ArticlePDF Available

Evaluating benthic survey techniques for validating maps of coral reefs derived from remotely sensed images

Authors:

Abstract and Figures

Field validation of maps derived from airborne or satellite imagery is essential to enable their use for monitoring and managing coral reef habitats. Methods of benthic survey in coral reef ecosystems have been documented elsewhere, yet a comparative evaluation of methods for integrating field data with remote sensing has not been completed. In addition to meeting standard field survey requirements, data for validation of maps produced from remotely sensed images have several unique requirements in terms of spatial coverage, timing, information content and positional accuracy. This study compares ten different methods used to determine benthic cover for verifying image-based maps in coral reef environments. At three locations the field survey techniques were applied to the same section of reef, under similar environmental conditions, to measure benthic cover characteristics. The techniques tested were: line intercept transect, point intercept transect, 0.5 x 0.5m quadrat with ten points; 0.5 x 0.5m quadrat visual estimates, photographic transect, photographs of 25 or five cells in a 5.0 x 5.0m grid and visual estimates of a 5.0 x 5.0m grid. Digital photographic analysis used two different methodologies based on 12 and 1024 point processing. The final comparison covered both field survey and data processing techniques, in terms of: benthic cover, time and cost, degree of expertise, spatial intensity and the power of the final result. From this comparison it was concluded that the photographic transect using 1024 point analysis was the overall best choice given no limits on resources and available expertise..This implies that the method is also sufficient for any imagery with lower spectral or spatial resolution. Our findings should enable scientists or managers to make a more informed selection of field survey for mapping and validating maps derived from remotely sensed data.
Content may be subject to copyright.
1
Evaluating Benthic Survey Techniques for Validating Maps of
Coral Reefs Derived from
Remotely Sensed Images
Chris M. ROELFSEMA1,2, Stuart R. PHINN1 and Karen E. JOYCE1
1 Biophysical Remote Sensing Group, Centre for Remote Sensing and Spatial Information Science, University of
Queensland, St.Lucia, 4072, QLD, Australia.
2 Centre for Marine Studies, University of Queensland, St.Lucia, 4072, QLD. Australia,
phone: 07 33652529, email: c.roelfsema@uq.edu.au
Abstract Field validation of maps derived from
airborne or satellite imagery is essential to enable their
use for monitoring and managing coral reef habitats.
Methods of benthic survey in coral reef ecosystems
have been documented elsewhere, yet a comparative
evaluation of methods for integrating field data with
remote sensing has not been completed. In addition to
meeting standard field survey requirements, data for
validation of maps produced from remotely sensed
images have several unique requirements in terms of
spatial coverage, timing, information content and
positional accuracy. This study compares ten different
methods used to determine benthic cover for verifying
image-based maps in coral reef environments. At three
locations the field survey techniques were applied to the
same section of reef, under similar environmental
conditions, to measure benthic cover characteristics.
The techniques tested were: line intercept transect, point
intercept transect, 0.5 x 0.5m quadrat with ten points;
0.5 x 0.5m quadrat visual estimates, photographic
transect, photographs of 25 or five cells in a 5.0 x 5.0m
grid and visual estimates of a 5.0 x 5.0m grid. Digital
photographic analysis used two different methodologies
based on 12 and 1024 point processing. The final
comparison covered both field survey and data
processing techniques, in terms of: benthic cover, time
and cost, degree of expertise, spatial intensity and the
power of the final result. From this comparison it was
concluded that the photographic transect using 1024
point analysis was the overall best choice given no
limits on resources and available expertise..This implies
that the method is also sufficient for any imagery with
lower spectral or spatial resolution. Our findings should
enable scientists or managers to make a more informed
selection of field survey for mapping and validating
maps derived from remotely sensed data.
Keywords: Remote sensing, field validation, benthic
surveys, digital photography
Introduction
Airborne and satellite remote sensing
technologies are increasingly being used for monitoring
coral reef habitats. To create accurate and reliable maps,
calibration of mapping algorithms and validation of
output maps is necessary (Green et al. 2000). A
common limitation of most image based mapping of
coral reefs, which may be responsible for its limited
uptake by managers, is an inadequate or absent
validation program (Ahmad and Neil 1994, Green et al.
2000, Holden and LeDrew 1998). A variety of benthic
survey methods have been used for validation, such as
visual checks (Mazel et al. 2003), line intercept
(Andréfouët et al. 2004), video (Louchard et al. 2003)
and digital still surveys (Joyce et al. 2004). The most
effective technique to implement is not always obvious
and there are numerous benthic survey methods from
which to chose (English et al. 1997, Hill and Wilkinson
2004).
Extensive work has been completed on sample
design and statistical requirements for field survey of
benthos and validation of maps of terrestrial
environments derived from remotely sensed images
(Curran and Williamson 1986, Long et al. 2004,
Stehman 1999). In contrast, there are few reviews
addressing field validation techniques for maps of coral
reefs derived from remotely sensed image data (Green
et al. 2000). Coral reef survey methods for estimating
2
the composition of benthos, along with its condition and
cover have been assessed in detail in terms of their cost,
time required, accuracy, precision and statistical power
in a number of reef systems worldwide (Brown et al.
1999, Hill and Wilkinson 2004).
Validating maps of coral reef composition, coral
condition and coral cover requires field survey data to
possess certain attributes: a spatial and temporal match
to the image data used to produce the map;
georeferencing precision and accuracy; and a match in
information content between field data and image based
variables (Congalton and Green 1999). The standard
techniques used in remote sensing for collecting
validation data and performing accuracy assessments
have been developed for use in terrestrial environments
(Atkinson 1991, Atkinson and Curran 1997, Congalton
and Green 1999, Foody 2002, Menges et al. 2002). With
few exceptions e.g. (Green et al. 2000), these validation
data collection techniques have not been developed for
use in multi-scale heterogeneous environments, such as
coral reefs, where it is difficult to determine where and
how intensively to sample. Standard image based
accuracy assessment, correlation analysis and root mean
square analysis techniques can be applied to image
maps of coral reef environments. To ensure effective
use of current and future remotely sensed data sets in
shallow coastal and marine environments, such as coral
reefs, there is a need for a validation data collection
technique that adequately represents the composition,
condition or cover of reef environments.
Collection of field validation is challenging in
marine environments due to additional constraints of
safety, water clarity, water depth, currents, remoteness
and logistics on the field survey process (Green et al.
2000). The most appropriate benthic survey validation
method to use will depend on the answers to the key
questions detailed below.
What benthic classes do you need to survey?
Remote sensing techniques are capable of
mapping coral reef communities (Palandro et al. 2003),
geomorphic zones (Andréfouët 2004), and impacts such
as bleaching (Andréfouët et al. 2002), with the most
current research differentiating blue and brown coral
types (Hochberg et al. 2003). Benthic classes can vary
from species level to description of geomorphic zones.
What resources are available to conduct the survey?
This concerns available funding for: logistics,
equipment and people and may range from a viewing
bucket (Roelfsema et al. 2002) to underwater
photography and video (Joyce et al. 2004)
What scale of the validation is required?
Determined by the area to be covered, the type of
information to be mapped, and the spatial resolution of
the sensor used (Andréfouët and Claereboudt 2000).
