ArticlePDF Available

Abstract and Figures

Shorebirds have declined severely across the East Asian–Australasian Flyway. Many species rely on intertidal habitats for foraging, yet the distribution and conservation status of these habitats across Australia remain poorly understood. Here, we utilised freely available satellite imagery to produce the first map of intertidal habitats across Australia. We estimated a minimum intertidal area of 9856 km2, with Queensland and Western Australia supporting the largest areas. Thirty-nine percent of intertidal habitats were protected in Australia, with some primarily within marine protected areas (e.g. Queensland) and others within terrestrial protected areas (e.g. Victoria). Three percent of all intertidal habitats were protected by both marine and terrestrial protected areas. To achieve conservation targets, protected area boundaries must align more accurately with intertidal habitats. Shorebirds use intertidal areas to forage and supratidal areas to roost, so a coordinated management approach is required to account for movement of birds between terrestrial and marine habitats. Ultimately, shorebird declines are occurring despite high levels of habitat protection in Australia. There is a need for a concerted effort both nationally and internationally to map and understand how intertidal habitats are changing, and how habitat conservation can be implemented more effectively.
Content may be subject to copyright.
The distribution and protection of intertidal habitats in Australia
Kiran L. Dhanjal-Adams
A,F
, Jeffrey O. Hanson
A
, Nicholas J. Murray
B
, Stuart R. Phinn
C
,
Vladimir R. Wingate
C,D
, Karen Mustin
A
, Jasmine R. Lee
A
, James R. Allan
A,C
,
Jessica L. Cappadonna
A
, Colin E. Studds
A,E
, Robert S. Clemens
A
, Chris M. Roelfsema
C
and Richard A. Fuller
A
A
School of Biological Sciences, University of Queensland, Brisbane, Qld 4072, Australia.
B
Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences,
University of New South Wales, Sydney, NSW 2052, Australia.
C
School of Geography, Planning and Environmental Management, University of Queensland,
Brisbane, Qld 4072, Australia.
D
Department of Environmental Science, University of Basel, Basel 4056, Switzerland.
E
Department of Geography and Environmental Systems, University of Maryland Baltimore County,
Baltimore, MD 21250, USA.
F
Corresponding author. Email: kiran.dhanjal.adams@gmail.com
Abstract. Shorebirds have declined severely across the East AsianAustralasian Flyway. Many species rely on intertidal
habitats for foraging, yet the distribution and conservation status of these habitats across Australia remain poorly understood.
Here, we utilised freely available satellite imagery to produce the rst map of intertidal habitats across Australia. We
estimated a minimum intertidal area of 9856 km
2
, with Queensland and Western Australia supporting the largest areas.
Thirty-nine percent of intertidal habitats were protected in Australia, with some primarily within marine protected areas
(e.g. Queensland) and others within terrestrial protected areas (e.g. Victoria). Three percent of all intertidal habitats were
protected by both marine and terrestrial protected areas. To achieve conservation targets, protected area boundaries must
align more accurately with intertidal habitats. Shorebirds use intertidal areas to forage and supratidal areas to roost, so a
coordinated management approach is required to account for movement of birds between terrestrial and marine habitats.
Ultimately, shorebird declines are occurring despite high levels of habitat protection in Australia. There is a need for a
concerted effort both nationally and internationally to map and understand how intertidal habitats are changing, and how
habitat conservation can be implemented more effectively.
Received 29 April 2015, accepted 24 December 2015, published online 10 March 2016
Introduction
Migratory shorebird populations are declining rapidly across
continental Australia (Clemens et al.2016), and also locally in
many places including Tasmania (Cooper et al.2012; Reid and
Park 2003), South Australia (Close 2008; Paton et al.2009),
Victoria (Minton et al.2012; Rogers and Gosbell 2006), the east
of the country (Nebel et al.2008; Wilson et al.2011) and in
Western Australia (Creed and Bailey 2009; Rogers et al.2011).
Based on the severity of their declines and a high likelihood
that threatening processes are continuing, both Eastern Curlew
(Numenius madagascariensis) and Curlew Sandpiper (Calidris
ferruginea) were recently up-listed to Critically Endangered
under the Environment Protection and Biodiversity Conservation
Act 1999 (EPBC Act; Department of the Environment 2015a,
2015b). At a broader scale, similar declines have also been
reported across the East AsianAustralasian Flyway (EAAF;
Amano et al.2010). This is particularly troubling as not only
does the EAAF have the greatest number of threatened species
and the largest number of shorebird populations among the
worldsyways, it also has the least information on conservation
status (Amano et al.2010; International Wader Study Group
2003; Wilson et al.2011). Therefore, the EAAF is arguably the
yway in greatest need of conservation evaluation and action
(Amano et al.2010).
The majority of migratory shorebirds rely on intertidal habitats
for foraging (Galbraith et al.2002), dened here as the area
between the high and low waterline (Murray et al.2012). Long
distance migrations are energetically demanding (Blem 1990),
and shorebirds must feed rapidly and store fat reserves before,
during and after migration to ensure survival and reproduction
(Drent and Piersma 1990). Relative to other habitat types, inter-
tidal habitats are limited to a narrow strip along the coastline,
leaving the species these habitats support vulnerable to extinction
(Lee and Jetz 2011; Purvis et al.2000). For migratory shorebirds,
the likelihood that a particular site will sustain large numbers of
birds is strongly correlated with the area of available intertidal
habitat, a key factor inuencing the availability of benthic prey
organisms (Evans and Dugan 1983; Galbraith et al.2002). Loss
CSIRO PUBLISHING
Emu
http://dx.doi.org/10.1071/MU15046
Journal compilation BirdLife Australia 2016 www.publish.csiro.au/journals/emu
of intertidal habitats could reduce the carrying capacity of a site,
decreasing the number of birds in an area and increasing the
risk of local extinctions (Iwamura et al.2013; Sheehy et al.2011;
Sutherland and Anderson 1993).
