Team » Dr. Gang Chen

Dr. Gang Chen

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Education

  • Ph.D. (2011) Geography, University of Calgary
  • M.S. (2006) Photogrammetry and Remote Sensing, Wuhan University
  • B.S. (2003) Geomatics Engineering, Wuhan University

Profile

As an environmental and geospatial scientist, Dr. Chen’s interests and capabilities are broadly emphasized on (i) developing novel geospatial big data analytical algorithms, and (ii) promoting geospatial technologies in studying landscape dynamics (e.g., forest ecological processes and land cover change) in both urban and natural environments. He has been working on a variety of projects, across several regions/countries including Antarctica, Brazil, Canada, China, Southeast Asia, Turkey, and the United States.

Dr. Chen is a full professor in the Department of Earth, Environmental and Geographical Sciences. His research has been supported by NSF, USDA, North Carolina Space Grant, NC Electric Cooperatives, National Geographic Society, Microsoft, NSERC, City of Charlotte, and UNC Charlotte. He currently serves as an Associate Editor for Remote Sensing of Environment (ranked #1 in Remote Sensing), and Ecological Processes (open access). He also serves on the editorial board for ISPRS Journal of Photogrammetry and Remote Sensing (the official journal of the world’s leading remote sensing society ISPRS), Remote Sensing (open access), and Forests (open access). He was the Chair of the AAG (American Association of Geographers) Landscape Specialty Group, and served as the President of the ASPRS (American Society for Photogrammetry & Remote Sensing) Potomac Region. He was a BOD (board of directors) member of CPGIS (International Association of Chinese Professionals in Geographic Information Sciences). Dr. Chen received the 2016 Early Career Scholar in Remote Sensing Award from AAG, the 2015 Junior Faculty Development Award from UNC Charlotte, the 2014 North Carolina Space Grant New Investigators Program Award, the 2011-2012 NSERC Visiting Fellowship from Natural Resources Canada, and the 2011 National Best Ph.D. Thesis Award from the Canadian Remote Sensing Society.