Download Comparison of remotely-sensed surveys vs. in situ plot

J Coast Conserv (2014) 18:299–308
DOI 10.1007/s11852-014-0319-y
Comparison of remotely-sensed surveys vs. in situ plot-based
assessments of sea grass condition in Barnegat Bay-Little Egg
Harbor, New Jersey USA
Richard G. Lathrop Jr. & Scott M. Haag &
Daniel Merchant & Michael J. Kennish & Benjamin Fertig
Received: 11 February 2014 / Accepted: 16 April 2014 / Published online: 11 May 2014
# Springer Science+Business Media Dordrecht 2014
Abstract With the increasing appreciation that sea grass habitats are in global decline, there is a great need to be able to
efficiently and effectively assess and characterize the status
and trends of sea grass in our coastal ecosystems. This paper
examines the utility of remotely sensed vs. in situ plot-based
monitoring using the Barnegat Bay-Little Egg Harbor (BBLEH), New Jersey, USA estuarine system as a case study.
Eelgrass (Zostera marina) is the dominant species, while
widgeon grass (Ruppia maritima) is also common in lower
salinity regions of the BB-LEH. Aerial imagery collected
during the months of July and August 2009 was interpreted
and mapped using object based image analysis techniques,
similar to techniques used in the 2003 mapping survey of this
system. Boat-based in situ monitoring data were collected
concurrently with the aerial photography to assist the image
interpretation and for an independent accuracy assessment.
We compared the remotely-sensed mapping of sea grass cover
change (in 2003 vs. 2009) vs. in situ plot-based monitoring
conducted from 2004 through 2009. Comparison of the
remotely-sensed vs. the in situ plot-change analysis suggests
that the two methodologies had broadly similarly results, with
the percent area showing declines in sea grass cover greater
than those that exhibited increases. In conclusion, the two
studies provide corroborating evidence that sea grass has
declined in percent cover in the BB-LEH system during the
decade of the 2000’s. While remotely-sensed surveys provide
synoptic information for a “big picture” view on sea grass
distribution, site specific in situ sampling is required to
R. G. Lathrop Jr. (*) : S. M. Haag : D. Merchant
Center for Remote Sensing & Spatial Analysis, Rutgers University,
New Brunswick, NJ, USA
e-mail: [email protected]
S. M. Haag : M. J. Kennish : B. Fertig
Institute of Marine and Coastal Sciences, Rutgers University, New
Brunswick, NJ, USA
determine other aspects of sea grass status, e.g. above vs.
below-ground biomass, blade length, shoot density, epiphytic
loading, etc. Either method alone gives an incomplete picture.
As demonstrated in this study, to fully characterize the spatial
extent, health, and density of sea grass meadows across the
entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the
most robust approach.
Keywords Sea grass status and trends . Eutrophication .
Estuaries . Object based image analysis
Introduction
Due to the important role that sea grasses play in estuaries,
there has been a considerable effort at developing sampling
and mapping techniques to quantify the spatial distribution,
biomass and health of sea grass communities and monitor
changes over time (Dobson et al. 1995; Short et al. 2002).
Sea grass is considered a useful measure of estuarine condition as it integrates environmental impacts over measurable
and definable timescales (Orth et al. 2006). Accordingly sea
grass has been adopted as an ecological indicator in
Chesapeake Bay (Orth et al. 2002, 2010), in a number of
Mid Atlantic USA coastal lagoons (Wazniak et al. 2007;
Fertig et al. 2013), in the Northern Gulf of Mexico (Handley
et al. 2007), and more broadly as part of the U.S. National
Estuarine Eutrophication Assessment (Bricker et al. 2003;
Scavia and Bricker 2006). With the increasing appreciation
that sea grass habitats are in global decline (Orth et al. 2006;
Waycott et al. 2009), there is a great need to efficiently and
effectively assess and characterize the status and trends of sea
grass in our coastal ecosystems.
To aid in monitoring sea grass meadows in a consistent and
comparable fashion, Sea grass Net (Short et al. 2002, 2006)
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developed a set of in situ sampling protocols. In situ sampling
can provide detailed, site-specific information on the aboveand below-ground biomass, blade length, areal cover and
related information on epiphytic load, macroalgae, and disease at specific locations. Alternatively, where a big picture
perspective is warranted, remote sensing approaches have
been employed for “wall-to-wall” mapping of sea grass
presence/absence or areal cover over large areas. The most
widely adopted approach for operational monitoring has been
the visual interpretation and mapping from either digital or
analog aerial photography (Ferguson et al. 1993; Dobson et al.
1995; Kendrick et al. 2000; Kurz et al. 2000; Moore et al.
