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Hill, V., Et Al. 2014. Evaluating Light Availability, Seagrass Biomass, and Productivity Using Hyperspectral Airborne Remote

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Hill, V., Et Al. 2014. Evaluating Light Availability, Seagrass Biomass, and Productivity Using Hyperspectral Airborne Remote Estuaries and Coasts (2014) 37:1467–1489 DOI 10.1007/s12237-013-9764-3 Evaluating Light Availability, Seagrass Biomass, and Productivity Using Hyperspectral Airborne Remote Sensing in Saint Joseph’sBay,Florida Victoria J. Hill & Richard C. Zimmerman & W. Paul Bissett & Heidi Dierssen & David D. R. Kohler Received: 29 October 2012 /Revised: 23 December 2013 /Accepted: 26 December 2013 /Published online: 30 January 2014 # Coastal and Estuarine Research Federation 2014 Abstract Seagrasses provide a number of critical ecosystem reduce biomass retrievals, underestimate productivity, and alter services, including habitat for numerous species, sediment sta- patch size statistics. Although data requirements for this ap- bilization, and shoreline protection. Ariel photography is a use- proach are considerable, water column optical modeling may ful tool to estimate the areal extent of seagrasses, but recent reduce the in situ requirements and facilitate the transition of this innovations in radiometrically calibrated sensors and algorithm technique to routine monitoring efforts. The ability to quantify development have allowed identification of benthic types and not just areal extent but also productivity of a seagrass meadow retrieval of absolute density. This study demonstrates the quan- in optically complex coastal waters can provide information on titative ability of a high spatial resolution (1 m) airborne the capacity of these environments to support marine food webs. hyperspectral sensor (3.2 nm bandwidth) in the complex coastal waters of Saint Joseph’s Bay (SJB). Several benthic types were Keywords Remote sensing . Seagrass . Hyperspectral . distinguished, including submerged and floating aquatic vege- Spatial patterns . Submarine landscape tation, 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. Introduction 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 Seagrasses form extensive meadows in shallow coastal envi- − seagrass production in the bay was 13,570 t C year 1 and ronments, where they provide a number of critical ecosystem represented 41 % of total marine primary production. The effects services, including a source of organic carbon, habitat for of coarser spatial resolution were investigated and found to numerous species of fish and invertebrates, sediment stabili- zation, and shoreline protection (Gillanders 2006; Marbá et al. Communicated by Wayne S. Gardner 2006). Despite the environmental and economic significance of these “ecosystem engineers,” seagrass populations are V. J. Hill (*) : R. C. Zimmerman Department Ocean, Earth and Atmospheric Sciences, Old Dominion threatened worldwide by coastal development and anthropo- University, 4600 Elkhorn Avenue, Norfolk, VA 23529, USA genic eutrophication and may be nearing a crisis with respect e-mail: [email protected] to global sustainability (Short and Wyllie-Echeverria 1996; Orth et al. 2006; Waycott et al. 2009). The overall health of the W. P. Bissett : D. D. R. Kohler Florida Environmental Research Institute, 10500 University Drive, seagrass system is driven by recruitment, mortality, and abi- Suite 140, Tampa, FL 33612, USA otic factors, such as light, mechanical disturbance, and nutri- ents. These biotic and abiotic factors combine to control the H. Dierssen spatial distribution and density of seagrass across the subma- Marine Sciences/Geography, Department of Marine Sciences, University of Connecticut, 1080 Shennecossett Road, rine landscape (Marba and Duarte 1995;Roseetal.1999; Groton, CT 06340, USA Orth et al. 2000; Robbins and Bell 2000; Harwell and Orth 2001). In turn, the mosaic nature of the seagrass meadows, Present Address: including the degree of patchiness, gap dynamics, habitat edge W. P. Bissett : D. D. R. Kohler WeoGeo, Inc., 2828 SW Corbett Ave., Suite 206, type, and connectivity, controls animal recruitment and move- Portland, OR 97201, USA ment among patches (Kurdziel and Bell 1992; With and Crist 1468 Estuaries and Coasts (2014) 37:1467–1489 1995;HovelandLipcius2001;Belletal.2006). The contin- across the visible spectrum (400 to 700 nm), often with gaps ued study of landscape dynamics as they apply to both the between the bands. Radiometrically calibrated hyperspectral health of the seagrass meadows and the animals they support sensors, such as AVIRIS, PHILLS, SAMSON, and HICO, depends on accurate, fine-scale, and quantitative measure- provide higher spectral resolution (15 nm or less) and continu- ments of areal coverage and seagrass density. Through metrics ous wavelength