Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Modelling Methods
2.4. Accuracy Assessment
2.5. Classification of Thermokarst Lakes
3. Results
3.1. Accuracy Assessment of the Distribution
3.2. Changes in Thermokarst Lakes from 2015 to 2020 via RF, GBDT, CART, and SVM
3.3. The Distribution of Thermokarst Lakes
4. Discussion
4.1. RF Performed Best in the Extraction of Thermokarst Lakes
4.2. Spatial and Temporal Variability of Thermokarst Lakes
4.3. The Environmental Factors Influencing the Formation of Thermokarst Lakes
4.4. Limitations and Future Perspectives
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Intergovernmental Panel on Climate Change. Climate Change 2021–The Physical Science Basis; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023. [Google Scholar]
- Mu, C.; Shang, J.; Zhang, T.; Fan, C.; Wang, S.; Peng, X.; Zhong, W.; Zhang, F.; Mu, M.; Jia, L. Acceleration of thaw slump during 1997–2017 in the Qilian Mountains of the northern Qinghai-Tibetan plateau. Landslides 2020, 17, 1051–1062. [Google Scholar] [CrossRef]
- Jorgenson, M.T.; Osterkamp, T.E. Response of boreal ecosystems to varying modes of permafrost degradation. Can. J. For. Res. 2005, 35, 2100–2111. [Google Scholar] [CrossRef]
- Liebner, S.; Welte, C.U. Roles of Thermokarst Lakes in a Warming World. Trends Microbiol. 2020, 28, 769–779. [Google Scholar] [CrossRef]
- Manasypov, R.M.; Pokrovsky, O.S.; Shirokova, L.S.; Auda, Y.; Zinner, N.S.; Vorobyev, S.N.; Kirpotin, S.N. Biogeochemistry of macrophytes, sediments and porewaters in thermokarst lakes of permafrost peatlands, western Siberia. Sci. Total Environ. 2021, 763, 144201. [Google Scholar] [CrossRef]
- Ren, Z.; Ma, K.; Jia, X.; Wang, Q.; Zhang, C.; Li, X. Metagenomics Unveils Microbial Diversity and Their Biogeochemical Roles in Water and Sediment of Thermokarst Lakes in the Yellow River Source Area. Microb. Ecol. 2023, 85, 904–915. [Google Scholar] [CrossRef]
- Mu, C.; Mu, M.; Wu, X.; Jia, L.; Fan, C.; Peng, X.; Ping, C.-L.; Wu, Q.; Xiao, C.; Liu, J. High carbon emissions from thermokarst lakes and their determinants in the Tibet Plateau. Glob. Change Biol. 2023, 29, 2732–2745. [Google Scholar] [CrossRef]
- Zhao, Y.-D.; Hu, X. The diversity and function of microbial community in the sediment and terrestrial area of thermokarst lakes. CATENA 2023, 233, 107505. [Google Scholar] [CrossRef]
- Zakharova, E.A.; Kouraev, A.V.; Stephane, G.; Franck, G.; Desyatkin, R.V.; Desyatkin, A.R. Recent dynamics of hydro-ecosystems in thermokarst depressions in Central Siberia from satellite and in situ observations: Importance for agriculture and human life. Sci. Total Environ. 2018, 615, 1290–1304. [Google Scholar] [CrossRef]
- Nitze, I.; Grosse, G.; Jones, B.M.; Arp, C.D.; Ulrich, M.; Fedorov, A.; Veremeeva, A. Landsat-Based Trend Analysis of Lake Dynamics Across Northern Permafrost Regions. Remote Sens. 2017, 9, 640. [Google Scholar] [CrossRef]
- Hu, J.; Huang, H.; Chi, Z.; Cheng, X.; Wei, Z.; Chen, P.; Xu, X.; Qi, S.; Xu, Y.; Zheng, Y. Distribution and Evolution of Supraglacial Lakes in Greenland During the 2016–2018 Melt Seasons. Remote Sens. 2022, 14, 55. [Google Scholar] [CrossRef]
