Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.2.1. Satellite Images and Preprocessing
2.2.2. Training Sample Data
2.2.3. Other Datasets
2.3. Methods
2.3.1. Extraction and Selection of Spatiotemporal Features
2.3.2. Random Forest for Peatland Mapping
2.3.3. Accuracy Assessment of Peatland Classification Results
3. Results
3.1. Peatland Features
3.2. Classification Accuracy of Peatland Categories
3.3. Area and Distribution of Peatlands on the TP
4. Discussion
4.1. Variable Importance of the Distribution of Peatlands
4.2. High-Resolution Peatland Mapping Outcomes for the Tibetan Plateau
4.3. Implications of Peatland Mappings
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature Category | Feature Name (Number) | Data Source | |
---|---|---|---|
Polarization | Polarization bands Polarization indexes | VV, VH (2) VV+VH, VH-VV, NDV_VV, NDV_VH, VH/VV (5) | Sentinel-1 |
Spectral | Spectral bands Spectral indexes Red-edge indexes Tasseled cap transformation | B1~B12 (13) NDVI, EVI, RVI, DVI, NDWI, MNDWI (6) NDVIre1, NDVIre1n, NDVIre2, NDVIre2n, NDVIre3, NDVIre3n, PSRI, CIre, NDre1, NDre1m, NDre2, NDre2m, SRre1, SRre2, MSRre, MSRren (16) Wetness, Greenness, Brightness (3) | Sentinel-2 |
Soil | SAVI | ||
Texture | ASM, Contrast, Entropy, Correlation, Dissimilarity (5) | Sentinel-2 | |
Terrain | Elevation, Slope, Roughness (3) | DEM | |
Temporal | SWO | Sentinel-1/2 |
Features | GL | PL | NPM |
---|---|---|---|
B1 | 0.24 | 0.24 | 0.25 |
B2 | 0.24 | 0.27 | 0.24 |
B3 | 0.25 | 0.28 | 0.25 |
B4 | 0.28 | 0.32 | 0.28 |
B5 | 0.30 | 0.34 | 0.30 |
B6 | 0.32 | 0.36 | 0.33 |
B7 | 0.33 | 0.37 | 0.35 |
B8 | 0.34 | 0.37 | 0.36 |
B8A | 0.35 | 0.39 | 0.37 |
B9 | 0.30 | 0.32 | 0.32 |
B10 | 0.15 | 0.14 | 0.14 |
B11 | 0.38 | 0.37 | 0.35 |
B12 | 0.33 | 0.31 | 0.27 |
VV | 23.57 | 26.03 | 24.96 |
VH | 13.69 | 15.60 | 14.29 |
Data | Class | PL | NPM | Total | UA | Kappa | OA |
---|---|---|---|---|---|---|---|
2023 | PL | 274 | 31 | 305 | 89.84% | 0.72 | 86.33% |
Non-PM | 73 | 383 | 456 | 83.99% | |||
Total | 347 | 414 | 761 | - | |||
PA | 78.96% | 92.51% | - | - |
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Pan, Z.; Xiang, H.; Shi, X.; Wang, M.; Song, K.; Mao, D.; Huang, C. Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery. Remote Sens. 2025, 17, 292. https://doi.org/10.3390/rs17020292
Pan Z, Xiang H, Shi X, Wang M, Song K, Mao D, Huang C. Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery. Remote Sensing. 2025; 17(2):292. https://doi.org/10.3390/rs17020292
Chicago/Turabian StylePan, Zihao, Hengxing Xiang, Xinying Shi, Ming Wang, Kaishan Song, Dehua Mao, and Chunlin Huang. 2025. "Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery" Remote Sensing 17, no. 2: 292. https://doi.org/10.3390/rs17020292
APA StylePan, Z., Xiang, H., Shi, X., Wang, M., Song, K., Mao, D., & Huang, C. (2025). Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery. Remote Sensing, 17(2), 292. https://doi.org/10.3390/rs17020292