…There has considerable divergence in the previous research on the effect of adding H to the biomass models to predict tree biomass: while some studies reported no improvement-or even deterioration [12,22,27]-other studies reported improvement [32,36]. Many workers suggest that including height in models will offset the site effect [37,38], but Dutcă et al reported that height inclusion reduces model site specificity only for stem biomass and increases site specificity for total trees and total aboveground biomass [39].…”

“…Wood density is considered to be an important variable in allometric model for biomass estimation, especially for general biomass models covering many tree species [24]. Some studies indicate that taking wood density as variables in allometric models can greatly improve accuracies of biomass model in tropical forests and subtropical evergreen broadleaved forest [23,32], but others demonstrated that adding wood density did not improve model performance [28]. In this study, combining WD and DBH as a single variable (DBH 2 WD) did not improve model fitness, but adding WD as a second variable to DBH could reduce the CV and Bias and increase the R 2 and perform the best fitness among all of the models.…”

“…Tree height (H) is often added to the biomass equation to improve the model accuracy, which can be used as the second variable in the equation [9,31] or combined with DBH, in the form of DBH 2 H, as one variable [28]. However, there is considerable divergence in previous research on the effect of adding H and its form of addition in allometric models for the prediction of tree biomass [27,32]. Wood density (WD) is a basic characteristic of trees and has been considered as important variable in allometric models for biomass estimation [24], but its performance in biomass prediction of subtropical evergreen broadleaved forest needs more examination.…”

Disturbance and Growth: Deciphering the Impact of Resin Tapping on Biomass Allocation and Allometric Growth Patterns in Pinus massoniana

“…Tree height (H) is usually added as a second parameter in biomass models to improve estimation accuracy, but previous studies have shown varying effects of H on model accuracy. Some studies indicate that H can improve model accuracy (Wang et al, 2021;Peng et al, 2017)， while others suggest that H may reduce or have no effect on model accuracy (Gou et al, 2017). Studies have shown that including H in biomass models can offset regional impacts, and even limited sampling of heights can be used to refine height-diameter allometries (Sullivan et al, 2018).…”

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

“…In forest inventories in general, DBH and height (H) are measured and registered in the field in order to predict the AGB using allometric models. AGB is then converted to carbon density for each field-reference tree (Chave et al 2014, Mensah et al 2016, Zhang et al 2016, Peng et al 2017. With ALS, it became possible to measure the heights of trees in large forests in a short time, which makes it more practical compared to filed-measured methods.…”