- Qin, Y.; Zhang, C.; Lu, P. A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images. Sci. Remote Sens. 2023, 8, 100111. [Google Scholar] [CrossRef]
- Janiec, P.; Nowosad, J.; Zwoliński, Z. A machine learning method for Arctic lakes detection in the permafrost areas of Siberia. Eur. J. Remote Sens. 2023, 56, 2163923. [Google Scholar] [CrossRef]
- Zou, D.; Zhao, L.; Sheng, Y.; Chen, J.; Hu, G.; Wu, T.; Wu, J.; Xie, C.; Wu, X.; Pang, Q.; et al. A new map of permafrost distribution on the Tibetan Plateau. Cryosphere 2017, 11, 2527–2542. [Google Scholar] [CrossRef]
- Ran, Y.; Li, X.; Cheng, G. Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau. Cryosphere 2018, 12, 595–608. [Google Scholar] [CrossRef]
- Lin, Z.; Luo, J.; Niu, F. Development of a thermokarst lake and its thermal effects on permafrost over nearly 10 yr in the Beiluhe Basin, Qinghai-Tibet Plateau. Geosphere 2016, 12, 632–643. [Google Scholar] [CrossRef]
- Luo, J.; Niu, F.; Lin, Z.; Liu, M.; Yin, G. Thermokarst lake changes between 1969 and 2010 in the Beilu River Basin, Qinghai–Tibet Plateau, China. Sci. Bull. 2015, 60, 556–564. [Google Scholar] [CrossRef]
- Luo, J.; Niu, F.; Lin, Z.; Liu, M.; Yin, G.; Gao, Z. Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years. CATENA 2022, 217, 106497. [Google Scholar] [CrossRef]
- Șerban, R.-D.; Jin, H.; Șerban, M.; Luo, D. Shrinking thermokarst lakes and ponds on the northeastern Qinghai-Tibet plateau over the past three decades. Permafr. Periglac. Process. 2021, 32, 601–617. [Google Scholar] [CrossRef]
- Wang, R.; Guo, L.; Yang, Y.; Zheng, H.; Liu, L.; Jia, H.; Diao, B.; Liu, J. Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods. Remote Sens. 2023, 15, 3331. [Google Scholar] [CrossRef]
- Niu, F.; Lin, Z.; Lu, J.; Luo, J.; Wang, H. Assessment of terrain susceptibility to thermokarst lake development along the Qinghai–Tibet engineering corridor, China. Environ. Earth Sci. 2015, 73, 5631–5642. [Google Scholar] [CrossRef]
- Li, R.; Zhang, M.; Pei, W.; Melnikov, A.; Zhang, Z.; Li, G. Risk evaluation of thaw settlement using machine learning models for the Wudaoliang-Tuotuohe region, Qinghai-Tibet Plateau. CATENA 2023, 220, 106700. [Google Scholar] [CrossRef]
- Yu, Y.; Hui, F.; Zhou, Y.; Liu, C.; Cheng, X. The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season. Big Earth Data 2024, 8, 302–332. [Google Scholar] [CrossRef]
- Wei, Z.; Du, Z.; Wang, L.; Zhong, W.; Lin, J.; Xu, Q.; Xiao, C. Sedimentary organic carbon storage of thermokarst lakes and ponds across Tibetan permafrost region. Sci. Total Environ. 2022, 831, 154761. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.-W.; Wang, Q.; Zhao, L.; Wu, X.-D.; Yue, G.-Y.; Zou, D.-F.; Nan, Z.-T.; Liu, G.-Y.; Pang, Q.-Q.; Fang, H.-B.; et al. Mapping the vegetation distribution of the permafrost zone on the Qinghai-Tibet Plateau. J. Mt. Sci. 2016, 13, 1035–1046. [Google Scholar] [CrossRef]
