A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space

By L. Eysn, M. Hollaus, Eva Lindberg, F. Berger, J. Monnet, Michele Dalponte, M. Kobal, M. Pellegrini, Emanuele Lingua, Domen Mongus and 1 more. 2015

226 citations recorded by the source corpus.

In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/68951b5cc1cac3717bbce12f173513e44f9ec5f0

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

1. Lianjun Zhang
2. Zhen Zhen
3. Arne Nothdurft
4. Lindi J. Quackenbush
5. Shenglian Guo
6. Vijay P. Singh
7. Shih-Chieh Kao
8. Rao S. Govindaraju

In Insect Science

1. Zhen Zhen
2. Lianjun Zhang
3. Arne Nothdurft
4. Lindi J. Quackenbush

In Nature and Landscape Conservation

1. Zhen Zhen
2. Lianjun Zhang
3. Arne Nothdurft
4. Lindi J. Quackenbush
5. Friedrich Leisch
6. J. Gove
7. M. Mathur

Citations in the corpus

• Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, background): “, [110,111]); and (4) research that concentrated on comparing different ITCD algorithms or reviewing current ITCD studies [112,113].”
• Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning (2017, background): “[56] reported omission errors between 42% and 65% and commission errors between 22% and 104%, while Dalponte et al.”