Department of Geosciences and Natural Resources
Associate Professor of Remote Sensing
Office Address: Stillwell 320
Download Dr. Styers's Curriculum Vitae
A bit about me:
I use remote sensing and geospatial approaches to analyze patterns of social and ecological processes that drive landscape changes affecting earth's resources. My current research focuses on analyzing ecosystem structure and function and examining changes over space and time, particularly in response to natural and human disturbances. This information will help support my ultimate goal of mapping, quantifying, and valuing ecosystem services.
My areas of expertise include: LiDAR data analysis, object-based image analysis (OBIA), MODIS and Landsat phenology and other time series analysis, multi-sensor integration, forest ecology (structure and function), human ecology mapping, air pollution effects on forest ecosystems, and dendroecology.
ES 480: Independent Study in Environmental Science
ES 495: Senior Research Seminar in Environmental Science
GEOG 150: Environmental Geography
GEOG 221: Introduction to Geospatial Analysis*
GEOG 300: Weather and Climate*
GEOG 324: Introduction to Remote Sensing*
GEOG 491/591: GIS for Mapping and Decision Making
NRM 140: Natural Resource Conservation & Management*
NRM 210: Methods in Natural Resource Management (Lab)
NRM 440: Integrated Resource Management
NRM 472: Geospatial Analysis*
NRM 480: Independent Study in Natural Resources*
NRM 493: Post-fire Ecology
* indicates courses taught regularly
Recent Publications (* indicates student author):
Biedenweg, K., K.A. Williams*, L.K. Cerveny, and D.M. Styers. 2019. Is recreation a landscape value?: Exploring underlying values in Landscape Values Mapping. Landscape and Urban Planning 185:24-27. doi:10.1016/j.landurbplan.2018.12.005.
Tanner, B.R., M.L. Douglas*, C.H. Greenberg, J.N. Chamberlin*, and D.M. Styers. 2018. A Macroscopic Charcoal and Multiproxy Record From Peat Recovered From Depression Marshes in Longleaf Pine Sandhills, Florida, USA. Quaternary 1(3):25. doi:10.3390/quat1030025.
Styers, D.M., G.R. Dobbs, L. Cerveny, and I. Hayes*. 2018. Geovisualization of Socio-Spatial Data on Outdoor Activities and Values in the Southern Appalachians. International Journal of Applied Geospatial Research 9(3):55-80. doi:10.4018/IJAGR.2018070104.
Bates, P.C., J.R. Miller, D.M. Styers, K. Langdon, C. Burda, R. Davis, T. Martin, B. Kloeppel, and S. McFarland. 2018. Natural Resource Condition Assessment for Great Smoky Mountains National Park. Natural Resource Report NPS/GRSM/NRR—2018/1626, 478 pp. National Park Service, Fort Collins, Colorado. https://irma.nps.gov/2253044.
Styers, D.M. 2018. Using big data to engage undergraduate students in authentic science. Journal of Geoscience Education, 66(1):12-24. doi:10.1080/10899995.2018.1411699.
Richards, S.L., J.G. Balanay, B.D. Byrd, M.H. Reiskind, D.M. Styers. 2017. Regional Survey of Mosquito Control Knowledge and Usage in North Carolina. Journal of the American Mosquito Control Association, 33(4):331-339. doi:10.2987/17-6669.1.
Bates, P.C., J.R. Miller, D.M. Styers, C. Burda, R. Davis, T. Martin, and B. Kloeppel. 2017. Natural Resource Condition Assessment for Carl Sandburg Home National Historic Site. Natural Resource Report NPS/CARL/NRR—2017/1373, 202 pp. National Park Service. Fort Collins, Colorado. https://irma.nps.gov/2237916.
Bates, P.C., J.R. Miller, D.M. Styers, C. Burda, R. Davis, T. Martin, and B. Kloeppel. 2017. Natural Resource Condition Assessment for Guilford Courthouse National Military Park. Natural Resource Report NPS/GUCO/NRR—2017/1372, 163 pp. National Park Service. Fort Collins, Colorado. https://irma.nps.gov/2237912.
Zhang, Z.*, A. Kazakova, L.M. Moskal, and D.M. Styers. 2016. Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data. Forests, Special Issue LiDAR Remote Sensing of Forest Resources, 7(6):122. doi:10.3390/f7060122.
Styers, D.M., L.M. Moskal, J.J. Richardson, and M.A. Halabisky*. 2014. Evaluation of the contribution of LiDAR data and post-classification procedures to object-based classification accuracy. Journal of Applied Remote Sensing, 8(1):083529 (2014). doi:10.1117/1.JRS.8.083529.
*Google Scholar Citations: https://scholar.google.com/citations?hl=en&user=qpLXwd4AAAAJ