Faculty and Staff
Cuizhen (Susan) Wang
College of Arts and Sciences
|Office:||Callcott, Room 310|
Curriculum Vitae [pdf]
Department of Geography
Dr. Wang joined the Dept. of Geography, University of South Carolina in 2013 as an Associate Professor. She 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 (previously Institute of Remote Sensing Applications), 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 leads the USGIF GEOINT Certificate Program at USC.
Remote Sensing; satellite time-series analysis; bio-environmental change
Dr. Wang’s primary research areas are bio-environmental remote sensing, GIS and spatial analysis. Particular interests are innovative modeling of optical/radar synergy in biophysical remote sensing, vegetation mapping, environmental stress monitoring and bioenergy. Her past research experience includes Land Use/Land Cover mapping, canopy radiative transfer modeling, and quantitative biophysical estimation with optical and microwave remotely sensed data. The associated field studies include China, Japan, Thailand and the United States. Example applications include weed invasion, drought and oak decline, rice mapping, prairie grassland conservation, alpine grassland (Tibet Plateau) mapping and soil/water quality assessment.
GEOG 105: The Digital Earth (Caroline Core – ARP course)
GEOG 345: Interpretation of Aerial Photographs
GEOG 551: Principles of Remote Sensing
GEOG 575: Digital Techniques and Applications in Remote Sensing
GEOG 755: Remote Sensing Modeling and Analysis
(More can be found at Dr. Wang’s research webpage)
Fan, Q., C. Wang*, D. Zhang and S. Zang, 2017. Environmental influences on forest fire regime in the Greater Hinggan Mountains, Northeast China. Forests, 8,372.
Wang, J., C. Wang*, and S. Zhang, 2017. Assessing re-composition of Xing’an larch in boreal forests after the 1987 fire, Northeast China. Remote Sensing, 9: 504.
Li, H., C. Wang*, L. Zhang, X. Li, and S. Zang, 2017. Satellite monitoring of boreal forest phenology and its climatic responses in Eurasia. International Journal of Remote Sensing, 38:19, 5446-5463. (1st author as MS advisee)
Li, Z., C. Wang, C. T. Emrich, D. Guo, 2017. A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina Floods. Cartography and Geographic Information Sciences. DOI: 10.1080/15230406.2016.1271356
Wang, C., Q. Fan, Q. Li, W. M. SooHoo, and L. Lu, 2017. Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands. ISPRS Journal of Photogrammetry and Remote Sensing, 124: 133-143.
SooHoo, W. M., C. Wang*, and H. Li, 2017. Geospatial assessment of bioenergy land use and its impacts on soil erosion in the U.S. Midwest. Journal of Environment Management, 190:188-196. (1st author as MS advisee)
Wang, C. (Review Article), 2016. A remote sensing perspective of alpine grasslands on the Tibetan Plateau: better or worse under Tibet Warming? Remote Sensing Applications: Society and Environment. 3: 36-44.
Bai, L., C. Wang, S. Zang, Y. Zhang, Q. Hao, and Y. Wu, 2016. Remote Sensing of soil alkalinity and salinity in the Wuyu’er Shuangyang River Basin, Northeast China. Remote Sensing, 8(2): 163. doi:10.3390/rs8020163
Li, Q., C. Wang, B. Zhang and L. Lu, 2015. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sensing, 7(12): 16091-16107.
Wang, C., H. Guo, L. Zhang, Y. Qiu, Z. Sun, J. Liao, G. Liu, and Y. Zhang, 2015. Improved alpine grassland mapping in the Tibetan Plateau with MODIS time series: a phenology perspective. International Journal of Digital Earth. 8(2), 133-152.
Wang, C., H. Guo, L. Zhang, S. Liu, Y. Qiu and Z. Sun, 2015. Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series. International Journal of Biometeorology. 49 (1): 11-23.