BioScape Mapping
The overarching goal of this NASA-funded project is to develop a hyperspectral radiometric approach for mapping the spatial distribution of phytoplankton functional types (PFTs) across key coastal environments along South Africa’s coastline. The study focuses on St Helena Bay in the Namaqua Bioregion, and Walker Bay and Algoa Bay in the Agulhas Bioregion, regions that span strong environmental and ecological gradients.
Field data will be used to disentangle the optical complexity of coastal waters by separating phytoplankton Rrs signals from those associated with other seawater constituents, such as mineral particles, colored dissolved organic matter, and particulate organic matter. The resulting residual hyperspectral signals attributable to phytoplankton pigments will form the basis for identifying and mapping distinct PFTs.
In addition to airborne hyperspectral observations, we will generate satellite-based PFT maps using multispectral data from MODIS-Aqua, Suomi-VIIRS, VIIRS-20, and PACE. Together, these datasets will enable us to test five key hypotheses across two thematic areas: (1) the distribution and abundance of marine biodiversity in South Africa’s Greater Cape Floristic Region (GCFR), and (2) the feedbacks between global change, biodiversity dynamics, and ecosystem services within the GCFR.
Our approach leverages the unique hyperspectral remote-sensing reflectance (Rrs) signatures produced by variability in phytoplankton pigments. These spectral fingerprints enable the detection and discrimination of specific PFTs, including those associated with harmful algal blooms (HABs). Algorithm development will integrate newly collected in situ optical and bio-optical measurements with microscopic analyses, phytoplankton pigment data, and environmental DNA (eDNA) observations.
The project will utilize narrow-band hyperspectral datasets from AVIRIS-NG and PRISM, flown during NASA’s BIOSCape field campaign. Our team will work closely with BIOSCape collaborators to plan flight lines and optimize data quality by minimizing the impacts of cloud cover, atmospheric aerosols and haze, sun glint, and atmospheric variability between transects.
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