- Ding, J.; Chen, L.; Ji, C.; Hugelius, G.; Li, Y.; Liu, L.; Qin, S.; Zhang, B.; Yang, G.; Li, F.; et al. Decadal soil carbon accumulation across Tibetan permafrost regions. Nat. Geosci. 2017, 10, 420–424. [Google Scholar] [CrossRef]
- Lu, H.; Wu, N.; Gu, Z.; Guo, Z.; Wang, L.; Wu, H.; Wang, G.; Zhou, L.; Han, J.; Liu, T. Distribution of carbon isotope composition of modern soils on the Qinghai-Tibetan Plateau. Biogeochemistry 2004, 70, 275–299. [Google Scholar] [CrossRef]
- Ni, J.; Wu, T.; Zhu, X.; Hu, G.; Zou, D.; Wu, X.; Li, R.; Xie, C.; Qiao, Y.; Pang, Q.; et al. Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models. J. Geophys. Res. Atmos. 2021, 126, e2020JD033402. [Google Scholar] [CrossRef]
- Ran, Y.; Li, X.; Cheng, G.; Nan, Z.; Che, J.; Sheng, Y.; Wu, Q.; Jin, H.; Luo, D.; Tang, Z.; et al. Mapping the permafrost stability on the Tibetan Plateau for 2005–2015. Sci. China Earth Sci. 2021, 64, 62–79. [Google Scholar] [CrossRef]
- Wulder, M.A.; Roy, D.P.; Radeloff, V.C.; Loveland, T.R.; Anderson, M.C.; Johnson, D.M.; Healey, S.; Zhu, Z.; Scambos, T.A.; Pahlevan, N.; et al. Fifty years of Landsat science and impacts. Remote Sens. Environ. 2022, 280, 113195. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Rodriguez-Galiano, V.F.; Chica-Olmo, M.; Abarca-Hernandez, F.; Atkinson, P.M.; Jeganathan, C. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 2012, 121, 93–107. [Google Scholar] [CrossRef]
- Liang, W.; Luo, S.; Zhao, G.; Wu, H. Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms. Mathematics 2020, 8, 765. [Google Scholar] [CrossRef]
- Were, K.; Bui, D.T.; Dick, Ø.B.; Singh, B.R. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol. Indic. 2015, 52, 394–403. [Google Scholar] [CrossRef]
- Drake, J.M.; Randin, C.; Guisan, A. Modelling ecological niches with support vector machines. J. Appl. Ecol. 2006, 43, 424–432. [Google Scholar] [CrossRef]
- González, C.; Mira-McWilliams, J.; Juárez, I. Important variable assessment and electricity price forecasting based on regression tree models: Classification and regression trees, Bagging and Random Forests. IET Gener. Transm. Distrib. 2015, 9, 1120–1128. [Google Scholar] [CrossRef]
- Frantz, D.; Haß, E.; Uhl, A.; Stoffels, J.; Hill, J. Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sens. Environ. 2018, 215, 471–481. [Google Scholar] [CrossRef]
- Wei, Z.; Du, Z.; Wang, L.; Lin, J.; Feng, Y.; Xu, Q.; Xiao, C. Sentinel-Based Inventory of Thermokarst Lakes and Ponds Across Permafrost Landscapes on the Qinghai-Tibet Plateau. Earth Space Sci. 2021, 8, e2021EA001950. [Google Scholar] [CrossRef]
- Plug, L.J.; Walls, C.; Scott, B.M. Tundra lake changes from 1978 to 2001 on the Tuktoyaktuk Peninsula, western Canadian Arctic. Geophys. Res. Lett. 2008, 35, L03502. [Google Scholar] [CrossRef]
- Belgiu, M.; Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016, 114, 24–31. [Google Scholar] [CrossRef]
- Wang, R.; Guo, L.; Yang, Y.; Zheng, H.; Jia, H.; Diao, B.; Li, H.; Liu, J. Thermokarst lake susceptibility assessment using machine learning models in permafrost landscapes of the Arctic. Sci. Total Environ. 2023, 900, 165709. [Google Scholar] [CrossRef] [PubMed]
