Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning
By Tim Ritter, M. Schwarz, Andreas Tockner, Friedrich Leisch, Arne Nothdurft. 2017
60 citations recorded by the source corpus.
Mapping of exact tree positions can be regarded as a crucial task of field work associated with forest monitoring, especially on intensive research plots. We propose a two-stage density clustering approach for the automatic mapping of tree positions, and an algorithm for automatic tree diameter estimates based on terrestrial laser-scanning (TLS) point cloud data sampled under limited sighting conditions. We show that our novel approach is able to detect tree positions in a mixed and vertically structured stand with an overall accuracy of 91.6%, and with omission- and commission error of only 5.7% and 2.7% respectively. Moreover, we were able to reproduce the stand’s diameter in breast height (DBH) distribution, and to estimate single trees DBH with a mean average deviation of ±2.90 cm compared with tape measurements as reference.
Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/7895fdf1e6d17b5fcbbc28518eccdac3e3b332f9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
In Nature and Landscape Conservation
Citations in the corpus
- Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, background): “[87] proposed a two-stage density clustering approach for the automatic mapping of tree positions based on terrestrial lidar point cloud data sampled under limited sighting conditions.”