Currently, migratory shorebirds are considered a matter of
national environmental signicance under the EPBC Act, owing
to their inclusion in bilateral migratory bird agreements with
China, Japan, and the Republic of Korea. Any development or
activity likely to cause signicant impact must be assessed under
the EPBC Act (Department of the Environment 2013), where the
concept of important habitatsplays a crucial role in protecting
shorebirds. Important habitats in Australia for migratory shore-
birds under the EPBC Act include those recognised as nationally
or internationally important, based on criteria adopted under the
Ramsar Convention on Wetlands (1971, available at http://www.
ramsar.org/sites/default/les/documents/library/scan_certied_e.
pdf, accessed 4 February 2016). According to this convention,
wetland habitats should be considered internationally important
if they regularly support 1% of the individuals in a population, or
a minimum of 20 000 individuals of all species combined. Na-
tionally important habitats can be dened using a similar approach
if they regularly support 0.1% of the EAAF population of a single
species, 2000 migratory shorebirds, or 15 migratory shorebird
species (Clemens et al.2010). However,with no formal evaluation
of the distribution and protection of intertidal habitats in Australia,
it remains difcult to assess how well such criteria are performing.
Mapping the occurrence and protection of intertidal habitats
is critical given their restricted distribution and importance to
migratory shorebirds. Indeed, formal evaluation of the distribu-
tion and extent of intertidal habitats will provide valuable data to
help assess the impact of alternative coastal development plans on
shorebird populations. Conserving intertidal habitats requires an
understanding of habitat distribution, as well as extent and current
levels of protection by both marine and terrestrial protected areas.
However, mapping intertidal habitats can be complicated using
any form of eld survey, airborne or satellite remote sensing, as
the waterline is highly dynamic, inundating the habitat once or
twice per day and exposing it to a varying extent. Although many
habitats have been effectively mapped in Australia, the distribu-
tion and status of intertidal habitats at a national scale, aside from
mangroves and saltmarsh, remain unknown below a resolution of
10 km
2
(http://www.ozcoasts.gov.au/nrm_rpt/habitat_extent.jsp,
accessed 20 January 2016).
Recent advances in the availability of satellite image archives
and multi-temporal image analysis techniques have led to the
development of a method for mapping the distribution of inter-
tidal habitats at continental scales (Murray et al.2012). This has
paved the way for a regional status assessment of tidal at habitats
in the Yellow Sea (Murray et al.2014; Murray et al.2015).
Murray et al.(2014) demonstrated that intertidal habitats in the
Yellow Sea have declined by 65% in the last ve decades, and by
28% since the 1980s. However, there is little information on
the extent of intertidal habitats outside the Yellow Sea. Here, we
use the methodology developed by Murray et al.(2012) to create
the rst map of intertidal habitats for Australia, and assess the
extent to which intertidal habitats are protected by marine and
terrestrial protected areas. This mapping (i) enables a better
understanding of the distribution and protection of intertidal
habitats in Australia, (ii) forms an exemplar for the development
of continent wide tidal at maps in other parts of the world, and
nally (iii) helps identify critical shorebird habitat at a national
scale.
Methods
The method we used to map the extent and distribution of
intertidal habitats in Australia was based on a continental-scale
mapping project conducted across Asia by Murray et al.(2012,
2014). We rst obtained the complete metadata of the freely
available Landsat Archive from USGS Earth Explorer (http://
earthexplorer.usgs.gov, accessed 20 January 2016). We con-
strained our analysis to the years spanning 19992014, to max-
imise coverage and permit the identication of images acquired
at low tidal elevations (see Fig. S1, available online as supple-
mentary material). We identied all Landsat images that inter-
sected the Australian coastline. Using the Tide Model Driver
(TMD) Matlab toolbox for tide modelling, we estimated the tidal
elevation at the time of image acquisition with the Indian Ocean,
Tasmania, and Northern Australia tide models available from the
Oregon State University suite of tide models (Egbert and Ero-
feeva 2002; Padman and Erofeeva 2005). Images acquired within
the upper and lower 10% of the tidal range were downloaded and
visually reviewed before being selected for the nal remote
sensing analysis. For Landsat images not available via Earth
Explorer, due to extensive cloud cover or other problems, we
obtained the ortho-corrected Landsat Archive images from Geo-
science Australia and the Department of Environmental Resource
Management (Filmer et al.2010). Image pre-processing, sorting
and pairing for intertidal mapping, followed the procedure in
Murray et al.(2012).
The nal image set consisted of 99 pairs of Landsat scenes
over 79 path-row footprints of 185 km 170 km each, with 170
Enhanced Thematic Mapper Plus (ETM+), and 28 Landsat
Thematic Mapper (TM) satellite images (Fig. S1). The mean
difference in acquisition time between high and low tide image
pairs was 1.49 1.18 years. The Normalised Differenced Water
Index (NDWI; McFeeters 1996) and, where possible, the Mod-
ied Normalised Differenced Water Index (MNDWI; Xu 2006)
were calculated for each pixel to maximise the likelihood of
differentiating between water and non-water areas, irrespective of
the substrate or benthos (McFeeters 1996;Xu2006). Each image
was then classied into a binary land/water image by manually
assigning a threshold that most effectively identied the waterline
in each image. Images were discarded if a suitable threshold could
not be found that consistently identied the waterline throughout
the image. The classied high and low tide images in each pair
were then differenced, resulting in a delineation of intertidal
habitats as the difference between the two input images (Murray
et al.2012). For further detail on the NDWI differencing method
refer to Murray et al.(2012).
The intertidal areas identied from all Landsat images were
merged to create the rst estimate of the intertidal habitat distri-
bution across Australia at a 30 m resolution (full dataset can be
found in Dhanjal-Adams et al.2015). Post-processing was
necessary to remove incorrectly classied pixels (Murray et al.
2012; Murray et al.2014). False positive classication errors
occurred both landward and seaward in many images. In part,
these were due to seasonal changes in water presence, such as
BEmu K. L. Khanjal-Adams et al.
ooding and inland ephemeral wetlands inland appearing in one
image but not the other, but most errors occurred when ocean was
classied as intertidal. Such errors resulted from cloud cover,
water turbidity, algal blooms and whitewash from waves being
classied as land, thus affecting the classication output. Such
limitations are inherent in delimiting tidal at and open water
features, but are easily corrected during post-processing (Liu et al.
2012; McFeeters 1996; Ryu et al.2002;Xu2006).
We completed an accuracy assessment on the nal intertidal
habitat map to measure classication error, by comparing the
mapped dataset with a reference set using a confusion matrix
(Congalton and Green 2008; Roelfsema and Phinn 2013). Using
stratied random sampling, we generated 204 sample locations
within 10 km of the coastline and within intertidal habitats. Each
point was assessed by an independent reviewer and labelled as
belonging to one of the two classes (intertidalor other)to
create a reference dataset based on a combination of ground-truth
information, including low tide Landsat imagery, Google Earth
imagery and Esri imagery. For each point, the mapped data were
extracted from the intertidal habitat map created in this study.
