Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations

ABSTRACT

Highly accurate, rapid forest inventory techniques are needed to enable forest managers to address the increasing demand for sustainable forestry. In the last two decades, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning have become internationally established as forest mapping and monitoring methods. However, recent advances in sensors and in image processing – particularly Structure from Motion (SfM) technology – have also enabled the extraction of dense point clouds from images obtained by Digital Aerial Photography (DAP). DAP is cheaper than ALS, especially when the systems are mounted on small unmanned aerial vehicles (UAVs), and the density of the point cloud can easily reach the levels yielded by ALS devices. The main objective of this study was to evaluate and compare the usefulness of ALS-derived and UAV(SfM)-derived high-density point clouds for detecting and measuring individual tree height in Eucalyptus spp. plantations established on complex terrain. A total of 325 reference trees were measured and located in 6 square plots (400 m²). The individual tree crown (ITC) delineation algorithm detected 311 from the ALS-derived data and 259 trees from the UAV(SfM)-derived data, representing accuracy levels of, respectively, 96% and 80%. The results suggest that at plot level, UAV(SfM)-generated point clouds are as good as ALS-derived point clouds for estimating individual tree height. Furthermore, analysis of the differences in digital elevation models at landscape level showed that the elevations of the UAV(SfM)-derived terrain surfaces were slightly higher than the ALS-derived surfaces (mean difference, 1.14 m and standard deviation, 1.93 m). Finally, we discuss how non-optimal UAV-image-acquisition conditions and slope terrain affect the ITC delineation process.

Acknowledgements

We gratefully acknowledge RAIZ and the Navigator Company for supplying the inventory databases and support the airborne surveys and TLS field work. We thank the Portuguese Science Foundation (SFRH/BD/52408/2013) for funding the research activities of Juan Guerra and the Galician Government and European Social Fund (Official Journal of Galicia – DOG No. 52, 17/03/2014 p. 11343, exp: POS-A/2013/049) for funding the postdoctoral research stays of Eduardo González-Ferreiro. This research was supported by SuFoRun project ‘Models and decision SUpport tools for integrated FOrest policy development under global change and associated Risk and Uncertaintʼ funded by the European Union’s H2020 research and innovation programme under the Marie Sklodowska Curie Grant Agreement No. 691149. We also acknowledge support from Terradrone Co. during the airborne survey. The research was carried out in the Centro de Estudos Florestais: a research unit funded by Fundação para a Ciência e a Tecnologia (Portugal) within UID/AGR/00239/2013.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Portuguese Science Foundation [SFRH/BD/52408/2013], Galician Government and European Social Fund: [Official Journal of Galicia – DOG No. 52, 17/03/2014], European Union’s H2020 research and innovation program [Marie Sklodowska Curie Grant Agreement No. 691149].