Oyster Aquaculture Site Selection Using Landsat 8-Derived Sea Surface Temperature, Turbidity, and Chlorophyll a

Snyder, Jordan; Boss, Emmanuel; Weatherbee, Ryan; Thomas, Andrew C.; Brady, Damian C.; Newell, Carter R.

doi:10.3389/fmars.2017.00190

Cited by 64 publications

(62 citation statements)

References 48 publications

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“…One kilometer resolution SST imagery was obtained via a combined MODIS/VIIRS-derived product for the Southern California Bight 4 ; while 100 m resolution data from the Landsat 8 TIRS thermal band was used to derive high spatial resolution SST products in the Santa Barbara Channel 5 . An atmospheric correction was applied to the Landsat 8 imagery by scaling brightness values with fully processed 1 km SST product data (Snyder et al, 2017). Clouds, cloud shadows and land were masked from the Landsat 8 SST imagery using the Fmask algorithm (Zhu et al, 2015), and fog banks and airplane contrails were manually removed.…”

Section: Satellite Imagerymentioning

confidence: 99%

“…Landsat 8 imagery is particularly useful for work in coastal environments because the thermal infrared sensor (TIRS) has a high signalto-noise ratio and 100-m spatial resolution. High-resolution SST from Landsat 8 can be accurately determined after accounting for atmospheric effects using coincident satellite imagery and have been used to aid in the siting of aquaculture, such as oyster farms in Maine (Snyder et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight

et al. 2020

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Offshore aquaculture of giant kelp (Macrocystis pyrifera) has been proposed by the US Department of Energy for large scale biofuel production along the west coast of California. The Southern Californian Bight provides an ideal area for offshore kelp aquaculture as the upwelling and advection of cool, nutrient-rich waters supports the growth of vast native giant kelp populations. However, concentrations of nutrients vary greatly across space, can be limiting for kelp growth over seasonal to interannual time scales, and inputs of nutrients to surface waters may be subject to local circulation processes. Therefore, it is important to understand both the spatiotemporal variability of seawater nitrate concentrations and the appropriate scale of observation in order for offshore kelp aquaculture to be successful. Here, we use a combination of satellite sea surface temperature imagery, in situ measurements, and modeling to determine seawater nitrate fields across multiple spatial and temporal scales. We then combine this information with known giant kelp physiological traits to develop a kelp stress index (KSI) for the optimal siting of offshore kelp aquaculture over seasonal to decadal scales. Temperature to nitrate relationships were determined from in situ measurements using generalized additive models and validated with buoy data. Summer and winter relationships were significantly different, and satellite-derived products compared well to buoy validations. Surface nitrate patterns, as derived from satellite temperature products, reveal the spatial variability in nitrate concentrations, and indicate areas that that may cause nutrient stress seasonally and during the negative phase of the North Pacific Gyre Oscillation. As the spatial scale of the surface nitrate product decreased, the negative bias increased and fine scale spatial variability was lost. Similarly, the averaging of daily nitrate concentration determinations over longer time scales increased Frontiers in Marine Science | www.frontiersin.org January 2020 | Volume 7 | Article 22 Snyder et al.Spatiotemporal Nutrient Patterns for Aquaculture the negative bias. We found that daily, 1 km spatial resolution nitrate products were most sufficient for identifying localized upwelling and areas of consistently high surface nitrate concentrations, and that areas in the northern and western-most portions of the Southern California Bight are the most suitable for sustained offshore kelp aquaculture.

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Section: Satellite Imagerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight

et al. 2020

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“…Landsat image of turbidity in Casco Bay taken on 12 April 2014 (Snyder et al ) showing the deflection of the Kennebec River plume to the mouth of Harpswell Sound. Location of Harpswell Sound, the LOBO mooring site, and the Kennebec River indicated.…”

Section: Materials and Proceduresmentioning

confidence: 99%

Correcting in situ chlorophyll fluorescence time‐series observations for nonphotochemical quenching and tidal variability reveals nonconservative phytoplankton variability in coastal waters

Carberry

Roesler

Drapeau

2019

Limnology & Ocean Methods

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Chlorophyll fluorometry is one of the most commonly implemented approaches for estimating phytoplankton biomass in situ, despite documented sources of natural variability and instrumental uncertainty in the relationship between in vivo fluorescence and chlorophyll concentration. A number of strategies are employed to minimize errors and quantify natural variability in this relationship in the open ocean. However, the assumptions underlying these approaches are unsupported in coastal waters due to the short temporal and small spatial scales of variability, as well as the optical complexity. The largest source of variability in the in situ chlorophyll fluorometric signal is nonphotochemical quenching (NPQ). Typically, unquenched nighttime observations are interpolated over the quenched daytime interval, but this assumes a spatial homogeneity not found in tidally impacted coastal waters. Here, we present a model that provides a tidally resolved correction for NPQ in moored chlorophyll fluorescence measurements. The output of the model is a time series of unquenched chlorophyll fluorescence in tidal endmembers (high and low tide extremes), and thus a time series of phytoplankton biomass growth and loss in these endmember populations. Comparison between modeled and measured unquenched time series yields quantification of nonconservative variations in phytoplankton biomass. Tidally modeled interpolation between these endmember time series yields a highly resolved time series of unquenched daytime chlorophyll fluorescence values at the location of the moored sensor. Such data sets provide a critical opportunity for validating the satellite remotely sensed ocean color chlorophyll concentration data product in coastal waters.

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“…Conventional water sampling methods are reliable but are ineffective in identifying detailed spatial variations of water quality, which renders comprehensive management infeasible [1][2][3]. Remote sensing techniques have been proven effective in the selection of aquaculture sites and the qualitative measurement of regional water parameters, including suspended sediment, chlorophyll-a (Chl-a), and pollutant loads [4][5][6]. Kuhn et al [7] used Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products to estimate turbidity over the Amazon, Columbia, and Mississippi rivers.…”

Section: Introductionmentioning

confidence: 99%

Spectral Feature Selection Optimization for Water Quality Estimation

Nguyen

Lin

Chu

et al. 2019

IJERPH

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The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

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Oyster Aquaculture Site Selection Using Landsat 8-Derived Sea Surface Temperature, Turbidity, and Chlorophyll a

Cited by 64 publications

References 48 publications

Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight

Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight

Correcting in situ chlorophyll fluorescence time‐series observations for nonphotochemical quenching and tidal variability reveals nonconservative phytoplankton variability in coastal waters

Spectral Feature Selection Optimization for Water Quality Estimation

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