TY - GEN
T1 - Estimation of Forest Carbon from Aerial Photogrammetry
AU - Pulido, Dagoberto
AU - Puettmann, Klaus
AU - Salas, Joaquín
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Carbon estimation
KW - Deep learning
KW - Remote sensing
KW - Tree detection
UR - http://www.scopus.com/inward/record.url?scp=85068340662&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21077-9_10
DO - 10.1007/978-3-030-21077-9_10
M3 - Contribución a la conferencia
AN - SCOPUS:85068340662
SN - 9783030210762
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 105
EP - 114
BT - Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
A2 - Salas, Joaquín
PB - Springer Verlag
T2 - 11th Mexican Conference on Pattern Recognition, MCPR 2019
Y2 - 26 June 2019 through 29 June 2019
ER -