coverage across the visible spectrum, allowing such as seagrass density and primary production, we can for the discrimination of subtle spectral characteristics. provide a direct relationship to population dynamics of juve- There are several techniques for processing remotely nile and adult invertebrate species (Warren et al. 2010; Ralph sensed imagery to SAV maps. Supervised thematic classifica- et al. 2013) and biogeochemical properties such as release of tion provides a computationally inexpensive technique based labile dissolved organic carbon (Moriarty et al. 1986) and on the similarity of spectral characteristics among picture removal or burial of particulate carbon (Mateo et al. 2006). elements (pixels) within a scene. These similarities may vary Remote sensing by aircraft or satellite has proven to be across time and space due to changes in solar illumination and useful at providing spatial information over large coastal areas water column optical characteristics, requiring expert local (Robbins 1997;Dekkeretal.2006;Ferwerdaetal.2007). The knowledge to match pixel characteristics to bottom types for spectral reflectance of seagrass leaves provides a marked con- each successive image (Mumby et al. 1997; Mumby and trast to the surrounding unvegetated environments (Fyfe 2003; Edwards 2002;Pasqualinietal.2005; Matarrese et al. Louchard et al. 2003;Zimmerman2003;Thorhaugetal.2007), 2008). Physics-based methods that use radiative transfer the- and submerged aquatic vegetation (SAV) is easily differentiated ory (modeling light absorption and scattering) attempt to from bright sand. These spectral characteristics are distorted by incorporate the effects of water depth and water column the overlying water column, so that the remotely sensed reflec- transparency and promise greater generality and accuracy, tance is derived from a combination of benthic reflectance and but they require radiometrically calibrated datasets (Dekker the optical properties of the water column itself (Lee et al. et al. 2011). Continued advances in computational speed and 1999). Changes in the quantity of phytoplankton biomass, memory capacity have dramatically reduced the practical suspended sediment, or colored dissolved organic material limitations associated with radiative transfer methods that within the water column affect the optical properties overlying simultaneously derive water depth, water column constituents, theseabed(Dierssenetal.2003; Han and Rundquist 2003; and benthic type (Lee et al. 2001;Mobleyetal.2005;Brando Vahtmae et al. 2006; Silva et al. 2008)shiftingthespectral et al. 2009; Sagawaa et al. 2010; Yangab et al. 2010). The characteristics of sea surface reflectance. application of individual algorithms to the imagery is depen- Radiometrically uncalibrated aerial photography is used with dent on study site complexity and access to in situ data; considerable success to map the relative abundance (e.g., per- knowledge or retrieval of bathymetry is considered critical cent cover) of SAV in shallow coastal environments (Kendrick in these methods (Dekker et al. 2011). Despite potentially et al. 2000;Meehanetal.2005; http://web.vims.edu/bio/sav/ demanding requirements, the retrieval of benthic reflectance index.html). However, the lack of radiometric calibration and vegetation type from these radiative transfer-based algo- generally precludes accurate retrieval of absolute density from rithms provides a pathway for the automated determination of the imagery. Further, the comparison of uncalibrated imagery density (leaf area index) biomass (in grams per square meter) across time and space can also be problematic, as differences in and the subsequent computation of net primary productivity solar illumination and water column optical properties will without the intervention of human experts required for super- distort the reflectance signal emanating from the submerged vised classification (Dierssen et al. 2003;Puetal.2012). vegetation (Dierssen et al. 2003). The goals of this work were to (a) test bio-optical methods Radiometrically calibrated sensors provide precise estimates for the retrieval of absolute seagrass abundance in optically of target radiance in absolute physical units (e.g., watts per complex coastal waters, using methods pioneered in extreme- square meter per steradian) that can be used to account for ly clear tropical waters of the Bahamas Banks (Dierssen et al. differences in illumination within and among images, allowing 2003;Dierssenetal.2010); (b) to estimate the contribution of for more reliable comparisons across time and space. They also SAV to overall productivity of the Saint Joseph’s Bay (SJB) permit the application of radiative transfer theory for retrieval of ecosystem; and (c) to explore the importance of spatial reso- target optical properties and
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