Locally invariant analysis of forest road quality using two different pulse density airborne laser scanning datasets

Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m(-2)) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m(-2)). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.

Automated summary

Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology for determining forest road quality, with the use of a reference DEM. The high pulse density dataset provided better classification results than the low pulse density dataset, and the use of a reference DEM increased the precision of road quality classification. The study compared four interpolation techniques and found that spline interpolation provided the best classification results, showing the potential for identifying poor quality road sections for maintenance purposes.