The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)
Date
2020
Authors
Yancho, J. Maxwell M.
Jones, Trevor Gareth
Gandhi, Samir R.
Ferster, Colin
Lin, Alice
Glass, Leah
Journal Title
Journal ISSN
Volume Title
Publisher
Remote Sensing
Abstract
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.
Description
Keywords
GEEMMM, mangroves, remote sensing, google earth engine, Myanmar, cloud computing, digital earth
Citation
Yancho, J. M. M., Jones, T. G., Gandhi, S. R., Ferster, C., Lin, A., & Glass, L. (2020). The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing, 12(22), 1-35. https://doi.org/10.3390/rs12223758.