- Zhang, K.; Feng, M.; Sui, Y.; Xu, J.; Yan, D.; Hu, Z.; Han, F.; Sthapit, E. Identifying thermokarst lakes using deep learning and high-resolution satellite images. Sci. Remote Sens. 2024, 10, 100175. [Google Scholar] [CrossRef]
- Zhou, L.; Yang, Y.; Zhang, D.; Yao, H. Recent advances in hydrology studies under changing permafrost on the Qinghai-Xizang Plateau. Res. Cold Arid. Reg. 2024, 16, 159–169. [Google Scholar] [CrossRef]
- Zhu, J.; Luo, J.; Zhang, H.; Zhang, J. Distribution and changes of thermokarst lakes along the Qinghai-Tibet Railway from 1991 to 2022. Res. Cold Arid. Reg. 2024. [Google Scholar] [CrossRef]
- Zhou, Y.; Dong, J.; Xiao, X.; Xiao, T.; Yang, Z.; Zhao, G.; Zou, Z.; Qin, Y. Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water 2017, 9, 256. [Google Scholar] [CrossRef]
- Liu, C.; Zhang, X.; Wang, T.; Chen, G.; Zhu, K.; Wang, Q.; Wang, J. Detection of vegetation coverage changes in the Yellow River Basin from 2003 to 2020. Ecol. Indic. 2022, 138, 108818. [Google Scholar] [CrossRef]
- Zabelina, S.A.; Shirokova, L.S.; Klimov, S.I.; Chupakov, A.V.; Lim, A.G.; Polishchuk, Y.M.; Polishchuk, V.Y.; Bogdanov, A.N.; Muratov, I.N.; Guerin, F.; et al. Carbon emission from thermokarst lakes in NE European tundra. Limnol. Oceanogr. 2021, 66, S216–S230. [Google Scholar] [CrossRef]
- Yin, G.; Luo, J.; Niu, F.; Zhou, F.; Meng, X.; Lin, Z.; Liu, M. Spatial Analyses and Susceptibility Modeling of Thermokarst Lakes in Permafrost Landscapes Along the Qinghai–Tibet Engineering Corridor. Remote Sens. 2021, 13, 1974. [Google Scholar] [CrossRef]
- Li, W.; Zhao, L.; Wu, X.; Wang, S.; Sheng, Y.; Ping, C.; Zhao, Y.; Fang, H.; Shi, W. Soil distribution modeling using inductive learning in the eastern part of permafrost regions in Qinghai–Xizang (Tibetan) Plateau. CATENA 2015, 126, 98–104. [Google Scholar] [CrossRef]
- Shi, Z.H.; Fang, N.F.; Wu, F.Z.; Wang, L.; Yue, B.J.; Wu, G.L. Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J. Hydrol. 2012, 454–455, 123–130. [Google Scholar] [CrossRef]
- Liu, R.; Yang, X.; Xu, C.; Wei, L.; Zeng, X. Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping. Remote Sens. 2022, 14, 321. [Google Scholar] [CrossRef]
- Lv, L.; Chen, T.; Dou, J.; Plaza, A. A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping. Int. J. Appl. Earth Obs. Geoinf. 2022, 108, 102713. [Google Scholar] [CrossRef]
Name | Source | Spatial Resolution | |
---|---|---|---|
Image | Landsat 7 | LANDSAT/LE07/C02/T1_L2 | 30 m |
Landsat 8 | LANDSAT/LC08/C02/T1_L2 | ||
Topography | DEM | USGS/SRTMGL1_003 | 30 m |
SLOPE | |||
ASPECT | |||
TPI | |||
Vegetation | NDVI | LANDSAT/LE07/C02/T1_L2 LANDSAT/LC08/C02/T1_L2 | 30 m |
EVI | |||
Hydrology | TWI | USGS/SRTMGL1_003 and WWF/HydroSHEDS/15ACC | 30 and 464 m |
NDWI | LANDSAT/LE07/C02/T1_L2 LANDSAT/LC08/C02/T1_L2 | 30 m | |
Soil | SOIL1 | ECMWF/ERA5_LAND/MONTHLY_AGGR | 11,132 m |
SOIL2 | |||
SOIL3 | |||
mean | |||
Climate | ST_B10 | LANDSAT/LE07/C02/T1_L2 LANDSAT/LC08/C02/T1_L2 | 30 m |
Pre mean | IDAHO_EPSCOR/TERRACLIMATE | 4638 m |
