DETERMINATION OF FOREST ROAD CUTTING-SLOPE GROUND MATERIAL TYPES USING MACHINE LEARNING METHODS IN UAV DATA

Abstract

Background: The cost of forest roads is practically estimated by determining the ground material types (GMT). Experts determine GMT by classifying soil, loose soil, and rocky ground classes (%) through in-situ measurements, which are both costly and time-intensive. This study aims to reduce cost and time loss by evaluating the effectiveness of high-resolution remote sensing (RS) data in determining GMT. Conducted on a forest road in Konuralp region of Düzce district in Türkiye, the study involved experts classifying the road's Soil, Loose Soil and Rocky ground classes (%) and collecting high-resolution RS data using UAV. The RS data was processed through Random Forest (RF) and Support Vector Machine (SVM) algorithms to classify the ground types, and their accuracy was assessed using the Kappa Coefficient, Overall Accuracy (%) and Conditional Kappa. The images were clipped at 20-meter intervals for detailed analysis. The RS data classifications were then compared with in-situ measurements using statistical analyses Index-of-Agreement (IA).

Results: The RF algorithm made the best identification, although the classification of the Loose Soil class was more difficult for both algorithms compared to the other classes. Both algorithms highest accuracy in identifying the Rocky class.

Conclusions: This study proposes methods to reduce time loss in cost calculations and enhance the use of RS images for estimating forest road costs.