Estimation of Forest Carbon from Aerial Photogrammetry
Quantifying tree biomass is a critical process for carbon stock estimation at the stand, landscape, and national levels. A major challenge for forest managers is the amount of effort involved to document carbon storage levels, especially in terms of human labor. In this paper, we propose a method to quantify the amount of carbon in forest stands. In our approach, we obtain aerial images from where we build 3D reconstructions of the terrain. Using the resulting orthomosaics, we identify individual trees and process their point clouds to extract information to estimate tree the height and to infer the diameter, which we employ in allometric equations to compute carbon content. We compare our results with carbon estimates obtained from allometric equations applied to manual tree diameter and height measurements.
KeywordsTree detection Carbon estimation Deep learning Remote sensing
Dagoberto Pulido thanks CONACYT for providing a scholarship for his studies. SIP-IPN 20196702 partially funded Joaquín Salas.
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