Then, using the mapped data and the reference dataset, we
populated a confusion matrix (Fig. S2) and quantied the map
category, users and producers accuracy, as well as the map
overall accuracy (Congalton and Green 2008).
Users accuracy represents the probability that a pixel on the
map is correctly classied as intertidal. Producers accuracy
represents a measure of omission error, i.e. the probability a pixel
was missed by the classication (Congalton and Green 2008).
Individual users accuracy for the intertidal class was 100% and
for the other class was 91.2% (Fig. S2), i.e. all the pixels in the
intertidal class were intertidal, but some pixels in the other class
were also intertidal. The producers accuracy for the intertidal
class was 91.9%, and for the other class was 100% (Fig. S2), i.e.
some intertidal habitats were found in the ocean class, while no
ocean was found in the intertidal class. This resulted in an overall
accuracy of 95.6%, which is well above the commonly cited
acceptable Landsat scale mapping accuracy level of 85% (Con-
galton and Green 2008; Foody 2009). These small errors highlight
false negative classication errors, where not all intertidal habitats
were picked up during the mapping process. These errors were,
in part, due to striping on Landsat ETM+ imagery as a result of
a sensor malfunction after May 2003, causing some images to
miss 22% data. We applied the standard approach used to
minimise striping, by merging 15 years of classication maps
together (Markham et al.2004). False negative classication
errors (omission errors) were also, in part, due to the image
selection process. To maximise the number of images used in
the analysis with the aim of maximising coverage, we used images
taken within 10% of the high and low tide, not the highest or
lowest possible tides. Therefore, small strips of intertidal habitats
were missing on the landward and seaward sides of the correctly
mapped intertidal habitats. Although we used highly accurate tide
models, errors were likely to remain in the tide predictions due
to tidal variation across the extent of each Landsat image, as
well as variability in timing of Landsat imagery. By combining
multiple images, these errors were again minimised. For further
discussion of errors associated with this remote sensing method,
refer to Murray et al.(2012).
Finally, to determine the level of protection of mapped
intertidal habitat, we acquired data from the Collaborative Aus-
tralian Protected Area Database (CAPAD) for 2014 (http://www.
environment.gov.au/land/nrs/science/capad/2014, accessed 20
January 2016) and estimated the area of intertidal habitats
protected by marine protected areas, terrestrial protected areas,
or both.
Results
Our map of the intertidal habitats of Australia achieved 91%
coverage of the Australian coastline with an overall classication
accuracy of 95.6% at a 30 m resolution (Table 1; Fig. S2).
However, 9% of the coastline remained unmapped particularly
in Western Australia (Fig. 1). Roebuck Bay for example, an
internationally and nationally important shorebird site was not
mapped due to a lack of good quality images of the area.
We identied a minimum total of 9856 km
2
of intertidal
habitat across Australia (Figs 1and 2; Table 1). The states with
the largest areas of intertidal habitat were, in decreasing order,
Queensland, Western Australia, Northern Territory and South
Australia with >0.2 km
2
per mapped kilometre of coastline
(Table 1; Fig. 1). Intertidal habitats were largely concentrated in
estuaries, embayed coastlines and areas protected by coral reefs
(Figs 1and 2).
Intertidal habitats were generally very well covered by pro-
tected areas, with 39% of all intertidal habitats across Australia
overlapping marine or terrestrial protected areas (Table 1; Fig. 2).
The Northern Territory had the lowest level of protection at 6%
and Victoria the highest at 80% (Table 1). There was marked
Table 1. Distribution and protection of mapped intertidal habitats in Australia. PA, Protected Area
State Mapped coastline
in km (Percentage
of total coastline)
Total intertidal
habitat in km
2
Area of intertidal
habitat per km
of coastline
mapped (km
2
)
Total PA in km
2
(Percentage of total
intertidal habitat)
Marine PA only
in km
2
(Percentage
of total PA)
Terrestrial PA
only in km
2
(Percentage
of total PA)
Marine and
terrestrial PA in
km
2
(Percentage
of total PA)
NSW 3793 (100.00) 95.6 0.03 47.6 (49.7) 27.5 (58.0) 17.7 (35.1) 3.3 (6.9)
NT 10 384 (96.68) 2235.1 0.22 129.5 (5.8) 24.3 (18.8) 105.2 (81.2) 0.0 (0.0)
Qld 11 235 (97.54) 2682.1 0.24 1608.6 (60.0) 1513.4 (94.1) 73.0 (4.5) 22.2 (1.4)
SA 4709 (99.99) 925.8 0.20 616.1 (66.5) 530.5 (86.1) 20.3 (3.3) 65.2 (10.6)
Tas 4235 (87.10) 91.8 0.02 47.5 (51.7) 8.2 (17.3) 39.3 (82.7) 0.0 (0.0)
Vic 2404 (99.99) 231.7 0.10 185.6 (80.1) 0.0 (0.0) 185.6 (100.0) 0.0 (0.0)
WA 15 611 (80.15) 3593.4 0.23 1226.1 (34.1) 659.6 (53.8) 555.1 (45.3) 11.3 (0.9)
Australia 52 372 (91.08) 9855.6 0.19 3860.9 (39.2) 2763.8 (71.6) 995.3 (25.8) 101.9 (2.6)
Intertidal habitats in Australia Emu C
variation in whether intertidal habitats were primarily represented
in marine or terrestrial protected areas. For example, of the
protected intertidal habitat in Queensland, 96% occurred
exclusively within marine protected areas. Yet in Victoria, only
terrestrial protected areas covered intertidal habitat (Table 1;
Fig. 2). Furthermore, 3% of protected intertidal habitats in
Australia were covered by both marine and terrestrial protected
areas, with up to 11% overlap between marine and terrestrial
protected areas in South Australia (Table 1; Fig. 2).
Discussion
We present the rst high spatial resolution map of intertidal
habitats in Australia, determining that intertidal habitats have a
minimum total area in Australia of 9856 km
2
(Table 1; Figs 1and
2). About 39% of the total extent of intertidal habitat is covered
by protected areas (Table 1; Fig. 2), suggesting these habitats are
well represented within the Australian protected area network.
This information is crucial for assessing how Australias coastal
protected area networks are contributing towards global targets
such as Aichi Target 11, laid out under Goal C of the Strategic Plan
for Biodiversity (https://www.cbd.int/sp/, accessed 20 January
2016) suggesting that 10% of coastal and marine environments
be protected by 2020.
