Bayesian Updating of Landcover Deviation (BULC-D)

BULC-D is based on the logic of the Bayesian Updating of Land Cover (BULC) algorithm  from Cardille & Fortin, 2016. It uses Bayesian updating to estimate whether every pixel in a study area is stable or changing from year to the next. Using an earlier year to build a harmonic expectation, BULC-D weighs the deviance of every image in the later year from the expected value at that same point in the year.  Values that are more unexpected (for example, an unusually low NBR) are stronger evidence of change.  

Scripts in Google Earth Engine (GEE)

Access the full repository: https://code.earthengine.google.com/?accept_repo=users/alemlakes/r-2909-BULC-Releases

• Link to presentation slides: https://docs.google.com/presentation/d/1sIAz_xrgomnVB-C9oxbNnHvRGAzlP-qpfRN6JUtpmqc/edit?usp=sharing

• Link to GEE script for Belize: https://code.earthengine.google.com/b0ed8807b36787b5aaca2a5db690a2e2

• Link to view cloud-free data by country: https://africa-uah.users.earthengine.app/view/spatialtemporalcloudfree

• BULC-D interface in GEE: https://code.earthengine.google.com/ab64b87e06ce223d1587dc95623cbc69

Cardille, J. A., & Fortin, J. A. (2016). Bayesian updating of land-cover estimates in a data-rich environment. Remote Sensing of Environment, 186, 234–249. https://doi.org/10.1016/j.rse.2016.08.021

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