Thermokarst Lakes | Non-Thermokarst Lakes | OA (%) | K | ||
---|---|---|---|---|---|
RF | PA (%) | 98.08 | 99.60 | 98.90 | 0.98 |
UA (%) | 99.51 | 98.40 | |||
F1 | 98.79 | 99.00 | |||
GBDT | PA (%) | 96.15 | 98.79 | 97.58 | 0.95 |
UA (%) | 98.52 | 96.83 | |||
F1 | 97.32 | 97.80 | |||
CART | PA (%) | 97.12 | 97.17 | 97.14 | 0.94 |
UA (%) | 96.65 | 97.56 | |||
F1 | 96.88 | 97.36 | |||
SVM | PA (%) | 75.00 | 87.45 | 81.76 | 0.63 |
UA (%) | 83.42 | 80.60 | |||
F1 | 78.99 | 83.89 | |||
Number of validation samples | 208 | 247 | 455 |
Lakes | STKs | MTKs | LTKs | VLTKs | Sum | |
---|---|---|---|---|---|---|
Model | ||||||
RF | 51.1% | 37.8% | 8.0% | 3.1% | 100.0% | |
GBDT | 49.7% | 41.4% | 6.8% | 2.1% | 100.0% | |
CART | 55.1% | 38.2% | 5.0% | 1.7% | 100.0% | |
Mean | 52.0% | 39.1% | 6.6% | 2.3% | 100.0% | |
Total number | 76,716 | 57,641 | 9210 | 3094 | 146,661 |
Lakes | STKs | MTKs | LTKs | VLTKs | Sum | |
---|---|---|---|---|---|---|
Model | ||||||
RF | 3.6% | 12.7% | 19.4% | 64.3% | 100.0% | |
GBDT | 4.5% | 17.8% | 21.1% | 56.6% | 100.0% | |
CART | 6.4% | 19.0% | 19.4% | 55.2% | 100.0% | |
Mean | 4.8% | 16.5% | 20% | 58.7% | 100.0% | |
Total area(km2) | 47.83 | 164.22 | 195.45 | 566.91 | 974.40 |
Lakes | STKs | MTKs | LTKs | VLTKs | Sum | |
---|---|---|---|---|---|---|
Model | ||||||
RF | 36.5% | 45.9% | 13.4% | 4.2% | 100.0% | |
GBDT | 39.9% | 45.5% | 11.3% | 3.3% | 100.0% | |
CART | 39.3% | 45.9% | 11.4% | 3.4% | 100.0% | |
Mean | 38.6% | 45.8% | 12.0% | 3.6% | 100.0% | |
Total number | 86,555 | 102,337 | 26,774 | 7981 | 223,647 |
Lakes | STKs | MTKs | LTKs | VLTKs | Sum | |
---|---|---|---|---|---|---|
Model | ||||||
RF | 1.7% | 11.6% | 21.4% | 65.3% | 100.0% | |
GBDT | 2.3% | 13.7% | 22.4% | 61.6% | 100.0% | |
CART | 2.2% | 13.7% | 22.4% | 61.7% | 100.0% | |
Mean | 2.0% | 13.0% | 22.1% | 62.9% | 100.0% | |
Total area(km2) | 52.73 | 334.77 | 568.22 | 1617.30 | 2573.02 |
STKs | MTKs | LTKs | VLTKs | Sum | |
---|---|---|---|---|---|
Increased number | 9839 | 44,696 | 17,564 | 4887 | 76,986 |
Increased area(km2) | 4.89 | 170.55 | 372.77 | 1050.39 | 1598.60 |
Rate of increase in number/year−1 | 1968 | 8939 | 3513 | 977 | 15,397 |
Rate of increase in area (km2/year−1) | 0.98 | 34.11 | 74.55 | 210.08 | 319.72 |
Percentage increase in number | 12.8% | 58.1% | 22.8% | 6.3% | 52.5% |
Percentage increase in area | 0.3% | 10.7% | 23.3% | 65.7% | 164.1% |
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Wei, R.; Hu, X.; Zhao, S. Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020. Remote Sens. 2025, 17, 1174. https://doi.org/10.3390/rs17071174
Wei R, Hu X, Zhao S. Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020. Remote Sensing. 2025; 17(7):1174. https://doi.org/10.3390/rs17071174
Chicago/Turabian StyleWei, Rongrong, Xia Hu, and Shaojie Zhao. 2025. "Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020" Remote Sensing 17, no. 7: 1174. https://doi.org/10.3390/rs17071174
APA StyleWei, R., Hu, X., & Zhao, S. (2025). Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020. Remote Sensing, 17(7), 1174. https://doi.org/10.3390/rs17071174