What type of reef environment is to be mapped?
The effectiveness of a survey is influenced by a
number of factors, some of which include: water clarity,
water depth, currents, and leeward or windward
position. Protected areas can be accessed any time,
others require careful planning. Surface and underwater
conditions influence safety.
Existing field survey programs, if suitable for the
type of mapping application, may also be used in the
validation activities, reducing survey costs and effort.
The aim of this study was, to provide a preliminary
comparison of benthic field sampling methods for
validating image based maps of coral reefs
environments. We have attempted here to provide a
basis to enable the choice of an optimum field data
collection method for validating image maps. This
should be of value for producers and users of satellite or
airborne image based maps.
Study Sites and Methodology
Study Sites
Surveys were conducted at sites where the
authors were involved in ongoing image and field data
collection programs. These sites included: Flinders Reef
in Moreton Bay, Australia (27º S, 153º E) and Heron
Reef, in the southern Great Barrier Reef , Australia (27º
S, 153º E) and Eagle Reef (27º S, 153º E) in Palau..
Benthic classes
For this study the benthic classes mapped were
chosen through a combination of previous research
(Joyce et al. 2004) and Reef Check classification
scheme (Hodgson et al. 2004). The classes used were:
branching corals; massive corals; plate corals;
encrusting corals; turf algae; macro algae; sand;
coralline Algae; and other. The class interpretation in
this study (underwater and above water) was conducted
by the same observer to reduce classification error.
Transect and Grid Surveys
The survey methods chosen for the comparison
originated from a variety of projects with which the
authors have been involved with (Ford et al. 2003,
Joyce et al. 2004, Joyce 2003, McMahon et al. 2002,
Mumby et al. 2004, Phinn and Neil 1998, Roelfsema et
al. in review).
The comparisons were based on either a 20m
transect line (conforming to the Reef Check protocol
(Hodgson et al. 2004)) or a 5.0 x 5.0m grid (Mumby et
al. 2004) (Figure 1). For the transect line, a 20 m section
of a standard 50 m measuring tape was used. The grid
consisted of 25 one meter grid cells in a five by five grid
constructed from thin ropes (Figure 1).The grid was
positioned, so that its diagonal was parallel to the
3
transect line and the grid centre point placed on the 10
m mark of the transect line.
Fig. 1 Graphical depiction of the six benthic survey
methods. a) Point intercept, b) Line intercept, c)
Quadrat, d) photo transect, e) 5.0 x 5.0m grid with 25
cells and f) 5.0 x 5.0m with five grid cells (four corners
and centre).
Percentage benthic cover was determined from
in-situ assessment or from analysis of photographs
from the transect and grid. Techniques for in-situ
assessment were: point intercept, line intercept, quadrat
(0.5 x 0.5m) estimate or ten points and visual estimates
of a 5.0 x 5.0m grid. For photographic assessment,
photos were captured of: the transect line, 25 cells or
five cells (corner and centre cell) in a 5.0 x 5.0m grid.
Benthic Surveys – In-situ assessment
Point Intercept
On the 20m transect at 0.5m intervals, benthic
cover type observed directly under the measurement
point and recorded on a dive slate. Percent cover of
different benthic cover types for the transect was
calculated by counting the occurrences of a benthic
cover class on the transect line (Greig-Smith 1983,
Hodgson et al. 2004) (Figure 1a).
Line Intercept
Along each 20m transect the distance at which
benthic cover changed from one cover type to another
was recorded on a dive slate. Percent cover was
calculated by determining the total distance on the
transect covered by each class (English et al. 1997)
(Figure 1b).
Quadrat Estimate and 10 point
On the 20m transect at 2.0m intervals a 0.5 x
0.5m quadrat was deployed. Percent benthic cover was
determined for the quadrat using two techniques: a)
visual estimate of percentage cover of the benthic cover
classes for the complete quadrat, and b) determining
benthic cover class for ten random points, marked
within the quadrat, from which the percentage cover
could be calculated (English et al. 1997) (Figure 1c).
5.0 x 5.0m Grid Estimate
For the 5.0 x 5.0m grid, benthic cover was
visually estimated for each of the 25 (1.0 x 1.0m) grid
cells (Mumby et al. 2004) (Figure 1e).
Benthic Surveys – Photographic assessment
Field component
For the digital photo surveys a SONY Cybershot
PC9 4.3 megapixel in a Marine Pack underwater
housing was used with a Sea and Sea 16 mm wide angle
lens. The camera recorded images at medium resolution.
For all surveys, photographs were captured of the
benthos, from positioning the camera vertically at 1.5m
above the bottom. The camera height above target was
chosen to enable replication of a surface area of 1 x 1m
within each image.
Photo transects
A photograph was captured every 2.0m on the
20m transect line. The position of the photograph on the
transect line could be determined by its number and/or
reading the distance from the transect tape (Joyce et al.
2004) (Figure 1d).
Photo of 25 or five cells (four corners and centre) of 5.0
x 5.0m grid
A photograph was captured of each of the 25
1.0m x 1.0m cells within the 5.0 x 5.0m grid. Each
4
cell’s photograph was followed by one of a magnetic
slate with the cell number on it to determine the position
of photo within grid (Figure 1e) (Mumby et al. 2004).
This method was repeated for only five cells (corners
and centre cells) (Figure 1f).
Photo Analysis
12 point photo
Benthic cover was determined for twelve points
in a regular grid which was superimposed onto each
photo. The regular grid was composed of three rows of
four points at equal distance from each other. Twelve
points were selected as the ‘optimum’ number for
sampling after testing from 1- 40 points and observing
the trade off between coral cover estimation accuracy
and time spent in the analysis (Joyce et al. 2004). The
benthic cover type was stored in a database using a
customised graphical user interface in Microsoft
Access. The database automatically calculates the
percent cover as the proportional cover times 100, for
each photo (Greig-Smith 1983, Joyce et al. 2004).
1024 point photo
Using the VidAna 1.0 (Hedley 2003) software
package, polygons were drawn covering each benthic
cover type present in the photo. Once the photo was
covered, percent cover was automatically calculated by
dropping a 1024 point regular grid onto the polygon and
counting the number of points in each polygon (Hedley
2003).
Comparison of Field Survey Methods
The comparison of the field validation methods
focussed on observed differences and similarities in
percent benthic cover estimates, time and cost of survey,
degree of expertise required for survey and analysis,
sampling intensity, sampling power and non-
quantifiable survey attributes.
Percent Benthic Cover
Percent cover for every class was calculated
using the appropriate analysis technique for each survey
method (English et al. 1997, Hedley et al. 2004,
Hodgson et al. 2004, Joyce et al. 2004, Mumby et al.
2004).
Time and Cost
To determine the effectiveness of each survey
method, time and air use of each dive were also noted
for the different parts of the survey process in the field
on a dive slate, e.g. deploying, surveying and retrieval.
For the processing component, time was noted for
downloading, interpretation and analysis of field notes
and/or digital photos.
Cost was determined for the survey methods and
their equipment needs. To place cost and time into
perspective for the different field survey methods an
example was established for a theoretical survey in a
realistic mapping program. The case study focussed on
validating the benthic cover for 25 sites at 5.0 m depth
around Heron Reef. Prices were calculated based on
personnel time, boat use and other necessary field
equipment within compliance of local work place health
and safety regulations.
