Mapping forest felling activities in Latvia from Sentinel-2 satellite imagery using machine learning
This thesis develops and evaluates a Random Forest (RF) machine learning classification framework for detecting clear-cut forest felling events using Sentinel-2 multispectral satellite data. While RF classification is widely applied in forest monitoring, its performance for operational clear-cut detection in Latvian semi-boreal forests remains insufficiently studied. This study aims to assess the
