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

Abstract

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

“…As a frequently used indirect biomass estimation method [46], the allometric biomass equations, where the quantitative relationships between the biomass and the growth variables of a plant are established [11], however, provide an alternative biomass sampling approach in an urban context. As they are reliable for determining tree biomass, a growing number of biomass equations have been proposed for various vegetation species across the world [47][48][49][50][51][52][53][54]. In this study, the allometric biomass equations were considered for calculating the biomass of each quadrat.…”

Aboveground Biomass Allometric Models for Evergreen Broad-Leaved Forest Damaged by a Serious Ice Storm in Southern China

“…The form of a power function or its logarithmic form is commonly used for allometric equations in biomass studies [29,30], DBH is generally the most common independent variable found in the available allometric models, and using DBH as a single variable in the biomass equation can provide a high estimation accuracy of tree biomass [30,31]. In this study, the tip of most of the damaged trees was lost in the ice storm and the measured tree height no longer reflected the real tree height, so we selected DBH alone as a predictor variable to build a biomass allometric model, and the equation is as follows…”

“…Statistical analyses in this study were performed using the statistical software R 3.3.0, package 'nlme' (Linear and Nonlinear Mixed Effects Models). The criteria for evaluating the performance and fitness of the models were the coefficient of determination (R 2 ), coefficient of variation (CV) and systematic errors (Bias) [30]…”

“…The criteria for evaluating the performance and fitness of the models were the coefficient of determination (R 2 ), coefficient of variation (CV) and systematic errors (Bias) [30]…”

The forensic landscape and the population genetic analyses of Hainan Li based on massively parallel sequencing DNA profiling

“…The initial peopling of Hainan island, where laid on one of the modern human northward migration routes from Southeast Asia to East Asia, was likely to the early Holocene and/or even the late Upper Pleistocene (around 7 -27 thousand years ago, kya) based on complete mtDNA genomes sequencing [15], when Hainan island was connected with the mainland southern China and/or northern Vietnam, and the sea level was around 80-120 meter below present day [6,15,20,21]. While, the archaeological findings in Luobi Cave of Sanya County (~10kya) and Xinjie Shell-heap site of Dongfang (~6kya) in Hainan island suggested that the definite traces of anatomically modern humans in Hainan island were evidenced and the colonization and human activities settled on Hainan island could be traced back to the Upper Paleolithic Period (~10kya) or the Upper Pleistocene (1-12.6kya) [22][23][24][25][26]. Due to the formation of the Qiongzhou Strait by the abrupt rise in sea level and the exact evidences which could be traced back to the Neolithic Period [4, 6-8, 21, 27, 28], Hainan island isolated from the mainland southern China during the Last Glacial Maximum (~14.5kya) and the colonization of Hainan island by the ancestors of the Li people, the direct descendants of the original migrants, could be at least traced back to the transition period between the Upper Paleolithic Period and the Early Neolithic Period (~ 10kya) [10-12, 15, 17-19, 24].…”