2000). As an alternative to visual interpretation and manual
digitizing, Lathrop et al. (2006) employed object based image
analysis of digital aerial photographic imagery to map sea
grass.
This paper examines the utility of remotely sensed vs. in
situ plot-based monitoring using the Barnegat Bay-Little Egg
Harbor (BB-LEH), New Jersey, USA estuarine system as a
case study. In 2003 and again in 2009, we acquired aerial
photographic imagery and employed an object-oriented image
segmentation approach (Lathrop et al. 2006) to assess change
in sea grass status in the BB-LEH estuary. During the intervening time period, an intensive in situ sea grass sampling
program was undertaken (Kennish et al. 2008, 2010; Fertig
et al. 2013). In this paper, we assess the comparative utility of
the remotely-sensed change mapping and the in situ monitoring data, either singly or in combination, for elucidating trends
in sea grass status.
Methods
Remotely-sensed surveys
Lathrop et al. (2006) used object based image analysis
(Blaschke 2010) of digital aerial photographic imagery to
map sea grass presence and characterize sea grass areal
cover. Employing similar terminology as Robbins and Bell
(1994), the spatial structure of the sea grass habitats was
conceptualized at 3 different levels, from the coarser to finer
scale: 1) meadow, a spatially contiguous area of sea grass beds
of varying % cover composition; 2) bed, a spatially contiguous area of overall similar % cover composition; and 3) patch,
a small discrete clump of sea grass. A “neighborhood-based”
approach of image segmentation was employed to delineate
coarser scale “image super-objects” (i.e., sea grass beds) and
finer scale “image objects” (i.e., patches and gaps). Rather
than manually classifying each fine scale patch individually,
some economy of scale is possible allowing the image analyst
to classify coarser scale super-objects and translate the results
downward through “super-object classification”. Finer scale
patches or gaps of density different than the enclosing sea
R.G. Lathrop et al.
grass bed are identified and then re classed as appropriate.
While the methodology was designed to be easily repeatable,
this present paper represents the first attempt at employing the
methodology for mapping and quantifying change in the
spatial distribution of sea grass area and cover.
Film aerial photography was collected by Air Photo
graphics, Inc. on June 28, July 14, and August 4, 2009 using
a Navajo HS airplane equipped with a Leica RC30 camera
shooting AGFA 100 color film. Unfavorable cloud and wind
conditions required several over flights to capture the entire
study area. The same plane and camera was used for all three
imaging missions. The resultant film was then processed and
scanned through a high resolution scanner resulting in a digital
image with 18,278 by 18,292 pixels in a scale of 1:2,000.
These scans were ortho-rectified and projected into Universal
Transverse Mercator (Zone 18 North, North American Datum
1983, GRS Spheroid of 1980) with a horizontal positional
accuracy at root mean square error of ±1–2 m. The resulting
geotiffs were mosaicked into 15 larger blocks for later
analysis.
During the summer of 2009, a number of field sites were
visited to collect reference information to enable the interpretation of the aerial photography (Fig. 1). Reference sites (n=
167) were selected to match a subset of the field references
sites selected during the 2003 study (Lathrop et al. 2006). Data
from 120 in situ sea grass transect monitoring points collected
in June 2009 (Kennish et al. 2010; described below) were also
used to aid in the interpretation and mapping. An additional
set of 124 field validation points were selected using a stratified random sampling design, stratified on the depth distribution of sea grass within the BB-LEH estuary. This validation
dataset was not used in the image mapping and classification
process but kept as an independent data set to compare with
the map.
Field collection for the reference and validation data set
was accomplished as follows. At each sampling location, a Lshaped 4 m × 5 m grid was lowered over the side of the boat.
The grid coordinate location was determined using Magellan
Mobile Mapper 6 (±2–5 m horizontal accuracy) and the grid
orientation recorded using a compass to allow for a precise
placement of the sampling grid on the benthos to a higher
level of accuracy than a boat-based GPS unit. The diver then
visited each of eight sub-grids and recorded information on
SAV presence/absence (yes no), percent cover of sea grass
species (R. maritima and Z. marina) (0 to 100 in 10 % increments), and percent coverage macro-algae (0 to 100 in 10 %
increments). A composite percent areal cover of sea grass was
then calculated. For more detail on the methods employed,
please refer to Lathrop and Haag (2011).
The rectified mosaicked color aerial photography was
imported into e Cognition™ to support image segmentation
and classification. e Cognition™ is an image analysis software
package that segments raster image data in an unsupervised
Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring
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Fig. 1 Map of Barnegat Bay –
Little Egg Harbor (BB-LEH)
study area with 2009 reference
and validation sites for remotelysensed survey
method minimizing the intra-polygon (image object) variance
while maximizing inter-polygon (image object) variance.
