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Article

The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)

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Blue Ventures Conservation—Mezzanine, The Old Library, Trinity Road, St Jude’s, Bristol BS2 0NW, UK
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Department of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Terra Spatialists, The Blue House, 660 West 13th Avenue, Vancouver, BC V5Z 1N9, Canada
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The Jolly Geographer, 7 Yorke Gate, Watford, Hertfordshire WD17 4NQ, UK
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Department of Geography, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC V8W 2Y2, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3758; https://doi.org/10.3390/rs12223758
Received: 1 October 2020 / Revised: 6 November 2020 / Accepted: 9 November 2020 / Published: 16 November 2020
(This article belongs to the Special Issue Remote Sensing in Mangroves)
Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem this loss. Remotely sensed data have a proven record for successfully mapping and monitoring mangroves, but conventional methods are limited by imagery availability, computing resources and accessibility. In addition, the variable tidal levels in mangroves presents a unique mapping challenge, particularly over geographically large extents. Here we present a new tool—the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)—an intuitive, accessible and replicable approach which caters to a wide audience of non-specialist coastal managers and decision makers. The GEEMMM was developed based on a thorough review and incorporation of relevant mangrove remote sensing literature and harnesses the power of cloud computing including a simplified image-based tidal calibration approach. We demonstrate the tool for all of coastal Myanmar (Burma)—a global mangrove loss hotspot—including an assessment of multi-date mapping and dynamics outputs and a comparison of GEEMMM results to existing studies. Results—including both quantitative and qualitative accuracy assessments and comparisons to existing studies—indicate that the GEEMMM provides an accessible approach to map and monitor mangrove ecosystems anywhere within their global distribution. View Full-Text
Keywords: GEEMMM; mangroves; remote sensing; google earth engine; Myanmar; cloud computing; digital earth GEEMMM; mangroves; remote sensing; google earth engine; Myanmar; cloud computing; digital earth
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MDPI and ACS Style

Yancho, J.M.M.; Jones, T.G.; Gandhi, S.R.; Ferster, C.; Lin, A.; Glass, L. The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sens. 2020, 12, 3758. https://doi.org/10.3390/rs12223758

AMA Style

Yancho JMM, Jones TG, Gandhi SR, Ferster C, Lin A, Glass L. The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing. 2020; 12(22):3758. https://doi.org/10.3390/rs12223758

Chicago/Turabian Style

Yancho, J. M.M., Trevor G. Jones, Samir R. Gandhi, Colin Ferster, Alice Lin, and Leah Glass. 2020. "The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)" Remote Sensing 12, no. 22: 3758. https://doi.org/10.3390/rs12223758

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