Preliminary forest tree species classification in northern provinces of Mongolia using Sentinel-2 and machine learning approach - Boldbaatar, Erdenetuya; Wężyk, Piotr; Krawczyk, Wojciech (Quaestiones Geographicae, 2026)
This research addresses the need for precise, wide-scale monitoring of Mongolia’s boreal forests, as a part of a critical ecosystem that stores nearly 30% of global terrestrial carbon. Covering approximately 7.37 Mha in Mongolia, these forests form the southern fringe of the Siberian taiga and are increasingly affected by wildfires (95.9% of total forest losses) and logging (2.5% of losses) since 2000. The study focuses on Selenge, Darkhan-Uul and Tuv provinces located in northern Mongolia, where traditional forest inventory methods do not work well due to the extensive and difficult or inaccessible terrain. To overcome these challenges and needs of precise forest monitoring, we developed a classification framework using Sentinel-2 (European Space Agency [ESA]) multi-temporal satellite imageries (period 2020–2024), acquiring key phenological stages. We applied a random forest (RF) algorithm to classify five dominant tree species, that is, Siberian pine (SBP) (Pinus sibirica Ledeb.), Scotch pine (SP) (Pinus sylvestris L.), Siberian Larch (SBL) (Larix sibirica Ledeb.), Siberian spruce (SBS) (Picea obovata Ledeb.) and Manchurian birch (MB) (Betula platyphylla Sukaczev), forming the forest stand cover. The results of Sentinel-2 imageries processing demonstrate very high classiication overall accuracy (OA = 96.19%, κ = 0.949). Compared with existing forest management maps (based on in situ surveys), SBS (P. obovata Ledeb.) area share was underestimated, whereas MB (B. platyphylla) area share was overestimated, indicating observable differences in traditional forest inventories. This Sentinel-2 classification approach offers timely, cost-effective and accurate data tailored to Mongolian conditions, supporting sustainable forest management, conservation, reforestation, afforestation and REDD + programmes (Reducing Emissions from Deforestation and Forest Degradation).