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remote sensing
Article
Quantifying Intertidal Habitat Relative Coverage in a
Florida Estuary Using UAS Imagery and GEOBIA
Michael C. Espriella 1, * , Vincent Lecours 1,2 , Peter C. Frederick 3, Edward V. Camp 1and
Benjamin Wilkinson 2
1
Fisheries and Aquatic Sciences Program, School of Forest Resources and Conservation, University of Florida,
Gainesville, FL 32653, USA; vlecours@ufl.edu (V.L.); edvcamp@ufl.edu (E.V.C.)
2Geomatics Program, School of Forest Resources and Conservation, University of Florida,
Gainesville, FL 32611, USA; benew@ufl.edu
3Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA;
pfred@ufl.edu
*Correspondence: michaelespriella@ufl.edu
Received: 17 December 2019; Accepted: 17 February 2020; Published: 19 February 2020
Abstract:
Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services
including shoreline erosion control, habitat provision, and water filtration. However, these systems
face significant global change as a result of a combination of anthropogenic stressors like coastal
development and environmental stressors such as sea-level rise and disease. Traditional intertidal
habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are
mapped in a way that is often insufficient to make informed management decisions. Unoccupied
aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a
platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was
used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure
from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface
model from the UAS imagery. These products were used in a geographic object-based image analysis
(GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a
more informed classification than traditional techniques by providing textural and geometric context
to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the
temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying
habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA
can differentiate intertidal habitats. This method allows for effective monitoring that can inform
management and restoration efforts.
Keywords:
geographic object-based image analysis; eastern oyster; unoccupied aircraft system; UAS;
drone; Florida; coastal habitat; habitat mapping; eCognition; UAV
1. Introduction
Salt marshes and oyster reefs are ecologically significant estuarine habitats that face significant
changes due to environmental and anthropogenic stressors. Oyster reefs are facing global decline as a
result of stressors such as diseases, overharvest, coastal development, and alterations to hydrological
flows caused by active water management inland [
1
]. Together, these stressors have caused an
estimated 85% decline worldwide in oyster reef coverage over the last 130 years [
2
]. The loss of oyster
reefs has wide-ranging consequences as reefs can provide habitat for over 300 species as well as a
variety of vital ecosystem services like shoreline erosion control and water filtration [
3
–
5
]. Like oyster
reefs, salt marshes provide critical ecosystem services such as habitat provision, wave attenuation, and
Remote Sens. 2020,12, 677; doi:10.3390/rs12040677 www.mdpi.com/journal/remotesensing
Remote Sens. 2020,12, 677 2 of 17
shoreline stabilization [
6
]. Salt marshes demonstrate more varied responses to environmental changes
as they are expanding and encroaching on coastal forests in some areas [
7
,
8
], while they are declining
in others [
9
]. As a result of these changes, salt marshes and oyster reefs are increasingly the subjects of
monitoring programs.
Northwestern peninsular Florida’s Gulf of Mexico coastal region is referred to as the Big Bend
(Figure 1). This is one of the continental United States’ most pristine coastal regions [
1
,
10
], and supports
intertidal eastern oyster (Crassostrea virginica) reef, salt marsh, and mudflat habitats. While salt marshes
are expanding in the Big Bend [
7
,
8
], oyster reefs in Florida’s Big Bend are deteriorating [
1
,
11
]. A 2011
study of this section of coastline estimated a net loss of 66% in oyster reef area since 1982, with an 88%
net loss of offshore reefs [
1
]. Since offshore reefs protect inshore ones, their decline will likely precipitate
further declines inshore [
12
]. Additionally, oyster harvest in the region has increased significantly in
fishing effort and landings [
13
]. These trends are likely to continue, given that other nearby oyster
fisheries have collapsed (see [14]).
Remote Sens. 2019, 11, x FOR PEER REVIEW 2 of 17
environmental changes as they are expanding and encroaching on coastal forests in some areas [7,8],
while they are declining in others [9]. As a result of these changes, salt marshes and oyster reefs are
increasingly the subjects of monitoring programs.
Northwestern peninsular Florida’s Gulf of Mexico coastal region is referred to as the Big Bend
(Figure 1). This is one of the continental United States’ most pristine coastal regions [1,10], and
supports intertidal eastern oyster (Crassostrea virginica) reef, salt marsh, and mudflat habitats. While
salt marshes are expanding in the Big Bend [7,8], oyster reefs in Florida’s Big Bend are deteriorating
[1,11]. A 2011 study of this section of coastline estimated a net loss of 66% in oyster reef area since
1982, with an 88% net loss of offshore reefs [1]. Since offshore reefs protect inshore ones, their decline
will likely precipitate further declines inshore [12]. Additionally, oyster harvest in the region has
increased significantly in fishing effort and landings [13]. These trends are likely to continue, given
that other nearby oyster fisheries have collapsed (see [14]).
Figure 1. The area within the red rectangle represents Florida’s Big Bend coastline. The black dot
represents the approximate location of the study site.
This waning of oyster reefs has been attributed to the increase in fishing effort and harvest as
well as the increasing frequency of low freshwater flow events in the area [10]; high-salinity events
expose oysters to higher levels of disease and predation, causing reef erosion that lessens the reefs’
ability to retain freshwater, which initiates a negative feedback loop of increased salinity, diseases,
predation, and erosion [12]. However, substantial uncertainty remains regarding how multiple
stressors such as oyster harvest, freshwater availability, and biotic interactions like predation, may
interact to affect oyster populations and the resilience of the structural reefs they develop [14]. This
Figure 1.
The area within the red rectangle represents Florida’s Big Bend coastline. The black dot
represents the approximate location of the study site.
