Bio 

Dr. Wang received her Ph.D. in Geography in 2004 from Michigan State University, and a Ph.D. in Photogrammetry and Remote Sensing in 1999 from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. From 2004 to 2013, she was an Assistant and Associate Professor in the Department of Geography at the University of Missouri. She is a USGIF Subject Matter Expert (SME) in Remote Sensing & Image Analysis, and the director the USGIF GEOINT Certificate Program at University of South Carolina.

Research 

GIScience; Remote Sensing; Environment Change; Big Earth Data

Dr. Wang’s primary research areas are bio-environmental remote sensing. Specific experiences include optical and radar remote sensing, satellite time series, image-based deep learning, small Unmanned Aircraft Systems (sUAS), and a wide spectrum of environmental applications such as fire/drought stress; marsh dieback; 3D landscape mapping. Dr. Wang’s research and education are embraced in Big Earth Data that harmonizes all kinds of Earth observations to promote environmental health and sustainability.

Teaching 

• GEOG 105: The Digital Earth (Caroline Core – ARP course)
• GEOG 345: Introduction to Remote Sensing
• GEOG 551: Remote Sensing of the Environment
• GEOG 595: Internship in Geography (Coordinator)
• GEOG 755: Remote Sensing Modeling and Analysis
  
  

Representative Publications

 Wang, C., J.T. Morris, and E. Smith, 2024.  A Lidar Biomass Index of Tidal Marshes from Drone Lidar Point Cloud. Remote Sensing, 16,1823. 

Wang, C., G.R. Morgan, and J.T. Morris, 2023. Drone Lidar deep learning for fine-scale bare earth surface and 3D marsh mapping in intertidal estuaries. Sustainability, 15, 15823.  

Wang, C., D. Guo and S. Zang, 2023. Thermal remote sensing for mapping the sub-Arctic permafrost and refining its southern limits. International Journal of Applied Earth observations and Geoinformation, 118, 103235

Wang, C., A. Wang, D. Guo, H. Li, and S. Zang, 2022. Off-peak NDVI correction to reconstruct Landsat time series for post-fire recovery in high-latitude forests. International Journal of Applied Earth observations and Geoinformation. 107,102704.

Li**, H., C. Wang, Q. Yu and E. Smith, 2022. Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990-2019). Journal of Environmental Management, 313, 114907. 

Wang, C., 2021. At-sensor Radiometric correction of a multispectral camera (RedEdge) for sUAS vegetation mapping. Sensors, 21, 8224.

Morgan, G.R., C. Wang, Z. Li, S.R. Schill, and D.R. Morgan. 2022. Deep learning of high-resolution aerial imagery for coastal marsh change detection: a comparative study. International Journal of Geo-Information. 11, 100. 

Li, H., C. Wang, Y. Cui, M. Hodgson, 2021. Mapping salt marsh along coastal South Carolina using U-Net. ISPRS Journal of Photogrammetry and Remote Sensing. 179:121-132.

Wang, C., G. Morgan, and M. E. Hodgson, 2021. sUAS for 3D tree surveying: comparative experiments on a closed-canopy earthen dam. Forests, 12, 659. 

Huang, X., C. Wang*, Z. Li, and H. Ning, 2020. A 100m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints. Big Earth Data, 1-22. 

Huang, X., and C. Wang, 2020. Estimates of exposure to the 100-year floods in the conterminous United States using national building footprints. International Journal of Disaster Risk Reduction. 50:101731. 

Li, H., Wang*, C., Ellis, J.T., C. Y., Miller, G. and Morris J.T., 2020. Identifying marsh dieback events from Landsat image series (1998-2018) with an Autoencoder in the NIWB estuary, South Carolina. International Journal of Digital Earth, 13(12), 1467-1483.