Multiple scale image objects can be created by running a
multiple resolution segmentation procedure. The fine scale
parameter was selected to meet the target minimum mapping
unit of .05 ha (500 m2). The minimum mapping unit defines
the smallest feature delineated in the map.
A super-object classification approach was employed
where the coarser scale image objects were first manually classified using e Cognition™. Each image object
was visually interpreted and assigned to one of four
classes of sea grass areal cover (high 100–80 % percent
cover, medium <80–40 % cover, sparse <40–10 % cover, and no sea grass <10–0 %). The field reference data
were used to inform the interpretation. Areas of extremely sparse sea grass (i.e., 5–10 % cover) were not
discernible from the remotely sensed imagery and were
thereby considered as no sea grass. The coarser scale
image object classifications were then forced down into
the finer scale image objects based on the nested polygon structure. Smaller image objects on edge areas and
internal to the larger image objects were then manually
reclassified when necessary. This method sped up the
manual classification effort allowing large contiguous
areas of sea grass to be classified quickly while also
allowing precise classification on sea grass edge and
gap areas. To create the final GIS dataset, the finerscale image objects were exported to Environmental
Research Institute ESRI™ shapefile format.
To determine how well the object based image analysis described sea grass presence/absence and areal cover across the BB-LEH, an accuracy assessment was
undertaken using the 124 validation sites. The validation
dataset was compared with the final sea grass cover
map to create an accuracy assessment matrix. Error of
omission and commission, overall accuracy assessment,
and a Kappa coefficient were calculated. The Kappa
coefficient is a measure of agreement between two
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categorical datasets correcting for the random chance
that categories will agree. An un-weighted Kappa statistic was used to normalize the influence of categories
that cover a disproportionate area.
R.G. Lathrop et al.
Table 1 Seagrass change matrix showing seven categories of percent
cover change categories between 2003 and 2009 (or 2004 and 2009 for
plot data). Note: that the original percent cover categories were: Dense
(80–100 %), Medium (40–80 %), Sparse (10–40 %) and No Seagrass
(<10 %)
In situ monitoring
During 2004–2009, in situ monitoring was conducted
along 12 transects in three disjunct sub-areas, and each
transect consisted of 10 sampling stations for a total of
120 sample points (Fig. 1) (Kennish et al. 2010). Each
station was geo located with a Differential Global
Positioning System (Trimble®GeoXTTM handheld unit).
Note that transects 1–6 were sampled in 2004, transects
7–12 were sampled in 2005, and transects 1–12 were
sampled in 2006 and 2008–2009. No samples were
collected in 2007. Sampling efforts were based on the
Sea grass Net monitoring and sampling protocols of
Short et al. (2002). The main modification of methods
was establishing transects perpendicular to shore rather
than parallel, to identify differences along a clearly
defined depth gradient. Eelgrass samples were collected
during each of 3 time periods (June–July, August–
September, and October–November) in all years. The
following eelgrass characteristics were recorded on all
sampling dates at each sampling station: above-ground
and below-ground biomass, shoot density, blade length,
and areal cover. For comparing the in situ vs. the
remotely-sensed approaches, we used the in situ measure of areal cover for the years 2004–2009. To determine areal cover of both eelgrass and macroalgae, a
0.25-m2 metal quadrat was randomly tossed overboard
at each sampling station. A diver estimated the percentage of the quadrate covered by eelgrass in increments of
5 along a scale of 0–100.
Comparing remotely-sensed surveys with in situ monitoring
The in situ monitoring data were binned into the same sea
grass percent areal cover categories as employed in the
remotely sensed surveys: Dense (80–100 %), Medium
(40–80 %), Sparse (10–40 %) and No Sea grass
(<10 %). To visualize changes in sea grass cover between
the 2003 and 2009 remotely-sensed surveys, we classed
the mapped data into seven change categories (Table 1).
The in situ monitoring data for the 2004 to 2009 time
period were also classed into the same seven change
categories (Table 1). The in situ plot data were geolocated and the corresponding remotely-sensed 2003–
2009 change category extracted for comparison purposes
with a final sample size of 120 plots.
Results
2009 remotely-sensed survey
The 2009 sea grass remotely-sensed survey (Fig. 2, Table 2)
classified 5,260 ha of sea grass (2,266 ha of sparse, 2,523 ha of
moderate, and 471 ha of dense cover). A high level of agreement was obtained (overall accuracy of 87 % and a Kappa
value of 0.73 between the mapped and in situ validation
presence/absence data (Table 3). A moderate level of agreement was obtained (overall accuracy of 70 % and a Kappa
statistic of 0.47) for the four-class sea grass density map,
(Table 4).