This waning of oyster reefs has been attributed to the increase in fishing effort and harvest as well
as the increasing frequency of low freshwater flow events in the area [
10
]; high-salinity events expose
oysters to higher levels of disease and predation, causing reef erosion that lessens the reefs’ ability to
retain freshwater, which initiates a negative feedback loop of increased salinity, diseases, predation,
Remote Sens. 2020,12, 677 3 of 17
and erosion [
12
]. However, substantial uncertainty remains regarding how multiple stressors such
as oyster harvest, freshwater availability, and biotic interactions like predation, may interact to affect
oyster populations and the resilience of the structural reefs they develop [
14
]. This scientific uncertainty
complicates management and restoration decisions [
15
]. The dramatic decline of oyster reefs has
made them the target of intensive monitoring programs in Florida (see [
12
]) and elsewhere [
16
,
17
].
While most of these monitoring programs focus almost exclusively on oysters, the intertwined and
complex dynamics among different coastal habitats like reefs and salt marshes underline the necessity
to study these habitats together as a system.
Mapping and monitoring coastal habitats like oyster reefs and salt marshes are critical to
improving the scientific understanding of the complex dynamics at play in these systems to then better
inform management and restoration efforts. However, current sampling techniques for mapping and
monitoring coastal habitats are often time- and cost-intensive. For instance, standard methods for
studying intertidal reefs include quadrat sampling to quantify oyster densities (e.g., [
18
]), the use of
GPS receivers to measure reef morphology in situ (e.g., [
19
]), and satellite imagery to quantify declines
at a broader spatial scale (e.g., [
20
]). Satellite imagery is used as a cost-effective alternative to in situ
sampling of intertidal reefs [
20
,
21
]; however, the coarse spatial resolution of data does not allow for
analyses that are detailed enough for practical, fine-scale monitoring, management, and restoration
efforts. Furthermore, satellite imagery does not always align with a favorable tidal stage in the area of
interest, limiting what is discernible in the imagery.
Unoccupied aircraft systems (UASs) provide a cost- and time-efficient alternative that can capture
very high-resolution imagery [
22
], while minimizing any potential harm caused by sampling the
habitats themselves. A benefit of UAS imagery is the ability to collect data at different spatial and
spectral resolutions depending on the sensor used and the flying height. While the collection of in
situ data may still be necessary, depending on the purpose of the surveys, UAS imagery provides a
spatial context for the ecological processes that may contribute to reef decline or marsh expansion
and can be analyzed alongside biological data. UAS imagery has been used successfully to manually
delineate habitats, and has proven to be reliable and cost-efficient when compared to traditional
methods (e.g., [
19
]); however, further decreases in processing time and cost may be possible with the
automation of image processing and habitat classification.
The goal of this study was to automate UAS imagery processing to classify intertidal habitats in
an estuary where oyster reefs are present. Geographic object-based image analysis (GEOBIA) [
23
] has
the potential to delineate and characterize reefs and surrounding habitats, and was used to address
the objective. While GEOBIA is well established in terrestrial settings [
24
–
26
], its potential has yet to
be realized in studying intertidal habitats. GEOBIA permits the segmentation of regions that have
one or more criteria of homogeneity in multiple dimensions [
24
,
27
], which changes the working unit
of a classification algorithm from an individual pixel that carries little context to a group of pixels
segmented into meaningful objects. These objects can then be characterized by layer values, geometry,
or texture, among other spatial and environmental associations [
25
,
28
]. Oyster reefs and salt marshes
present unique challenges from a remote sensing perspective because of their spectral similarity to
each other, and GEOBIA has proven effective in areas with little spectral separability in ecosystem
features [
25
]. Understanding how these different types of habitat patches shrink or grow is essential
to understanding the interactions within the ecosystem and predicting how ecosystem services may
change over time.
2. Materials and Methods
2.1. Study Site and Image Acquisition
The coastal system studied was Little Trout Creek (29
◦
15
0
34.98”N, 83
◦
4
0
29.68”W), which is located
within Florida’s Big Bend coastline, north of Cedar Key and south of the mouth of the Suwannee River
(Figure 2). A compilation of oyster presence data from various studies is available through the Florida
Remote Sens. 2020,12, 677 4 of 17
Fish and Wildlife Conservation Commission [
29
]. While informative, this dataset does not provide a
clear or detailed outlook of oyster reefs in the region as the data were not necessarily updated and
were produced using different methods and scales.
Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 17
Figure 2. This historical footprint of eastern oyster reefs near Cedar Key, Florida, USA is the most
comprehensive oyster reef map available of the area. Reefs are not represented to scale.
Georeferenced UAS imagery was collected at the mouth of Little Trout Creek using a DJI Inspire
2 equipped with a Zenmuse X7 35 mm RGB sensor on 8 December 2018. The sensor captures images
with pixel dimensions of 6016 × 4008 and a radiometric resolution of 8 bits per band. Habitats that
were observed included low-energy shoreline habitats like mudflats, salt marshes, and oyster reefs
(Figure 3). The UAS was flown 60 m above ground level and imagery was collected at nadir. The
conditions at the time of collection were overcast with minimal wind. Data collection began at 8:30
am when the tide was at its daily low. The National Oceanic and Atmospheric Administration
(NOAA) tide station in Cedar Key recorded a −0.006 m water level height relative to the mean lower
low water datum at 8:24 am local time [30]. High overlap of 80% along-track and 75% across-track
was used to limit difficulties with image matching over homogenous areas such as water and mud
during the subsequent photogrammetric processing of images. High image overlap is also known to
improve digital surface model (DSM) quality [31]. Four checkered targets were evenly distributed
across the scene, and their respective geographical location was determined using real-time kinematic
(RTK) positioning with a Trimble 5800 RTK system.
Figure 2.
This historical footprint of eastern oyster reefs near Cedar Key, Florida, USA is the most
comprehensive oyster reef map available of the area. Reefs are not represented to scale.
