Evaluating light availability, seagrass biomass, and productivity using hyperspectral airborne remote sensing in Saint Joseph’s Bay, Florida
Hill, V.J.; Zimmerman, R.C.; Bissett, W.P.; Dierssen, H.M.; Kohler, D.D.R. (2014). Evaluating light availability, seagrass biomass, and productivity using hyperspectral airborne remote sensing in Saint Joseph’s Bay, Florida. Est. Coast. 37(6): 1467-1489. https://dx.doi.org/10.1007/s12237-013-9764-3 In: Estuaries and Coasts. Estuarine Research Federation: Port Republic, Md.. ISSN 1559-2723; e-ISSN 1559-2731, more |  |
| Keyword | | | Author keywords | Remote sensing, Seagrass, Hyperspectral, Spatial patterns, Submarine landscape |
| Authors | | Top | - Hill, V.J.
- Zimmerman, R.C.
- Bissett, W.P.
| - Dierssen, H.M., more
- Kohler, D.D.R.
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| Abstract | Seagrasses provide a number of critical ecosystem services, including habitat for numerous species, sediment stabilization, and shoreline protection. Ariel photography is a useful tool to estimate the areal extent of seagrasses, but recent innovations in radiometrically calibrated sensors and algorithm development have allowed identification of benthic types and retrieval of absolute density. This study demonstrates the quantitative ability of a high spatial resolution (1 m) airborne hyperspectral sensor (3.2 nm bandwidth) in the complex coastal waters of Saint Joseph’s Bay (SJB). Several benthic types were distinguished, including submerged and floating aquatic vegetation, benthic red algae, bare sand, and optically deep water. A total of 23.6 km2 of benthic vegetation was detected, indicating no dramatic change in vegetation area over the past 30 years. SJB supported high seagrass density at depths shallower than 2 m with an average leaf area index of 2.0 ± 0.6 m2 m−2. Annual seagrass production in the bay was 13,570 t C year−1 and represented 41 % of total marine primary production. The effects of coarser spatial resolution were investigated and found to reduce biomass retrievals, underestimate productivity, and alter patch size statistics. Although data requirements for this approach are considerable, water column optical modeling may reduce the in situ requirements and facilitate the transition of this technique to routine monitoring efforts. The ability to quantify not just areal extent but also productivity of a seagrass meadow in optically complex coastal waters can provide information on the capacity of these environments to support marine food webs. |
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