Quantification of spatial and temporal changes in forest cover is an essential component of
forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar
(SAR) is an ideal source of information on forest dynamics in countries with near-constant
cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation
in landscapes with highly sparse and fragmented forest cover. In this study, the
potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo)
in Ireland is investigated and compared to forest cover estimates derived from three national
(Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006)
and one global forest cover (Global Forest Change) product. Two machine-learning
approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random
Forests and Extremely Randomised Trees classification accuracies were high (98.1–
98.5%), with differences between the two classifiers being minimal (