We discovered large differences in the extent to which inter-
tidal habitats are protected among states, with some states pro-
tecting over 60% of their intertidal area (Victoria, South Australia
and Queensland), and others less than 6% (Northern Territory;
Table 1; Fig. 2). The lowest levels of protection however occurred
in the Northern Territory, where some of the largest numbers of
shorebirds (Chatto 2003; Clemens et al.2016) and largest areas of
intertidal habitats (0.22 km
2
/km mapped coastline; Table 1) were
observed. The Northern Territory is however aiming to increase
the exploitation of energy and mineral resources (Northern
Territory Government 2013), and low levels of protection could
be detrimental to already declining shorebird populations
if development is not planned strategically.
Variations between states probably highlight differences in
protected area designation and management, potentially as a
result of the socio-political context. Queensland, for instance,
has particularly high levels of protection as a result of the Great
Barrier Reef being designated as a United Nations Educational,
10°0'0"S20°0'0"S30°0'0"S40°0'0"S
120°0'0"E110°0'0"E 130°0'0"E 140°0'0"E 150°0'0"E 160°0'0"E
10°0'0"S20°0'0"S30°0'0"S40°0'0"S
120°0'0"E 130°0'0"E 140°0'0"E 150°0'0"E
Area of intertidal habitat in square kilometres
0–1
1–10
10–50
50–166
Landsat footprint 0 280 560 1120 km
Tasmania
Western
Australia
Northern
Territory
South
Australia
New South
Wales
Queensland
N
Victoria
Fig. 1. Net area of intertidal habitats across Australia mapped at a 14 km grid resolution. (For colour gure, see online version
available at http://www.publish.csiro.au/nid/17.htm.)
DEmu K. L. Khanjal-Adams et al.
Scientic and Cultural Organization (UNESCO) World Heritage
Site. However, it is unclear how such designations can benet
shorebirds when they are not specically targeted at shorebird
management.
Some intertidal habitats were primarily managed as part of a
marine protected area, while others as part of a terrestrial protected
area (Table 1; Fig. 2). There is a clear potential for such differ-
ences to lead to inadequate management, as terrestrial protected
areas might not always prioritise their marine environments
and marine parks might underplay the importance of supratidal
habitats that function as shorebird breeding or roosting sites
(Department of Environment Water and Natural Resources
2014; Department of National Parks Recreation Sport and Racing
2014; Department of Parks and Wildlife 2014; Department of
Primary Industries Parks Water and Environment 2014;Ofce of
Environment and Heritage 2014; Parks and Wildlife Commission
of the Northern Territory 2014; Parks Victoria 2014). Further-
more, some intertidal habitats are managed under both marine
and terrestrial protected area designations (Table 1; Fig. 2). In
Australia, this occurs for 3% of all protected intertidal habitats.
In South Australia in particular, where there are large areas of
intertidal habitats (0.20 km
2
per km of coastline mapped; Table 1),
10% fall under the jurisdiction of both terrestrial and marine
protected areas. Such overlap could lead to confusion, with
neither management agency taking full responsibility for the
conservation of intertidal habitats and the shorebirds reliant on
them. Alternatively, overlap has the potential to lead to better
protection when both agencies manage intertidal habitats togeth-
er. Indeed, shorebirds move between intertidal habitats to forage
and inland wetlands to roost, so combined management of
terrestrial and marine environments will be critical for ensuring
healthy shorebird populations. There is a strong need for sus-
tained collaboration between terrestrial and marine protected area
managers, as well as other stakeholders, to ensure that protected
area boundaries align more sensibly with intertidal habitats to
benet shorebirds. Accurate, spatially comprehensive maps de-
rived from satellite imagery such as ours are therefore important
for identifying habitat, delineating protected area boundaries,
and facilitating targeted management of migratory shorebirds in
intertidal habitats.
Shorebirds congregate in large numbers in roost sites, which
can be readily identied as important habitat under the EPBC Act,
but disperse during feeding. Densities while foraging in intertidal
areas are typically far lower, making it more difcult to delineate
important habitat, because the birds rarely concentrate in suf-
ciently large numbers to trigger the criteria. Such conservation
criteria are therefore often inappropriate for protecting intertidal
habitats from developments, despite their importance to shore-
120°0'0"E 130°0'0"E 140°0'0"E 150°0'0"E
120°0'0"E110°0'0"E 130°0'0"E 140°0'0"E 150°0'0"E 160°0'0"E
0 285 570 1140 km
Western
Australia
South
Australia
Northern
Territory
Queensland
New South
Wales
Victoria
Tasmania
10°0'0"S20°0'0"S30°0'0"S40°0'0"S
10°0'0"S20°0'0"S30°0'0"S40°0'0"S
Terrestrial PA
Terrestrial and marine PA
Unprotected
Marine PA
Primary source of protection
N
Fig. 2. Primary source of protection of intertidal habitats across Australia mapped at a 14 km grid resolution. PA, Protected Area.
Intertidal habitats in Australia Emu E
birds. In such cases, determination of important habitat could
usefully occur at a broader scale, for example with all intertidal
habitats within an important estuarine system being classied as
important habitat. Not all shorebirds rely on intertidal habitats,
and such criteria also apply to supra-tidal habitats, including
saltworks and ephemeral wetlands, which are critically important
for shorebirds in Australia. Intertidal habitat usage both inside
and outside of protected areas needs to be formally assessed for
all nationally important shorebird species, as not all intertidal
habitats are used equally by different species. Finally, greater
understanding of how protected areas are designated and regu-
lated is needed, and how these vary between states is an important
step towards coordinating management at the national scale.
Ultimately, protection of intertidal habitats across Australia
remains essential to the long-term conservation of EAAF shore-
bird species. However, shorebirds are declining across Australia
despite the apparent high level of protection of intertidal habitats
(Clemens et al.2016). There is mounting evidence that these
declines are driven by loss of intertidal habitats from migratory
stop-over sites outside Australia, such as the Yellow Sea (Ma et al.
2014; MacKinnon et al.2012; Moores et al.2008; Murray et al.
2014). Any threat impacting such restricted habitats, particularly
in stop-over sites, is likely to have a disproportionate effect on
abundance (Iwamura et al.2013; Sheehy et al.2011; Sutherland
and Anderson 1993). Mapping of the Yellow Sea, for example,
has already revealed declines of 65% in extent of tidal ats in
the last ve decades (Murray et al. 2014). It remains unclear to
what degree these changes in habitat availability are being
mirrored throughout the EAAF. Mapping of intertidal habitats
is urgently needed across the entire yway to inform coordinated
protection of shorebirds and to identify population bottlenecks
during migration. Well managed and well connected intertidal
habitats across the yway are essential if we are to prevent further
migratory shorebird extinctions within our lifetimes.