Comparison of 2009 and 2003 remotely-sensed surveys
Comparison of the results of the 2009 and 2003 remotelysensed surveys indicate that the overall area of mapped sea
grass (i.e., sea grass present) was similar (5,122 ha in 2003 vs.
5,260 ha in 2009) (Table 2). 1,463 ha were mapped as sea
grass in 2003 (and not in 2009), 1,601 in 2009 (and not in
2003) and 3,659 ha were mapped in both 2003 and 2009
(Fig. 2). Examination of the more detailed four-class sea grass
cover maps shows a decline in the area of dense (80–100 %
cover) sea grass in 2009 vs. 2003 (471 ha in 2009 vs. 2,074 ha
in 2003; a nearly 60 % decline). The loss in area of dense
cover translated to an increase in medium (40–80 % cover)
density meadows (1,093 ha in 2003 vs. 2,523 ha in 2009; an
increase of 130 %). The 2009 imagery collection was a
challenge due to meteorological events (cloud cover), which
caused three separate imaging attempts (June 28th, July 7th,
and August 4th) before good aerial photography could be
obtained. Areas of high water turbidity were observed in
Little Egg Harbor and southern Barnegat Bay, obscuring the
Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring
303
Fig. 2 Sea grass habitat mapped
in 2003 and 2009 over the study
area
clear delineation of bottom features and complicating the
interpretation of the sea grass areal coverage. The degree to
which this apparent thinning in the density of the sea grass
meadows is real or an artifact of the poorer image quality in
the 2009 imagery and the resulting lower accuracy in mapping
dense sea grass meadows is uncertain.
Table 2 Area of seagrass cover types mapped in the 2009 and 2003
remote sensing surveys
Seagrass cover
Sparse (10–39 %)
Moderate (40–79 %)
Dense (80–100 %)
Total
2009
2003
Class
(ha)
% Total
seagrass
Class
(ha)
% Total
seagrass
2,266
2,523
471
5,260
43 %
48 %
9%
1,955
1,093
2,074
5,122
38 %
22 %
40 %
Comparison of remotely-sensed vs. in situ trend results
Comparison of the remotely-sensed vs. the in situ plot change
analysis suggests that the two methodologies had broadly
similar results with the overall percent area showing decreases
in percent sea grass cover greater than those that exhibited
increases (Table 5; Fig. 3). The remotely sensed change analysis showed approximately 47 % of the study area with a
decrease in density vs. 37 % that exhibited an increase (Fig. 3).
Examining the 120 plots only, the remote sensed change
analysis showed 65 plots (54 %) with a decrease in cover vs.
19 plots (16 %) with an increase (Table 5). The in situ plotbased change analysis showed approximately 56 plots (47 %)
with a decrease in density vs. 27 (22 %) that exhibited an
increase. Note that 13 out of the 120 plots (11 %) were not
mapped as sea grass in either 2003 or 2009 and thereby
classed as no change. Likewise, 6 of the in situ plots (5 %)
did not contain sea grass at any point during the 2004 to 2009
time period and were classed as no change. Plot-by-plot level
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R.G. Lathrop et al.
Table 3 Presence/absence accuracy assessment matrix for the 2009
seagrass survey. Un-weighted Kappa statistic 0.73
GIS MAP
Field reference
Seagrass absent
Seagrass present
Producer’s accuracy
Seagrass
absent
Seagrass
present
User’s
accuracy
69
7
91 %
9
39
81 %
88 %
85 %
87 %
comparison between the mapped vs. in situ plot results shows
a low degree of correspondence with only 27 (22.5 %) of the
plots matching the mapped results (Table 5). Given the difference in scales with the minimum mapping unit for the
remotely-sensed survey at 500 m2 (0.05 ha) vs. the <1 m2 size
for the in situ plots, we relaxed the definition of correctness to
include a more “fuzzy similarity” (i.e., exact match as well as
± one class difference). The fuzzy similarity showed greater
degree of correspondence with 75 (62.5 %) of the plots ± one
class from the remotely-sensed data (Table 5).
Discussion
NOAA protocols (Finkbeiner et al. 2001) suggest that overall
thematic accuracy should be greater than 85 % and a Kappa of
>0.5. The sea grass presence/absence map meets these criteria,
while the four-class sea grass density map is slightly below.
