Wheel rut measurements by forest machine-mounted LiDAR sensors - accuracy and potential for operational applications?

被引：33

作者：

Salmivaara, Aura ^{[1
]}

Miettinen, Mikko ^{[2
]}

Finer, Leena ^{[3
]}

Launiainen, Samuli ^{[1
]}

Korpunen, Heikki ^{[4
]}

Tuominen, Sakari ^{[1
]}

Heikkonen, Jukka ^{[5
]}

Nevalainen, Paavo ^{[5
]}

Siren, Matti ^{[1
]}

Ala-Ilomaki, Jari ^{[1
]}

Uusitalo, Jori ^{[4
]}

机构：

[1] Nat Resources Inst Finland, Latokartanonkaari 9, FI-00790 Helsinki, Finland

[2] Argone Ltd, Lempaala, Finland

[3] Nat Resources Inst Finland, Joensuu, Finland

[4] Nat Resources Inst Finland, Tampere, Finland

[5] Univ Turku, Dept Future Technol, Turku, Finland

来源：

INTERNATIONAL JOURNAL OF FOREST ENGINEERING | 2018年 / 29卷 / 01期

基金：

芬兰科学院;

关键词：

Forest trafficability; forest machine instrumentation; LiDAR sensor; rut depth; rut measurement; SOIL COMPACTION; PHYSICAL-PROPERTIES; DISTURBANCE; RESISTANCE; MODEL;

D O I：

10.1080/14942119.2018.1419677

中图分类号：

S7 [林业];

学科分类号：

0829 ; 0907 ;

摘要：

Soil rutting caused by forest operations has negative economic and ecological effects and thus limits for rutting are set by forest laws and sustainability criteria. Extensive data on rut depths are necessary for post-harvest quality control and development of models that link environmental conditions to rut formation. This study explored the use of a Light Detection and Ranging (LiDAR) sensor mounted on a forest harvester and forwarder to measure rut depths in real harvesting conditions in Southern Finland. LiDAR-derived rut depths were compared to manually measured rut depths. The results showed that at 10-20 m spatial resolution, the LiDAR method can provide unbiased estimates of rut depth with root mean square error (RMSE) < 3.5 cm compared to the manual rut depth measurements. The results suggest that a LiDAR sensor mounted on a forest vehicle can in future provide a viable method for the large-scale collection of rut depth data as part of normal forestry operations.

引用

页码：41 / 52

页数：12