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Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change

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Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine Implications for assessment of channel floodplain dynamics in a time of rapid environmental change Journal of[.]

Journal of Environmental Management 278 (2021) 111559 Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman Research article Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change Jiaguang Li a, b, *, Stephen Tooth c, Kun Zhang d, Yang Zhao a a Key Laboratory of Tectonics and Petroleum Resources of the Ministry of Education, China University of Geosciences, Wuhan, 430074, China Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, Wuhan, 430074, China Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, SY23 3DB, UK d Institute of Geological Survey of Qinghai Province, Xining, 810012, China b c A R T I C L E I N F O A B S T R A C T Keywords: Channel dynamics Flood mapping Floodplain Google Earth Engine Meandering river Unvegetated channel Given rapid environmental change, the development of new, data-driven, interdisciplinary approaches is essential for improving assessment and management of river systems, especially with respect to flooding In the world’s extensive drylands, difficulties in obtaining field observations of major hydrological events mean that remote sensing techniques are commonly used to map river floods and assess flood impacts Such techniques, however, are dependent on available cloud-free imagery during or immediately after peak discharge, and single images may omit important flood-related hydrogeomorphological events Here, we combine multiple Landsat images from Google Earth Engine (GEE) with precipitation datasets and high-resolution (

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