Table 3 suggests that most of the errors of omission (i.e.,
producer’s accuracy) and commission (i.e., user’s accuracy)
for the presence/absence sea grass map are similar. Table 4
suggests that the four-class map does not consistently differentiate between moderate and dense sea grass habitat and
thereby underestimates the amount of dense sea grass. While
every effort was undertaken to reduce the spatial error in
relating the field reference data to the imagery (i.e., ±1–2 m
for imagery geo registration and ±2–5 m for geo locating the
field reference data collection location), this positional error
Table 4 Class accuracy assessment matrix for the 2009 seagrass survey.
Un-weighted Kappa statistic 0.47
GIS MAP
Field reference
Seagrass Seagrass Seagrass Seagrass User’s
absent
sparse
moderate dense
accuracy
Seagrass absent
Seagrass sparse
Seagrass moderate
Seagrass dense
Producer’s accuracy
69
5
2
0
91 %
7
7
4
0
39 %
2
4
6
2
43 %
0
1
10
5
31 %
88
41
27
71
70
%
%
%
%
%
coupled with the fine scale patchiness of some sea grass beds
can result in a disagreement between the reference data and
the mapping. Increasing the size of the sampling grid used in
the remotely sensed field surveys, while more cumbersome to
deploy, we deemed to be more effective in capturing the fine
scale spatial heterogeneity for comparison with the remotely
sensed imagery. The procedure to select the validation sites
(random vs. targeted) can drive which error (omission and
commission vs. categorical) is better constrained. In the 2009
survey, we opted for a stratified random sampling to provide a
better estimate on the total errors of omission and commission
of sea grass presence/absence across the entire estuary.
Consequently, the sample sizes within the individual sea grass
density categories (i.e., sparse, moderate and dense) were
limited while areas of no sea grass were oversampled.
Future work should attempt to collect a larger number of
samples in known sea grass habitat to provide more information on the accuracy of the areal cover density mapping.
Over the past decade, both periodic remotely-sensed surveys and in situ monitoring have been employed to chart
trends in the a real cover and spatial extent of sea grass within
the BB-LEH estuary. One objective of this paper is to characterize the change in sea grass distribution and cover as determined from remotely-sensed surveys taken in 2003 and 2009.
We employed similar aerial photographic imagery and an
object based image analysis approach to assess change in
sea grass distribution for both time periods. The remotelysensed change analysis suggests that the overall area of
mapped sea grass cover was similar between the two time
periods. In directly comparing the two mapped data sets, there
were areas of apparent gain and loss, though the bulk of the
sea grass area was stable (i.e., was present in both 2003 and
2009) (Fig. 2). Differences in the seasonal period of image
acquisition may account for some of the differences in the
mapped area as imagery for the 2003 survey was acquired
early in the growing season (May 4–5th), while the 2009
survey was acquired later in the growing season on July 14,
and August 4 (northern section of the BB study area). The
remotely-sensed 2003–2009 trend map (Fig. 4) suggests an
increase in the percent cover density of sea grass meadows in
the north-central portions of BB-LEH, while sea grass
meadows in the central portions (near the Barnegat Inlet area)
and in the southern portions of Barnegat Bay and Little Egg
Harbor experienced declines in percent cover. The areas in
northern Barnegat Bay mapped as increasing sea grass cover
are likely due to an artifact of the later image acquisition date
for the 2009 survey. Our calibration plots document that
Ruppia dominates in these lower salinity waters of northern
Barnegat Bay and the mid-summer image acquisition (as
compared to mid-spring acquisition in 2003) may have
mapped higher cover of this later maturing species due to a
timing issue and not a “real” change (Lathrop and Haag
2011). Unfortunately, we do not have long-term in situ
Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring
305
Table 5 Site-level comparison between the mapped vs. plot change classification results
monitoring plots in this portion of the study area to
serve as comparison.
While the overall area of sea grass appears to be comparatively stable (as mapped in 2003 and 2009), both the remotely
sensed and in situ monitoring document an overall declining
trend in percent cover in the BB-LEH system during the decade
of the 2000’s. Analysis of the more detailed four-class sea grass
cover change data (Fig. 4) shows a decline in the area of dense
sea grass in 2009 vs. 2003 (i.e., 471 ha in 2009 vs. 2,074 ha in
2003; a nearly 60 % decline). The degree to which this apparent
thinning in the density of the sea grass meadows is real or an
artifact of the poorer image quality in the 2009 imagery (in
selected locations) and the resulting lower accuracy in mapping
Fig. 3 Comparison of remotelysensed (2003–2009; absolute area
in ha and by % of total) vs. in situ
plot (2004–2009; number of plots
and by % of total))
dense sea grass is uncertain. However, comparison of the
remotely-sensed surveys vs. the in situ monitoring at a series
of permanent plots during roughly the same time period (between 2004 and 2009) provide corroborating evidence that sea
grass has declined in percent cover. The remotely-sensed and in
situ change analysis showed 54 and 47 % of the monitoring
plots, respectively, with some decrease in percent cover vs. only
16 and 22 %, respectively, exhibited an increase (Table 5). The
in situ monitoring data also documents a decline in biomass and
other indicators of sea grass health (Fertig et al. 2013). Short
(2007) found similar patterns in Great Bay, NH with Zostera
biomass and percent cover declining between 1995 and 2005,
while sea grass distribution remained relatively constant. It is
306
R.G. Lathrop et al.
