Abstract
The Tibetan Plateau is known as the “Asian water tower”, and changes in its surface water distribution are important indicators of global climate change and the regional response to these changes. Dynamic monitoring of the surface water on the Tibetan Plateau is an important part of the research on the functions of the “third pole” of the earth and the Asian water tower. With the support of the Google Earth Engine cloud platform, this study used a spectral index-based fast extraction method to obtain surface water data from multi-temporal Landsat (Landsat 4, 5, and 8) satellite remote sensing images. Based on the extracted surface water data, we analyzed the spatiotemporal variations in the surface water of the Tibetan Plateau from 1980s to 2019. In this study, surface water area refers to the maximum coverage area of the surface water extracted from remote sensing images for one year, hereafter referred to as the surface water area. The results show that since 1980s, the overall surface water area of the Tibetan Plateau has increased, but not in a linear fashion. After a slight decrease from 1980s to 1995, the surface water area of the Tibetan Plateau increased steadily, except for a slight decrease in 2015, which may have been caused by the El Niño phenomenon. In terms of spatiotemporal distribution, different patterns exist in the various ecological regions of the Tibetan Plateau. The Inner ecological region had the greatest changes of surface water area among the ten ecological regions, accounting for 71.0% of the total surface water area increase from 1980s to 2019. The surface water bodies in the cold desert and the dry-winter subtropical climatic regions underwent the most changes, with their coefficients of variation being more than 20%. This study can provide data support for dynamic monitoring of surface water in the Tibetan Plateau.
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References
Beck HE, Zimmermann NE, McVicar TR, et al. (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214. https://doi.org/10.1038/sdata.2018.214
Bian D, Bian BCR, La B, et al. (2010) The response of water level of Selin Co to climate change during 1975–2008. Acta Geogr Sin 65(3): 313–319. https://doi.org/10.11821/xb201003006
Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment 113(5): 893–903. https://doi.org/10.1016/j.rse.2009.01.007
He GJ, Wang GZ, Long TF, et al. (2018a) Opening and sharing of big Earth observation data: challenges and countermeasures. Bulletin of Chinese Academy of Sciences 8: 783–790. https://doi.org/10.16418/j.issn.1000-3045.2018.08.003
He GJ, Wang LZ, Ma Y, et al. (2015) Processing of earth observation big data: challenges and countermeasures. Chin Sci Bull 60: 470–478. https://doi.org/10.1360/N972014-00907
He GJ, Zhang ZM, Jiao WL, et al. (2018b) Generation of ready to use (RTU) products over China based on Landsat series data. Big Earth Data 2(1):56–64. https://doi.org/10.1080/20964471.2018.1433370
Jiang JH, Huang Q (2004) Distribution and variation of lakes in Tibetan Plateau and their comparison with lakes in other part of China. Water Resources Protection 6: 24–27. https://doi.org/10.3969/j.issn.1004-6933.2004.06.006
Jiang W, He GJ, Long TF, et al. (2018) Multilayer perceptron neural network for surface water extraction in Landsat 8 OLI satellite images. Remote Sens 10(5). https://doi.org/10.3390/rs10050755
Liao AP, Chen LJ, Chen J, et al. (2014) High-resolution remote sensing mapping of global land water. Sci China Earth Sci 57:2305–2316. https://doi.org/10.1007/s11430-014-4918-0
Liao XH, Shi J C (2014) 2013 Global Ecosystem and Environment Observation Analysis Research Cooperation. China Science Press, Beijing, China.
Liu P, Zhang H, Eom KB (2017) Active deep learning for classification of hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 10(2): 712–724. https://doi.org/10.1109/JSTARS.2016.2598859
Lu SL, Xiao GH, Jia L, et al. (2016) Extraction of the spatial-temporal lake water surface dataset in the Tibetan Plateau over the past 10 years. Remote Sensing for Land and Resources 28(3): 181–187. https://doi.org/10.6046/gtzyyg.2016.03.28
Ma RH, Yang GH, Duan HT, et al. (2011) China’s lakes at present: Number, area and spatial distribution. Science China Earth Sciences 54:283–289. https://doi.org/10.1007/s11430-010-4052-6
Ma XQ, Lu SL, Ma J, et al. (2019) Lake water storage estimation method based on topographic parameters: A case study of Nam Co Lake. Remote Sensing for Land and Resources 31(4): 167–173. https://doi.org/10.6046/gtzyyg.2019.04.22
McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7): 1425–1432. https://doi.org/10.1080/01431169608948714
Qiao B, Zhu L, Wang J, et al. (2017) Estimation of lakes water storage and their changes on the northwestern Tibetan Plateau based on bathymetric and Landsat data and driving force analyses. Quat Int 454: 56–67. https://doi.org/10.1016/j.quaint.2017.08.005
Wan W, Xiao PF, Feng XZ, et al. (2014) Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data. Chin Sci Bull 59(10): 1021–1035. https://doi.org/10.1007/s11434-014-0128-6
Xu HQ (2005) A study on information extraction of water body with the modified normalized difference water index. Journal of Remote Sensing 9(5): 589–595. https://doi.org/10.3321/j.issn:1007-4619.2005.05.012
Zhang GQ, Yao TD, Xie HJ, et al. (2014) Lakes’ state and abundance across the Tibetan Plateau. Chin Sci Bull 59(24): 3010–3021. https://doi.org/10.1007/s11434-014-0258-x
Zhang GQ, Yao TD, Xie, HJ, et al. (2013) Increased mass over the Tibetan Plateau: From lakes or glaciers? Geophys Res Lett 40: 2125–2130. https://doi.org/10.1002/grl.50462
Zhang GQ, Yao TD, Xie, HJ, et al. (2020) Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms. Earth Sci Rev 208. https://doi.org/10.1016/j.earscirev.2020.103269
Zhang JY, Liu JF, Jin JL, et al. (2019) Evolution and trend of water resources in Qinghai-Tibet Plateau. Bulletin of Chinese Academy of Sciences 34(11): 1264–1273. https://doi.org/10.16418/j.issn.1000-3045.2019.11.009
Zhu LP, Qiao BJ, Yang RM, et al. (2017) New recognition of water storages and physicochemical property of the lakes on the Tibetan Plateau. Chinese Journal of Nature 39(3): 166–172. https://doi.org/10.3969/j.issn.0253-9608.2017.03.002
Zhu LP, Xie MP, Wu YH (2010) Quantitative analysis of lake area variations and the influence factors from 1971 to 2004 in the Nam Co basin of the Tibetan Plateau. Chin Sci Bull 55(13): 1294–1303. https://doi.org/10.1007/s11434-010-0015-8
Acknowledgements
This study was funded by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No.2019QZKK0307), the National Natural Science Foundation of China (NSFC, Grant No.61731022), and the National Key Research and Development Program of China (Grant No.2016YFA0600302).
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Liu, Hc., He, Gj., Peng, Y. et al. Dynamic monitoring of surface water in the Tibetan Plateau from 1980s to 2019 based on satellite remote sensing images. J. Mt. Sci. 18, 2833–2841 (2021). https://doi.org/10.1007/s11629-020-6482-8
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DOI: https://doi.org/10.1007/s11629-020-6482-8