CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity
Liane Guild, NASA ARC, liane.s.guild@nasa.gov (Presenter)
Juan Torres-Pérez, NASA ARC, juan.l.torresperez@nasa.gov
Jeremy Kravitz, Bay Area Environmental Research Institute, jeremy.kravitz@nasa.gov (Presenter)
In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web and include photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment for the overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity.
Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. We will be testing operational and emerging advanced algorithms for use with the BioSCape airborne campaign data. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV.
The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.
Poster: Poster_Guild_3-7_177_35.pdf
Associated Project(s):
Poster Location ID: 3-7
Presentation Type: Poster
Session: Poster Session 3
Session Date: Thu (May 11) 3:00-5:00 PM
CCE Program: BDEC