Salt marshes offer important ecosystem services to coastal populations and are key organismal habitats, but are under threat as a result of drowning related to sea level rise. The extent to which any given marsh is resilient to sea level rise depends on its ability to produce vertical accretion. This is primarily driven by belowground biomass (BGB) production, which explains why BGB may serve as an early warning sign of vulnerability to marsh drowning. Declines in plant productivity may occur in BGB before aboveground biomass (AGB), indicating that BGB may serve as an early warning sign of vulnerability to marsh drowning. However, landscape assessments of BGB are rare, as BGB is difficult to measure and has high spatiotemporal variability. The Belowground Ecosystem Resiliency Model (BERM) is a geospatial informatics tool to estimate whole-plant biomass (AGB and BGB) with satellite, climate, tide, and elevation data at a 30 m spatial scale and monthly time step. BERM was built using machine learning algorithms and extensive ground-truth calibration datasets in U.S. Georgia Spartina alterniflora marshes. Here, we aimed to characterize landscape salt marsh resilience with BERM. To do this, we generated S. alterniflora AGB and BGB predictions across the Georgia coast, covering an area of 691 km2, from 2014-2023 and identified biomass trends. We found broad declines in BGB alongside gains in AGB. A total of 74% of the marsh experienced a decrease in BGB, with an average annual trend of -0.91%. Simultaneously, 88% of the marsh increased in AGB at an average rate of 0.66% per year. We classified much of the marsh (27% of area) as vulnerable to drowning (defined as a decline in BGB that exceeded model error). We also investigated biomass trends against flooding frequency, where flooding was derived via a remote sensing-based model. BGB losses were greater with increasing flooding frequencies, suggesting that accelerated SLR will further reduce productivity. Based on BERM predictions, early stage marsh drowning is likely widespread, and management actions to conserve ecosystem services are an urgent need.

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality and public health hazard worldwide. Untreated CyanoHABs can severely affect human health due to their toxin producing ability, causing physiological and neurological disorders such as non-alcoholic liver disease, dementia to name a few. Transfer of these cyanotoxins via food-chain only accelerates public health hazards. CyanoHABs can potentially also lead to a decline in aquatic and animal life, hampering recreational activities at waterbodies and ultimately affecting the country’s economy gravely. CyanoHABs require nutrient rich warm aquatic environments to bloom and their proliferation in increasingly warmer areas of the world can be an indirect indicator of global climate change. Many lakes in the United States have been experiencing such CyanoHABs in the summers, which only grow severe every coming year, and this is consistently leading to increased public health implications. A recent study (September, 2021) by the Centre for Disease Control quantified hospital visits with the trend of such CyanoHABs to indeed observe a strong correlation between the two. This necessitates a need for a user-friendly and accessible infrastructure to monitor inland and coastal waterbodies throughout the U.S for such blooms. We present a remote sensing-based approach wrapped in a lucid web-app, “CyanoTRACKER”, which can help detect CyanoHABs on a global level and act as an early warning system, potentially preventing/lessening public health implications. CyanoHABs are dominated by the Phycocyanin pigment, which absorbs sunlight strongly around 620 nm wavelength. Owing to this specific absorption characteristic and the availability of a satellite band at exactly 620 nm, we use the opensource Sentinel-3 OLCI satellite data to detect the presence of CyanoHABs. CyanoTracker is a user-friendly Google Earth Engine dashboard, which is easily accessible via only a browser and an internet connection and allows for a variety of near-daily analysis options such as: a) select any location throughout the world and view satellite image based on date-range of choice, b) click on any pixel in the satellite image and detect presence/absence of cyanobacteria, c) visualize the spatial spread as well as the temporal phenology of an ongoing bloom or a potential incoming bloom. This dashboard is easily accessible to water-managers and in fact, anyone who wishes to use it with minimal training and can effectively serve as an early warning system to CyanoHAB induced disease outbreaks.