An Object-Oriented Approach to Extracting Productive Fossil Localities from Remotely Sensed Imagery

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Robert Anemone, Professor and Department Head (Creator)
Brett A. Nachman, Research Scientist (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: 2015-2016 UNCG University Libraries Open Access Publishing Fund Grant Winner. Most vertebrate fossils are rare and difficult to find and although paleontologists andpaleoanthropologists use geological maps to identify potential fossil-bearing deposits, theprocess of locating fossiliferous localities often involves a great deal of luck. One way to reducethe role of serendipity is to develop predictive models that increase the likelihood of locatingfossils by identifying combinations of geological, geospatial, and spectral features that arecommon to productive localities. We applied GEographic Object-Based Image Analysis(GEOBIA) of high resolution QuickBird and medium resolution images from the Landsat 8Operational Land Imager (OLI) along with GIS data such as slope and surface geology layers toidentify potentially productive Eocene vertebrate fossil localities in the Great Divide Basin,Wyoming. The spectral and spatial characteristics of the image objects that represent a highlyproductive locality (WMU-VP-222) were used to extract similar image objects in the areacovered by the high resolution imagery and throughout the basin using the Landsat imagery.During the 2013 summer field season, twenty-six locations that would not have been spottedfrom the road in a traditional ground survey were visited. Fourteen of the eighteen localities thatwere fossiliferous were identified by the predictive model. In 2014, the GEOBIA techniqueswere applied to Landsat 8 imagery of the entire basin, correctly identifying six new productivelocalities in a previously unsurveyed part of the basin.

Additional Information

Remote Sensing
Language: English
Date: 2015
paleoanthropology, vertebrate paleontology, object-oriented image analysis, feature extraction, Eocene, predictive models

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