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).
Table 1.
Basic statistic for area of interest (AOI).
| No. | Class | Number 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 | |||||||
| 1 | Manchurian birch (B. platyphylla Sukaczev) | 40 | 0.3 | 12 | 47.6 | 494 | 17.2 |
| 2 | Siberian larch (L. sibirica Ledeb.) | 51 | 0.3 | 8 | 49.5 | 388 | 22.0 |
| 3 | Scotch pine (P. sylvestris L.) | 36 | 2.7 | 14 | 57.1 | 514 | 15.5 |
| 4 | Siberian pine (P. sibirica Ledeb.) | 43 | 0.3 | 16 | 84.6 | 667 | 18.5 |
| 5 | Siberian spruce (P. obovata Ledeb.) | 32 | 0.7 | 5 | 28.3 | 168 | 13.8 |
| Other land use and land cover classes | |||||||
| 1 | Non-forest | 30 | 0.1 | 9 | 59.4 | 314 | 12.9 |
Fig. 7.
Mean NDVI values of AOIs of analysed tree species in test site. AOIs, area of interest.
Table 2.
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).
| Classification | Training samples (train) | Testing samples (test) | Total | Overall percentage (%) | |
|---|---|---|---|---|---|
| LULC forest: Tree species | |||||
| 1 | Manchurian birch | 34,132 | 14,627 | 48,759 | 21.9 |
| 2 | Siberian larch | 26,313 | 11,277 | 37,590 | 16.9 |
| 3 | Scotch pine | 35,723 | 15,309 | 51,032 | 22.9 |
| 4 | Siberian pine | 40,829 | 17,497 | 58,326 | 26.2 |
| 5 | Siberian spruce | 11,066 | 4742 | 15,808 | 7.1 |
| Other LULC classes | |||||
| 1 | Non-forest | 7949 | 3406 | 11,355 | 5.1 |
| Total | 156,012 | 66,858 | 22,287 | 100.0 | |
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).
Table 3.
Classification results using RF approach (bold values represent the total area and overall percentage across all classes).
| Class name | Manchurian birch | Siberian larch | Scotch pine | Siberian pine | Siberian spruce | Sum of forest mask | Nonforest | Other | Total (km2) | |
|---|---|---|---|---|---|---|---|---|---|---|
| Sentinel 2 21.07.2020 | Area (km2) | 2188.5 | 1178.1 | 736.0 | 664.1 | 243.4 | 5010.0 | 1142.3 | 1233.9 | 7386.3 |
| Percentage (%) | 29.6 | 15.9 | 10.0 | 9.0 | 3.3 | 67.8 | 15.5 | 16.7 | 100.0 | |
| Sentinel 2 30.08.2021 | Area (km2) | 1900.7 | 1614.3 | 692.5 | 626.5 | 280.8 | 5114.7 | 1037.6 | 1233.9 | 7386.3 |
| Percentage (%) | 25.7 | 21.9 | 9.4 | 8.5 | 3.8 | 69.2 | 14.0 | 16.7 | 100.0 | |
| Sentinel 2 30.08.2022 | Area (km2) | 2107.1 | 1600.3 | 602.4 | 589.7 | 243.0 | 5142.4 | 1009.9 | 1233.9 | 7386.3 |
| Percentage (%) | 28.5 | 21.7 | 8.2 | 8.0 | 3.3 | 69.6 | 13.7 | 16.7 | 100.0 | |
| Sentinel 2 30.08.2023 | Area (km2) | 2127.3 | 1555.1 | 699.0 | 612.5 | 241.7 | 5235.5 | 9168 | 1233.9 | 7386.3 |
| Percentage (%) | 28.8 | 21.1 | 9.5 | 8.3 | 3.3 | 70.9 | 12.4 | 16.7 | 100.0 | |
| Sentinel 2 24.08.2024 | Area (km2) | 2008.0 | 1367.6 | 769.6 | 685.1 | 281.0 | 5111.3 | 1041.1 | 1233.9 | 7386.3 |
| Percentage (%) | 27.2 | 18.5 | 10.4 | 9.3 | 3.8 | 69.2 | 14.1 | 16.7 | 100.0 | |
Table 4.
Classification validation metrics.
| Metric | Range | Interpretation |
|---|---|---|
| Accuracy | 0.939–0.962 | All pixels were classified correctly, showing that the overall performance of the model is very reliable. |
| Kappa | 0.924–0.949 | Demonstrates very strong agreement beyond chance, which confirms the consistency and reliability of the classification results |
| P-value | < 2.2e-16 | The findings are extremely statistically significant. |
| Sensitivity (high class) | 0.963–0.980 | The model is highly effective in detecting the actual presence of species in these categories. |
| Sensitivity (low class) | 0.867–0.917 | Slightly lower sensitivity for species like Spruce, meaning it’s harder to correctly identify them. |
| Specificity | 0.975–0.997 | Very high species are rarely misclassified as each other. |
| Fl-score | 0.935–0.952 | Demonstrating excellent performance and a strong balance between precision and recall. |
Fig. 11.
Confusion matrices for forest type classification (2020–2024).
Table 5.
Results of S-2 classification vs. forest inventory data (bold values represent the total area and overall percentage across all classes).
| Class | Forest inventory map (2019) | S-2 | Difference | S-2 | Difference | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| 21/07/2020 | 24/08/2024 | |||||||||
| km2 | % | km2 | % | km2 | % | km2 | % | km2 | % | |
| Manchurian birch | 2773.9 | 53.8 | 2188.5 | 43.7 | –585.4 | –21.1 | 2008.0 | 39.3 | –765.9 | –27.6 |
| Siberian larch | 912.3 | 17.7 | 1178.1 | 23.5 | 265.8 | 29.1 | 1367.6 | 26.8 | 455.3 | 49.9 |
| Scotch pine | 902.7 | 17.5 | 735.9 | 14.7 | –166.8 | –18.5 | 769.6 | 15.1 | –133.1 | –14.7 |
| Siberian pine | 517.4 | 10.0 | 664.1 | 13.3 | 146.7 | 28.4 | 685.1 | 13.4 | 167.7 | 32.4 |
| Siberian spruce | 50.9 | 1.0 | 243.4 | 4.9 | 192.5 | 378.2 | 281.0 | 5.5 | 230.1 | 452.1 |
| Total | 5157.2 | 100.0 | 5010.0 | 100.0 | 5111.3 | 100.0 | ||||