Abstract
Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.
Highlights
Forest biomass is an important indicator of forest productivity, carbon storage and forest carbon sequestration capacity, and it has been widely investigated by the scientific community [1,2,3]
We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the above-ground biomass (AGB) of trees by using specific basic wood density values
Our estimates showed that most of the diameter at breast height (DBH) of the ten tree species obtained from the terrestrial laser scanning (TLS) data were below the 1:1 dashed line, indicating that the DBH estimated from point cloud data was smaller than the one measured in the field (Figure 4a)
Summary
Forest biomass is an important indicator of forest productivity, carbon storage and forest carbon sequestration capacity, and it has been widely investigated by the scientific community [1,2,3]. China has taken measures to increase forest biomass and carbon storage by Forests 2019, 10, 936; doi:10.3390/f10110936 www.mdpi.com/journal/forests. The assessment of forest biomass includes the estimation of both above-ground biomass (AGB) and underground biomass. The underground biomass is difficult to quantify, but it is relatively small to the AGB [6]. The estimation of AGB has always been the main focus in biomass research. AGB calculations rely on tree structure parameters, such as diameter at breast height (DBH), tree height, crown radius, etc., form which the AGB can be calculated using allometric biomass models, which can be very effective when applied to tree species and productivity ranges with reliable calibration data
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