Georeferenced UAS imagery was collected at the mouth of Little Trout Creek using a DJI Inspire 2
equipped with a Zenmuse X7 35 mm RGB sensor on 8 December 2018. The sensor captures images
with pixel dimensions of 6016
×
4008 and a radiometric resolution of 8 bits per band. Habitats that
were observed included low-energy shoreline habitats like mudflats, salt marshes, and oyster reefs
(Figure 3). The UAS was flown 60 m above ground level and imagery was collected at nadir. The
conditions at the time of collection were overcast with minimal wind. Data collection began at 8:30 am
when the tide was at its daily low. The National Oceanic and Atmospheric Administration (NOAA)
tide station in Cedar Key recorded a
−
0.006 m water level height relative to the mean lower low water
datum at 8:24 am local time [
30
]. High overlap of 80% along-track and 75% across-track was used
to limit difficulties with image matching over homogenous areas such as water and mud during the
subsequent photogrammetric processing of images. High image overlap is also known to improve
digital surface model (DSM) quality [
31
]. Four checkered targets were evenly distributed across the
scene, and their respective geographical location was determined using real-time kinematic (RTK)
positioning with a Trimble 5800 RTK system.
Remote Sens. 2020,12, 677 5 of 17
Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of 17
Figure 3. (A) Mudflat, (B) salt marsh with fringing oysters, and (C) intertidal oyster reef habitats.
Images were taken from a UAS at Little Trout Creek.
2.2. Image Processing and Geographic Object-Based Image Analysis
The UAS collected a total of 963 images, and the size of the dataset was approximately 8.57 GB.
The flying height allowed for a ground sampling distance of 0.66 cm. The imagery was used to
generate an orthomosaic and a DSM using techniques from structure from motion photogrammetry.
The digital surface model was used rather than the digital terrain model (DTM) as the DTM would
remove objects like vegetation, and it was necessary to preserve these features to assist in
differentiating the salt marsh habitat. Photogrammetric processing was conducted in Pix4D Mapper
v. 4.2.26 [32]. In pre-processing, the ground control targets were located in the imagery and associated
with their respective coordinates to enhance the spatial accuracy of outputs [19]. Pix4D Mapper was
used to estimate the position, angular orientation, and camera calibration parameters of the images,
and to subsequently generate an orthomosaic and DSM. The ground control targets were located and
digitized in the orthomosaic using ArcGIS Pro v. 2.4 [33]. The differences from the RTK-acquired
coordinates were calculated to determine the spatial accuracy of the orthomosaic.
GEOBIA was then used to delineate and characterize coastal habitats from the orthomosaic and
the DSM. GEOBIA is a two-step process that first segments pixels into meaningful objects using
spectral and structural characteristics and then classifies these objects based on a ruleset [24]. The
process allows for a more robust analysis than traditional pixel-based analysis techniques by
including values such as the geometry and texture of the generated objects, thus potentially better
representing natural features. Figure 4 displays the workflow used to classify habitats with GEOBIA.
The segmentation process was applied in multiple steps. First, a Laplacian filter, which is used
as an edge detection method, was applied to the orthophoto using a 3 × 3 pixel window in ArcGIS
Pro. The Laplacian-filtered orthomosaic and the original mosaic (i.e., the combination of the red,
green, and blue (RGB) bands) were both used as inputs to a first GEOBIA segmentation. The
workflow was developed in eCognition Developer 9 software, a software package for object-based
image analysis applications with built-in segmentation and classification algorithms [34]. The scene
was segmented using the multi-resolution segmentation algorithm with a scaling parameter of 2500,
a shape parameter of 0.1, and compactness of 0.5. The layer weighting was equal for the Laplacian-
filtered mosaic and the three RGB bands from the original orthomosaic.
Figure 3.
(
A
) Mudflat, (
B
) salt marsh with fringing oysters, and (
C
) intertidal oyster reef habitats.
Images were taken from a UAS at Little Trout Creek.
2.2. Image Processing and Geographic Object-Based Image Analysis
The UAS collected a total of 963 images, and the size of the dataset was approximately 8.57 GB.
The flying height allowed for a ground sampling distance of 0.66 cm. The imagery was used to generate
an orthomosaic and a DSM using techniques from structure from motion photogrammetry. The digital
surface model was used rather than the digital terrain model (DTM) as the DTM would remove objects
like vegetation, and it was necessary to preserve these features to assist in differentiating the salt marsh
habitat. Photogrammetric processing was conducted in Pix4D Mapper v. 4.2.26 [
32
]. In pre-processing,
the ground control targets were located in the imagery and associated with their respective coordinates
to enhance the spatial accuracy of outputs [
19
]. Pix4D Mapper was used to estimate the position,
angular orientation, and camera calibration parameters of the images, and to subsequently generate
an orthomosaic and DSM. The ground control targets were located and digitized in the orthomosaic
using ArcGIS Pro v. 2.4 [
33
]. The differences from the RTK-acquired coordinates were calculated to
determine the spatial accuracy of the orthomosaic.
GEOBIA was then used to delineate and characterize coastal habitats from the orthomosaic and
the DSM. GEOBIA is a two-step process that first segments pixels into meaningful objects using spectral
and structural characteristics and then classifies these objects based on a ruleset [
24
]. The process
allows for a more robust analysis than traditional pixel-based analysis techniques by including values
such as the geometry and texture of the generated objects, thus potentially better representing natural
features. Figure 4displays the workflow used to classify habitats with GEOBIA.
The segmentation process was applied in multiple steps. First, a Laplacian filter, which is used as
an edge detection method, was applied to the orthophoto using a 3
×
3 pixel window in ArcGIS Pro.
The Laplacian-filtered orthomosaic and the original mosaic (i.e., the combination of the red, green,
and blue (RGB) bands) were both used as inputs to a first GEOBIA segmentation. The workflow was
developed in eCognition Developer 9 software, a software package for object-based image analysis
applications with built-in segmentation and classification algorithms [
34
]. The scene was segmented
using the multi-resolution segmentation algorithm with a scaling parameter of 2500, a shape parameter
of 0.1, and compactness of 0.5. The layer weighting was equal for the Laplacian-filtered mosaic and the
three RGB bands from the original orthomosaic.
