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We want to know what influences the distribution of species across landscapes, and how abiotic and biotic processes affect the morphology of a landscape. Answers to these questions differ with scale. Selecting an appropriate scale of analysis is therefore fundamental to investigating and understanding the ecological processes that drive landscape morphology, assembly patterns of primary producers and the distribution and habitat uses of consumers at higher trophic levels.

The different processes that operate and interact at different scales across the landscape often require cross-scale and multi-scale modeling of ecological processes. For these spatio-temporal models, we rely heavily on remote sensing methods that are supplemented with permanent plot and transect data at a finer scale of analysis.

The Everglades landscape and its surrounding seascapes in Florida Bay and the Gulf of Mexico provide a diverse mosaic of ecosystems. They offer a unique laboratory to study ecological processes that interrelate the marine ecosystems of coral reefs and seagrass beds to coastal mangrove forests and vast terrestrial, graminoid-dominated marshes and prairies, tree islands, and cypress and pine forests. Most of our projects are focused on ecotones to better understand ecosystem shifts in the Everglades landscape mosaic that are driven by changing environmental conditions including interactions of sea-level rise, management practices of freshwater restoration, fire regimes and passing tropical storm events.

Projects

(detailed descriptions coming soon)

Title: Landscape-Scale Plant-Community Succession in Wetlands under Changing Hydrology and Fire Regimes.

Funded by: Everglades National Park

Lead PIs: Dr. Daniel Gann, Dr. Jennifer Richards


Title: Effects of Sea-Level Rise on Mangrove Distribution in Everglades National Park

Funded by: Everglades National Park

Lead PIs: Dr. Daniel Gann, Dr. Tiffany Troxler, Dr. Jennifer Richards 


Title: Tree Island Dynamics in Everglades National Park

Funded by: U.S Army Corps of Engineers

Lead PIs: Dr. Jay Sah, Dr. Daniel Gann, Dr. Michael Ross 


Title: Rapid Assessment of Vegetation Changes in Storm Water Treatment Areas (STAs)

Funded by: South Florida Water Management District

Lead PI: Dr. Daniel Gann 


Title: Optimization of LiDAR Data Processing Algorithms for Wetland
Graminoid Marsh and Prairie Vegetation

Funded by: Everglades National Park

Lead PIs: Dr. Daniel Gann, Dr. Keqi Zhang, Dr. Paulo Olivas, Dr. Shimon Wdowinski, Dr. Jennifer Richards


Title: MRI: Development of an instrument for student and faculty research on Multimodal
Environmental Observations

Funded by: National Science Foundation

Lead PIs: Dr. Naphtalie Rishe, Dr. Daniel Gann, Dr. Shahin Vasigh, Dr. Sitharama Iyengar, Dr. Todd Crowl

Publications & Datasets

To view all publications, please visit Dr. Daniel Gann's profile on FIU Digital Commons.

  • Publications & Reports

    Journal Articles

    Biswas, H., Zhang, K., Ross, M. S., & Gann, D. (2020). Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs. Remote Sensing, 12(13), 2086. https://doi.org/10.3390/rs12132086

    Gann, D. (2019). Quantitative spatial upscaling of categorical information: The multi‐dimensional grid‐point scaling algorithm. Methods in Ecology and Evolution / British Ecological Society, 10(12), 2090–2104. https://doi.org/10.1111/2041-210X.13301

    Zhang, K., Gann, D., Ross, M., Biswas, H., Li, Y., & Rhome, J. (2019). Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area. Remote Sensing, 11(7), 876. https://doi.org/10.3390/rs11070876

    Zhang, K., Gann, D., Ross, M., Robertson, Q., Sarmiento, J., Santana, S., Rhome, J., & Fritz, C. (2019). Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment, 225, 290–306. https://doi.org/10.1016/j.rse.2019.02.028

    Wendelberger, K. S., Gann, D., & Richards, J. H. (2018). Using Bi-seasonal worldview-2 multi-spectral data and supervised random forest classification to map coastal plant communities in everglades national park. Sensors , 18(3). https://doi.org/10.3390/s18030829

    Zhang, K., Thapa, B., Ross, M., & Gann, D. (2016). Remote sensing of seasonal changes and disturbances in mangrove forest: A case study from South Florida. Ecosphere , 7(6). https://doi.org/10.1002/ecs2.1366

    Gann, D., & Richards, J. (2015). Quantitative Comparison of Plant Community Hydrology Using Large-Extent, Long-Term Data. Wetlands, 35(1), 81–93. https://doi.org/10.1007/s13157-014-0594-2 

    Book Chapters

    Gann, D., Richards, J., Lee, S., & Gaiser, E. (2015). Detecting Calcareous Periphyton Mats in the Greater Everglades Using Passive Remote Sensing Methods. In J. A. Entry, A. D. Gottlieb, K. Jayachandran, & A. Ogram (Eds.), Microbiology of the Everglades Ecosystem (pp. 350–372). CRC Press. https://doi.org/10.1201/b18253-17

    Reports

    Gann, D., Richards, J. H., & Harris, B. (2019). Vegetation Change along Everglades National Park Boundary Areas of Northeast Shark River Slough, Between 2010/13 and 2016/17. Everglades National Park.

    Richards, J., Gann, D., & Sadle, J. (2015). Vegetation Trends in Indicator Regions of Everglades National Park (p. 171). Everglades National Park. http://digitalcommons.fiu.edu/gis/29

    Gann, D. (2014). Remote Sensing Supported Vegetation Detection in the Hole-in-the-Donut Restoration Areas (p. 31). Everglades National Park. http://digitalcommons.fiu.edu/gis/24

    Gann, D., Richards, J. H., & Biswas, H. (2012). Determine the effectiveness of vegetation classification using WorldView 2 satellite data for the Greater Everglades (p. 62 pp). Division/RECOVER, Everglades. http://digitalcommons.fiu.edu/gis/25

  • Datasets

    In order to access datasets related to FIU, please visit the FIU Research Data Portal.