Forest biophysical parameter estimation via machine learning and neural network approaches - ÜNSALAN, CEM (My University, 2023)
This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.
Keywords: Jeofizik Mühendisliği; Bilgisayar Bilimleri; Mühendislik ve Teknoloji; Geophysical Engineering; Computer Sciences; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Temel Bilimler (SCI); Bilgisayar Bilimi; Yerbilimleri; JEOKİMYA VE JEOFİZİK; Engineering, Computing & Technology (ENG); Natural Sciences (SCI); COMPUTER SCIENCE; GEOSCIENCES; GEOCHEMISTRY & GEOPHYSICS; Bilgisayar Bilimi Uygulamaları; Fizik Bilimleri; Genel Yer ve Gezegen Bilimleri; Computer Science Applications; Physical Sciences; General Earth and Planetary Sciences; Artificial Intelligence; Forest; global; machine learning; map