Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN

Ana Malta (), José Lopes, Raúl Salas-González (), Beatriz Fidalgo, Torres Farinha and Mateus Mendes ()
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Ana Malta: RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
José Lopes: Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
Raúl Salas-González: RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Beatriz Fidalgo: RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Torres Farinha: RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Mateus Mendes: RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal

Sustainability, 2023, vol. 15, issue 24, 1-17

Abstract: Pinus pinaster , commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.

Keywords: Pinus pinaster; wood volume; pine tree volume; Mask R-CNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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