Degree of Expertise
The degree of expertise required to complete the
benthic survey method was determined by the authors.
This was done by rating on a scale of one to five, the
different components of the survey process, including
planning and preparation; mobilisation to the field site;
survey work; demobilising; and downloading and
analysing the data). A rating of one represents minimum
expertise, taking minimum time, effort and no special
knowledge and skills.
Sampling Intensity for Satellite Image Data
In the context of this paper, sampling intensity was
calculated as the surface area covered by the survey
method, divided by the surface area of a pixel or the
case study area. For example, the ratio between the
surface area covered by each survey method and the
surface area of a single 4.0 x 4.0 m Ikonos multispectral
pixel or a single 30 x 30 m Landsat 7 (Enhanced
Thematic Mapper) ETM pixel. For the case study area,
Heron over the Heron Reef (28 km2) image.
Power Analysis
Power analysis was conducted to measure the
probability of several different benthic survey methods
to correctly reject the null hypothesis (Ho), when Ho =
no difference in coral cover % between two sample
transects (Sheppard 1999). Previous power analyses of
commonly used coral cover field measurement
techniques were reported in Brown et al. (1999) for the
Hawai’ian Coral Reef Assessment and Monitoring
Program, indicating that fixed photo-quadrats had the
highest statistical power, while the length of transect
could be varied to maximise statistical power depending
on the level of heterogeneity and coral cover in the area
to be sampled. G-power software (Buchner et al. 1997)
was applied to conduct a post-hoc power analysis on
each of the ten survey methods using a two-tailed t-test
(α = 0.05) for comparing mean coral cover.
Results
Percentage Benthic Cover
Different study sites varied significantly in their
benthic cover (Figure 2). Each survey technique
produced notably different results for Heron Reef which
can be explained by the higher degree of spatial
heterogeneity at this site in comparison to Eagle (Palau)
and Flinders Reefs. For Palau and Flinders Reef, the
5
methods gave comparable results. For Heron Reef,
observed variations in percent-cover of coral was
dependent on the method used. Estimation of percent
benthic cover from photos using 1024 points showed
similar cover distributions to the 12 sample point
method on Heron Reef. The results for Eagle and
Flinders Reef data (resulting from photo interpretation)
were similar to the results of other methods.
0 255075100
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat es timated
Grid photo 12 pt
Grid photo 1024 pt
Grid estimate
Grid 5 cells photo
% cover Heron
HC-T HC-M HC-B SC DC RB
SD OT RCC RCW TURF FT
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photo (5 cells)
0 25 50 75 100
% cover Flinders
0255075100
% cover Palau
Hard Coral Table
Hard Coral Massive
Hard Coral Branching
Soft Coral
Dead Coral
Other
Sand
Rubble Rock White
Algae
Turf Coralline
0 255075100
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat es timated
Grid photo 12 pt
Grid photo 1024 pt
Grid estimate
Grid 5 cells photo
% cover Heron
HC-T HC-M HC-B SC DC RB
SD OT RCC RCW TURF FT
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photo (5 cells)
0 25 50 75 100
% cover Flinders
0255075100
% cover Palau
0 25 50 75 100
% cover Flinders
0255075100
% cover Palau
Hard Coral Table
Hard Coral Massive
Hard Coral Branching
Soft Coral
Dead Coral
Other
Sand
Rubble Rock White
Algae
Turf Coralline
Hard Coral Table
Hard Coral Massive
Hard Coral Branching
Soft Coral
Dead Coral
Other
Sand
Rubble Rock White
Algae
Turf Coralline
Figure 2: Percent benthic cove r recorded for each of the three sites for each survey method
. Time and Cost
All survey methods took a similar amount of time for
one site, with the obvious difference being the extra
time needed for analyzing photos (Figure 3).
0 20406080
Line inter cept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photos (5 cells)
Benthic field
survey methods
Time (min)
Total Dive
Total Office
Figure 3: Time needed to conduct survey and
processing for one site per benthic survey method
From Figure 4 it can be seen that, in the case of
the theoretical study of 25 sites, the field cost is similar
for almost all methods and is the highest in relation to
analysis and equipment cost. This is due to the cost of
diving and boating time and personnel. As a rough
comparison, the cost of boat based video survey was
added into Figure 4 for reference. For this survey type a
visual benthic assessment was made from a monitor in
the boat so that nobody has to enter the water.
$0.00 $4,000.00 $8,000.00 $12,000.00
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photos (5 cells)
Boat video survey
Benthic field
survey methods
Cos t in US dollars
Fieldwork
Analysis
Equipment
Figure 4: Estimated cost for validating 25 sites
at Heron Reef for each benthic survey method. Boat
based video survey was added to the cost comparison
since no diving and snorkelling is involved.
Degree of Expertise
Benthic survey methods using a grid were ranked
the highest in terms of skill level required for data
collection and analysis (Figure 5). All photo-based
survey methods also required a high degree of expertise.
Line and point intercept methods were the easiest to
conduct in relation to grid photo with 1024 point
interpretation.
6
0 5 10 15 20 25
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photos (5 cells)
Benthic field
survey methods
Degree of expertise
Preparation
Survey
Clean up
Download
Processin
g
Figure 5: Degree of expertise required to conduct
survey and processing for each form of benthic survey
method. Each stage of the survey and processing was
scaled on score of one to five, where one was
considered minimum expertise needed and five the
maximum.
Sampling Intensity for Satellite Image Data
Figure 6 shows two different groupings: one
group contains line intercept, point intercept, transect
photo 12 point, transect quadrat 10 point, and grid photo
12 point. A second group includes: transect photo 1024
point, transect quadrat estimated, grid photo 1024 point,
grid 5 cell photo, grid 5 cell estimate. Only the transect
photo 1024 point and the grid methods have a 100%
sampling intensity for the Ikonos pixel.
1.E-05 1.E-03 1.E-01 1.E+01 1.E+03
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Transect quadrat estimated
Grid photo 12 pt (25 cells)
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photos (5 cells)
Benthic field
survey methods
Log scale of sampling intensit
y
Ikonos 4m
p
ixel Landsat ETM 30m
p
ixel Heron 28 km2
Figure 6: Spatial intensity of different benthic
cover survey methods in relation to a representative
multispectral Ikonos or Landsat Enhanced Thematic
Mapper pixel or a case study where 25 sites on Heron
Reef need to be validated.
Power Analysis
Results of the post-hoc sample power analysis
(Figure 7) concurred with previous power analyses and
comparisons of field survey techniques (Brown et al.
1999, Mumby 2002).
25
n.d.
n.d.
n.d.
11
11
11
n.d.
10
20
00.20.40.60.81
Line intercept
Point intercept
Transect photo 12 pt
Transect photo 1024 pt
Transect Quadrat 10 pt
Trans ec t qua drat estimated
Grid photo 12 pt (25 c ells )
Grid photo 1024 pt (25 cells)
Grid estimate (25 cells)
Grid photos (5 cells)
Benthic field
survey
methods
Power
power
n
Figure 7: Power analysis results of benthic field
validation methods for mapping differences in live coral
cover, conducted using a two-tailed post-hoc power
analysis. With for each method the number of samples.
