Assessing Handheld Mobile Laser Scanners for Forest Surveys

Ryding, Joseph; Williams, Emily; Smith, Martin J.; Eichhorn, Markus P.

Abstract

Abstract: A handheld mobile laser scanning (HMLS) approach to forest inventory surveying allows virtual reconstructions of forest stands and extraction of key structural parameters from beneath the canopy, significantly reducing survey time when compared against static laser scan and fieldwork methods. A proof of concept test application demonstrated the ability of this technique to successfully extract diameter at breast height (DBH) and stem position compared against a concurrent terrestrial laser scan (TLS) survey. When stems with DBH > 10 cm are examined, an HMLS to TLS modelling success rate of 91% was achieved with the root mean square error (RMSE) of the DBH and stem position being 1.5 cm and 2.1 cm respectively. The HMLS approach gave a survey coverage time per surveyor of 50 m 2/min compared with 0.85 m2/min for the TLS instrument and 0.43 m2/min for the field study. This powerful tool has potential applications in forest surveying by providing much larger data sets at reduced operational costs to current survey methods. HMLS provides an efficient, cost effective, versatile forest surveying technique, which can be conducted as easily as walking through a plot, allowing much more detailed, spatially extensive survey data to be collected.

Citation

Ryding, J., Williams, E., Smith, M. J., & Eichhorn, M. P. (2015). Assessing Handheld Mobile Laser Scanners for Forest Surveys. Remote Sensing, 7(1), 1095-1111. https://doi.org/10.3390/rs70101095

• Journal Article Type: Article
• Acceptance Date: Jan 12, 2015
• Online Publication Date: Jan 19, 2015
• Publication Date: Jan 19, 2015
• Deposit Date: Jun 16, 2016
• Publicly Available Date: Jun 16, 2016
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Pages: 1095-1111
• DOI: https://doi.org/10.3390/rs70101095
• Public URL: https://nottingham-repository.worktribe.com/output/742977
• Publisher URL: http://www.mdpi.com/2072-4292/7/1/1095
• Contract Date: Jun 16, 2016