Mud, Glorious Mud! Mapping Tidal Flats Globally
Dr Nick Murray, University of New South Wales,
and A/Prof Richard Fuller, University of Queensland
In a project funded by Google, the University of Queensland and the University of New South Wales are making a global, 30-m map of the intertidal zone. This map will mark a major leap forward in our ability to manage and conserve the coastal zone, and will likely prove a critical resource for investigating the drivers of change in migratory waterbird populations across the world. Also, it will identify previously unknown areas of habitat for waterbirds, as it includes coverage of some of the most remote regions of our planet.
The classification algorithm ingests all Landsat 4, 5, 7, and 8 data for a region and identifies those areas that undergo regular tidal inundation. With the use of Google’s supercomputers, we are able to process all the imagery rapidly in a parallel computing environment. This means that we can use enormous amounts of data:
the map will use about 10,000 computers and 2,000 terabytes of data when run globally. This is impossible on a single computer. Indeed, if we could try it on one computer it would take more than 100 years to run!
For now we are making a single global map. Our future plan is to make a time-series back to 1985, which will essentially be a global version of our paper on Yellow Sea intertidal habitat loss published in 2014. We are aiming to complete this project in 2017. For a sample go to: http://intertidal-app.appspot.com/
These data can be used for improved coastal protected area delineation and management,
identification of potential waterbird habitat, prioritisation of monitoring and survey work,
building models of migration pathways and much more.
Murray NJ, Clemens RS, Phinn SR, Possingham HP & Fuller RA (2014) Tracking the rapid loss of tidal wetlands in the Yellow Sea. Frontiers in Ecology and the Environment, 12, 267-272.
Murray NJ, Phinn SR, Clemens RS, Roelfsema CM & Fuller RA (2012) Continental scale
mapping of tidal flats across East Asia using the Landsat archive. Remote Sensing, 4,
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