Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data.
Funding
Winifred Violet Scott Charitable Trust
History
Publication title
IEEE Transactions on Geoscience and Remote Sensing
Volume
52
Issue
12
Pagination
7619-7628
ISSN
0196-2892
Department/School
School of Geography, Planning and Spatial Sciences
Publisher
Ieee-Inst Electrical Electronics Engineers Inc
Place of publication
445 Hoes Lane, Piscataway, USA, Nj, 08855
Rights statement
Copyright 2014 IEEE
Repository Status
Restricted
Socio-economic Objectives
Assessment and management of freshwater ecosystems