Developing Sensor-based Models for Mapping Greenhouse Gas Exchanges and Evapotranspiration from Wetlands in the Greater Everglades
Funded by FSGC and collaborated with Dr. Xavier Comas and USGS, we are developing innovative machine learning models to upscale CH4, CO2, and ET measured from eddy covariance towers and field platforms in south Florida Everglades to investigate the response of fluxes across multiple wetland ecosystem to climate change and disturbance such as wildfires.
Predicting Hot Spots and Hot Moments of Biogenic Gas Accumulation and Release in a Subtropical Ecosystem Using Airborne Ground-Penetrating Radar (GPR) (on-going)
Funded by DOE and collaborated with Dr. Xavier Comas at FAU, we are exploring modern drone techniques with a GPR payload to measure greenhouse gases and their coupling effects with vegetation composition and structure in the Florida Everglades.
Characterizing Permafrost Terrains Using Machine Learning Techniques (on-going)
Funded by U.S. Army Corps Engineering (USACE), we are developing and applying machine learning classification and regression techniques to model permafrost thaw depth, snow depth, and ecotype using multiple remote sensing data sources like hyperspectral, lidar, WorldView-2, Sentinel -1 and 2, and field measurements to look at impacts of climate warming and fire disturbance on permafrost degradation and expansion.
Development of Automated Methods for Using Satellite Imagery to Monitor Changes in Vegetative Communities (on-going)
Funded by FL Water District, we develop object-based machine learning ensemble approach to map a large number of wetland communties in natural areas managed by the St. Johns River Water Management District using WorldView-2 satellite imagery, historical vegetation maps, drone surveyed data, and field data.
Quantification and Mapping Sawgrass Marsh Aboveground Biomass in the Coastal Everglades
This project investigated sawgrass biomass estimation using Landsat data and developed an object-based ensemble approach for better biomass mapping in the coastal Everglades.
Examining the Effects of Salinity, Nutrients, and Sea Level Rise on Vegetation Using Remote Sensing Spectroscopy Technique.
This project examines the effects of salinity, nutrients, and sea level rise on plant stress using spectroscopy techniques by collecting data under controlled environments. The project was completed at FAU Greenhouse and hyperspectral laboratory by Dr. Donna Selch, a former Ph.D. student of Dr. Zhang.
Mapping Benthic Habits in the Florida Keys Using Hyperspectral Remote Sensing Techniques
This project explores the potential of the application of fine spatial resolution hyperspectral imagery alone, and fusion of 20-m hyperspectral imagery and 1-foot aerial photography for benthic habitat mapping in the Florida Keys.
Research and Technical Assistance for Assessing: Climate Change, Sea Level Rise and Salinity Dynamics in the Greater Everglades
Funded by USGS and collaborated with CES. South Florida is one of the world’s most vulnerable areas to climate change, especially sea level rise. The potential adaptation to sea level rise is complicated by the porous limestone geology of the region, permitting salt water intrusion into important aquifers and by the low level terrain in many areas which make even a relatively small sea level rise problematic. Already past sea level rise is impacting canal outfalls in Dade County. Scientist, city and county officials and agencies that work in the affected area are well aware of the pressing nature of these issues. The activities of this project include: 1) synthesis, workshops and working groups on climate change, sea level rise and salinity dynamics, and 2) changing salinity as an indicator of restoration and climate change impacts on the Greater Everglades.
Salinity Assessment in Northeastern Florida Bay Using Landsat TM Data
This project examines the potential of Landsat Thematic Mapper (TM) for salinity assessment in the Greater Everglades of South Florida with the Northeastern Florida Bay as study site.
Water Quality Assessment in Florida Bay Using Remote Sensing Techniques
The objective of this project is to examine the potential of several remote sensors for assessing water quality in Florida Bay, and test the sensitivity of spectral signatures measured by a spectroradiometer for water samples collected from regions with different nutrient concentrations and/or different seagrass species compositions.
Mapping Urban Composites Using Medium Resolution Optical Imagery
This project developed object-based mixture analysis techniques for mapping and quantifying urban composites (impervious surface and vegetation) using multispectral Landsat imagery and hyperspectral EO-1/Hyperion imagery.