Fig. 4 Sea grass 2003–2009
remotely-sensed change map for
entire BB-LEH study area
interesting to note that Macomber and Allen (1979), using
visual interpretation of summer season black & white aerial
photography (field checked by low altitude sea plane reconnaissance), mapped over 6,550 ha of dense (>80 %) cover
Zostera and Ruppia meadows in the BB-LEH system
(Lathrop et al. 2001). Comparing the 1979 with the 2003 and
2009 mapping (which must be done with caution due to the
differences in methodology employed) suggests a decline of
dense sea grass meadows of approximately 68 and 93 % in
2003 and 2009, respectively.
While the remotely-sensed and in situ monitoring showed
similar trends, a lot of spatial and temporal dynamics can be
missed in the intervening years between remotely sensed
surveys (i.e., in this case between 2003 and 2009). In situ
monitoring done in the intervening years (2004–2010)
showed a declining sea grass cover and biomass across the
estuary, reaching a low point in 2006 (Fertig et al. 2013).
There was some rebound by 2008 followed by consistent
declines with 2010 levels reaching the lowest recorded over
the six-year time period. Fertig et al. (2013, 2014) suggest that
this consistent estuarine-wide rate of decline was related to
nutrient loading and associated symptoms of eutrophication.
Examination of the remotely sensed trend map (Fig. 4) and the
in situ monitoring data shows areas of moderate to major
Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring
decrease in southern Barnegat Bay and in Little Egg Harbor
that closely align with areas of high total nitrogen concentration (as displayed in Fertig et al. 2013), strongly supporting
their general conclusion that eutrophication is the major factor
in sea grass decline in the overall BB-LEH system. However,
the hotspot of decreasing sea grass cover bay ward of
Barnegat Inlet observed in both the remotely sensed survey
(Fig. 4) and in situ monitoring does not follow this general
pattern. Fertig et al. (2013) found consistently low total
nitrogen concentrations and Lathrop et al. (2001) observed
higher water transparency where ocean waters entering
through Barnegat Inlet are continually mixing with bay waters. The spatial patterns of change in sea grass cover and
distribution near Barnegat Inlet as revealed in the remotely
sensed imagery suggest that tidal inlet/delta dynamics (i.e.,
shifting currents and sediment deposition/erosion) may be
playing a more important role than eutrophication in
explaining the decline of sea grass meadows in this section
of the BB-LEH system.
Conclusions
Trends in Sea grass areal extent (primarily Zostera) within the
northeastern United States is highly variable with some locations showing new growth and expansion of existing or
transplanted beds, such as in the Virginia and Maryland
Coastal Bays (Orth and McGlathery 2012; Wazniak et al.
2007), while other areas such as Piscataqua River NH and
Chesapeake Bay (Beem and Short 2008; Orth et al. 2010)
have experienced significant declines in extent. Though there
have been considerable spatial dynamics with areas of both
increase and decline, the results of our study suggest an
overall declining trend in percent sea grass cover in the
Barnegat Bay-Little Egg Harbor (BB-LEH) system during
the decade of the 2000’s. The primary causes for sea grass
decline and or expansion have been related to site specific
changes in water quality, temperature, direct alterations, and
or disease (Burkholder et al. 2007; Short 2007; Beem and
Short 2008; Orth et al. 2010). Determining the causative
factors for sea grass decline and expansion can be difficult in
that sea grass habitat integrates the ecological signal over a
multi-year period of time. While nutrient enrichment and
accompanying estuary-wide eutrophication may build over a
multi-decadal time period, other phenomena such as harmful
algae blooms (Gastrich et al. 2004) or tidal channel dynamics
may be more restricted in space and time.
To understand what causative factors drive sea grass distribution and health and in a specific estuary, it is necessary to
have a dedicated monitoring program. This should be done on
an annual or semiannual basis to avoid missing major changes
in the extent and health of sea grass habitats. As Orth et al.