Coral cover estimates from fixed photographs
over a relatively homogeneous area exhibited maximum
statistical power (Brown et al. 1999, Carleton and Done
1995). Increasing the number of sample points per
photograph and the number of sample photographs
increased statistical power. The 10 point quadrat and
more detailed analysis of the same transects using photo
quadrats resulted in similar statistical power levels.
Discussion
Selection of an optimal field validation method
for image based maps requires consideration of their
strengths and weaknesses in relation to the intended
application. This will often require trade-offs, for
example, validation of maps derived from high spatial
resolution image data requires positional accuracy as the
critical factor. Since the trade-offs varying and are
influenced by who and for what type of validation it will
be used, it is not possible to specify an optimal
technique. The results presented in this paper are an
initial attempt at quantifying the strengths/weaknesses
of field survey techniques for use in coral reef remote
sensing.
To discuss which field validation methods may
be optimal the results of this research are summarised in
table 1 and in figure 8.
Figure 8 summarises key attributes for several
representative survey techniques compared in this work
using a multi-dimensional plot. Each axes of the graph
represents one of the measured attributes of each field
survey technique. The values along the axes are
normalised to their maximum value with low values at
the centre of the graph.
7
Table 1: Comparison of benthic field survey methods for validating in remote sensing programs. The processing
time is divided in L=Long, A=Average and S=Short.
Transect 5 x 5 m Grid
Method
Line intercept
Point intercept
Transect photo
12 pt
Transect photo
1024 pt
Transect
Quadrat 10 pt
quadrat
estimated
Grid photo 12 pt
(25 cells)
Grid photo 1024
pt (25 cells)
Grid estimate
(25 cells)
Grid photos (5
cells)
Boat video
survey
Processing time S S A L S S A L S A S
General 75 75 225 225 120 120 375 375 375 375 2250
Camera 1275 1275 1275 1275 1275 3750
1.51.41.72.01.71.23.73.92.72.4 1.2
Length (m) 20 20 20 20 20 20 5 5 5 5 1
Width (m) 0.05 0.05 1 1 0.5 0.5 5 5 5 5 1
Archival   
Equipment Transect line 
needs Grid 
Quadrat
Slate pencil  
Magnetic Wipe board
Digital camera and software  
Field computer
Camera maintenance    
General Surface expert verification   
Underwater ID needed   
Spatial viewing dimensions 3d 3d 2d 2d 3d 3d 2d 2d 3d 2d 2d
Representative of an image area 
of a pixel area 
People needs Advanced diver 
Experienced advanced diver 
ID expert above water  
ID expert under water  
Area
Initial Equipment
cost (US$)
Degree of expertise (scale 1 to 5)
0.0
1.5
Total Time
Tota l Co st
Degree expertise
Area covered
Spatial IntensityLength
Width
Archive
Power
Transect photo 12 pt Point intercept
Grid photo 12 pt (25 cells) Transect Quadrat 10 pt
Grid photo 1024 pt (25 cells) Grid estimate (25 cells)
Figure 8: Comparison of six benthic validation survey
methods in terms of measured survey attributes.
Attribute (axes) are normalised by to maximum levels.
Attributes are: Total time (in field and office), Total cost
(equipment+soft/hardware+labour), Degree expertise
(average rating to conduct method), Area covered
(surface area visually assessed), Spatial intensity (area
covered / Ikonos pixel size), Length (length covered by
survey), Width (widest with), Archive (photographs
were captured resulting in an historical archive), Power
= probability of methods to correctly discriminate
percentage coral cover..
By analysing Figure 8, it can be seen that none of
the benthic validation methods compared were clearly
“optimal”. However, each method has its own
characteristic attributes (table 1 and figure 8) which
make it suitable to a specific environment, image type
and mapping problem.
Cost, Time, and Degree of Expertise
Transect-based methods were more suitable in
this context, as their cost, time and degree of expertise
are low in comparison to grid based methods. This was
mostly due to simple survey methods which reduce the
equipment cost and underwater time needed. Analysis
of digital photos with 12 or 1024 points significantly
increases the total time needed.
For validation of image based maps, significant
attention needs to be paid to the number and distribution
of sample site locations, a topic given extensive
attention in terrestrial remote sensing applications
8
(Atkinson 1991, Carleton and Done 1995, Green et al.
2000).Transect methods were faster and easier to
implement than grid based methods. Combined with a
reduction of in-water expertise by using digital
photography, transects come close to the attributes
required for an optimal method.
Area Covered, Spatial Intensity, Length and Width
Ideally the surface area covered is large,
covering the target scene and enabling sufficient sapling
for high res pixels. Not all sensors have a high spatial
resolution and the sampling intensity for other survey
method in relation to a Landsat 7 ETM pixel was low.
Manta tow survey (English et al. 1997) could cover a
large spatial extent in a short time. However, this
method has limited use due to: lack of detailed
positional data, validation error and the fact that this
technique is not supported by local workplace health
and safety guidelines. Hence, the validation of image
pixels with moderate to low spatial resolution (10m –
1km pixels) requires further consideration. In this case
a survey method should cover several pixels. Transect
based methods where the length of the survey area
covered extends over several pixels should be suitable.
Which method is optimal?
With the information discussed it can be seen
that an optimal field validation method is not obvious as
it depends on a variety of factors. The choice can be
narrowed down by knowing which type of image data
will be used in combination with a set classification
scheme, and by having access to a digital camera. With
these variables in mind and the findings described in
this manuscript, Table 2 was created. This table assists
in selecting the type of field validation method to apply.
Table 2: Optimal benthic field validation methods for: a specific image types, benthic detail thought and
camera availability. Spatial resolution = High < 10 m < Low 10 m. Spectral resolution = High > 8 bands > Low. Image
extent = Small < 30 km2 < Large. Benthic classification detail = High > 20 classes > Medium > 8 classes > Low.
Sensor type 1 2 3 4
Spatial resolution High High Low Low
Spectral resolution High Low High Low
Image extent small small Large large
Example CASI Quickbird EO-1 Hyperion
Landsat Thematic
Mapper
Classification detail High Medium Medium Low
Camera Ideal
method
Photo grid (25 cells)
with 1024 points photo
analysis
Photo transect or
grid (25 cells) with
12 points photo
analysis
Photo transect
with 12 points
photo analysis
Photo transect with
12 points photo
analysis
Why
High level of detail,
grid covers several
pixel and many sites
visits due to small
image size.
Reasonable high
level of detail and
many sites visits
due to small image
size.
Enough detail and
can cover many
sites since
analysis will go
faster.
Enough detail and
can cover many
sites since analysis
will go faster.
No
camera
Ideal
method
10 point Quadrat
transect
10 point Quadrat
transect
Point intercept Point intercept
Why
Relatively High level
of detail and almost no
analysis time could
therefore visit more
sites.
Relatively High
level of detail and
almost no analysis
time could therefore
visit more sites,
Enough detail and
almost no analysis
time could
therefore visit
more sites.