The objects produced by this first segmentation were used to distinguish water from exposed
intertidal habitats through a classification process. A combination of the DSM and a water index derived
from the RGB data were used to mask the water out of the scene to facilitate the subsequent classification
process. The water index was adapted from Upadhyay (2016): Red-Blue +Green/Blue +Red +
Green [
35
]. All objects with an average water index value smaller than 0.325 or an average elevation of
less than 0.8 m were classified as water. A binary vector file identifying objects (i.e., polygons) as “water”
and “not water” was then exported from eCognition and imported into ArcGIS Pro. The “Extract by
Mask” tool was used to remove water areas from both the orthomosaic and the DSM. Following the
removal of water from the scene, the new orthomosaic and DSM were returned to eCognition.
Remote Sens. 2020,12, 677 6 of 17
Remote Sens. 2019, 11, x FOR PEER REVIEW 6 of 17
Figure 4. Workflow diagram detailing imagery and geographic object-based image analysis
processing.
The objects produced by this first segmentation were used to distinguish water from exposed
intertidal habitats through a classification process. A combination of the DSM and a water index
derived from the RGB data were used to mask the water out of the scene to facilitate the subsequent
classification process. The water index was adapted from Upadhyay (2016): Red-Blue + Green/Blue +
Red + Green [35]. All objects with an average water index value smaller than 0.325 or an average
elevation of less than 0.8 m were classified as water. A binary vector file identifying objects (i.e.,
polygons) as “water” and “not water” was then exported from eCognition and imported into ArcGIS
Pro. The “Extract by Mask” tool was used to remove water areas from both the orthomosaic and the
DSM. Following the removal of water from the scene, the new orthomosaic and DSM were returned
to eCognition.
A second segmentation was performed using the same multi-resolution algorithm, shape
parameter, and compactness parameter as the initial one, but this time using the updated, water-free
orthomosaic. The scaling parameter was changed to 1500 for a finer segmentation to capture the
transitions between habitats. Following the multi-resolution segmentation, a spectral difference
segmentation was performed using a maximum spectral difference parameter of two on the RGB
layers. Equal weighting was applied to all three RGB bands. After this segmentation, the feature-
space optimization tool in eCognition was used to select the variables best fit to recognize mud, oyster
reef, and marsh habitats in the imagery. Twenty objects were selected for each habitat type to train
the feature-space optimization. Thirty-one variables were included in the feature-space optimization
(cf. Appendix A). Object features built into eCognition as well as a variety of spectral indices listed in
Figure 4.
Workflow diagram detailing imagery and geographic object-based image analysis processing.
A second segmentation was performed using the same multi-resolution algorithm, shape
parameter, and compactness parameter as the initial one, but this time using the updated, water-free
orthomosaic. The scaling parameter was changed to 1500 for a finer segmentation to capture the
transitions between habitats. Following the multi-resolution segmentation, a spectral difference
segmentation was performed using a maximum spectral difference parameter of two on the RGB
layers. Equal weighting was applied to all three RGB bands. After this segmentation, the feature-space
optimization tool in eCognition was used to select the variables best fit to recognize mud, oyster reef,
and marsh habitats in the imagery. Twenty objects were selected for each habitat type to train the
feature-space optimization. Thirty-one variables were included in the feature-space optimization
(cf. Appendix A). Object features built into eCognition as well as a variety of spectral indices listed in
Louhaichi (2001), Mandal (2016), Upadhyay (2016), and Tucker (1979) were used in the feature-space
optimization [
35
–
38
]. All combinations of up to ten variables were allowed, as features beyond the
top ten typically do not enhance the discriminative power of the algorithm [
39
]. The most significant
variables in establishing distinct signatures for the three habitat types were then used as inputs for
a standard nearest neighbor classification. Following the standard nearest neighbor classification,
neighboring objects of the same class were merged. Then, any object entirely enclosed by a different
class was reclassified as the enclosing class.
Remote Sens. 2020,12, 677 7 of 17
2.3. Accuracy Assessment
Classification accuracy was assessed using stratified random sampling per class. The sample
size was determined from an equation based on a multinomial distribution with a confidence level
of 95% [
40
,
41
]. The equation indicated that 663 samples should be randomly selected to assess the
accuracy. To allow an equal number of samples for each class, 664 points were generated in ArcGIS Pro
and equally distributed among the classes so that each class had 166 validation points. For consistency,
a strict definition of all classes was used in the accuracy assessment (i.e., the cover type was considered
water even if mud or oysters were visible beneath the surface). A confusion matrix was developed to
visualize the ruleset’s performance. Producer’s accuracies, user’s accuracies, overall accuracy, and a
kappa coefficient of agreement were calculated from the confusion matrix [40,42].
3. Results
The total area surveyed covered approximately 116,000 m
2
. Figure 5shows the resulting
orthomosaic and DSM. Pix4D generated a root mean square error (RMSE) of 0.3 cm in longitude,
0.3 cm in latitude, and 0.1 cm in elevation for the residuals of the control points. The difference in the
digitized ground control points and RTK coordinates resulted in an RMSE of 2.1 cm in latitude and
1.6 cm in longitude.
Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 17
Louhaichi (2001), Mandal (2016), Upadhyay (2016), and Tucker (1979) were used in the feature-space
optimization [35–38]. All combinations of up to ten variables were allowed, as features beyond the
top ten typically do not enhance the discriminative power of the algorithm [39]. The most significant
variables in establishing distinct signatures for the three habitat types were then used as inputs for a
standard nearest neighbor classification. Following the standard nearest neighbor classification,
neighboring objects of the same class were merged. Then, any object entirely enclosed by a different
class was reclassified as the enclosing class.
2.3. Accuracy Assessment
Classification accuracy was assessed using stratified random sampling per class. The sample size
was determined from an equation based on a multinomial distribution with a confidence level of 95%
[40,41]. The equation indicated that 663 samples should be randomly selected to assess the accuracy.
