Journal of Mountain Science

, Volume 15, Issue 1, pp 13–24 | Cite as

An automated method for glacial lake mapping in High Mountain Asia using Landsat 8 imagery

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Abstract

Glacial lakes in the High Mountain Asia (HMA) are sensitive to global warming and can result in much more severe flood disasters than some largesized lakes. An accurate and robust method for the extraction of glacial lakes is critical to effective management of these natural water resources. Conventional methods often have limitations in terms of low spectral contrast and heterogeneous backgrounds in an image. This study presents a robust and automated method for the yearly mapping of glacial lake over a large scale, which took advantage of the complementarity between the modified normalized difference water index (MNDWI) and the nonlocal active contour model, required only local homogeneity in reflectance features of lake. The cloud computing approach with the Google Earth Engine (GEE) platform was used to process the intensive amount of Landsat 8 images from 2015 (344 path/rows and approximately 7504 scenes). The experimental results were validated by very high resolution images from Chinese GaoFen-1 (GF-1) panchromatic multi-spectral (PMS) and appeared a general good agreement. This is the first time that information regarding the spatial distribution of glacial lakes over the HMA has been derived automatically within quite a short period of time. By integrating it with the relevant indices, it can also be applied to detect other land cover types such as snow or vegetation with improved accuracy.

Keywords

Glacial lake Landsat 8 Modified Normalized Difference Water Index Nonlocal active contour 

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Notes

Acknowledgements

The research reported in this manuscript was funded by the National Natural Science Foundation Project (Grant Nos. 41701481 and 41401511).