Enough detail and
almost no analysis
time could therefore
visit more sites,
9
When analyzing Table 2, in combination with the
findings of this paper, one can conclude that the
photographic transect field method with 1024 photo
analysis was optimal. The method would give sufficient
information to validate images from both the higher and
lower spectral and spatial resolution sensors. The choice
of this method does not consider available funding and
accuracy needed in combination with spatial statistical
analysis. The field component of this method is
relatively fast, as it can easily cover several pixels (e.g.
Landsat 7 ETM scale) and does not require highly
trained personnel. The processing component on the
other hand requires more time and higher degree of
expertise due to image processing skills needed and
benthic identification capabilities. Processing time can
be reduced by using a 12 point photo analysis but this
will result in less detailed analysis of photos. The photos
are valuable since one would have a permanent archive
and during classification process the photos could
explain mis-classifications. Although not covered in the
table above, benthic heterogeneity should also be
considered when selecting a suitable technique. If the
area to be surveyed is heterogeneous a photo survey will
provide sufficient detail, while in homogenous areas
point intercept or random spot checks (Andréfouët al.
2002) may be more suitable.
Acknowledgements
World Bank GEF Coral Reefs Project, Coral
Remote Sensing, ARC Small Grant, Natural Vision and
Remote Sensing, ARC Linkage Grant, Heron Island
Research Station, Palau International Coral Reef
Research Centre and Sean Ivermee for field assistance
and the reviewers of this manuscript.
References
Ahmad W, Neil DT (1994) An evaluation of Landsat
Thematic Mapper (TM) digital data for discriminating
coral reef zonation: Heron Reef (GBR). International
Journal of Remote Sensing 15: 2583-2597
Andréfouët S (2004) Keynote Address: The Diversity and
Extent of Planet Earth’s Modern Coral Reefs as View
from Space. International Coral Reef Symposium.
Andréfouët S, Berkelmans R, Odriozola L, Done T, Oliver J,
Muller-Karger F (2002) Choosing the appropriate
spatial resolution for monitoring coral bleaching
events using remote sensing. Coral Reefs 21: 147–154
Andréfouët S, Claereboudt M (2000) Objective class
definitions using correlation of similarities between
remotely sensed and environmental data. International
Journal of Remote Sensing 21: 1925 - 1930
Andréfouët S, Zubia M, Payri C (2004) Mapping and biomass
estimation of the invasive brown algae Turbinaria
ornata(Turner) J. Agardh and Sargassum
mangarevense (Grunow) Setchell on heterogeneous
Tahitian coral reefs using 4-meter resolution IKONOS
satellite data. Coral Reefs 23: 26–38
Atkinson PM (1991) Optimal ground-based sampling for
remote sensing investigations: estimating the regional
mean. International Journal of Remote Sensing 12:
559 - 567
Atkinson PM, Curran PJ (1997) Choosing an appropriate
spatial resolution for remote sensing investigations.
Photogrammetric Engineering Remote Sensing 63:
1345-1351
Brown E, Cox E, Tissot B, Rodgers K, Smith W (1999)
Evaluation of benthic sampling methods considered
for the coral reef assessment and monitoring program
(CRAMP) in Hawai’i. International Conference on
Scientific Aspects of Coral Reef Assessment
Monitoring and Restoration: 14-16.
Buchner A, Erdfelder E, Faul F (1997) How to Use G*Power
Carleton J, Done T (1995) Quantitative video sampling of
coral reef benthos: large scale application. Coral Reefs
14: 35-46
Congalton R, Green K (eds.) (1999) Assessing the accuracy of
remotely sensed data: principles and practices. Lewis
Publishers, Boca Rotan FL (137)
Curran PJ, Williamson HD (1986) Sample Size for Ground
and Remotely Sensed Data. Remote Sensing of
Environment 20: 31 - 41
English S, Wilkinson CR, Baker V (1997) Survey Manual for
Tropical Marine Resources. Australian Institute of
Marine Science, Townsville
Foody GM (2002) Status of land cover classification accuracy
assessment. Remote Sensing of Environment 80: 185 -
201
Ford S, Langridge m, Roelfsema c, Bansemer c, Pierce s,
Gomez k, Fellegara i, McMahon k, Keller m, Joyce k,
Aurish n, Prebble c (2003) Surveying Habitats Critical
to the Survival of Grey Nurse Sharks in South-East
Queensland. University of Queensland Underwater
Club, Unidive, Brisbane (59)
Green EP, Mumby PJ, Edwards ED, Clark CD (2000) Remote
sensing handbook for tropical coastal management.
UNESCO Publishing (316)
Greig-Smith P (1983) Quantitative plant ecology. Blackwell
Scientific, London
Hedley J (2003) VidAna 1.0 Software for cover analysis from
video footage or still images. Marine Spatial Ecology
Lab, School of Biological & Chemical Sciences,
University of Exeter, UK (Free open source software
tool for performing cover analysis form video footage
or still images. Vidana was created om tje cpmtext
preliminary work for the world bank gef targeted coral
reef researchjb project. Vidana has now been made
available as a free download from this site(see below)
and is released under the terms of the GPL version2.
The source code is freely available (below) and the
rpogram may be modified and redistributed under the
terms of the GPL.)
Hedley JD, Mumby PJ, Joyce KE, Phinn SR (2004) Spectral
unmixing of coral reef benthos under ideal conditions.
Coral Reefs 23: 60-73
Hill J, Wilkinson C (2004) Methods for Ecological Monitoring
of Coral Reefs. Australian Institute of Marine Science
and Reef Check., Townsville
Hochberg EJ, Atkinson MJ, Andréfouët S (2003) Spectral
reflectance of coral reef bottom-types worldwide and
implications for coral reef remote sensing. Remote
Sensing of Environment in press
Hodgson G, Kiene W, Mihaly J, Liebeler J, Shuman C, Maun
L (2004) Reef Check Instruction Manual: A Guide to
Reef Check Coral Reef Monitoring. Reef Check,
Institute of the Environment, University of California
at Los Angeles, Los Angeles (92)
10
Holden H, LeDrew E (1998) The scientific issues surrounding
remote detection of submerged coral ecosystems.
Progress in Physical Geography 22: 190-221
Joyce KE, Phinn SR, Roelfsema C, Neil DT, Dennison WC
(2004) Combining Landsat ETM and Reef Check for
mapping coral reefs: An example from the southern
Great Barrier Reef, Australia. Coral Reefs 23: 21-25
Joyce KE, Phinn, S.R, Roelfsema, C, Scarth, P.F (2003) A
method for determining live coral cover using remote
sensing. International symposium for remote sensing
of the environment November, 2003.
Long BL, Andrews G, Suharsono YW (2004) Sampling
accuracy of reef resource inventory technique. Coral
Reefs 23: 378–385
Louchard EC, Reid PR, Stephens CF, Davis CO, Leathers RA,
Downes VT (2003) Optical remote sensing of benthic
habitats and bathymetry in coastal environments at Lee
Stocking Island, Bahamas: A comparative spectral
classification approach. Limnol. Oceanogr 48: 511–
521
Mazel C, Strand MP, Lesser MP, Crosby MP, Coles B, Nevis
AJ (2003) High-resolution determination of coral reef
bottom cover from multispectral fluorescence laser
line scan imagery. Limnol. Oceanogr 48: 522–534
McMahon K, Levy D, Bansemer C, Fellegara I, Keller M,
Kerswell A, Kwik J, Longstaff B, Roelfsema CM,
Thomas J, Stead J (2002) A baseline assessment of the
flora and fauna of North Stradbroke Island dive sites,
Queensland. Coastcare Project. Queensland University
Diveclub Unidive, Brisbane (72)
Menges C, Bartolo RE, Phinn S, Hill G (2002) A Spatial
statistic to Determine Appropriate Field Sampling Size
for Linking Image and Field Data. Proceedings of the
11th Australasian Remote Sensing and
Photogrammetry Conference.