(2010) have demonstrated, annual aerial mapping surveys
307
over several decades (1984 to present) have allowed researchers within the Chesapeake Bay to assess the trends in
sea grass distribution through time, compare the success of
various transplant and relocation efforts, and compare sea
grass decline to in situ water quality parameters. However,
for many estuary systems annual remote sensed surveys are
cost prohibitive and are undertaken on a more irregular basis.
For the BB-LEH system, mapping surveys every five to ten
years are more the norm (Lathrop et al. 2001; 2006). Intensive
in situ monitoring (i.e., multiple visits spaced over the growing season to scores of plots over multiple years) is potentially
even more expensive. Unfortunately, this level of sampling is
not routinely collected in the BB-LEH system, nor in many
estuaries, due to budgetary constraints.
When monitoring sea grass, coastal management agencies
have a choice, do they invest in remotely sensed surveys or in
situ sampling or can they do both? Based on our results, we
conclude that object based image analysis provides an effective alternative approach, as compared to visual interpretation
and manual digitizing, to map sea grass on a repetitive basis.
This methodology provides useful quantitative information on
both areal extent and percent cover, though the accuracy of the
latter needs further improvement. While remotely-sensed surveys provide synoptic information for a “big picture” view on
sea grass distribution, site specific in situ sampling is required
to determine other aspects of sea grass status, e.g. above vs.
below-ground biomass, blade length, shoot density, epiphytic
loading, etc. Either method alone gives an incomplete picture.
As demonstrated in this study, to more fully characterize the
spatial extent, health, and density of sea grass meadows across
the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the
most robust approach.
Acknowledgments This project was funded by the Barnegat Bay Partnership, the New England Interstate Water Pollution Control Commission
and the New Jersey Agricultural Experiment Station. We express our
sincere appreciation for the assistance of Gina Petruzzelli, Greg
Sakowicz, Chris Huch and the staff at Rutgers University Marine Field
Station in the collection of the field reference data.
References
Beem NT, Short FT (2008) Sub tidal eelgrass declines in the great bay
estuary, new Hampshire and Maine, USA. Estuar Coasts 32:202–
205
Blaschke T (2010) Object based image analysis. ISPRS J Photogramm
Remote Sens 65(1):2–16
Bricker SB, Ferreira JG, Simas T (2003) An integrated methodology for
assessment of estuarine trophic status. Ecol Model 169:39–60
Burkholder J, Tomasko D, Touchette B (2007) Sea grass and eutrophication. J Exp Mar Biol Ecol 350:46–72
Dobson JE, Bright EA, Ferguson RL, Field DW, Wood LL, Haddad KD,
Iredale H, Jensen JR, Klemas VV, Orth RJ, Thomas JP (1995)
NOAA coastal change analysis program (C-CAP): guidance for
308
regional implementation. NOAA Technical Report NMFS 123,
Washington
Ferguson RL, Wood LL, Graham DB (1993) Monitoring spatial change
in sea grass habitat with aerial photography. Photogramm Eng
Remote Sens 59:1033–1038
Fertig B, Kennish MJ, Sakowicz GP (2013) Changing eelgrass (Zostera
marina L.) characteristics in a highly eu trophic temperate coastal
lagoon. Aquat Bot 104:70–79
Fertig, B, Kennish MJ, Sakowicz GP, Reynolds LK (2014) Mind the Data
Gap: Identifying and assessing drivers of changing eutrophication
condition. Estuaries and Coasts Online First.
Finkbeiner, M, Stevenson B, Seaman R (2001) Guidance for benthic
habitat mapping: an aerial photographic approach. NOAA CSC
20117-PUB. http://www.csc.noaa.gov/benthic/mapping/pdf/
bhmguide.pdf
Gastrich MD, Lathrop RG, Haag S, Weinstein MP, Danko M, Caron D,
Schaffner R (2004) Assessment of brown tide blooms, caused by
Aureococcus anophagefferens, and contributing factors in New
Jersey coastal bays: 2000–2002. Harmful Algae Special Issue
Brown Tides 3:305–320
Handley L, Altsman D, DeMay R (eds) (2007) Seagrass status and trends
in the Northern Gulf of Mexico: 1940–2002: U.S. Geological
Survey Scientific Investigations Report 2006–5287, 267 p.