Acknowledgements
We thank Tony Gill and staff from the Joint Remote Sensing Research
Program for their time, expertise, and access to data and computer processing
systems. Landsat data are freely available from the United States Geological
Survey and pre-processed and ortho-rectied Landsat images were provided
by Geoscience Australia and the Department for Environmental Resource
Management. We also thank Amelie Corriveau, Maggie McKeown and
Madeleine Stigner for their involvement in the mapping component of the
project. This project was supported by project CA130019 of the Gladstone
Ports Corporation Limited, and an Australian Research Council Linkage
Grant LP150101059, co-funded by the Burnett-Mary Regional Group, the
Queensland Department of Environment and Heritage Protection, and
the Queensland Wader Study Group. Additional support was provided by
Birds Queensland, and the CSIRO Climate Adaptation Flagship.
References
Amano, T., Szekely, T., Koyama, K., Amano, H., and Sutherland, W. J.
(2010). A framework for monitoring the status of populations: an example
from wader populations in the East AsianAustralasian yway. Biological
Conservation 143(9), 22382247. doi:10.1016/j.biocon.2010.06.010
Blem, C. (1990). Avian energy storage. Current Ornithology 7(5), 59113.
Chatto, R. (2003) The Distribution and Status of Shorebirds Around the Coast
and Coastal Wetlands of the Northern Territory. Parks and Wildlife
Commission of the Northern Territory, Palmerston, Australia.
Clemens, R. S., Weston, M. A., Haslem, A., Silcocks, A., and Ferris, J. (2010).
Identication of signicant shorebird areas: thresholds and criteria.
Diversity & Distributions 16(2), 229242. doi:10.1111/j.1472-4642.
2009.00635.x
Clemens, R. S., Hansen, B. D., Rogers, D., Gosbell, K., Minton, C., Straw, P.,
Bamford,M., Woehler, E. J., Milton, D., Weston,M., Venables, B.,Weller,
D., Purnell,C., Hassell, C., Rutherford,B., Onton, K., Herrod,A., Skilleter,
G., and Fuller, R. A. (2016). Local changes in shorebird abundance in
Australia driven largely by external factors. Emu doi:10.1071/MU15056
Close, D. H. (2008). Changes in wader numbers in the Gulf St Vincent, South
Australia, 19792008. Stilt 54,2427.
Congalton, R. G., and Green, K. (2008) Assessing the Accuracy of
Remotely Sensed Data: Principles and Practices.(CRC press: Boca
Raton, FL)
Cooper, R., Clemens, R., Oliveira, N., and Chase, A. (2012). Long-term
declines in migratory shorebird abundance in northeast Tasmania. Stilt
61,1929.
Creed, K. E., and Bailey, M. (2009). Continuing decline in wader populations
at Pelican Point, Western Australia, since 1971. Stilt 56,1014.
Department of Environment Water and Natural Resources (2014) Annual
Report 20132014. Department of Environment, Water and Natural
Resources, Adelaide, Australia.
Department of National Parks Recreation Sport and Racing (2014) Annual
Report 20132014. Brisbane, Australia.
Department of Parks and Wildlife (2014) 20132014 Annual Report. Ken-
sington, Australia.
Department of Primary Industries Parks Water and Environment (2014)
Annual Report 20132014. Hobart, Australia.
Department of the Environment (2013) Matters of National Environmental
Signicance. Australian Government, Department of the Environment,
Canberra, Australia.
Department of the Environment (2015a)Calidris ferruginea (Curlew Sand-
piper) Conservation Advice. Australian Government, Department of the
Environment, Canberra, Australia.
Department of the Environment (2015b)Numenius madagascariensis (East-
ern Curlew) Conservation Advice. Australian Government, Department
of the Environment, Canberra, Australia.
Dhanjal-Adams, K. L., Hanson, J. O., Murray, N. J., Phinn, S. R., Wingate,
V. R., Mustin, K., Lee, J. R., Allan, J. R., Cappadonna, J. L., Studds, C. E.,
Clemens, R. S., Roelfsema, C. M., and Fuller, R. A. (2015) Mapped
distribution of intertidal habitats in Australia between 1999 and 2014.
Available at http://doi.pangaea.de/10.1594/PANGAEA.845726 [Veri-
ed 20 January 2015].
Drent, R., and Piersma, T. (1990) An exploration of the energetics of leap-frog
migration in Arctic breeding waders. In Bird Migration.(Ed. E Gwinner)
pp. 399412. (Springer Berlin Heidelberg)
Egbert, G. D., and Erofeeva, S. Y. (2002). Efcient Inverse Modeling of
Barotropic Ocean Tides. Journal of Atmospheric and Oceanic Technology
19(2), 183204. doi:10.1175/1520-0426(2002)019<0183:EIMOBO>2.
0.CO;2
Evans, P., and Dugan, P. (1983) Coastal birds: numbers in relation to food
resources. In Coastal Waders and Wildfowl in Winter.(Eds P Evans,
J Goss-Custard and W Hale) pp. 828. (Cambridge University Press:
Cambridge)
Filmer, M. S., Featherstone, W. E., and Kuhn, M. (2010). The effect of
EGM2008-based normal, normal-orthometric and Helmert orthometric
height systems on the Australian levelling network. Journal of Geodesy
84(8), 501513. doi:10.1007/s00190-010-0388-0
Foody, G. M. (2009). Sample size determination for image classication
accuracy assessment and comparison. International Journal of Remote
Sensing 30(20), 52735291. doi:10.1080/01431160903130937
Galbraith, H., Jones, R., Park, R., Clough, J., Herrod-Julius, S., Harrington, B.,
and Page, G. (2002). Global climate change and sea level rise: potential
losses of intertidal habitat for shorebirds. Waterbirds 25(2), 173183.
doi:10.1675/1524-4695(2002)025[0173:GCCASL]2.0.CO;2
FEmu K. L. Khanjal-Adams et al.
International Wader Study Group (2003). Are waders world-wide in
decline? Reviewing the evidence. Wader Study Group Bulletin 101
(102), 812.
