TY - GEN
T1 - A Dataset for Individual Tree Delineation from 3D Point Cloud data
AU - Song, Qian
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - LiDAR scanning data, which is able to acquire the vertical structures of forests, is of great potential in forest monitoring and biodiversity quantification. Besides, the derivation of some forest indices, such as biomass, relies on individual tree delineation (ITD). In this paper, we generated a dataset for individual tree delineation using LiDAR-derived point clouds. This dataset can be used to fairly compare different ITD methods and to develop deep learning algorithms for tree segmentation. The acquired LiDAR data consist of 0.94 billion points covering an area of about 31 km2 in the Netherlands. We first used a rule-based algorithm to remove non-tree points. And then a mean shift clustering method is utilized to segment the points. Besides, we proposed a method that compares the highest point in the same cluster to evaluate the delineation results. In the future, the derived segmentation result will be compared with existing individual tree delineation algorithms.
AB - LiDAR scanning data, which is able to acquire the vertical structures of forests, is of great potential in forest monitoring and biodiversity quantification. Besides, the derivation of some forest indices, such as biomass, relies on individual tree delineation (ITD). In this paper, we generated a dataset for individual tree delineation using LiDAR-derived point clouds. This dataset can be used to fairly compare different ITD methods and to develop deep learning algorithms for tree segmentation. The acquired LiDAR data consist of 0.94 billion points covering an area of about 31 km2 in the Netherlands. We first used a rule-based algorithm to remove non-tree points. And then a mean shift clustering method is utilized to segment the points. Besides, we proposed a method that compares the highest point in the same cluster to evaluate the delineation results. In the future, the derived segmentation result will be compared with existing individual tree delineation algorithms.
KW - forest
KW - forest monitoring
KW - Individual tree delineation (ITD)
KW - point cloud
UR - http://www.scopus.com/inward/record.url?scp=85178376618&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282259
DO - 10.1109/IGARSS52108.2023.10282259
M3 - Conference contribution
AN - SCOPUS:85178376618
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1369
EP - 1372
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -