patrickbdevaney/mia9 · Datasets at Hugging Face

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Importantly, Florida Bay has undergone major changes over the
past century as a result of anthropogenic impacts associated with
the construction of the railroad across the Florida Keys and
drainage and impoundment of freshwater wetlands upstream
(Fourqurean and Robblee, 1999), resulting in increased salinity,
decreased water exchange, and changes in benthic macrophyte
communities (Rudnick et al., 2005; Madden et al., 2009).
Today, a large portion of Florida Bay functions as a reverse
estuary with chronic hypersalinity conditions prevailing in the
north-central part of the bay during the low precipitation and
freshwater inflow periods of the dry season (December–May;
Kelble et al., 2007). Relative to historical conditions, freshwater
flows have been reduced by 60%, with nearshore present-day
salinities being 5–20 ppt higher than pre-drainage (Marshall et al.,
2009). These conditions make the bay vulnerable to drought
events, which in 1987 and 2015 resulted in massive seagrass
die-off events, affecting approximately 30% of the bay (Zieman
et al., 1999; Hall et al., 2016). Two basins, Johnson and Rankin,
were chosen as focal basins for this study because they are
located in the north-central part of the bay affected by the 2015
die-off, and because long long-term seagrass cover monitoring
data exists, which are useful to examine recovery trends (Hall
et al., 2016; Figure 2). Additionally, the basins were affected by
Hurricane Irma, which passed through Florida Bay as a category
4 hurricane in September 2017, disturbing benthic communities
and significantly altering the circulation of water in the bay
(Liu et al., 2020).
Satellite Imagery Processing
LandSat imagery from three LandSat missions (LandSat 5,
LandSat 7, and LandSat 8)1 were used to map the sediment plume.
LandSat satellites are a very popular tool in coastal mapping
due to a relatively short revisit rate (2 weeks), high resolution
(30 m), long time series (LandSat 5 was launched in 1985), and
availability. LandSat satellites collect data in 7 bands across the
visible and infrared spectrums. Two LandSat images per year
1https://landsat.gsfc.nasa.gov
Frontiers in Marine Science \| www.frontiersin.org 3 July 2021 \| Volume 8 \| Article 633240
Rodemann et al. Sediment Plume and Seagrass Resilience
FIGURE 2 \| (A) Map of Florida Bay showing the location of our two study basins (Rankin and Johnson), the extent of the 2015 seagrass die-off, and the track of
Hurricane Irma in 2017. Everglades National Park is denoted by green shading. Extent of the sediment plume overlaid over satellite imagery shows the (B) smallest
(119.6 km2
; 10/03/2009), and (C) largest plume areas (249.2 km2
; 11/29/2018) observed in the study.
from 2008 to 2020 were used to map the plume expansion
for a total of 24 images. 2008 was chosen as the early cutoff
since it matches the time series of total seagrass cover data
provided by the Florida Fish and Wildlife Commission Fish and
Wildlife Research Institute (FWC-FWRI). Google Earth Engine,
an online coding platform, was used to process each satellite
image (Gorelick et al., 2017). Google Earth Engine is a popular
tool for seagrass mapping at regional scales due to its availability,
ease of use, and batch processing that allows multiple satellite
images to be analyzed at once (Lyons et al., 2012; Zhang et al.,
2019; Wang et al., 2020).
Atmospherically and geometrically corrected LandSat images
(courtesy of the U.S. Geological Survey) were loaded into Google
Earth Engine to mask clouds, land, and shallow banks. Masking
each image removes the areas of the satellite image that are not
the focus of the study. Images were chosen based on a visual
inspection of cloud cover, where images needed to have less
than 10% cloud cover to be considered. Clouds were masked via
the U.S. Geological Survey (USGS) quality assessment algorithm,
which uses the CFMask algorithm (Foga et al., 2017) to calculate
pixels with cloud cover and shadow. Land and shallow banks
were masked using a Florida Bay Basin shapefile provided by
FWC-FWRI (Figure 3A). Banks were masked due to the lack
of seagrass data and difficulty of differentiating sediment plume
from bottom. Masked LandSat images were downloaded from
Google Earth Engine, keeping the bands in the visible and nearinfrared spectrums.
Sediment Plume Delineation
The areal extent of the sediment plume was delineated using
manual digitalization. Manual digitization, also known as photo
interpretation, has been used in coastal mapping for many
decades (Roelfsema et al., 2009) and continues to be a popular
method of coastal ecosystem mapping (Sherwood et al., 2017).
Manual delineation was used in this study due to a lack of field
training data as well as the presence of optically similar areas
(i.e., sandy bottom vs. sediment plume). Two image interpreters
were trained to delineate two classes: sediment plume with no
light penetration (i.e., the bottom was not visible) and sediment
plume with some light penetration (i.e., the bottom was visible;
Figure 3B). Algal blooms within our area of study were not
delineated. Accuracy assessment was performed by comparing
delineations from 2017 to 2020 with turbidity measured from
grab samples taken within the area of the plume by ENP.
A turbidity measurement of >8 NTU was considered turbid,
corresponding to an average Secchi depth of less than 1 m (Effler,
1988).
To aid in delineation, approximate suspended sediment
concentration (SSC) was mapped using the algorithm developed
in Islam et al. (2001). This algorithm assumes a linear relationship
Frontiers in Marine Science \| www.frontiersin.org 4 July 2021 \| Volume 8 \| Article 633240
Rodemann et al. Sediment Plume and Seagrass Resilience
FIGURE 3 \| Illustration of the methods employed in this study. LandSat images were loaded into Google Earth Engine and (A) masked to remove clouds, land, and
shallow banks. (B) The masked satellite images and an approximate suspended sediment concentration (SSC) map were downloaded used for sediment plume
delineation. (C) A total of 24 images were delineated from 2008 to 2020, GAMs were used to model the expansion of the plume, breakpoint analysis was used to
determine a significant change in sediment plume size while ANOVAs and correlation analysis were used to determine the impact of the sediment plume on seagrass
recovery.
between the red band and the sediment concentration and was
chosen because it best visually represented the sediment plume in

Importantly, Florida Bay has undergone major changes over the

past century as a result of anthropogenic impacts associated with

the construction of the railroad across the Florida Keys and

drainage and impoundment of freshwater wetlands upstream

(Fourqurean and Robblee, 1999), resulting in increased salinity,

decreased water exchange, and changes in benthic macrophyte

communities (Rudnick et al., 2005; Madden et al., 2009).

Today, a large portion of Florida Bay functions as a reverse

estuary with chronic hypersalinity conditions prevailing in the

north-central part of the bay during the low precipitation and

freshwater inflow periods of the dry season (December–May;

Kelble et al., 2007). Relative to historical conditions, freshwater

flows have been reduced by 60%, with nearshore present-day

salinities being 5–20 ppt higher than pre-drainage (Marshall et al.,

2009). These conditions make the bay vulnerable to drought

events, which in 1987 and 2015 resulted in massive seagrass

die-off events, affecting approximately 30% of the bay (Zieman

et al., 1999; Hall et al., 2016). Two basins, Johnson and Rankin,

were chosen as focal basins for this study because they are

located in the north-central part of the bay affected by the 2015

die-off, and because long long-term seagrass cover monitoring

data exists, which are useful to examine recovery trends (Hall

et al., 2016; Figure 2). Additionally, the basins were affected by

Hurricane Irma, which passed through Florida Bay as a category

4 hurricane in September 2017, disturbing benthic communities

and significantly altering the circulation of water in the bay

(Liu et al., 2020).

Satellite Imagery Processing

LandSat imagery from three LandSat missions (LandSat 5,

LandSat 7, and LandSat 8)1 were used to map the sediment plume.

LandSat satellites are a very popular tool in coastal mapping

due to a relatively short revisit rate (2 weeks), high resolution

(30 m), long time series (LandSat 5 was launched in 1985), and

availability. LandSat satellites collect data in 7 bands across the

visible and infrared spectrums. Two LandSat images per year

1https://landsat.gsfc.nasa.gov

Frontiers in Marine Science | www.frontiersin.org 3 July 2021 | Volume 8 | Article 633240

Rodemann et al. Sediment Plume and Seagrass Resilience

FIGURE 2 | (A) Map of Florida Bay showing the location of our two study basins (Rankin and Johnson), the extent of the 2015 seagrass die-off, and the track of

Hurricane Irma in 2017. Everglades National Park is denoted by green shading. Extent of the sediment plume overlaid over satellite imagery shows the (B) smallest

(119.6 km2

; 10/03/2009), and (C) largest plume areas (249.2 km2

; 11/29/2018) observed in the study.

from 2008 to 2020 were used to map the plume expansion

for a total of 24 images. 2008 was chosen as the early cutoff

since it matches the time series of total seagrass cover data

provided by the Florida Fish and Wildlife Commission Fish and

Wildlife Research Institute (FWC-FWRI). Google Earth Engine,

an online coding platform, was used to process each satellite

image (Gorelick et al., 2017). Google Earth Engine is a popular

tool for seagrass mapping at regional scales due to its availability,

ease of use, and batch processing that allows multiple satellite

images to be analyzed at once (Lyons et al., 2012; Zhang et al.,

2019; Wang et al., 2020).

Atmospherically and geometrically corrected LandSat images

(courtesy of the U.S. Geological Survey) were loaded into Google

Earth Engine to mask clouds, land, and shallow banks. Masking

each image removes the areas of the satellite image that are not

the focus of the study. Images were chosen based on a visual

inspection of cloud cover, where images needed to have less

than 10% cloud cover to be considered. Clouds were masked via

the U.S. Geological Survey (USGS) quality assessment algorithm,

which uses the CFMask algorithm (Foga et al., 2017) to calculate

pixels with cloud cover and shadow. Land and shallow banks

were masked using a Florida Bay Basin shapefile provided by

FWC-FWRI (Figure 3A). Banks were masked due to the lack

of seagrass data and difficulty of differentiating sediment plume

from bottom. Masked LandSat images were downloaded from

Google Earth Engine, keeping the bands in the visible and nearinfrared spectrums.

Sediment Plume Delineation

The areal extent of the sediment plume was delineated using

manual digitalization. Manual digitization, also known as photo

interpretation, has been used in coastal mapping for many

decades (Roelfsema et al., 2009) and continues to be a popular

method of coastal ecosystem mapping (Sherwood et al., 2017).

Manual delineation was used in this study due to a lack of field

training data as well as the presence of optically similar areas

(i.e., sandy bottom vs. sediment plume). Two image interpreters

were trained to delineate two classes: sediment plume with no

light penetration (i.e., the bottom was not visible) and sediment

plume with some light penetration (i.e., the bottom was visible;

Figure 3B). Algal blooms within our area of study were not

delineated. Accuracy assessment was performed by comparing

delineations from 2017 to 2020 with turbidity measured from

grab samples taken within the area of the plume by ENP.

A turbidity measurement of >8 NTU was considered turbid,

corresponding to an average Secchi depth of less than 1 m (Effler,

1988).

To aid in delineation, approximate suspended sediment

concentration (SSC) was mapped using the algorithm developed

in Islam et al. (2001). This algorithm assumes a linear relationship

Frontiers in Marine Science | www.frontiersin.org 4 July 2021 | Volume 8 | Article 633240

Rodemann et al. Sediment Plume and Seagrass Resilience

FIGURE 3 | Illustration of the methods employed in this study. LandSat images were loaded into Google Earth Engine and (A) masked to remove clouds, land, and

shallow banks. (B) The masked satellite images and an approximate suspended sediment concentration (SSC) map were downloaded used for sediment plume

delineation. (C) A total of 24 images were delineated from 2008 to 2020, GAMs were used to model the expansion of the plume, breakpoint analysis was used to

determine a significant change in sediment plume size while ANOVAs and correlation analysis were used to determine the impact of the sediment plume on seagrass

recovery.

between the red band and the sediment concentration and was

chosen because it best visually represented the sediment plume in