Iwamura, T., Possingham, H. P., Chadès, I., Minton, C., Murray, N. J., Rogers,
D. I., Treml, E. A., and Fuller, R. A. (2013). Migratory connectivity
magnies the consequences of habitat loss from sea-level rise for shore-
bird populations. Proceedings of the Royal Society B: Biological Sciences
280(1761), 20130325. doi:10.1098/rspb.2013.0325
Lee, T. M., and Jetz, W. (2011). Unravelling the structure of species extinction
risk for predictive conservation science. Proceedings of the Royal Society
B: Biological Sciences 278(1710), 13291338. doi:10.1098/rspb.2010.
1877
Liu, Y., Song, P., Peng, J., and Ye, C. (2012). A physical explanation of the
variation in threshold for delineating terrestrial water surfaces from multi-
temporal images: Effects of radiometric correction. International Journal
of Remote Sensing 33(18), 58625875. doi:10.1080/01431161.2012.
675452
Ma, Z., Melville, D. S., Liu, J., Chen, Y., Yang, H., Ren, W., Zhang, Z.,
Piersma, T., and Li, B. (2014). Rethinking Chinas new great wall.
Science 346(6212), 912914. doi:10.1126/science.1257258
MacKinnon, J., Verkuil, Y. I., and Murray, N. J. (2012) IUCN Situation
Analysis on East and Southeast Asian Intertidal Habitats, with Particular
Reference to the Yellow Sea (Including the Bohai Sea). IUCN, Gland,
Switzerland and Cambridge, UK.
Markham, B. L., Storey, J. C., Williams, D. L., and Irons, J. R. (2004). Landsat
sensor performance: History and current status. Geoscience and Remote
Sensing.IEEE Transactions on Geoscience and Remote Sensing 42(12),
26912694.
McFeeters, S. (1996). The use of the Normalized Difference Water Index
(NDWI) in the delineation of open water features. International Journal
of Remote Sensing 17(7), 14251432. doi:10.1080/01431169608948714
Minton, C., Dann, P., Ewing, A., Taylor, S., Jessop, R., Anton, P., and
Clemens, R. (2012). Trends of shorebirds in Corner Inlet, Victoria,
19822011. Stilt 61,318.
Moores, N., Rogers, D., Koh, C. H., Ju, Y. K., Kim, R. H., and Park,
M. N. (2008) The 20062008 Saemangeum Shorebird Monitoring Pro-
gram Report. Birds Korea, Busan, South Korea.
Murray, N., Phinn, S., Clemens, R., Roelfsema, C., and Fuller, R. A. (2012).
Continental scale mapping of tidal ats across East Asia using the Landsat
archive. Remote Sensing 4(12), 34173426. doi:10.3390/rs4113417
Murray, N. J., Clemens, R. S., Phinn, S. R., Possingham, H. P., and Fuller,
R. A. (2014). Tracking the rapid loss of tidal wetlands in the Yellow Sea.
Frontiers in Ecology and the Environment 12(5), 267272. doi:10.1890/
130260
Murray, N. J., Ma, Z., and Fuller, R. A. (2015). Tidal ats of the Yellow Sea:
A review of ecosystem status and anthropogenic threats. Austral Ecology
40(4), 472481. doi:10.1111/aec.12211
Nebel, S., Porter, J. L., and Kingsford, R. T. (2008). Long-term trends in
shorebird populations in eastern Australia and impacts of freshwater
extraction. Biological Conservation 141(4), 971980. doi:10.1016/j.bio
con.2008.01.017
Northern Territory Government (2013) Northern Territory Economic Devel-
opment Strategy, Northern Territory Government, Darwin, Australia.
Ofce of Environment and Heritage (2014) Annual Report 20132014.
Ofce of Environment and Heritage, Sydney, Australia.
Padman, L., and Erofeeva, S. (2005) Tide Model Driver (TMD) Manual
Earth & Space Research. Available at http://www.esr.org/polar_tide_
models/README_TMD.pdf [Veried 27 January 2016].
Parks and Wildlife Commission of the Northern Territory (2014) Annual
report 20132014. Alice Springs, Australia.
Parks Victoria (2014) Parks Victoria Annual Report 20132014. Melbourne,
Australia.
Paton, D., Rogers, D., Hill, B., Bailey, C., and Ziembicki, M. (2009).
Temporal changes to spatially stratied waterbird communities of the
Coorong, South Australia: Implications for the management of heterog-
enous wetlands. Animal Conservation 12(5), 408417. doi:10.1111/
j.1469-1795.2009.00264.x
Purvis, A., Gittleman, J. L., Cowlishaw, G., and Mace, G. M. (2000).
Predicting extinction risk in declining species. Proceedings. Biological
Sciences 267(1456), 19471952. doi:10.1098/rspb.2000.1234
Reid, T., and Park, P. (2003). Continuing decline of Eastern Curlew, Nume-
nius madagascariensis, in Tasmania. Emu 103(3), 279283. doi:10.1071/
MU00079
Roelfsema, C., and Phinn, S. (2013) Validation. In Coral Reef Remote
Sensing.(Eds JA Goodman, SJ Purkis and SR Phinn) pp. 375401.
(Springer Netherlands)
Rogers, K. G., and Gosbell, K. (2006). Demographic models for red-necked
stint and curlew sandpiper in Victoria. Stilt 50, 205214.
Rogers, D. I., Hassell, C. J., Boyle, A., Gosbell, K., Minton, C. D. T., Rogers,
K. G., and Clarke, R. H. (2011). Shorebirds of the Kimberley Coast
Populations, key sites, trends and threats. Journal of the Royal Society
of Western Australia 94, 377391.
Ryu, J.-H., Won, J.-S., and Min, K. D. (2002). Waterline extraction from
Landsat TM data in a tidal at: a case study in Gomso Bay, Korea. Remote
Sensing of Environment 83(3), 442456. doi:10.1016/S0034-4257(02)
00059-7
Sheehy, J., Taylor, C. M., and Norris, D. R. (2011). The importance of
stopover habitat for developing effective conservation strategies for
migratory animals. Journal für Ornithologie 152(Suppl. 1), 161168.
doi:10.1007/s10336-011-0682-5
Sutherland, W. J., and Anderson, C. W. (1993). Predicting the distribution
of individuals and the consequences of habitat loss: The role of prey
depletion. Journal of Theoretical Biology 160(2), 223230. doi:10.1006/
jtbi.1993.1015
Wilson, H. B., Kendall, B. E., Fuller, R. A., Milton, D. A., and Possingham,
H. P. (2011). Analyzing variability and the rate of decline of migratory
shorebirds in Moreton Bay, Australia. Conservation Biology 25(4),
758766. doi:10.1111/j.1523-1739.2011.01670.x
Xu, H. (2006). Modication of normalised difference water index (NDWI) to
enhance open water features in remotely sensed imagery. International
Journal of Remote Sensing 27(14), 30253033. doi:10.1080/014311606
00589179
Intertidal habitats in Australia Emu G
www.publish.csiro.au/journals/emu
... Although the training dataset includes labeled water points, the sand-sea boundary classification needs to be addressed correctly. To this aim, we followed an approach commonly adopted in the literature, where a band associated with a spectral index is used to create a mask for the land and detect the shoreline (e.g., [5,9,[21][22][23][24][25][26][27][28]). The selection of the most suitable spectral index to discriminate land from water is highly debated in the literature. ...