To allow an equal number of samples for each class, 664 points were generated in ArcGIS Pro and
equally distributed among the classes so that each class had 166 validation points. For consistency, a
strict definition of all classes was used in the accuracy assessment (i.e., the cover type was considered
water even if mud or oysters were visible beneath the surface). A confusion matrix was developed to
visualize the ruleset’s performance. Producer’s accuracies, user’s accuracies, overall accuracy, and a
kappa coefficient of agreement were calculated from the confusion matrix [40,42].
3. Results
The total area surveyed covered approximately 116,000 m
2
. Figure 5 shows the resulting
orthomosaic and DSM. Pix4D generated a root mean square error (RMSE) of 0.3 cm in longitude, 0.3
cm in latitude, and 0.1 cm in elevation for the residuals of the control points. The difference in the
digitized ground control points and RTK coordinates resulted in an RMSE of 2.1 cm in latitude and
1.6 cm in longitude.
Figure 5. Orthomosaic (left) and digital surface model (right) developed from the collected imagery.
The yellow triangles on the orthomosaic represent the placement of ground targets. Difficulties with
finding image matching points resulted in some artifacts over water in the digital surface model
because of the extensive interpolation.
The Laplacian filter captured the water–habitat interface relatively well (Figure 6). The first
segmentation produced 436 objects, 135 of which were classified as water. Intertidal habitats were
Figure 5.
Orthomosaic (
left
) and digital surface model (
right
) developed from the collected imagery.
The yellow triangles on the orthomosaic represent the placement of ground targets. Difficulties with
finding image matching points resulted in some artifacts over water in the digital surface model because
of the extensive interpolation.
The Laplacian filter captured the water–habitat interface relatively well (Figure 6). The first
segmentation produced 436 objects, 135 of which were classified as water. Intertidal habitats were
thus estimated to cover 49% of the surveyed area. The masked orthomosaic and DSM are presented in
Appendix A.
For the second segmentation—the one applied using the water-free orthomosaic and DSM—the
feature-space optimization indicated that the water index, standard deviation of the red band, main
direction, asymmetry, vegetation index, standard deviation of the blue band, max difference, radius
of largest enclosed ellipse, spectral slope, and border index was the most effective combination of
variables. Table 1displays the separability of the three classes at each of the ten dimensions allowed in
Remote Sens. 2020,12, 677 8 of 17
the feature-space optimization. The distance matrix produced by the optimization indicated that the
marsh and oyster classes had the lowest separability at 2.45. Mud and oysters had a separability of
2.62, and marsh and mud had a separability of 4.04.
Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 17
thus estimated to cover 49% of the surveyed area. The masked orthomosaic and DSM are presented
in Appendix A.
Figure 6. Laplacian filter (left) used for edge detection with a detailed section (top right) and its
corresponding area on the orthomosaic (bottom right). The red rectangle represents the extent of the
detailed section.
For the second segmentation—the one applied using the water-free orthomosaic and DSM—the
feature-space optimization indicated that the water index, standard deviation of the red band, main
direction, asymmetry, vegetation index, standard deviation of the blue band, max difference, radius
of largest enclosed ellipse, spectral slope, and border index was the most effective combination of
variables. Table 1 displays the separability of the three classes at each of the ten dimensions allowed
in the feature-space optimization. The distance matrix produced by the optimization indicated that
the marsh and oyster classes had the lowest separability at 2.45. Mud and oysters had a separability
of 2.62, and marsh and mud had a separability of 4.04.
The use of the selected variables as inputs for the standard nearest neighbor classification in
eCognition resulted in the habitat map presented in Figure 7. Overall, 51% (59,691 m
2
) of the surveyed
area was classified as water, 13% (15,195 m
2
) as marsh habitat, 18% (20,858 m
2
) was classified as
oysters, and 17% (20,010 m
2
) as mud. Less than 1% (254 m
2
) was left unclassified. The final ruleset
was saved as a file within eCognition and is available in
Supplementary Materials
; it can be used by
anyone who has access to the software.
Table 1. Object features that allow for the most separation in classes at each dimension with the
corresponding separation value. Dimension refers to the number of object features contributing to the
separation analysis. The higher the separation value, the greater the differentiation between the
classes as represented by the randomly selected samples.
Dimension Separation Object Features
1 0.108 water index
2 0.469 water index, standard deviation green
3 0.855 water index, standard deviation red, main direction
4 1.117 water index, standard deviation red, main direction, asymmetry
Figure 6.
Laplacian filter (
left
) used for edge detection with a detailed section (
top right
) and its
corresponding area on the orthomosaic (
bottom right
). The red rectangle represents the extent of the
detailed section.
Table 1.
Object features that allow for the most separation in classes at each dimension with the
corresponding separation value. Dimension refers to the number of object features contributing to the
separation analysis. The higher the separation value, the greater the differentiation between the classes
as represented by the randomly selected samples.
Dimension Separation Object Features
1 0.108 water index
2 0.469 water index, standard deviation green
3 0.855 water index, standard deviation red, main direction
4 1.117 water index, standard deviation red, main direction, asymmetry
5 1.517 water index, standard deviation green, main direction, asymmetry,
vegetation index
6 1.684 water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue
7 1.835 water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference
8 2.028
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse
9 2.173
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse, spectral slope
10 2.257
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse, spectral slope, border index
Remote Sens. 2020,12, 677 9 of 17
The use of the selected variables as inputs for the standard nearest neighbor classification in
eCognition resulted in the habitat map presented in Figure 7. Overall, 51% (59,691 m
2
) of the surveyed
area was classified as water, 13% (15,195 m
2
) as marsh habitat, 18% (20,858 m
2
) was classified as oysters,
and 17% (20,010 m
2
) as mud. Less than 1% (254 m
2
) was left unclassified. The final ruleset was saved
as a file within eCognition and is available in Supplementary Materials; it can be used by anyone who
has access to the software.
Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 17
5 1.517
water index, standard deviation green, main direction, asymmetry,
vegetation index
6 1.684
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue
7 1.835
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference
8 2.028
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse
9 2.173
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse, spectral slope
10 2.257
water index, standard deviation red, main direction, asymmetry,
vegetation index, standard deviation blue, max difference, radius of
largest enclosed ellipse, spectral slope, border index
Figure 7. Habitat map produced from the standard nearest neighbor classification.
The confusion matrix of the classification is presented in Table 2. An overall accuracy of 79% was
achieved for the classification, with a Kappa coefficient of 0.72. Table 2 also displays the producer’s
and user’s accuracies. Producer’s accuracy is a measure of the error of omission, identifying how
often real features are represented in the classification [43,44]. Oyster and water demonstrated high
producer’s accuracies, indicating that areas of oyster and water were infrequently excluded from their
respective classes. User’s accuracy is a measure of the error of commission, measuring how often
objects are erroneously included in a class [43,44]. Mud and marsh had a high user’s accuracy,
indicating that these classes are reliable for the end user.
Figure 7. Habitat map produced from the standard nearest neighbor classification.
The confusion matrix of the classification is presented in Table 2. An overall accuracy of 79% was
achieved for the classification, with a Kappa coefficient of 0.72. Table 2also displays the producer’s and
user’s accuracies. Producer’s accuracy is a measure of the error of omission, identifying how often real
features are represented in the classification [
43
,
44
]. Oyster and water demonstrated high producer’s
accuracies, indicating that areas of oyster and water were infrequently excluded from their respective
classes. User’s accuracy is a measure of the error of commission, measuring how often objects are
erroneously included in a class [
43
,
44
]. Mud and marsh had a high user ’s accuracy, indicating that
these classes are reliable for the end user.
Remote Sens. 2020,12, 677 10 of 17
Table 2.
Confusion matrix of the actual and classified habitat covers with the accuracy of results.
UA =user’s accuracy PA =producer’s accuracy, and OA =overall accuracy.
Actual
Oyster Marsh Mud Water UA (%)
Oyster 133 33 14 6 71.51
Marsh 17 130 2 1 86.67
Classified Mud 6 3 119 17 82.07
Water 10 0 31 142 77.60
PA (%) 80.12 78.31 71.69 85.54
OA (%)
Kappa
78.92
0.72
4. Discussion
4.1. GEOBIA Classification
The GEOBIA classification produced promising results that suggest the technique can be used to
monitor relative changes in intertidal habitat cover. Between the three intertidal habitat covers, most
misclassifications occurred between the oyster and marsh habitats, which can be explained by the
lower separation represented in the difference matrix of the three habitat classes. Future research can
further emphasize the textural characteristics to better differentiate oysters and marsh, or topographical
characteristics such as rugosity, which is a measure of the local variation in a surface’s height [
45
].
However, we note that this may remain challenging as these two habitat types often spatially overlap
and create mixed, transitional areas. In areas of high marsh, for instance, oysters may be partially
or entirely obscured in UAS imagery. In cases with integrated habitat patches, fuzzy logic and
classifications may be more representative of natural transitional zones than traditional techniques
that can oversimplify complex systems by imposing discrete boundaries to habitats [
46
]. The area that
demonstrated the most difficulty in differentiating habitats was the eastern border of the scene, which
is composed of marsh habitats. This is likely due in part to the artificially smooth edges on the scene’s
border, which bias geometric features that are used in the classification.
GEOBIA works more like the human mind than traditional pixel-based techniques as it allows
for the classification of distinct, meaningful objects, rather than uniform pixels. eCognition provides
valuable tools to identify variables that separate classes from one another and goes beyond the value
of a layer, allowing for the exploration of standard deviations, maximums, minimums, and modes
of spectral layers as well as geometric or textural attributes. For example, the standard deviation of
all three spectral bands identified an upper threshold that separated mud from marsh and oyster.
The lower standard deviation demonstrates how mud is a more homogenous land cover than the other
two habitats, and it is the exploration of these attributes that allows for a more informed classification.
Features such as standard deviation and average elevation could have been assigned upper and lower
thresholds for classes to produce a higher accuracy, but this would have limited the generality and
replicability of the ruleset, which we intended to be applicable to any other study site. We note,
however, that while this study aimed to produce a general and replicable ruleset that is as automated
as possible, it may be useful for end-users to adapt the ruleset provided in Supplementary Materials to
make it more specific for the monitoring of a particular site.
While developing a GEOBIA workflow requires significant time in data exploration, one benefit
lies within the repeatability of the ruleset if data are collected at the same site multiple times. While it
is repeatable, there are initial considerations as several inputs and variables can be used to achieve
similar success. For example, local rugosity measured by the variation in elevation at a 3
×
3 window
size could have served as an edge detection input as it outlined habitats well and the values were low
over the smooth water regions. One of the primary decisions in GEOBIA is selecting which layers
serve as initial inputs, but this is in part facilitated by the use of feature-space optimization.
Remote Sens. 2020,12, 677 11 of 17
4.2. Limitations and Considerations
Despite promising results, there are some notable shortcomings of the GEOBIA workflow outlined.
The water masking had difficulty differentiating between mudflats that were partially inundated and
contiguous areas of water. A few small patches of oysters were also misclassified as water, while the
more extensive reefs demonstrated no misclassifications in the masking step. This may be remedied by
using a finer scale when segmenting the scene, but this comes with its tradeoffs (e.g., processing time).
Like mud, water is homogenous in texture and spectral variability, making the delineation challenging.
An important consideration when studying morphology is the scale of analysis. During the
data exploration stage, we conducted a multi-scale analysis in which samples of each habitat type
were selected and local terrain attributes such as rugosity and relative position were calculated at a
spectrum of scales ranging from the resolution of the imagery (0.66 cm) to multiple meters. While some
signatures provided a level of separation between habitats, the classification was not improved when
this information was input into the GEOBIA workflow. The role of scale and multiscale variables
should be further explored as some variables that seemingly provide no information on differentiating
classes may be relevant at larger or smaller windows of analysis that better capture the structural
patterns of habitats [
47
,
48
], especially considering the fine spatial resolution that is now attainable
with UASs.
