Reference : Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile ...
Scientific journals : Article
Life sciences : Phytobiology (plant sciences, forestry, mycology...)
http://hdl.handle.net/2268/198776
Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning
English
[fr] Inventaire forestier à partir de LiDAR terrestre: une comparaison d'un scanner LiDAR statique avec un mobile portable à main
Bauwens, Sébastien mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
Bartholomeus, Harm mailto [Wageningen University > Laboratory of Geo-information Science and Remote Sensing > > >]
Calders, Kim mailto [National Physical Laboratory > Climate and Optical Group > Earth Observations > >]
Lejeune, Philippe mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
21-Jun-2016
Forests
MDPI
7
6
Forest Ground Observations through Terrestrial Point Clouds
127
Yes
International
Basel
Switzerland
[en] forestry ; terrestrial laser scanning ; hand-held mobile laser scanning ; HMLS ; TLS ; SLAM ; digital elevation model ; stem mapping
[en] The application of static terrestrial laser scanning (TLS) in forest inventories is becoming
more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract
forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this study,
we assessed and compared a hand-held mobile laser scanner (HMLS) with two TLS approaches
(single scan: SS, and multi scan: MS) for the estimation of several forest parameters in a wide range
of forest types and structures. We found that SS is competitive to extract the ground surface of
forest plots, while MS gives the best result to describe the upper part of the canopy. The whole
cross-section at 1.3 m height is scanned for 91% of the trees (DBH > 10 cm) with the HMLS leading
to the best results for DBH estimates (bias of 0.08 cm and RMSE of 1.11 cm), compared to no
fully-scanned trees for SS and 42% fully-scanned trees for MS. Irregularities, such as bark roughness
and non-circular cross-section may explain the negative bias encountered for all of the scanning
approaches. The success of using MLS in forests will allow for 3D structure acquisition on a larger
scale and in a time-efficient manner.
DynaFfor, PreREDD+
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/198776
10.3390/f7060127
http://www.mdpi.com/1999-4907/7/6/127

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