NSF Drone Mapping and GIS for coastal seagrass

I am co-leading with Dr. Timothy Hawthorne the UAV mapping of coastal seagrass sites along the Pacific (west) coast of North America through a $1.3 million collaborative NSF grant for the UCF portion, one of the earliest attempts to employ UAV mapping in coastal management and seagrass conservation. This interdisciplinary project aims to assess the interactions of three major stressors to coastal ecosystems (climate warming, altered biodiversity, and disease) on the local and regional health of seagrass integrate ecological, microbiological, computational, geospatial analysis, and UAV remote sensing.

Several research institutes/universities are involved in interdisciplinary collaborations, including Smithsonian MarineGeo, Cornell University, UC Davis, University of Alaska Fairbanks, Oregon State University, San Diego State University, Hakai institute. Our collaborators include: Carla Gomes (Cornell), Deanna Beatty (UCD), Drew Harvell(Cornell), Emmett Duffy (SI), Fiona Tomas Nash (OSU), Ginny Eckert (UAF), John Stachowicz (UCD), Kevin Hovel (SDSU), Leah Harper (SI), Lillian Aoki (Cornell), Lia Domke (UAF), Margot Hessing-Lewis (Hakai), Olivia Graham (Cornell),

I have been leading a drone mapping team travel along the Pacific coast and collected over 10,000 drone remote sensing images along the U.S. Pacific coast. The drone remote sensing and field sampling sites include (From north to south): (1) six sites around Prince of Wales Island, Alaska; (2) five sites on the Central Coast of British Columbia (by Hakai team); (3) five sites in the San Juan Islands; (4) three sites in Yaquina Bay, Oregon; (5) two sites in Coos Bay, Oregon; (6) six sites in Bodega Bay and Tomales Bay, northern California; and (7) six sites in San Diego and Mission Bay in southern California.

For more info and viewing the data, please visit our Project Page and ArcGIS Story Map.

Geospatial data science & machine learning for environmental science

For my doctoral dissertation, I developed and implemented a novel geo-statistical method that used to assimilate multi-scale data sets with different temporal sampling frequencies and different spatial densities. The algorithm has been made available in Python and ArcGIS packages with a user-friendly interface. High-performance computing on supercomputer and parallel computing are utilized to enhance the efficiency of the algorithm.

K12 outreach, participatory GIS research, and Citizen Science

I am co-leading the NSF RET project to build mutually rewarding partnerships with K-12 science teachers to transfer teachers’ experience in cutting edge research to the broader impact content in the classroom. Iwork with teachers participating fieldworks and developing science lessons using fieldwork data and drone mapping principles to support inquiry-based learning with students.