scholarly journals New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

2021 ◽  
Vol 8 (2) ◽  
pp. 201458
Author(s):  
Andrew Burt ◽  
Matheus Boni Vicari ◽  
Antonio C. L. da Costa ◽  
Ingrid Coughlin ◽  
Patrick Meir ◽  
...  

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

2020 ◽  
Author(s):  
A. Burt ◽  
M. Boni Vicari ◽  
A. C. L. da Costa ◽  
I. Coughlin ◽  
P. Meir ◽  
...  

AbstractA large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly due to the difficulty of making direct, whole-tree measurements. We harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in a natural closed forest stand in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We found approximately 40 % of green mass was water, and the majority of AGB was most often found in the crown, but varied from 42–62 %. Woody tissue density varied substantially intra-tree, with both height and radius, but variations were not systematic inter-tree. Terrestrial lidar data were collected pre-harvest, from which volume-derived AGB estimates were retrieved. These estimates were more accurate than traditional allometric counterparts (mean tree-scale relative error: 3 % vs. 15 %). Error in lidar-derived estimates remained constant across tree size, whilst error in allometric-derived estimates increased up to 4 −fold over the diameter range. Further, unlike allometric estimates, the error in lidar estimates decreased when up-scaling to the cumulative AGB of the four trees. Terrestrial lidar methods therefore can help reduce uncertainty in tree- and stand-scale AGB estimates, which would substantially advance our understanding of the role of tropical forests in the global carbon cycle.


Author(s):  
Ellen M. Douglas ◽  
Kate Sebastian ◽  
Charles J. Vorosmarty ◽  
Stanley Wood ◽  
Kenneth M. Chomitz
Keyword(s):  

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.


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