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Preliminary Forest Tree Species Classification in Northern Provinces of Mongolia Using Sentinel-2 and Machine Learning Approach Cover

Preliminary Forest Tree Species Classification in Northern Provinces of Mongolia Using Sentinel-2 and Machine Learning Approach

Open Access
|Mar 2026

Figures & Tables

Fig. 1.

Study area: (A) Overview map; (B) Provinces in test area: Selenge, Darkhan-Uul and Tuv; (C) Test area over sub-provinces: Mandal, Bayangol, Javkhlant, Yruu, Khuder, Khongor, Shariin gol and Erdene (Mongolian Environmental Database, 2025).

Fig. 2.

Examples of dominant tree species in northern Mongolia: (A) MB; (B) SBL; (C) SP; (D) SBP; (E) SBS. MB, Manchurian birch; SBL, Siberian larch; SBP, Siberian pine; SBS, Siberian spruce; SP, Scotch pine.

Fig. 3.

Main drivers of forest loss in Selenge, Darkhan-Uul and Tuv provinces (2001–2024).

Fig. 4.

Flow chart of data processing.

Fig. 5.

The Sentinel-2A (ESA) imageries during LEAF-ON (Leaf-on growing season) period used in the study: (A) 21.07.2020; (B) 30.08.2021; (C) 30.08.2022; (D) 30.08.2023; (E) 24.08.2024. ESA, European Space Agency.

Fig. 6.

Example of the Mongolian forest digital forest map containing data form field inventory visualised in ArcGIS Pro software (Esri, Redlands, CA, USA) (Data source: Mongolian Agency of Forestry 2019).

Fig. 7.

Mean NDVI values of AOIs of analysed tree species in test site. AOIs, area of interest.

Fig. 8.

Forest tree species classification maps for analysed periods: (A) 21.08.2020; (B) 30.08.2021; (C) 30.08.2022; (D) 30.08.2023; (E) 24.08.2024.

Fig. 9.

Change detection map of forest tree species classification: (A) From 2020 to 2021; (B) From 2021 to 2022; (C) From 2022 to 2023; (D) From 2023 to 2024.

Fig. 10.

Dynamic of classified (random forest) forest main tree species classes (single species/total area; km2) on S-2 image (European Space Agency).

Fig. 11.

Confusion matrices for forest type classification (2020–2024).

Classification results using RF approach (bold values represent the total area and overall percentage across all classes)_

Class nameManchurian birchSiberian larchScotch pineSiberian pineSiberian spruceSum of forest maskNonforestOtherTotal (km2)
Sentinel 2 21.07.2020Area (km2)2188.51178.1736.0664.1243.45010.01142.31233.97386.3
Percentage (%)29.615.910.09.03.367.815.516.7100.0
Sentinel 2 30.08.2021Area (km2)1900.71614.3692.5626.5280.85114.71037.61233.97386.3
Percentage (%)25.721.99.48.53.869.214.016.7100.0
Sentinel 2 30.08.2022Area (km2)2107.11600.3602.4589.7243.05142.41009.91233.97386.3
Percentage (%)28.521.78.28.03.369.613.716.7100.0
Sentinel 2 30.08.2023Area (km2)2127.31555.1699.0612.5241.75235.591681233.97386.3
Percentage (%)28.821.19.58.33.370.912.416.7100.0
Sentinel 2 24.08.2024Area (km2)2008.01367.6769.6685.1281.05111.31041.11233.97386.3
Percentage (%)27.218.510.49.33.869.214.116.7100.0

Results of S-2 classification vs_ forest inventory data (bold values represent the total area and overall percentage across all classes)_

ClassForest inventory map (2019)S-2DifferenceS-2Difference
21/07/202024/08/2024
km2%km2%km2%km2%km2%
Manchurian birch2773.953.82188.543.7–585.4–21.12008.039.3–765.9–27.6
Siberian larch912.317.71178.123.5265.829.11367.626.8455.349.9
Scotch pine902.717.5735.914.7–166.8–18.5769.615.1–133.1–14.7
Siberian pine517.410.0664.113.3146.728.4685.113.4167.732.4
Siberian spruce50.91.0243.44.9192.5378.2281.05.5230.1452.1
Total5157.2100.05010.0100.0 5111.3100.0

Number of S2 rasters (10 m GSD) used for training and testing in random forest classification (bold values represent the total area and overall percentage across all classes)_

ClassificationTraining samples (train)Testing samples (test)TotalOverall percentage (%)
LULC forest: Tree species
1Manchurian birch34,13214,62748,75921.9
2Siberian larch26,31311,27737,59016.9
3Scotch pine35,72315,30951,03222.9
4Siberian pine40,82917,49758,32626.2
5Siberian spruce11,066474215,8087.1
Other LULC classes
1Non-forest7949340611,3555.1
Total156,01266,85822,287100.0

Classification validation metrics_

MetricRangeInterpretation
Accuracy0.939–0.962All pixels were classified correctly, showing that the overall performance of the model is very reliable.
Kappa0.924–0.949Demonstrates very strong agreement beyond chance, which confirms the consistency and reliability of the classification results
P-value< 2.2e-16The findings are extremely statistically significant.
Sensitivity (high class)0.963–0.980The model is highly effective in detecting the actual presence of species in these categories.
Sensitivity (low class)0.867–0.917Slightly lower sensitivity for species like Spruce, meaning it’s harder to correctly identify them.
Specificity0.975–0.997Very high species are rarely misclassified as each other.
Fl-score0.935–0.952Demonstrating excellent performance and a strong balance between precision and recall.

Basic statistic for area of interest (AOI)_

No.ClassNumber of AOI (pcs)Min AOI area (ha)Mean AOI area (ha)Max AOI area (ha)Total AOI area (ha)Percentage of total samples (%)
Land use and land cover forest: Tree species
1Manchurian birch(B. platyphylla Sukaczev)400.31247.649417.2
2Siberian larch(L. sibirica Ledeb.)510.3849.538822.0
3Scotch pine(P. sylvestris L.)362.71457.151415.5
4Siberian pine(P. sibirica Ledeb.)430.31684.666718.5
5Siberian spruce(P. obovata Ledeb.)320.7528.316813.8
Other land use and land cover classes
1Non-forest300.1959.431412.9
DOI: https://doi.org/10.14746/quageo-2026-0009 | Journal eISSN: 2081-6383 | Journal ISSN: 2082-2103
Language: English
Page range: 139 - 154
Submitted on: Sep 19, 2025
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Published on: Mar 12, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2026 Erdenetuya Boldbaatar, Piotr Wężyk, Wojciech Krawczyk, published by Adam Mickiewicz University
This work is licensed under the Creative Commons Attribution 4.0 License.