Long-term Annual Aerial Surveys of Submersed Aquatic Vegetation (SAV) Support Science, Management, and Restoration

Orth, Robert J.; Dennison, William C.; Gurbisz, Cassie; Hannam, Michael; Keisman, Jennifer; Landry, J. Brooke; Lefcheck, Jonathan S.; Moore, Kenneth A.; Murphy, Rebecca R.; Patrick, Christopher J.; Testa, Jeremy M.; Weller, Donald E.; Wilcox, David J.; Batiuk, Richard A.

doi:10.1007/s12237-019-00651-w

Cited by 8 publications

(4 citation statements)

References 47 publications

(72 reference statements)

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“…To characterize landscape features, we analyzed aerial data products and satellite imagery in QGIS 3.14.0 (QGIS.org 2020). We used seagrass density maps based on high-resolution aerial photographs taken in 2019 (ground sample distance [GSD] = 24 cm; Orth et al 2019) and salt marsh maps developed from 2011 field surveys and 2019 aerial photography (GSD = 30 cm; Berman et al 2011;Planet Team 2019). We also used maps of intertidal oyster reefs created from 2002 and 2007 aerial imagery (GSD = 1 m; Ross and Luckenbach 2009) that were validated in 2015-2017 with field and LiDAR surveys (GSD < 1 m; Hogan and Reidenbach 2019; subtidal oyster reefs are not present in our study region).…”

Section: Seagrass and Landscape Measurementsmentioning

confidence: 99%

Coastal Vegetation and Bathymetry Influence Blue Crab Abundance Across Spatial Scales

Cheng

Tedford

Smith

et al. 2022

Estuaries and Coasts

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Blue crabs (Callinectes sapidus) are highly mobile, ecologically-important mesopredators that support multimillion-dollar fisheries along the western Atlantic Ocean. Understanding how blue crabs respond to coastal landscape change is integral to conservation and management, but such insights have been limited to a narrow range of habitats and spatial scales. We examined how local-scale to landscape-scale habitat characteristics and bathymetric features (channels and oceanic inlets) affect the relative abundance (catch per unit effort, CPUE) of adult blue crabs across a > 33 km2 seagrass landscape in coastal Virginia, USA. We found that crab CPUE was 1.7 × higher in sparse (versus dense) seagrass, 2.4 × higher at sites farther from (versus nearer to) salt marshes, and unaffected by proximity to oyster reefs. The probability that a trapped crab was female was 5.1 × higher in sparse seagrass and 8 × higher near deep channels. The probability of a female crab being gravid was 2.8 × higher near seagrass meadow edges and 3.3 × higher near deep channels. Moreover, the likelihood of a gravid female having mature eggs was 16 × greater in sparse seagrass and 32 × greater near oceanic inlets. Overall, we discovered that adult blue crab CPUE is influenced by seagrass, salt marsh, and bathymetric features on scales from meters to kilometers, and that habitat associations depend on sex and reproductive stage. Hence, accelerating changes to coastal geomorphology and vegetation will likely alter the abundance and distribution of adult blue crabs, challenging marine spatial planning and ecosystem-based fisheries management.

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Section: Seagrass and Landscape Measurementsmentioning

confidence: 99%

Coastal Vegetation and Bathymetry Influence Blue Crab Abundance Across Spatial Scales

Cheng

Tedford

Smith

et al. 2022

Estuaries and Coasts

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“…The Massachusetts Division of Marine Fisheries guidelines (Evans and Leschen 2010) recommend planting using ¼ m 2 plots with plant densities of 200 shoots/m 2 and planted plots arranged in a checkerboard pattern (Orth et al 2019). Plants must be maintained in water prior to transplantation to prevent desiccation and should be transplanted within 72 hr of harvesting.…”

Section: Restoration and In-kind Compensatory Mitigation Initiativesmentioning

confidence: 99%

Eelgrass functions, services, and considerations for compensatory mitigation

Altman¹,

Balazik²,

Thomas³

2023

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Coastal-marine eelgrass habitat is a critical resource within New England and throughout the world. Eelgrass habitat provides functions and services including providing structure, biogeochemical cycling, erosion reduction, habitation provision, and water quality improvement. Declines in eelgrass distribution are often due to anthropogenic processes impacting temperature and water quality. Declines in distribution and abundance highlight the importance of protecting the existing eelgrass, improving environmental conditions allowing for ecosystem restoration, and identifying viable in-kind and out-of-kind compensatory mitigation measures. Considering the limited availability of New England sites for in-kind compensatory mitigation, additional approaches for out-of-kind compensatory mitigation should be considered. These include (1) creation of alternative plant or kelp habitat, (2) using a multi-pronged, multi-habitat and structure approach, (3) contributing to the development of water quality improvement initiatives to encourage current eelgrass bed expansion over time, (4) reduce physical impacts to eelgrass habitat, (5) and identifying locations for future eelgrass habitat suitability based on climate predictions and investing to create future compensatory mitigation habitat in these locations.

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“…Numerous remote sensing methods have been developed to detect SAV for large-scale and long-term monitoring. These methods range from pixel to object-oriented approaches, and from manual to machine learning methods [44][45][46]. Due to the rapid development of remote sensing techniques, it is essential to use consistent methodologies so as to minimize mapping discrepancies and errors, and to enable comparisons through time.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

Liu,

Sevick,

Jung

et al. 2023

Remote Sensing

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Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 105 tonne CO2e y−1 and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 105 tonne CO2e y−1) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

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Long-term Annual Aerial Surveys of Submersed Aquatic Vegetation (SAV) Support Science, Management, and Restoration

Cited by 8 publications

References 47 publications

Coastal Vegetation and Bathymetry Influence Blue Crab Abundance Across Spatial Scales

Coastal Vegetation and Bathymetry Influence Blue Crab Abundance Across Spatial Scales

Eelgrass functions, services, and considerations for compensatory mitigation

Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

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