Mumby PJ (2002) Statistical power of non-parametric tests: A
quick guide for designing sampling strategies. Marine
Pollution Bulletin 44: 85-87
Mumby PJ, Hedley JD, Chisholm JRM, Clark CD, Ripley H,
Jaubert J (2004) The cover of living and dead corals
from airborne remote sensing. Coral Reefs 23: 171-
183
Palandro D, Andréfouët S, Muller-Karger F, Dustan P, Hu C,
Hallock P (2003) Detection of changes in coral reef
communities using Landsat 5/TM and Landsat
7/ETM+ Data. Canadian Journal of Remote Sensing
29: 201-209
Phinn SR, Neil D (1998) Differentiation of reef substrate types
and condition from the "next generation" of earth
monitoring satellite and airborne imaging systems.
Proceedings of 1998 Australian Coral Reef Society
Annual Scientific Conference.
Roelfsema CM, Phinn SR, Dennison WC, Dekker A, Brando
V (2002) Monitoring cyanobacterial blooms of
Lyngbya Majuscula in Moreton Bay, Australia by
combining field techniques with remote sensing.
Proceedings of the 11th Australasian Remote Sensing
and Photogrammetry Conference.
Roelfsema CM, Phinn SR, Dennison WC, Dekker A, Brando
V (in review) Monitoring Toxic Cyano-bacteria L.
majuscula in Moreton Bay, Australia by Integrating
Satellite Image Data and Field Mapping. Harmfull
Algae
Stehman SV (1999) Basic probability sampling designs for
thematic map accuracy assessment. International
Journal of Remote Sensing 20: 2423 - 2441
... The field data were collected by the photo-transect method, which is very efficient in terms of cost, time, and energy (Roelfsema et al., 2006). Photos of benthic habitat were captured using an underwater camera by the surveyors while snorkeling in the optically shallow water. ...
Article
Full-text available
Spatial information on the varying composition of coral reefs is beneficial for the management and preservation of natural resources in coastal areas. Its availability is inseparable from environmental management goals; however, it can also be used as a means of supporting tourism activities and predicting the emergence of certain living species. A satellite image is one of the effective and efficient data sources that provide spatial information on coral reef variations. This study aimed to evaluate the classification scheme of coral reef life-form using images with different spatial resolutions on Parang Island, Karimunjawa Islands, Central Java. These images were from PlanetScope (3m), PlanetScope resampling (6m), and Sentinel-2A MSI (10m), whose spatial resolutions functioned as the base for building the 3m, 6m, and 10m classification schemes producing 12, 11, and 9 classes, respectively. As for the classification method, it integrated both object-based and pixel-based approaches. The results showed that the highest overall accuracy (60%) was obtained using Sentinel-2A MSI image (10m), followed by PlanetScope (3m) with 48% accuracy, and PlanetScope resampling (6m) with 40% accuracy. This finding indicates that multiresolution images can be used to produce complex coral reef life-form maps with different levels of information details. Keywords: Coral reef; Life-form; Planetscope; Spatial resolution; Classification scheme Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
... Impor- tantly, however, Van Rein et al. [50] and Perkins et al. [51] suggest that, while a higher number of points per image can increase the detection rate of more organisms within an image, increasing the number of scored images using fewer points is likely to have a similar effect. Ide- ally, increasing both the number of images scored and the number of points scored within an image would result in greater power to improve the accuracy and precision of epifaunal cover estimates [52]. Unfortunately, the adoption of this approach is likely to result in substantial increases in processing time and therefore cost. ...
Article
Full-text available
Efficient monitoring of organisms is at the foundation of protected area and biodiversity management. Such monitoring programs are based on a systematically selected set of survey locations that, while able to track trends at those locations through time, lack inference for the overall region being “monitored”. Advances in spatially-balanced sampling approaches offer alternatives but remain largely untested in marine ecosystems. This study evaluated the merit of using a two-stage, spatially-balanced survey framework, in conjunction with generalized additive models, to estimate epifauna cover at a reef-wide scale for mesophotic reefs within a large, cross-shelf marine park. Imagery acquired by an autonomous underwater vehicle was classified using a hierarchical scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI). At a realistic image subsampling intensity, the two-stage, spatially-balanced framework provided accurate and precise estimates of reef-wide cover for a select number of epifaunal classes at the coarsest CATAMI levels, in particular bryozoan and porifera classes. However, at finer hierarchical levels, accuracy and/or precision of cover estimates declined, primarily because of the natural rarity of even the most common of these classes/morphospecies. Ranked predictor importance suggested that bathymetry, backscatter and derivative terrain variables calculated at their smallest analysis window scales (i.e. 81 m²) were generally the most important variables in the modeling of reef-wide cover. This study makes an important step in identifying the constraints and limitations that can be identified through a robust statistical approach to design and analysis. The two-stage, spatially-balanced framework has great potential for effective quantification of epifaunal cover in cross-shelf mesophotic reefs. However, greater image subsampling intensity than traditionally applied is required to ensure adequate observations for finer-level CATAMI classes and associated morphospecies.
... Van Rein et al. (2011) and suggest that, while a higher number of points per image can increase the detection rate of more organisms within an image, increasing the number of scored images using fewer points is likely have a similar (or greater) effect. Ideally, increasing both the number of images scored and the number of points scored within an image would result in greater power (Roelfsema et al. 2006), but preference is usually for increasing the number of images . Unfortunately, the adoption of this approach is likely to result in substantial increases in processing time and thus cost. ...
