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Curriculum Vitae (CV) pdf

About:

• Ph.D. in Geospatial Information Sciences (University of Texas at Dallas) 
• MS in Marine Geology (Ocean University of China) 
• BS in Marine Geology (Ocean University of China) 

Research Interests: 

• Hyperspectral and Lidar Remote Sensing
• Coastal Vulnerability to Sea Level Rise and Hurricanes
• Machine Learning Classification and Modeling
• Object-Based Image Analysis

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• GIS 6127C: Hyperspectral Remote Sensing
• GIS 6032C: LiDAR Remote Sensing
• GIS 4021C/GEO 6938: Photogrammetry and Aerial Photography Interpretation
• GIS 4037C/GIS 5033C: Digital Image Analysis

• GIS 4035C/GIS 5038C: Remote Sensing of Environment

Book

• Zhang, C ., 2020. Multi-sensor System Applications in the Everglades Ecosystem. CRC Press/Taylor and Francis Group, ISBN:1498711774; ISBN-13: 9781498711777; 334 pages.

Recent Publication 

• Zhang, C., T. A. Douglas, D. Brodylo, L. V. Bosche, and M. Torre Jorgenson, 2024. Combining a Climate-Permafrost Model with Fine Resolution Remote Sensor Products to Quantify Active-Layer Thickness at Local Scales.  Environmental Research Letters,  .
• Zhang, C., T. A. Douglas, D. Brodylo, M. T. Jorgenson, 2023. Linking Repeat Lidar with Landsat Products for Large Scale Quantification of Fire-induced Permafrost Thaw Settlement in Interior Alaska.  Environmental Research Letters, 18, 015003.
• Zhang, C.,  D. Brodylo, M. Rahman, M. A. Rahman, T. A. Douglas, and X. Comas, 2022. Using an Object-based Machine Learning Ensemble Approach to Upscale Evapotranspiration Measured from Eddy Covariance Towers in a Subtropical Wetland.  Science of The Total Environment, 831, 154969.
• Zhang, C., T. A. Douglas, and J. Anderson, 2021. Modeling and Mapping Permafrost Active Layer Thickness using Field Measurements and Remote Sensing Techniques.  International Journal of Applied Earth Observations and Geoinformation, 102, 102455. https://doi.org/10.1016/j.jag.2021.102455 (open access)
• Douglas, T. A., and  Zhang, 2021. Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska.  Environmental Research Letters, 16, 065014.
• Zhang, C., D. Brodylo, M. J. Sirianni, T. Li, X. Comas, T. Douglas, and G. Starr, 2021. Mapping CO2  Fluxes of Cypress Swamp and Marshes in the Greater Everglades Using Eddy Covariance Measurements and Landsat Data.  Remote Sensing of Environment, 262, 112523.
• Zhang, C., X. Comas, and D. Brodylo, 2020. A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland. Journal of Geophysical Research: Biogeosciences, 125, e2020JG006002, https://doi.org/10.1029/2020JG006002.
• Zhang, C., H. Su, T. Li, W. Liu, D. Mitsova, S. Nagarajan, R. Teegavarapu, Z. Xie, F. Bloetscher, and Y. Yong, 2020. Modeling and Mapping High Water Table for a Coastal Region in Florida Using Lidar DEM Data. Groundwater, .
• Durgan, S*., C. Zhang, A. Duecaster, F. Fourney, and H. Su, 2020. Unmanned Aircraft System Photogrammetry for Mapping Diverse Vegetation Species in a Heterogeneous Coastal Wetland. Wetlands, .
• Durgan, S*., C. Zhang, and A. Duecaster, 2020. Evaluation and Enhancement of Unmanned Aircraft System Photogrammetric Data Quality for Coastal Wetlands. GIScience & Remote Sensing, https://doi.org/10.1080/15481603.2020.1819720.
• Zhang, C., 2019. Combining Ikonos and Bathymetric LiDAR Data to Improve Reef Habitat Mapping in the Florida Keys. Papers in Applied Geography, 5, 256-271.
• Zhang, C., S. Durgan, and D. Lagomasino, 2019. Modeling Risk of Mangroves to Tropical Cyclones: A Case Study of Hurricane Irma. Estuarine, Coastal, and Shelf Science, 224, 108-116.
• Zhang, C., D. R. Mishra, and S. Pennings, 2019. Mapping Salt Marsh Soil Properties Using Imaging Spectroscopy. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 221-234.
• Zhang, C., S. Denka, and D. R. Mishra, 2018. . International Journal of Remote Sensing, 39: 5600-5618.
• Zhang, C., S. Denka, H. Cooper, and D. R. Mishra, 2018. Quantification of Sawgrass Marsh Aboveground Biomass in the Coastal Everglades Using Object-Based Ensemble Analysis and Landsat Data. Remote Sensing of Environment, 204, 366-379.
• Zhang, C., M. Smith, and C. Fang, 2018.  . GIScience & Remote Sensing, 55, 90-109.
• Zhang, C., M. Smith, J. Lv, and C. Fang, 2017. Applying Time Series Landsat Data for Vegetation Change Analysis in the Florida Everglades Water Conservation Area 2A during 1996-2016. International Journal of Applied Earth Observations and Geoinformation, 57, 214-223.