There are also important considerations regarding the collection of imagery such as time of day,
cloud cover, and the effect of shadows [
49
]. Collecting images on overcast days can aid in minimizing
shadows [
50
]. Wind conditions also affect imagery collection as high winds can be detrimental to
the stability of the UAS, and therefore the quality of images. Additionally, wind can cause waves
that may alter the spectral response of the habitats by spraying them. Conditions should be kept as
constant as possible when surveying a site as part of a time series dataset. In addition to atmospheric
conditions, working in an intertidal environment poses challenges with establishing ground control
points and checkpoints. The American Society for Photogrammetry and Remote Sensing recommends
20 horizontal and vertical checkpoints for areas less than 500 square km [
51
]. Due to the challenges of
sampling this environment, our only checkpoints were the four ground control targets. While this
limitation is not of great concern given our objective of characterizing relative coverage, this becomes a
more significant concern if this workflow was to be used for temporal monitoring.
Another consideration is the computing capabilities necessary to conduct this analysis efficiently,
as processing these data is computationally intensive. Details on the computation time and hardware
used in processing can be found in Appendix A. Additionally, there are few open-source solutions that
allow the same capabilities as a software suite such as eCognition. As open-source options for GEOBIA
advance, this process will become even less cost-intensive. Researchers have used a Python-based
open-source workflow for GEOBIA [
52
], highlighting the growing capabilities of open-source solutions.
4.3. Future Directions
There is potential for future studies to improve upon classification accuracies by collecting imagery
from different spectral bands. This study aimed to minimize costs as much as possible and as a result,
selected to use only RGB imagery. However, if available, near-infrared imagery could be utilized for
more accurate water classifications. A higher spectral resolution may also aid in differentiating oysters
and marsh, which caused the most misclassifications for reefs.
The spatial resolution of this dataset allows for analysis within a reef system itself. While developing
accurate distributions of oyster reefs is valuable to assess change over time, analyzing the structure of a
thriving reef is also an essential component. This is particularly relevant in areas such as the Big Bend
of Florida where reef collapse can lead to an increase in reef extent as broken shells get dispersed [
12
].
While reefs are increasing in extent, the availability of a stable substrate for larvae is decreasing [
12
].
The NOAA Oyster Restoration Workgroup suggests monitoring metrics such as population structure,
recruitment, disease, and vertical relief of oyster reefs to provide a more complete outlook on the health
of reefs and populations [
7
,
12
]. UASs can provide informative data on these metrics, the most direct
Remote Sens. 2020,12, 677 12 of 17
application being vertical relief and morphology, which has been conducted successfully for eastern
oyster reefs in other areas such as off the coast of North Carolina, USA [19].
As reefs continue to erode due to anthropogenic and environmental stressors, there is often a
concentrated restoration effort in regions losing reefs such as in the Big Bend of Florida. Restoration
often involves installing a hard substrate for the oysters to settle on including boulders and recycled
oyster shells [
53
]. These restoration activities are typically conducted in areas where reefs used to
exist. However, given a changing environment, these may not be the ideal areas today, emphasizing
the importance of current information regarding where extant oyster reefs are and are not as well as
the structural integrity of these reefs. The multi-scale capabilities of this workflow not only allow for
the selection of sites where reefs can be successful, but also enable the characterization of a thriving
reef’s structure.
5. Conclusions
Developing an expedient workflow to monitor coastal environments is essential as stressors
continue to alter coastal systems. As we continue to extract and divert water resources, estuarine
systems that are reliant on freshwater inflow are of particular concern. Remote sensing techniques can
cut the costs and time of monitoring these ecosystems, and UASs present an accessible alternative to
traditional methods or less precise and broader-scale satellite imagery. The composition of coastal
ecosystems is unique and dynamic, again highlighting the benefit of UASs as a tool because they can
collect localized data to analyze the complexities of specific sites. Morphology, hydrodynamics, and
coastal development are all influencing factors on how these habitats expand or decline, and UASs can
efficiently capture data to document changes.
Our study showed that UAS imagery paired with GEOBIA could inform management by
quantifying the relative coverage of different coastal habitat types. Designing a GEOBIA ruleset that
is specific to a study site allows for a high level of repeatability, making long-term monitoring more
feasible at a given site. This workflow can be extended to other areas of Florida’s Big Bend coastline
to monitor changes. The GEOBIA workflow we developed incorporated specific steps such as the
water masking step to address difficulties with classifications in an intertidal environment that has
little spectral separability between habitats. Connecting changes in habitats by using quantifiable
measurements such as reef morphology or changes in area can inform restoration efforts and identify
areas prone to shoreline erosion. Understanding which spatiotemporal variables contribute to reef
success can provide valuable information to restoration and monitoring efforts alike. While coastal
research using UASs is gaining momentum, the procedure is just as important as the mechanism, and
GEOBIA serves as a streamlined approach to classify coastal habitats and quantify changes.
Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/4/677/s1.
Author Contributions:
Conceptualization, V.L. and M.C.E.; Methodology, M.C.E.; and V.L.; Validation, M.C.E.;
Formal analysis, M.C.E.; Resources, P.C.F. and V.L.; Writing—original draft preparation, M.C.E.; Writing—review
and editing, V.L., P.C.F., B.W., and E.V.C.; Visualization, M.C.E.; and V.L.; Supervision, V.L., P.C.F., B.W., and E.V.C.;
Funding acquisition, V.L. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by a Gulf Research Program Early-Career Research Fellowship awarded to V.
Lecours by the National Academies of Sciences, Engineering, and Medicine. We also acknowledge the support
of the University of Florida College of Agricultural and Life Sciences through an assistantship to M. Espriella.