... Kelly and Gontz [7] indicate the MNDWI as the best index and several works have successfully adopted it [9,29]. Nevertheless, to the best of our knowledge, most of the literature works have preferred the NDWI so far [21][22][23][26][27][28]. As Sagar et al. [26] report, the combined use of NIR and a visible band allows for the effective detection of the waterline, despite the high environmental (and spectral) variability of intertidal zones. ...
Article
Full-text available
In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses.
... To obtain instantaneous tide heights for each pixel in our satellite time series, we used the OTPS TPXO 8 global tide model Erofeeva, 2002, 2010) to model tide heights at the exact time of each satellite image acquisition. TPXO 8 has been validated to <0.12 m root mean square error (RMSE) accuracy against tide gauges in Australia (Rogers et al., 2017), and has been previously used for mapping intertidal extent and elevation at continental scale (Murray et al., 2012;Dhanjal-Adams et al., 2016;Sagar et al., 2017;Bishop-Taylor et al., 2019b). Tide heights were modelled using a 2 × 2 km grid covering the entire Australian coastal zone study region (Fig. 2b). ...
... Improved shoreline mapping in macrotidal environments will likely require improvements to the tidal modelling framework used to assign tide heights to each pixel in the 32-year Landsat time series. We employed the TPXO 8 global tidal model Erofeeva, 2002, 2010) in this study, a model previously used to map intertidal extent and topography using earth observation data at continental scale (Murray et al., 2012;Dhanjal-Adams et al., 2016;Sagar et al., 2017;Bishop-Taylor et al., 2019b). Seifi et al. (2019) compared TPXO 8 and nine other global tide models against satellite altimetry and coastal tide gauges across north-eastern Australia, finding that modelling accuracy varied significantly between models in response to local bathymetry and distance offshore. ...
Article
Full-text available
Accurate, robust and consistent coastline mapping is critical for characterising and managing coastal change. Satellite earth observation provides an unparalleled source of freely available data for studying dynamic coastlines through time and across large spatial extents. However, previous satellite-derived shoreline mapping approaches have been challenged by two key limitations: the medium spatial resolution of freely available satellite data, and the confounding influence of tides that can obscure longer term patterns of coastal change. In this study we present Digital Earth Australia Coastlines, a new continental dataset documenting three decades of coastal change across Australia. We combine sub-pixel waterline extraction with a new pixel-based tidal modelling method to seamlessly map almost 2 million km of tide-datum shorelines along the entire Australian coast from 1988 to 2019. Our tidally-constrained median composite approach maps the dominant annual position of the shoreline at 0 m Above Mean Sea Level each year, suppressing the short-term influence of tides and sub-annual shoreline variability. Using this robust mid-term shoreline proxy, long-term coastal change rates spanning the last three decades were accurately quantified and mapped at the continental scale. We find that 22% of Australia's non-rocky coastline has retreated or grown significantly since 1988, with 16% changing at greater than 0.5 m per year. Although trends of retreat and growth were closely balanced across the Australian continent, our results highlight significant regional variability and extreme local hotspots of coastal change. Our findings provide new insights into patterns and trends of coastal change across Australia, and highlight advantages and limitations of tide modelling and composite-based methods for extracting consistent shoreline data and long-term coastal trends from earth observation data at continental scale. Digital Earth Australia Coastlines is made available to the public as free and open interactive tools and code to support future coastal research and management across Australia, and any coastal region globally with access to free and open medium resolution satellite data.
... They provide unique ecosystem services, for instance, defensing against storm surge, maintaining shoreline, filtering pollutant, and promoting carbon storage (Barbier et al. 2008;Deegan et al., 2000). They also serve as feeding grounds for migrating birds, spawning and nursery habitats for fishes and other marine wildlife (Dhanjal-Adams et al. 2016;Ghosh et al. 2016). However, as one of the most ecologically and economically important ecosystems, tidal flats are vulnerable in the world (Mitch and Gosselink 2007;Tiner 2013). ...
... However, due to data availability, these approaches would be difficult to be applied to large areas. With increased capacities in image acquisition, storage, and computing power, studies have been reported in mapping tidal flats over large areas, such as in coastal Australia (Dhanjal-Adams et al. 2016;Sagar et al. 2017), as well as at continental and global scales (Murray et al. 2014;Murray et al. 2019). Generally, methodologies of tidal flat mapping derived from optical imagery can be divided into three approaches, including tide model or terrain-based (Murray et al. 2012;Han et al. 2019), training sample-based machine learning (Zhang et al., 2013;Zhang et al. 2019), and knowledge-based decision tree (Wang et al., 2020a;Wang et al., 2020b). ...
Article
Tidal flats are threatened by tidal reclamation and climatic changes around the world. Particular challenges exist in China where tidal flats are changing rapidly along with accelerated economic development in coastal regions. The unique and important ecosystem functions and services that tidal flats provide in coastal regions warrant the necessary of mapping such a particular land cover type in high precision and accuracy. Existing national tidal flat maps of China, which were derived from the 30-m resolution Landsat imagery and auxiliary data, are insufficient to support practical management efforts. In this study, in order to produce an accurate tidal flat map with finer spatial resolution, we employed 28,367 scenes of time series Sentinel-2 images acquired in 2019 and 2020 along the entire coastal line of China. The short revisit cycle (2–5 days) of the Sentinel-2 improved the opportunities of obtaining the highest and lowest tide images, and the finer spatial resolution (10-m) enhanced the capacity of precision tidal flat extraction. A rapid, robust, and automated tidal flat mapping approach is essential to large-scale applications. In this study, we developed an approach by integrating the maximum spectral index composite (MSIC) and the Otsu algorithm (OA), and so named MSIC-OA. By GEE platform, we automated the execution of MSIC-OA to Sentinel-2 images, and produced an up-to-date 10-m spatial resolution tidal flat map of China (China_Tidal Flat, CTF). Validated by massive field-based observations and selected edge-points, the CTF map achieved an overall accuracy of 95% and the F1 score of 0.93. As we calculated, the total area of tidal flats in China was 858,784 ha, and Jiangsu Province accounted the largest proportion (24%) of the national total. This study is the first attempt to delineate tidal flats automatically at a 10-m spatial resolution. The CTF map can provide essential information for management of coastal ecosystems and facilitate the implementations of coastal and marine related Sustainable Development Goals.