Book
Full-text available
Australia has one of the world’s largest marine estates that includes many vulnerable habitats and a high biodiversity, with many endemic species crossing a wide latitudinal range. The marine estate is used by a variety of industries including fishing, oil & gas, and shipping, in addition to traditional, cultural, scientific and recreational uses. The Commonwealth government has recently established the Australian Marine Parks (AMPs), the largest network of marine protected areas in the world, complementing existing networks in State and Territory waters. Monitoring the impacts of these uses on the marine environment is a massive shared responsibility that can only be achieved by making the best use of all the information that is collected. Australia now has a number of significant long-term marine monitoring and observing programs, as well as a national ocean data network. Without some common and agreed standards, much of the information collected will not be comparable with other areas or sectors. This may reduce its value to regional and national management, while the individual project or survey may lose the opportunity to interpret results in a regional or national context. We have therefore developed a suite of field manuals for the acquisition of marine benthic (i.e. seafloor) data from a variety of frequently-used sampling platforms so that data can become directly comparable in time and through space, thus supporting nationally relevant monitoring in Australian waters and the development of a monitoring program for the AMP network. This objective integrates with one of the eight high-level priorities identified by the National Marine Science Plan (2015-25): the establishment of national baselines and long-term monitoring. Due to the large geographic area, diverse flora and fauna, and range of environmental conditions represented by the Australian marine estate, a single method of sampling is neither practical nor desirable. For this reason, we present a standard operating procedure (SOP) for each of six key marine benthic sampling platforms that were identified based on their frequency of use in previous sampling and monitoring programs, as well as a pilot pelagic sampling platform included due to its similarity with benthic BRUVs: • Multibeam sonar (MBES) provides bathymetry and backscatter data that are used to map the seafloor. • Autonomous Underwater Vehicles (AUVs) acquire high-resolution continuous imagery of the seafloor and its associated habitats and organisms. • Benthic Baited Remote Underwater Video (BRUV) systems acquire video of demersal fish attracted to a baited camera system dropped to the seafloor. • Pelagic BRUVs acquire video of pelagic fish and other fauna that are attracted to a baited camera system suspended in the water column. This platform is included as an emergent sampling method for pelagic ecosystems. • Towed cameras acquire video or still imagery of the seafloor and its associated habitats and organisms. • Grabs and box corers collect sediment samples that can be analysed for biological, geochemical, or sedimentological variables. • Sleds and trawls collect benthic or demersal fauna near the seafloor. The main challenge in the development of these manuals was to find a balance between being overly prescriptive (such that people prefer to follow their own protocol and ignore the manuals) and overly flexible (such that data is not consistent and therefore not comparable). A collaborative approach was paramount to addressing this concern. Ultimately, over 70 individuals from over 30 organisations contributed to the field manual package. By engaging researchers, managers, and technicians from multiple agencies with a variety of experience, sea time, and subject matter expertise, we strove to ensure the field manuals represented the broader marine science community of Australia. This not only improved the content but also increased the potential for adoption across multiple agencies and monitoring programs. Future work is based on the understanding that SOPs should be periodically checked and revised, lest they become superseded or obsolete. Resources are available to develop a Version 2 of this field manual package, due for completion in late 2018, following additional community consultation and input. As part of this version, a long-term plan for managing the field manuals will be developed, including maintenance, version control, and the scoping of further SOPs as new sampling platforms are ready for use in monitoring programs.
... Van Rein et al. (2011) andPerkins et al. (2016) suggest that, while a higher number of points per image can increase the detection rate of more organisms within an image, increasing the number of scored images using fewer points is likely have a similar (or greater) effect. Ideally, increasing both the number of images scored and the number of points scored within an image would result in greater power (Roelfsema et al. 2006), but preference is usually for increasing the number of images (Perkins et al. 2016). Unfortunately, the adoption of this approach is likely to result in substantial increases in processing time and thus cost. ...
... Van Rein et al. (2011) andPerkins et al. (2016) suggest that, while a higher number of points per image can increase the detection rate of more organisms within an image, increasing the number of scored images using fewer points is likely have a similar (or greater) effect. Ideally, increasing both the number of images scored and the number of points scored within an image would result in greater power (Roelfsema et al. 2006), but preference is usually for increasing the number of images (Perkins et al. 2016). Unfortunately, the adoption of this approach is likely to result in substantial increases in processing time and thus cost. ...
... Terhadap peta ini selanjutnya dilakukan pengujian akurasi. Uji ini sangat penting dilakukan untuk mendapatkan peta yang dapat dipercaya (Chris et al., 2006). Uji akurasi yang digunakan menggunakan matrik kesalahan (error matrix). ...
Article
Full-text available
p>Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m<sup>2</sup>at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18 ind/m<sup>2</sup> at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation: LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation</p
... Five benthic classes were defined as bare sand, sparse, moderate, medium-dense and dense; corresponding to Z. marina covers of < 1%, 1-25%, 25-50%, 50-75% and > 75%, respectively; following Roelfsema et al. (2006) ...
Article
Seagrass meadows provide ecosystem services that contribute to climate mitigation and ecosystem resilience in coastal environments, being recognised among the most effective carbon sink ecosystems on Earth. Although seagrass meadows are declining worldwide at alarming rates, direct measurements of the consequences of habitat degradation on the sedimentary carbon stock remains still scarce. The aim of this study is to investigate the effects of the physical disturbance caused by clam harvesting on the capacity of a subtidal Zostera marina meadow to accumulate organic matter and sequester carbon. Biotic (seagrass cover, density and biomass) and abiotic factors (water depth and sediment grain size distribution) of the studied meadow were characterized and carbon stocks in control and impacted areas of vegetated and bare sand zones were compared. The physical disturbance resulted in a significant reduction in shoot density (63%) and biomass (64%) in the impacted area with respect to the adjacent zone not affected by the disturbance, whereas the sedimentary carbon stock was reduced by 50%, reaching similar levels to those recorded in un-vegetated areas. The meadow exposed to the harvesting activity showed a decreasing capacity to sequester carbon by reducing the seagrass standing stock and carbon preservation in the associated sediments. Thus, clam harvesting activity not only eroded the historical carbon stock accumulated over decades but also endangers further potential accumulation. Therefore, sustainable management of the exploited area should take into account not only the durability of clam stocks but also the resilience of the seagrass meadow and its capacity to provide critical ecosystem services.
... Nadon e Stirling (2006) apontam as grandes variações espaciais em um mesmo recife como uma das dificuldades associadas a conectar estimativas visuais com dados quantitativos de alterações locais. Estas duas metodologias (PIT e FOT) são muitas vezes descritas como correspondentes (e.g Roelfsema et al, 2006), porém ao longo deste trabalho, verificou-se que adaptações específicas para a área se fazem necessárias. Ainda não há um consenso total sobre qual metodologia é a melhor ou mais preciso na determinação de coberturas específicas em recifes de corais (Nadon e Stirling, 2006). ...
Article
O presente trabalho teve como objetivo principal a comparação da estrutura da comunidade bentônica e cobertura coralínea entre 2010 (ano de potencial branqueamento) e 2013 nos recifes de corais de Maragogi. A pesquisa de campo foi realizada nas cristas recifais por duas metodologias do Protocolo Reef Check, que foram: point intercept transect (PIT) e foto quadrats (FOT). As anomalias de temperatura superficial no mar na área não foram suficientes para causar branqueamento nos corais dos sítios estudados em 2010. Ambos os métodos revelaram que a cobertura total de corais se manteve estável nos três anos de pesquisa. Os métodos revelaram resultados diferentes a nível de cobertura específica de corais e de cobertura de categorias indicadoras, que pôde ser em parte atribuído a dificuldade de amostragem do hidrocoral Millepora alcicornis. Ademais, comprovou-se a necessidade de correções e adaptações nos métodos atualmente utilizados, dado a variação estrutural dos recifes da área. Apenas desta forma, será possível a detecção de alterações de pequena e média escala nos recifes de corais de Maragogi, estratégia muito importante para o manejo sustentável da área. Palavras chave: recife de coral, APA Costa dos Corais, branqueamento, cobertura de corais, metodologia de campo
Article
Full-text available
This study aims to understand the spatial distribution of coral reefs in the central region of Viet Nam. We classified live coral cover in Son Tra Peninsula (ST) and Cu Lao Cham Island (CLC) in the South-Central Coast Region of Viet Nam using the Maximum Likelihood Classifier on 3 m Planetscope imagery. Confusion matrices and the accuracy of the classifier were assessed using field data (1,543 and 1,560 photographs in ST and CLC, respectively). The results showed that the reef’s width ranged from 30 to 300 m across the study site, and we were able to detect live coral cover across a depth gradient of 2 to 6 m below the sea surface. The overall accuracies of the classifier (the Kappa coefficient) were 76.78% (0.76) and 78.08% (0.78) for ST and CLC, respectively. We found that 60.25% of coral reefs in ST were unhealthy and the live coral cover was less than 50%, while 25.75% and 11.46% of those in CLC were in good and excellent conditions, respectively. This study demonstrates the feasibility of utilizing Planetscope imagery to monitor shallow coral reefs of small islands at a high spatial resolution of 3 m. The results of this study provide valuable information for coral reef protection and conservation.
