Publications

Publications in Google Scholar

Book

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

Selected Peer-reviewed Journal Articles (* advisee)

  • 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, https://doi.org/10.1088/1748-9326/ad31dc .
  • 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. (https://www.fau.edu/newsdesk/articles/alaska-permafrost-thaw.php)
  • 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, https://doi.org/10.1016/j.scitotenv.2022.154969.
  • 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 C. 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, https://doi.org/10.1016/j.rse.2021.112523.
  • 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, 59 (2) 190-198.
  • 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, 40 (6), 2621-2633.
  • Durgan, S*., C. Zhang, and A. Duecaster, 2020. Evaluation and Enhancement of Unmanned Aircraft System Photogrammetric Data Quality for Coastal Wetlands. GIScience & Remote Sensing, 57, 865-881.
  • 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.
  • Cooper, H*., Zhang, S.E. Davis, and T.G. Troxler, 2019. Object-based Correction of LiDAR DEMs Using RTK-GPS Data and Machine Learning Modeling in the Coastal Everglades. Environmental Modeling & Software, 112, 179-191.
  • Zhang, C., S. Denka, and D. R. Mishra, 2018. Mapping Freshwater Marsh Species in the Wetlands of Lake Okeechobee using Very High-resolution Aerial Photography and Lidar Data. 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. Evaluation of Goddard’s LiDAR, Hyperspectral, and Thermal Data Products for Mapping Urban Land-cover Types. 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.
  • Zhang, C., 2016. Multiscale Quantification of Urban Composition from EO-1/Hyperion Data Using Object-based Spectral Unmixing. International Journal of Applied Earth Observations and Geoinformation, 47, 153-162.
  • Zhang, C., D. Selch, and H. Cooper, 2016. A Framework to Combine Three Remotely Sensed Data Sources for Vegetation Mapping in the Central Florida Everglades. Wetlands, 36, 201-213.
  • Zhang, C., 2015. Applying Data Fusion Techniques for Benthic Habitat Mapping and Monitoring in a Coral Reef Ecosystem. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 213-223.
  • Zhang, C., Y. Zhou, and F. Qiu, 2015. Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory. Remote Sensing, 7, 7892-7913.
  • Cooper, H*., Zhang, and D. Selch, 2015. Incorporating Uncertainty of Groundwater Modeling in Sea-level Rise Assessment: A Case Study in South Florida. Climatic Change, 129, 281-294.
  • Zhang, C., 2014. Combining Hyperspectral and LiDAR Data for Vegetation Mapping in the Florida Everglades. Photogrammetric Engineering & Remote Sensing, 80 (8), 733-743. (This paper won the 2015 John I. Davidson President’s Award from ASPRS).
  • Zhang, C., H. Cooper, D. Selch, et al., 2014. Mapping Urban Land Covers Using Object-based Multiple Endmember Spectral Mixture Analysis. Remote Sensing Letters, 5 (6), 521-529.
  • Zhang, C., and Z. Xie, 2014. Data Fusion and Classifier Ensemble Techniques for Vegetation Mapping in the Coastal Everglades. Geocarto International, 29 (3), 228-243.
  • Zhang, C., and Z. Xie, 2013. Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques. Wetlands, 33 (2), 233-244.
  • Zhang, C., D. Selch, Z. Xie, C. Roberts, H. Cooper, and G. Chen, 2013. Object-based Benthic Habitat Mapping in the Florida Keys from Hyperspectral Imagery. Estuarine, Coastal and Shelf Science, 134, 88-97.
  • Zhang, C., Z. Xie, and D. Selch, 2013. Fusing LiDAR and Digital Aerial Photography for Object-based Forest Mapping in the Florida Everglades. GIScience & Remote Sensing, 50 (5), 562-573.
  • Xie, Z., C. Zhang, and L. Berry, 2013. Geographically Weighted Modeling of Surface Salinity in Florida Bay Using Landsat TM Data. Remote Sensing Letters, 4 (1), 76-84.
  • Zhang, C., and F. Qiu, 2012a. Mapping Individual Tree Species in an Urban Forest Using Airborne LiDAR Data and Hyperspectral Imagery. Photogrammetric Engineering & Remote Sensing, 78 (10), 1079-1087. (This paper won the Early Career Paper Award of Remote Sensing Specialty Group (RSSG) of AAG in 2012, and the First Place of ERDAS Award for Best Scientific Paper in Remote Sensing from ASPRS in 2013).
  • Zhang, C., and F. Qiu, 2012b. Unsupervised Hyperspectral Image Classification with a Neuro-fuzzy System. Journal of Applied Remote Sensing, 6, 063515.
  • Zhang, C., and Z. Xie, 2012. Combining Object-based Texture Measures with a Neural Network for Vegetation Mapping in the Everglades from Hyperspectral Imagery. Remote Sensing of Environment, 124, 310-320.
  • Zhang, C., Z. Xie, C. Roberts, L. Berry, and G. Chen, 2012. Salinity Assessment in Northeastern Florida Bay Using Landsat TM Data. Southeastern Geographer, 52 (3), 267-281.
  • Qiu, F., C.Zhang, and Y. Zhou, 2012. The Development of an Areal Interpolation ArcGIS Extension and a Comparative Study. GIScience & Remote Sensing, 49(5), 644-663.
  • Zhang, C., and F. Qiu, 2011. A Point-based Intelligent Approach to Areal Interpolation. The Professional Geographer, 63 (2), 262-276.