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How do abiotic and biotic processes affect the spatial morphology of a landscape and what drives the distribution of species and communities?

Answers to these questions vary across ecosystems and with scale. Selecting an appropriate scale of analysis is fundamental to investigating and understanding the ecological processes that drive landscape morphology, assembly patterns of primary producers, and the spatial distribution of habitats used by consumers of higher trophic levels.

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The Everglades landscape and its surrounding seascapes in Florida Bay and the Gulf of Mexico provide a diverse mosaic of ecosystems that 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.

We focus 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.

The different processes that operate and interact at different scales across the landscape often require cross-scale and multi-scale modeling of ecological processes and patterns. To understand ecosystem processes we maintain permanent transects and plots that provide long-term time series at a fine spatial scale. What we learn about processes at a fine scale we scale up to the ecosystem and landscapes using remote sensing data at various spatial resolutions and scaling methods that allow for larger-scale inferences, and prediction.

Crucial for system-wide scaling are methods that allow for estimation and reduction of information loss. The effects of categorical data scaling and the establishment of multi-scale quantitative classification systems are the focus of our research.

Research Interests:

(1) Categorical Data Scaling

(2) Plant Community Transitions:

(a) Glycophytic - Halophytic Community Dynamics in Response to Sea-Level Rise

(b) Effects of Hydroperiod Length and Fire on Spatio-Temporal Dynamics of Freshwater Wetland Communities

(3) Woody and Graminoid Species Co-Existence Patterns in Wetland Ecosystems

Projects

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: Modeling and Correcting Vegetation-Induced Bias for LiDAR-Derived Digital Terrain Models in Water Conservation Areas

Funded by: South Florida Water Management District

Lead PIs: Dr. Daniel Gann, Dr. Danielle Ogurcak 

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: 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

    Gann, D. & Richards, J.H. (2023). Scaling of classification systems-effects of class precision on detection accuracy from medium-resolution multispectral data. Landscape Ecology, 38, 659–687 (2023). https://doi.org/10.1007/s10980-022-01546-1 

    Lamb, L. M., Gann, D., Velazquez, J. T., & Troxler, T. G. (2022). Detecting Vegetation to Open Water Transitions in a Subtropical Wetland Landscape from Historical Panchromatic Aerial Photography and Multispectral Satellite Imagery. Remote Sensing, 14, 3976. https://doi.org/10.3390/rs14163976

    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.