An automated method for glacial lake mapping in High Mountain Asia using Landsat 8 imagery
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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.
KeywordsGlacial lake Landsat 8 Modified Normalized Difference Water Index Nonlocal active contour
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The research reported in this manuscript was funded by the National Natural Science Foundation Project (Grant Nos. 41701481 and 41401511).
- 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
- 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
- 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
- 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
- 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
- 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
- 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