Publication of this article was funded in part by the University of Florida Open Access Publishing Fund.
Acknowledgments:
Thanks are due to Steve Beck, Sean Denney, Lindsey Garner, and Andrew Ortega who led
the UAS imagery and RTK data collection.
Conflicts of Interest:
The authors declare no conflicts of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
Remote Sens. 2020,12, 677 13 of 17
Appendix A
Table A1.
Description of object features used in the feature-space optimization and their corresponding
descriptions adapted from the eCognition reference book and Bialas 2015.
Object Feature Description
Asymmetry A measure of the variance in the x-direction and y-direction of an
approximated ellipse around the object
Border index Measures how jagged an object is; border length of object compared to
border length of smallest enclosing rectangle
Brightness Index 1((R2+G2+B2)/3)0.5
Compactness Product of the length and width, divided by the number of pixels
Density Describes distribution in space of the pixels in an object; number of pixels
forming object divided by its radius
Elliptic fit Measures what falls inside versus outside an ellipse with the same length
and width of and object
Green leaf index 4(2G −R−B)/(2G +R+B)
Hue index 1(2 * R −G−B)/(G −B)
Length/width The length of the object divided by the width
Main direction The direction of the eigenvector belonging to the larger of the two
eigenvalues
Max difference The maximum difference between mean values for layers of an object
divided by the brightness of the respective objects
Mean blue Mean reflectance of the blue band of all pixels in an object
Mean brightness Mean brightness of all pixels in an object
Mean DSM Mean elevation of all pixels in an object in the DSM layer
Mean green Mean reflectance of the green band of all pixels in an object
Mean red Mean reflectance of the red band of all pixels in an object
Normalized green red difference index 2(G −R)/(G +R)
Number of neighbors Number of neighbors in which an object shares a common border
Radius of largest enclosed ellipse
Measures an object’s similarity to an ellipse; ratio of largest enclosed ellipse
radius to the radius of an ellipse with the same area as the object
Radius of smallest enclosing ellipse How much of an object is similar to an ellipse; ratio of smallest enclosing
ellipse radius to radius of and ellipse with the same area as the object
Rectangular fit
Describes how well an object fits into a rectangle of similar size; are of image
object inside versus outside a rectangle that has the same length and width
as the object
Redness Index 1R2/(B*G3)
Relative border to image border Border length an object shares with outer boundary of entire image
Roundness Describes how similar an object is to an ellipse; difference of enclosing
ellipse and enclosed ellipse
Shape index Shape complexity; border length
Spectral slope 2(R −B)/(R +B)
Standard deviation blue Standard deviation of blue band reflectance values over all pixels in an
object
Standard deviation brightness Standard deviation of the brightness values over all pixels in an object
Standard deviation DSM Standard deviation of elevation values over all pixels in an object
Standard deviation green Standard deviation of green band reflectance values over all pixels in an
object
Standard deviation red
Standard deviation of red band reflectance values over all pixels in an object
Standard deviation of length of edges Measures how lengths of edges deviate from mean value
Vegetation index 3(B +R−G)/(B +R+G)
Water index 3(R −B+G)/(B +R+G)
1
Spectral indices from Mandal (2016) input into eCognition as arithmetic features;
2
Spectral indices from Tucker
(1979) input into eCognition as arithmetic features;
3
Spectral indices from Upadhyay (2016) input into eCognition
as arithmetic features; 4Spectral indices from Louhaichi (2001) input into eCognition as arithmetic features.
Remote Sens. 2020,12, 677 14 of 17
Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 17
Standard deviation
green
Standard deviation of green band reflectance values over all pixels in
an object
Standard deviation red Standard deviation of red band reflectance values over all pixels in an
object
Standard deviation of
length of edges Measures how lengths of edges deviate from mean value
Vegetation index
3
(B + R − G)/(B + R + G)
Water index
3
(R − B + G)/(B + R + G)
1
Spectral indices from Mandal (2016) input into eCognition as arithmetic features;
2
Spectral indices from Tucker
(1979) input into eCognition as arithmetic features;
3
Spectral indices from Upadhyay (2016) input into
eCognition as arithmetic features;
4
Spectral indices from Louhaichi (2001) input into eCognition as arithmetic
features.
Figure A1. Orthomosaic and DSM following the water masking step.
Table A2. Hardware and processing time for image processing conducted in Pix4D.
Description
CPU Intel Xeon CPU E5-2630 v4 2.20
GHz
RAM 192 GB
GPU NVIDIA Quadpro P4000
Initial processing time (image calibration, finding keypoints) 1 h 37 min
Point cloud densification 5 h 25 min
DSM generation 2 h 17 min
Orthomosaic generation 1 h 37 min
Total processing time 10 h 56 min
Table A3. Hardware and processing time for image processing conducted in eCognition.
Description
CPU Intel Xeon Silver 4114 2.20 GHz 2.19 GHz (2 processors)
RAM 256 GB
GPU NVIDIA Quadpro P60000
Segmentation 2 h
Figure A1. Orthomosaic and DSM following the water masking step.
Table A2. Hardware and processing time for image processing conducted in Pix4D.
Description
CPU Intel Xeon CPU E5-2630 v4 2.20 GHz
RAM 192 GB
GPU NVIDIA Quadpro P4000
Initial processing time (image calibration, finding keypoints) 1 h 37 min
Point cloud densification 5 h 25 min
DSM generation 2 h 17 min
Orthomosaic generation 1 h 37 min
Total processing time 10 h 56 min
Table A3. Hardware and processing time for image processing conducted in eCognition.
Description
CPU Intel Xeon Silver 4114 2.20 GHz 2.19 GHz (2 processors)
RAM 256 GB
GPU NVIDIA Quadpro P60000
Segmentation 2 h
Feature-space optimization 3+h
Classification 2 h 5 min
Exporting results 5 min
Total processing time 7+h
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