Kendrick GA, Hegge BJ, Wyllie A, Davidson A, Lord DA (2000)
Changes in sea grass cover on success and parmelia banks,
Western Australia between 1965 and 1995. Estuar Coast Shelf Sci
50:341–353
Kennish MJ, Haag SM, Sakowicz GP (2008) Seagrass demographic and
spatial habitat characterization in little egg harbor, New Jersey, using
fixed transects. J Coast Res SI 55:148–170
Kennish MJ, Haag SM, Sakowicz GP (2010) Seagrass decline in New
Jersey coastal lagoons: a response to increasing eutrophication. In:
Kennish MJ, Paerl HW (eds) Coastal lagoons: critical habitats of
environmental change. Taylor and Francis Publishers, Boca Raton,
pp 167–201
Kurz RC, Tomasko DA, Burdick D, Ries TF, Patterson K, Finck R (2000)
Recent trends in sea grass distributions in southwest Florida coastal
waters. In: Bortone SA (ed) Sea grasses: monitoring, ecology,
physiology and management CRC Press, Boca Raton, FL, pp
157–166.
Lathrop RG, Haag SM (2011) Assessment of Seagrass Status in the
Barnegat Bay – Little Egg Harbor Estuary System: 2003 and
2009. Center for Remote Sensing & Spatial Analysis, Rutgers
University, New Brunswick, NJ. 56 p. http://crssa.rutgers.edu/
projects/coastal/sav/downloads/CRSSAreport2011-01_
Assessment_Seagrass_in_BBAY_LEH_2003_and_2009.pdf
Lathrop RG, Styles RM, Seitzinger SP, Bognar JA (2001) Use of GIS
mapping and modeling approaches to examine the spatial distribution of sea grasses in Barnegat Bay, New Jersey. Estuaries 24(6):
904–916
R.G. Lathrop et al.
Lathrop RG, Montesano P, Haag SM (2006) A multi-scale segmentation approach to mapping sea grass habitat using airborne
digital camera imagery. Photogramm Eng Remote Sens 72(6):
665–675
Macomber RT, Allen D (1979) The New Jersey submerged aquatic
vegetation distribution atlas final report. Earth Satellite
Corporation, Washington
Moore KA, Wilcox DJ, Orth R (2000) Analysis of abundance of submersed aquatic vegetation communities in the Chesapeake Bay.
Estuaries 23:115–127
Orth RJ, Batiuk RA, Bergstrom PW, Moore KA (2002) A perspective on
two decades of policies and regulations influencing the protection
and restoration of submerged aquatic vegetation in Chesapeake Bay,
USA. Bull Mar Sci 71:1391–1403
Orth RJ, Carruthers TJB, Dennison WC, Duarte CM, Fourqurean JW,
Heck KL, Hughes AR, Kendrick GA, Kenworthy WJ, Olyarnik S,
Short FT, Waycott M, Williams SL (2006) A global crisis for sea
grass ecosystems. BioScience 56(12):987–996
Orth R, Marion SR, Moore KA, Wilcox DJ (2010) Eelgrass (Zostera
marina L.) in the Chesapeake Bay region of the Mid-Atlantic coast
of the USA: challenges in conservation and restoration. Estuaries
and Coasts 33:139–150
Orth RJ, McGlathery KJ (2012) Eelgrass recovery in the coastal
bays of the Virginia Coast Reserve, USA. Mar Ecol Prog Ser
448:173–176
Robbins BD, Bell SS (1994) Seagrass landscapes: a terrestrial
approach to the marine sub tidal environment. Trends Ecol
Evol 9:301–304
Scavia D, Bricker SB (2006) Coastal eutrophication assessment in the
United States. Biogeochemistry 79:187–208
Short FT (2007) Eelgrass Distribution in the Great Bay Estuary: 2005.
Final Report to the New Hampshire Estuaries Project. University of
New Hampshire, Durham, NH. http://www.prep.unh.edu/resources/
pdf/eelgrass_distribution_in-unh-07.pdf
Short FT, McKenzie LJ, Coles RG, Vidler KP (2002) Sea grass
net manual for scientific monitoring of sea grass habitat.
QDPI, Cairns
Short FT, McKenzie LJ, Coles RG, Vidler KP, Gaeckle JL (2006) Sea
grass Net Manual for Scientific Monitoring of Seagrass Habitat,
Worldwide edition. University of New Hampshire Publication.
75 pp. http://marine.unh.edu/jel/faculty/fred2/seagrassnet-manuals/
seagrassnet-manual-08.pdf
Waycott M, Duarte CM, Carruthers TJB, Orth RJ, Dennison WC,
Olyarnik S, Calladine A, Fourqurean JW, Heck KL, Hughes AR,
Kendrick GA, Kenworthy WJ, Short FT, Williams SL (2009)
Accelerating loss of sea grasses across the globe threatens coastal
ecosystems. PNAS 106(3):12377–12381
Wazniak CE, Hall MR, Carruthers TJB, Sturgis B, Dennison WC, Orth
RJ (2007) Linking water quality to living resources in a MidAtlantic lagoon system. Ecol Appl 17(5):S64–S78