Airborne LiDAR-derived elevation data in terrain trafficability mapping

Heavy off-road traffic causes soil compaction and rutting, which can significantly reduce the yield of forest stands. Reliable information on terrain trafficability, that is, the ability of terrain to support the passage of vehicles, would enable significant enhancement of wood procurement planning...

Full description

Saved in:
Bibliographic Details
Published in:Scandinavian journal of forest research 2017-11, Vol.32 (8), p.762-773
Main Authors: Niemi, Mikko T., Vastaranta, Mikko, Vauhkonen, Jari, Melkas, Timo, Holopainen, Markus
Format: Article
Language:English
Subjects:
airborne laser scanning
Damage
Elevation
Feasibility
Feasibility studies
Forest management
Forests
Lidar
Mapping
Moisture content
open data
Predictions
Procurement
Regression models
remote sensing
Resistance factors
risk modelling
soil bearing capacity
Soil compaction
Soil mapping
Soil resistance
spatial analysis
Statistical analysis
Sustainable forest management
Sustainable forestry
Terrain
Traffic information
Traffic models
Wood
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Heavy off-road traffic causes soil compaction and rutting, which can significantly reduce the yield of forest stands. Reliable information on terrain trafficability, that is, the ability of terrain to support the passage of vehicles, would enable significant enhancement of wood procurement planning and reduction of soil damage. The objective here was to determine the feasibility of airborne scanning light detection and ranging (LiDAR)-derived digital terrain models (DTM) in terrain trafficability mapping. Soil damage was inventoried from a total of 13 km of forwarding trails, and a logistic regression model was fitted for predicting the event of soil damage. DTM-derived soil wetness indices performed well as predictor variables, and DTM-derived local binary patterns also proved useful in terrain trafficability mapping. A prediction accuracy of 83.6% (Cohen's kappa of 0.38) was observed for soil damage probability modelling, using only DTM-derived predictors, and a corresponding accuracy of 85.0% (kappa of 0.45) was achieved when an existing soil map was used as well. In addition to the topography-related features, soil stoniness proved to be a critical factor affecting soil resistance to rutting. Our results indicate that the utilisation of LiDAR-derived elevation data for terrain trafficability mapping is a feasible method in sustainable forest management.
ISSN:0282-7581
1651-1891
DOI:10.1080/02827581.2017.1296181