References

  1. Arendt A, Bolch T, Cogley JG, et al. (2015) Randolph Glacier Inventory–A dataset of global glacier outlines: Version 5.0. GLIMS Technical Report. pp 3–25. (https://www.glims.org/RGI/00_rgi50_TechnicalNote.pdf, accessed on 2015-07)Google Scholar
  2. Bhardwaj A, Singh MK, Joshi PK, et al. (2015) A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment. International Journal of Applied Earth Observation & Geoinformation 38: 150–163. https://doi.org/10.1016/j.jag.2015.01.004CrossRefGoogle Scholar
  3. Dong JW, Xiao X, Kou W, et al. (2015) Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sensing of Environment 160(160): 99–113. https://doi.org/10.1016/j.rse.2015.01.004CrossRefGoogle Scholar
  4. Feyisa GL, Meilby H, Fensholt R, et al. (2014) Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140(1): 23–35. https://doi.org/10.1016/j.rse.2013.08.029CrossRefGoogle Scholar
  5. Fisher A, Flood N, Danaher T (2016) Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment 175: 167–182. https://doi.org/10.1016/j.rse.2015.12.055CrossRefGoogle Scholar
  6. Fujita K, Sakai A, Nuimura T, et al. (2009) Recent changes in Imja Glacial Lake and its damming moraine in the Nepal Himalaya revealed by in situ surveys and multi-temporal ASTER imagery. Environmental Research Letters 4(4): 940–941. https://doi.org/10.1088/1748-9326/4/4/045205CrossRefGoogle Scholar
  7. Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031CrossRefGoogle Scholar
  8. Ji L, Zhang L, Wylie B (2009) Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing 75(11): 1307–1317. https://doi.org/10.14358/pers.75.11.1307CrossRefGoogle Scholar
  9. Jiang H, Feng M, Zhu Y, et al. (2014) An automated method for extracting rivers and lakes from Landsat imagery. Remote Sensing 6(6): 5067–5089. https://doi.org/10.3390/rs6065067CrossRefGoogle Scholar
  10. Jung M, Peyré G, Cohen LD (2012) Non-local active contours. SIAM Journal on Imaging Sciences 5(3): 255–266. https://doi.org/10.1007/978-3-642-24785-9_22CrossRefGoogle Scholar
  11. Klein I, Dietz AJ, Gessner U, et al. (2014) Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data. International Journal of Applied Earth Observation & Geoinformation 26(2): 335–349. https://doi.org/10.1016/j.jag.2013.08.004CrossRefGoogle Scholar
  12. Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Transactions on Image Processing 17(11): 2029–2039. https://doi.org/10.1109/tip.2008.2004611CrossRefGoogle Scholar
  13. Li C, Kao CY, Gore JC, et al. (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing 17(10): 1940–1949. https://doi.org/10.1109/tip.2008.2002304CrossRefGoogle Scholar
  14. Li J, Sheng YW (2012) An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: A case study in the Himalayas. International Journal of Remote Sensing 33(16): 5194–5213. https://doi.org/10.1080/01431161.2012.657370CrossRefGoogle Scholar
  15. Mcfeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7): 1425–1432. https://doi.org/10.1080/01431169608948714CrossRefGoogle Scholar
  16. Mergili M, Müller JP, Schneider JF (2013) Spatio-temporal development of high-mountain lakes in the headwaters of the Amu Darya River (Central Asia). Global & Planetary Change 107(5): 13–24. https://doi.org/10.1016/j.gloplacha.2013.04.001CrossRefGoogle Scholar
  17. Maiersperger TK, Scaramuzza PL, Leigh L, et al. (2013) Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sensing of Environment 136:1–13. https://doi.org/10.1016/j.rse.2013.04.007CrossRefGoogle Scholar
  18. Masek JG, Vermote EF, Saleous NE, et al. (2006) A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geoscience & Remote Sensing Letters 3(1):68–72. https://doi.org/10.1109/lgrs.2005.857030CrossRefGoogle Scholar
  19. Hansen MC, Potapov PV, Moore R, et al. (2014) High-resolution global maps of 21st-century forest cover change. Science 342(6160): 850–853. https://doi.org/10.1126/science.1244693CrossRefGoogle Scholar
  20. Nie Y, Sheng YW, Liu Q, et al. (2017) A regional-scale assessment of Himalayan glacial lake changes using satellite observations from 1990 to 2015. Remote Sensing of Environment 189: 1–13. https://doi.org/10.1016/j.rse.2016.11.008CrossRefGoogle Scholar
  21. NASA (2016) Landsat 7 science data users handbook. pp 9-17. (http://landsathandbook.gsfc.nasa.gov/orbit_coverage/prog_sect 5_2.html, accessed on 2016-11-16)Google Scholar
  22. Patel NN, Angiuli E, Gamba P, et al. (2015) Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation & Geoinformation 35 (Part B):199–208. https://doi.org/10.1016/j.jag.2014.09.005CrossRefGoogle Scholar
  23. Pekel JF, Cottam A, Gorelick N, et al. (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633): 418–422. https://doi.org/10.1038/nature20584CrossRefGoogle Scholar
