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Remote Sens. 2015, 7(12), 16555-16570; doi:10.3390/rs71215848

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

1
Department of Geography, Western Michigan University, 1903 W. Michigan Avenue, Kalamazoo, MI 49008-5424, USA
2
W.E. Upjohn Institute for Employment Research, 3oo S. Westnedge Avenue, Kalamazoo, MI 49007, USA
3
Department of Anthropology, The University of Texas at Austin, 2201 Speedway Stop C3200, Austin, TX 78712, USA
4
Department of Anthropology, University of North Carolina at Greensboro, 426 Graham Building, Greensboro, NC 27412, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: James Jin-King Liu, Yu-Chang Chan, Norman Kerle and Prasad S. Thenkabail
Received: 21 September 2015 / Revised: 24 November 2015 / Accepted: 1 December 2015 / Published: 8 December 2015
(This article belongs to the Special Issue Remote Sensing in Geology)
View Full-Text   |   Download PDF [12372 KB, uploaded 8 December 2015]   |  

Abstract

Most vertebrate fossils are rare and difficult to find and although paleontologists and paleoanthropologists use geological maps to identify potential fossil-bearing deposits, the process of locating fossiliferous localities often involves a great deal of luck. One way to reduce the role of serendipity is to develop predictive models that increase the likelihood of locating fossils by identifying combinations of geological, geospatial, and spectral features that are common to productive localities. We applied GEographic Object-Based Image Analysis (GEOBIA) of high resolution QuickBird and medium resolution images from the Landsat 8 Operational Land Imager (OLI) along with GIS data such as slope and surface geology layers to identify potentially productive Eocene vertebrate fossil localities in the Great Divide Basin, Wyoming. The spectral and spatial characteristics of the image objects that represent a highly productive locality (WMU-VP-222) were used to extract similar image objects in the area covered 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 spotted from the road in a traditional ground survey were visited. Fourteen of the eighteen localities that were fossiliferous were identified by the predictive model. In 2014, the GEOBIA techniques were applied to Landsat 8 imagery of the entire basin, correctly identifying six new productive localities in a previously unsurveyed part of the basin. View Full-Text
Keywords: paleoanthropology; vertebrate paleontology; object-oriented image analysis; feature extraction; Eocene; predictive models paleoanthropology; vertebrate paleontology; object-oriented image analysis; feature extraction; Eocene; predictive models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Emerson, C.; Bommersbach, B.; Nachman, B.; Anemone, R. An Object-Oriented Approach to Extracting Productive Fossil Localities from Remotely Sensed Imagery. Remote Sens. 2015, 7, 16555-16570.

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