Determination of forest road cut slope surface material types using machine learning methods in UAV data
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Federal Lavras-Ufla
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Background: The cost of forest roads is practically estimated by determining the Surface Material Types (SMT). Experts determine SMT by classifying soil, loose soil, and rocky surface material 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 SMT. Conducted on a forest road in Konuralp region of D & uuml;zce district in T & uuml;rkiye, the study involved experts classifying the road's Soil, Loose Soil and Rocky surface material 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 surface material 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.
Açıklama
Anahtar Kelimeler
Random Forest, Support Vector Machine, Remote Sensing, Classification Algorithms
Kaynak
Cerne
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
31