Article
Remote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.
Article
Full-text available
According to the 1993 colloquium on the ‘Global status of coral reefs', our understanding of the global role of coral reefs is inadequate. To increase our understanding, an accurate large-scale mapping and monitoring programme is necessary. Historically, coastal zones have been mapped using traditional surveying tools such as topographic maps, nautical charts, existing aerial photographs and direct observations. Although less expensive than digital imagery, exclusive use of these traditional tools may not be practical for monitoring large or remote coral reef ecosystems accurately. Researchers are attempting to develop an adequate coral reef mapping system based on digital remote sensing, but are impeded by issues such as effects of the intervening water column and spectral distinction of bottom types. The two variables discussed, which will contribute to our understanding of the global role of coral reefs, are: 1) remote sensing of submerged coral reefs in general; and 2) remote sensing of coral bleaching in particular. A summary of radiative transfer theory is presented and case studies of attempts at mapping remotely the geographic extent and health of submerged ecosystems, as well as a discussion of the remote estimation of water depth and quality. Problems in the translation and delivery of information to the end user are presented, and possible solutions suggested.
Article
Choosing rationally the spatial resolution for remote sensing requires a formal relation between the size of support and some measure of the information content. The local variance in the image has been used to help choose an appropriate spatial resolution. Here we choose spatial resolutions to map continuous variation in properties, such as biomass, using the variogram. The experimental variogram can be separated into components of underlying spatially dependent variation and measurement error. The spatially dependent component can be deregularized to a punctual support, and then regularized to any spatial resolution. The regularized variogram summarizes the information attainable by imaging at that spatial resolution because information exists in the relations between observations only. The investigator can use it to select a combination of spatial resolution and method of analysis for a given investigation. Two examples demonstrate the method.
Article
Satellite remote sensing is increasingly used to map and monitor coral reefs. From 1984 to the present, Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+) images provide the longest time series available for change detection analysis over coral reefs. A time series of four Landsat-5 images and one Landsat-7 image spanning 1984-2000 was analyzed to detect changes in "coral-dominated", "sand", "algae", and "substrate" benthic classes for Carysfort Reef in the Florida Keys. To properly analyze this time series, a set of corrections was undertaken, which included noise-reduction correction, atmospheric correction, and TM-ETM+ data normalization. All images were classified with a Mahalanobis distance classifier using statistics from the 1984 image to identify the four benthic classes. The results were compared with historical ground-truthing data, a combination of high-resolution aerial photography and Ikonos satellite data, and results from a temporal texture change detection analysis. All data sets provided consistent results, with an extreme loss in coral cover between 1982 and 2000. The Landsat time series provided across-time progression and locations of the coral-dominated zones for all of Carysfort Reef. This study demonstrates the feasibility and utility of combining Landsat-5 TM and Landsat-7 ETM+ images for coral reef community scale change detection studies at a decadal scale. It opens the possibility of a cost-effective larger scale study, which could include an entire reef tract.
Article
Remote sensing is a valuable tool for rapid identification of benthic features in coastal environments. Past ap- plications have been limited, however, by multispectral models that are typically difficult to apply when bottom types are heterogeneous and complex. We attempt to overcome these limitations by using a spectral library of remote sensing reflectance ( Rrs), generated through radiative transfer computations, to classify image pixels according to bottom type and water depth. Rrs spectra were calculated for water depths ranging from 0.5 to 20 m at 0.5- to 1.0-m depth intervals using measured reflectance spectra from sediment, seagrass, and pavement bottom types and inherent optical properties of the water. To verify the library, computed upwelling radiance and downwelling irra- diance spectra were compared to field measurements obtained with a hyperspectral tethered spectral radiometer buoy (TSRB). Comparisons between simulated spectra and TSRB data showed close matches in signal shape and magnitude. The library classification method was tested on hyperspectral data collected using a portable hyper- spectral imager for low light spectroscopy (PHILLS) airborne sensor near Lee Stocking Island, Bahamas. Two hyperspectral images were classified using a minimum-distance method. Comparisons with ground truth data indicate that library classification can be successful at identifying bottom type and water depth information from hyper- spectral imagery. With the addition of diverse sediments types and different species of corals, seagrass, and algae, spectral libraries will have the potential to serve as valuable tools for identifying characteristic wavelengths that can be incorporated into bottom classification and bathymetry algorithms.
Article
A prototype in-water laser line-scanning multispectral fluorescence imaging system was evaluated for its ability to provide data that could be used to determine the quantitative distribution and abundance of various functional groups on coral reefs. The system collected fluorescence imagery in three spectral bands with 1 cm 2 resolution at sites in Florida and the Bahamas. Fluorescence excitation was at 488 nm, and imagery was collected in emission bands centered at 520, 580, and 685 nm. Ground truth data on bottom cover was collected by divers using con- ventional line transect and photographic quadrat methods. A set of classification rules based on the relative signal levels in the three fluorescence channels was developed to assign the image pixels to functional groups. Once the image was classified, percent cover data for the groups were computed for the full image and for subsets of the image chosen to simulate line transect, grid survey, and photographic quadrat surveys. The statistics of percent cover of various bottom types derived from the fluorescence image compared favorably with those determined by diver survey techniques. The results demonstrate that fluorescence imaging has the long-term potential to provide coverage of large spatial areas of coral reefs at high resolution, with automated classification and quantification of functional groups in the image.
Article
Coral reefs exhibit patterns of zonation. In this study we have evaluated the usefulness of Landsat-TM digital data as a tool for discrimination and mapping of reef zones. Classification, on bands 1, 2 and 3, and grouping of classes into reef zones was carried out with the aid of canonical variate analysis and minimum spanning trees. Thirteen reef zones can be identified and mapped, at a spatial scale relevant to their dimensions, with confidence. These zones can be further subdivided and mapped as spatially coherent subzones, in order to provide detailed information regarding the density of coral cover on the reef flat. In addition, the canonical variate analysis provides the basis for the aggregation of classes into sub-zones on interpreted primary productivity gradients, which is of relevance to coral reef management, monitoring and research.
Article
Remote sensing investigations often involve sampling on the ground to estimate the mean of some property within ground resolution elements. Investigators have used classical statistics to determine the size of sample required to produce a desired precision. However, classical statistics is based on assumptions that do not hold when the target population is spatially dependent. Remotely sensed data and ground cover are usually spatially correlated, and in these circumstances the size of sample required will be less when sampling is done on a regular grid. This is demonstrated for several variables measured at the ground.