Michael E. Hodgson
College of Arts and Sciences
|Office:||Callcott, Room 322|
|Resources:||Curriculum Vitae [pdf]
Department of Geography
Michael E. Hodgson received the B.A. from the University of Tennessee in 1980, the M.S. from the University of South Carolina (with coursework at the University of Georgia) in 1984, and the Ph.D. from the University of South Carolina in 1987. Prior to his tenure at the University of South Carolina he was Team Leader at the Oak Ridge National Laboratories, Assistant Professor at the University of Colorado, and Geographer at Henningson, Durham, and Richardson. In addition to his research and teaching in the USA he has served as Visiting Professor and taught GIScience courses at the University of Salzburg University (Austria) and the University of Padova (Italy).
My research interests are broadly in geographical information science with particular interest in the use of remote sensing approaches (e.g. LiDAR) for environmental problems. My funded research has focused on the development of innovative approaches and techniques for rapidly or more accurately extracting information from imagery and geospatial data. Recent research has utilized survey methods, cognitive studies, and GIS-based modeling for probing theoretical questions and pragmatic solutions to geographic problems. For instance, how do the state and counties emergency operations centers utilize geospatial approaches in disaster response and recovery (survey methods)? How does a trained image interpretation expert recognize objects and patterns on aerial imagery (cognitive studies)? And how can we model this cognitive image interpretation process and implement in an automated solution? What is the appropriate design and implementation for a 4-dimensional (space and time) satellite-sensor image collection model (GISci-modeling)? How can we reliably and rapidly, with estimates of confidence and uncertainty, map information from airborne LiDAR data sources?
My current research interests utilize GIS-based modeling approaches to environmental
problems and the use of sUAS imagery and LiDAR data for mapping and monitoring the
- GEOG 285: Introduction to Drones for Airborne Spatial Data
- GEOG 363: Introduction to Geographic Information Systems
- GEOG 552: LiDARgrammetric And Photogrammetric Digital Surface Mapping
- GEOG 564: GIS Based Modeling
- GEOG 565: GIS Databases and Their Use
- GEOG 763: Seminar in GIS
- GEOG 863: Advanced Seminar in GIS
Hodgson, M.E. and D. Sella-Villa, 2021. State-level Statutes Governing Unmanned Aerial Vehicle Use for Academic/Research in the USA, International Journal of Remote Sensing, 42(14): 5370-5399, https://dx.doi.org/ 10.1080/01431161.2021.1916121
Hodgson, M.E. and S.E. Piovan, 2021. An Indoor Landscape for Instruction of 3-D Aerial Drone Imagery, Journal of Geography in Higher Education, https://doi.org/10.1080/03098265.2021.1900084
Morgan, G. and M.E. Hodgson, 2021. A Post Classification Change Detection Model with Confidences in High Resolution Multi-Date sUAS Imagery in Coastal South Carolina. International Journal of Remote Sensing, 42(11): 4309-4336, http://dx.doi.org/10.1080/01431161.2021.1890266
Hodgson, M.E. and G. Morgan, 2021. Modeling Sensitivity of Topographic Change with sUAS Imagery, Geomorphology, 375(15), https://doi.org/10.1016/j.geomorph.2020.107563.
Hodgson, M.E., 2020. On the Accuracy of Low-Cost Dual-Frequency GNSS Network Receivers and Reference Data. GIScience & Remote Sensing, 57 (7): 907-923, https://doi.org/10.1080/15481603.2020.1822588.
Piovan, S.E., M. Filippini, M.E. Hodgson, 2020. Loss of Wetlands in the Southern Venetian Plain: a Geo-Historical Perspective. Bollettino dell’Associazione Italiana di Cartografia,168: 29-48, http://hdl.handle.net/10077/30963.
Derakhshan, S., M.E. Hodgson, and S.L. Cutter, 2020. Vulnerability of Populations
Exposed to Seismic Risk in the State of Oklahoma. Applied Geography, 124,
https://doi.org/10.1016/j.apgeog.2020.102295, online 2 October 2020.
Ning, H., Z. Li, M.E. Hodgson, C. Wang, 2020. Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning, International Journal of Geographic Information, 9(2): 104, https://doi.org/10.3390/ijgi9020104.
Merschdorf, H., M.E. Hodgson, T. Blaschke, 2020. Modeling Quality of Urban Life Using a Geospatial Approach, Urban Science, 4(1), 5, https://doi.org/10.3390/urbansci4010005.
Xu, H., M.E. Hodgson, S. Piovan, D. Tufford, 2018. The Potential of Using LiDAR and CIR Aerial Imagery for Palustrine Wetland Typology and Change, GIScience and Remote Sensing, 55: 477-501, https://dx.doi.org/10.1080/15481603.2017.1412145.
Contreras, D., T. Blaschke, M.E. Hodgson, 2017. Lack of Spatial Resilience in a Recovery Process: The Case of L’Aquila, Italy, Technological Forecasting & Social Change, 121:76-128, https://doi.org/10.1016/j.techfore.2016.12.010.
Li, Z., M.E. Hodgson, W. Li., 2017. A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data, International Journal of Digital Earth, http://dx.doi.org/10.1080/17538947.2016.1269842, 10: 1-22.
Liu, S. and M.E. Hodgson, 2016. Satellite Image Acquisition Planning for Large Area Disaster Emergency Response", ISPRS Journal of Photogrammetry and Remote Sensing, http://dx.doi.org/10.1016/j.isprsjprs.2016.04.007, 118: 13-21.
Shook, E., M.E. Hodgson, S. Wang, B. Behzad, K. Soltani, A.L. Hiscox, and J. Ajayakumar, 2016. Parallel Cartographic Modeling: A Methodology for Parallelizing Spatial Data Processing, International Journal of Geographical Information Science, http://dx.doi.org/10.1080/13658816.2016.1172714.
Piovan, S. and M.E. Hodgson, 2016. How many Carolina bays? An analysis of Carolina bays from USGS topographic maps at different scales, Cartography and Geographic Information Science, http://dx.doi.org/10.1080/15230406.2016.1162670.
Hodgson, M.E., B. A. Davis, D. Accardo, H. Xu, K. Beidel, S.E. Piovan, 2015. Application of Mobile Data Capture with Imagery Support, Time Sensitive Remote Sensing; Chris Lippitt and Doug Stow, editors (Springer-Verlag).
Im, J., Z. Lu, J. Rhee, and M.E. Hodgson, 2014. Building Type Classification Using Spatial and Landscape Attributes Derived from LiDAR Remote Sensing Data, Landscape and Urban Planning, 130:134-148.
Liu, S., and M.E. Hodgson, 2013. Optimizing Large Area Coverage From Multiple Satellite-Sensors, GIScience & Remote Sensing.50(6): 652-666.