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A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment
Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.ORCID iD: 0000-0002-2502-6384
Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
Space Applications Centre, ISRO, Ahmedabad, Atmospheric and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad.
Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
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2015 (English)In: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 38, p. 150-163Article in journal (Refereed) Published
Abstract [en]

Glacial lakes show a wide range of turbidity. Owing to this, the normalized difference water indices (NDWIs) as proposed by many researchers, do not give appropriate results in case of glacial lakes. In addition, the sub-pixel proportion of water and use of different optical band combinations are also reported to produce varying results. In the wake of the changing climate and increasing GLOFs (glacial lake outburst floods), there is a need to utilize wide optical and thermal capabilities of Landsat 8 data for the automated detection of glacial lakes. In the present study, the optical and thermal bandwidths of Landsat 8 data were explored along with the terrain slope parameter derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version2 (ASTER GDEM V2), for detecting and mapping glacial lakes. The validation of the algorithm was performed using manually digitized and subsequently field corrected lake boundaries. The pre-existing NDWIs were also evaluated to determine the supremacy and the stability of the proposed algorithm for glacial lake detection. Two new parameters, LDI (lake detection index) and LF (lake fraction) were proposed to comment on the performances of the indices. The lake detection algorithm (LDA) performed best in case of both, mixed lake pixels and pure lake pixels with no false detections (LDI = 0.98) and very less areal underestimation (LF= 0.73). The coefficient of determination (R-2) between areal extents of lake pixels, extracted using the LDA and the actual lake area, was very high (0.99). With understanding of the terrain conditions and slight threshold adjustments, this work can be replicated for any mountainous region of the world.

Place, publisher, year, edition, pages
2015. Vol. 38, p. 150-163
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Aerospace Engineering
Research subject
Atmospheric science
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URN: urn:nbn:se:ltu:diva-4852DOI: 10.1016/j.jag.2015.01.004Local ID: 2da77462-3f35-48ab-8b04-117f9567d746OAI: oai:DiVA.org:ltu-4852DiVA, id: diva2:977726
Note
Upprättat; 2015; 20160831 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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Bhardwaj, Anshuman

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