Submerged aquatic vegetation (SAV) is an important indicator of freshwater and marine water quality in almost all shallow water aquatic environments. Throughout the world the diversity of submerged aquatic vegetation appears to be in decline, although sufficient historical data, of sufficient quantitative quality is lacking. Hyperspectral remote sensing technology, available from low altitude aircraft sensors, may provide a basis to improve upon existing photographic regional assessments and monitoring concerned with the aerial extent and coverage of SAV. In addition, modern low altitude remote sensing may also help in the development of environmental satellite requirements for future satellite payloads. This paper documents several important spectral reflectance signature features which may be useful in developing a protocol for remote sensing of SAV, and which is transferable to other shallow water aquatic habitats around the world. Specifically, we show that the shape or curvature of the spectral reflectance absorption feature centered near the chlorophyll absorption region of ˜ 675 nm is strongly influenced not only by the relative backscatter region between 530-560 nm, but by a "submerged vegetation red edge" that appears in the 695 to 700 nm region in extremely high density vegetative areas in very shallow waters (= 0.5m depth). This "aquatic biomass red edge" is also observable in deeper waters where there is a shallow subsurface algal boom as demonstrated in this paper. Use of this submerged aquatic red edge feature will become an important component of SAV remote sensing in shallow aquatic habitats, as well as in phytoplankton-related water quality remote sensing applications of surface phytoplankton blooms .

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Seagrasses are the foundation of many coastal ecosystems and are in global decline because of anthropogenic impacts. For the Indian River Lagoon (Florida, U.S.A.), we developed competing multistate statistical models to quantify how environmental factors (surrounding land use, water depth, and time [year]) influenced the variability of seagrass state dynamics from 2003 to 2014 while accounting for time-specific detection probabilities that quantified our ability to determine seagrass state at particular locations and times. We classified seagrass states (presence or absence) at 764 points with geographic information system maps for years when seagrass maps were available and with aerial photographs when seagrass maps were not available. We used 4 categories (all conservation, mostly conservation, mostly urban, urban) to describe surrounding land use within sections of lagoonal waters, usually demarcated by land features that constricted these waters. The best models predicted that surrounding land use, depth, and year would affect transition and detection probabilities. Sections of the lagoon bordered by urban areas had the least stable seagrass beds and lowest detection probabilities, especially after a catastrophic seagrass die-off linked to an algal bloom. Sections of the lagoon bordered by conservation lands had the most stable seagrass beds, which supports watershed conservation efforts. Our results show that a multistate approach can empirically estimate state-transition probabilities as functions of environmental factors while accounting for state-dependent differences in seagrass detection probabilities as part of the overall statistical inference procedure.

Society needs information about how vegetation communities in coastal regions will be impacted by hydrologic changes associated with climate change, particularly sea level rise. Due to anthropogenic influences which have significantly decreased natural coastal vegetation communities, it is important for us to understand how remaining natural communities will respond to sea level rise. The Cape Canaveral Barrier Island complex (CCBIC) on the east central coast of Florida is within one of the most biologically diverse estuarine systems in North America and has the largest number of threatened and endangered species on federal property in the contiguous United States. The high level of biodiversity is susceptible to sea level rise. Our objective was to model how vegetation communities along a gradient ranging from hydric to upland xeric on CCBIC will respond to three sea level rise scenarios (0.2 m, 0.4 m, and 1.2 m). We used a probabilistic model of the current relationship between elevation and vegetation community to determine the impact sea level rise would have on these communities. Our model correctly predicted the current proportions of vegetation communities on CCBIC based on elevation. Under all sea level rise scenarios the model predicted decreases in mesic and xeric communities, with the greatest losses occurring in the most xeric communities. Increases in total area of salt marsh were predicted with a 0.2 and 0.4 m rise in sea level. With a 1.2 m rise in sea level approximately half of CCBIC’s land area was predicted to transition to open water. On the remaining land, the proportions of most of the vegetation communities were predicted to remain similar to that of current proportions, but there was a decrease in proportion of the most xeric community (oak scrub) and an increase in the most hydric community (salt marsh). Our approach provides a first approximation of the impacts of sea level rise on terrestrial vegetation communities, including important xeric upland communities, as a foundation for management decisions and future modeling.

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