  24. Qiu J (2008) China: The third pole. Nature 454(7203): 393–396. https://doi.org/10.1038/454393aCrossRefGoogle Scholar
  25. Rodríguez E, Morris CS, Belz JE (2006) A global assessment of the SRTM performance. Photogrammetric Engineering & Remote Sensing 72(3): 249–260. https://doi.org/10.14358/pers.72.3.249CrossRefGoogle Scholar
  26. Roy DP, Wulder MA, Loveland TR, et al. (2014) Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment 145: 154–172. https://doi.org/10.1016/j.rse.2014.02.001CrossRefGoogle Scholar
  27. Ryu JH, Won JS, Min KD (2002) Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea. Remote Sensing of Environment 83(3): 442–456. https://doi.org/10.1016/s0034-4257(02)00059-7CrossRefGoogle Scholar
  28. Salerno F, Thakuri S, Agata CD, et al. (2012) Glacial lake distribution in the Mount Everest region: Uncertainty of measurement and conditions of formation. Global & Planetary Changes 92-93(1): 30–39. https://doi.org/10.1016/j.gloplacha.2012.04.001CrossRefGoogle Scholar
  29. Tian BS, Li Z, Zhang MM, et al. (2017) Mapping thermokarst lakes on the Qinghai–Tibet Plateau using nonlocal active contours in Chinese GaoFen-2 multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(5): 1687–1700. https://doi.org/10.1109/jstars.2017.2666787CrossRefGoogle Scholar
  30. Tulbure MG, Broich M (2013) Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS Journal of Photogrammetry & Remote Sensing 79(330): 44–52. https://doi.org/10.1016/j.isprsjprs.2013.01.010CrossRefGoogle Scholar
  31. Wang W, Xiang Y, Gao Y, et al. (2015) Rapid expansion of glacial lakes caused by climate and glacier retreat in the Central Himalayas. Hydrological Processes 29(6): 859–874. https://doi.org/10.1002/hyp.10199CrossRefGoogle Scholar
  32. Wang X, Ding Y, Liu S, et al. (2013) Changes of glacial lakes and implications in Tian Shan, central Asia, based on remote sensing data from 1990 to 2010. Environmental Research Letters 8(4): 575–591. https://doi.org/10.1088/1748-9326/8/4/044052Google Scholar
  33. Wang X, Chai KG, Liu SY, et al. (2017) Changes of glaciers and glacial lakes implying corridor-barrier effects and climate change in the Hengduan Shan, southeastern Tibetan Plateau. Journal of Glaciology 63(239):1–8. https://doi.org/10.1017/jog.2017.14Google Scholar
  34. Wang X, Liu SY, Guo WQ, et al. (2012) Using remote sensing data to quantify changes in glacial lakes in the Chinese Himalaya. Mountain Research & Development 32 (2):203–212. https://doi.org/10.1659/mrd-journal-d-11-00044.1CrossRefGoogle Scholar
  35. Westoby MJ, Glasser NF, Brasington J, et al. (2014) Modelling outburst floods from moraine-dammed glacial lakes. Earth-Science Reviews 134(1): 137–159. https://doi.org/10.1016/j.earscirev.2014.03.009CrossRefGoogle Scholar
  36. Xia GS, Liu G, Yang W, et al. (2015) Meaningful object segmentation from SAR images via a multiscale nonlocal active contour model. IEEE Transactions on Geoscience & Remote Sensing 54(3): 1860–1873. https://doi.org/10.1109/tgrs.2015.2490078CrossRefGoogle Scholar
  37. Xie X, Wu J, Jing M (2013) Fast two-stage segmentation via nonlocal active contours in multiscale texture feature space. Pattern Recognition Letters 34(11): 1230–1239. https://doi.org/10.1016/j.patrec.2013.04.016CrossRefGoogle Scholar
  38. Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14): 3025–3033. https://doi.org/10.1080/01431160600589179CrossRefGoogle Scholar
  39. Yang Y, Liu Y, Zhou M, et al. (2015) Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sensing of Environment 171: 14–32. https://doi.org/10.1016/j.rse.2015.10.005CrossRefGoogle Scholar
  40. Yao TD, Thompson L, Yang W, et al. (2012) Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change 2(9): 663–667. https://doi.org/10.1038/nclimate1580CrossRefGoogle Scholar
  41. Zhang GQ, Yao TD, Xie H, et al. (2015) An inventory of glacial lakes in the Third Pole region and their changes in response to global warming. Global & Planetary Change 131: 148–157. https://doi.org/10.1016/j.gloplacha.2015.05.013CrossRefGoogle Scholar
  42. Zhang GQ, Li JL, Zheng GX (2017) Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods. International Journal of Remote Sensing 38 (3):742–772. https://doi.org/10.1080/01431161.2016.1271478CrossRefGoogle Scholar
  43. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recognition 43(4): 1199–1206. https://doi.org/10.1016/j.patcog.2009.10.010CrossRefGoogle Scholar
  44. Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment 159: 269–277. https://doi.org/10.1016/j.rse.2014.12.014CrossRefGoogle Scholar
  45. Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118 (6):83–94. https://doi.org/10.1016/j.rse.2011.10.028CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesSanyaChina

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