Precise aboveground biomass estimation of plantation forest trees using the novel allometric model and UAV-borne LiDAR

Abstract

IntroductionPlantation forest is an important component of global forest resources. The accurate estimation of tree aboveground biomass (AGB) in plantation forest is of great significance for evaluating the carbon sequestration capacity. In recent years, UAV-borne LiDAR has been increasingly applied to forest survey, but the traditional allometric model for AGB estimation cannot be directly used without the diameter at breast height (DBH) of individual trees. Therefore, it is practicable to construct a novel allometric model incorporating the crown structure parameters, which can be precisely extracted from UAV LiDAR data. Additionally, the reduction effect of adjacent trees on crown area (Ac) should be taken into account.MethodsIn this study, we proposed an allometric model depending on the predictor variables of Ac and trunk height (H). The UAV-borne LiDAR was utilized to scan the sample plot of dawn redwood (DR) trees in the test site. The raw point cloud was first normalized and segmented into individual trees, whose Acs and Hs were sequentially extracted. To mitigate the effects of adjacent trees, the initial Acs were corrected to refer to the potential maximum Acs under undisturbed growth conditions. Finally, the corrected Acs (Acc) and Hs were input into the constructed allometric model to achieve the AGBs of DR trees.Results and discussionAccording to accuracy assessment, coefficient of determination (R2) and root mean square error (RMSE) of extracted Hs were 0.9688 and 0.51 m; R2 and RMSE of calculated AGBs were 0.9432 and 10.91 kg. The unrestricted growth parts of the tree crowns at the edge of a plantation forest could be used to derive the potential maximum Ac. Compared with the allometric models for AGB estimation relying only on trunk H or on initial Ac and H, the novel allometric model demonstrated superior performance in estimating the AGBs of trees in a plantation forest.

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

“…Conversely, the greatest random pattern was observed in P3, presenting the lowest CFL values (median value of 10.71 kg/m 2 ). These differences limit the use of linear and allometric model regression because tree spatial distribution patterns prevent generalizable models from being obtained [89,90]. Other studies have identified that mixed forests tend to present random distributions following a disturbance occurrence and they are reverted to clusters over time [91]; therefore, these may be the cause of differences in the spatial distribution patterns of trees in our study area.…”

Evaluating the Research Status of the Remote Sensing-Mediated Monitoring of Forest Biomass: A Bibliometric Analysis of WOS

“…The blue cluster is related to LiDAR remote sensing, including the keywords Li-DAR, airborne LiDAR, and height. LiDAR technology is able to obtain three-dimensional structural information of forest trees, realizing the leap from the two-dimensional to threedimensional study of forest trees [13], and it is one of the most effective and accurate techniques for estimating forest biomass [56,57]; LiDAR data overcome the saturation of vegetation indices and SAR in inverted biomass in high-density forest areas [58]. Based on LiDAR data, forest biomass is usually inverted using either direct or indirect methods; the direct method is to establish a regression model with forest biomass by using the characteristic factors such as density, intensity, and height extracted from the point cloud data, and the indirect method is to firstly obtain the structural parameters of the forest, and then to establish the relationship between the forest parameters and the forest biomass [59].…”