... Overlap between PA designation and jurisdiction types suggests the potential for conflicting management goals (Margules & Pressey 2000;Dhanjal-Adams et al. 2016). Overlapping PAs of different jurisdictions or designations may compete for funding, undertake conflicting management actions, or inefficiently use their resources (Deguignet et al. 2017). ...
... Overlapping PAs of different jurisdictions or designations may compete for funding, undertake conflicting management actions, or inefficiently use their resources (Deguignet et al. 2017). Furthermore, the narrowness and position of the coastal zone between realms presents the situation that neither marine nor terrestrial PAs were developed to prioritize tidal flat conservation (Dhanjal-Adams et al. 2016). Alternatively, jurisdictional overlap affords the opportunity for coordinated landscape-level management and could provide additional benefits to tidal flat protection if all jurisdictions make a concerted effort to protect these areas (Stoms et al. 2005 ...
Article
Full-text available
Tidal flats are a globally distributed coastal ecosystem important for supporting biodiversity and ecosystem services. Local to continental-scale studies have documented rapid loss of tidal habitat driven by human impacts, but assessments of progress in their conservation are lacking. We analysed human pressure on tidal flats, and measured their representation in protected areas using a newly developed, internally-consistent estimate of distribution and change for the world's tidal flats. We discovered that 68% of the current extent of tidal flats is subject to moderate to very high human pressure (Human Modification Index > 0.1), but that 31% of tidal flat extent occurred within protected areas, far exceeding percent protection of the marine (6%) and terrestrial (13%) realms. Net change of tidal flat extent inside protected areas was similar to tidal flat net change outside protected areas between 1999 and 2016. Substantial shortfalls in tidal flat protection occurred across Asia, where large intertidal extents coincide with high to very high human pressure (Human Modification Index > 0.4-1), and net tidal flat losses up to 86.4 km² (83.9 km²-89.0 km²; 95% confidence interval) occurred inside individual protected area boundaries within the study period. Taken together, our results show substantial progress in protected area designation for tidal flats globally, but that protected area status alone does not prevent all habitat loss. Safeguarding the world's tidal flats will thus require deeper understanding of the factors that govern their dynamics and effective policy that promotes holistic coastal and catchment management strategies. Article impact statement: 31% of the world's tidal flats are protected, but more is needed to counter human pressure and avoid further global tidal flat loss. This article is protected by copyright. All rights reserved.
... Remoting-sensing methods based on satellite images could monitor land cover and its changes in near real time and over extensive ranges. Using satellite imagery, land cover mapping concerned with tidal flats has been achieved at local [7,[20][21][22][23], national [2][3][4]8,[24][25][26], continental [10,27], and global scales [1]. At the same time, various tidal flat mapping methodologies have been proposed. ...
Article
Full-text available
Tidal flats are one of the most productive ecosystems on Earth, providing essential ecological and economical services. Because of the increasing anthropogenic interruption and sea level rise, tidal flats are under great threat. However, updated and large-scale accurate tidal flat maps around the Bohai and Yellow Seas are still relatively rare, hindering the assessment and management of tidal flats. Based on time-series Sentinel-2 imagery and Google Earth Engine (GEE), we proposed a new method for tidal flat mapping with the Normalized Difference Water Index (NDWI) extremum composite around the Bohai and Yellow Seas. Tidal flats were derived from the differences of maximum and minimum water extent composites. Overall, 3477 images acquired from 1 Oct 2020 to 31 Oct 2021 produced a tidal flat map around the Bohai and Yellow Seas with an overall accuracy of 94.55% and total area of 546,360.2 ha. The resultant tidal flat map at 10 m resolution, currently one of the most updated products around the Bohai and Yellow Seas, could facilitate the process of sustainable policy making related to tidal flats and will help reveal the processes and mechanisms of its responses to natural and human disturbance.
... The waterline was extracted in an automated process utilising an adaptive thresholding method on the NIR band. Multitemporal image analysis requires adaptive thresholds for segmenting an image (Dhanjal-Adams et al., 2016;Liu and Jezek, 2010;Murray et al., 2012;Sagar et al., 2017;Sousa et al., 2018) due to the varying nature of atmospheric noise and interference (Liu et al., 2012). Although atmospheric correction methods have been shown to minimise the temporal variations in atmosphere, correction techniques are recognised to be imperfect (Ji, Zhang, and Wylie, 2009). ...
... The quality and the inconsistent availability of input images at various temporal and spatial scales has posed significant challenges to land cover mapping (Gong et al., 2013;Hansen et al., 2000). Utilizing a sun-synchronous satellite such asSentinel-2 and Landsat, it is inevitable that the sensor will only observe a portion of the full tidal range at any location, with less frequent observations at the extremely low and hightides (Dhanjal-Adams et al., 2016;Sagar et al., 2017). In addition, mNDWI plus AEWlsh were used to detect pure vegetation, pure tidal flat, and pure water in the study so that mixed pixels. ...
Preprint
Full-text available
Based on the cloud platform of Google Earth Engine (GEE), this study selected Landsat 5/8 and Sentinel-2 remote sensing images and used Support Vector Machine (SVM) classification method to classify the 35 years of intertidal salt marshes in China, and verified the classification results in combination with field survey. Finally, combining with various driving factors, the reasons and laws affecting the changes of salt marshes species and area were discussed and analyzed. The main results of the study are as follows:The main types of salt marshes plants in China include Phragmites australis, Spartina alterniflora, Suaeda salsa, Scirpus mariquete, Tamarix chinensis, Cyperus malaccensis and Sesuvium portulacastrum. The results salt marshes classification indicated that 166999.32 ha in 1985, 172893.87 ha in 1990, 174952.29 ha in 1995, 125567.51 ha in 2000, 93257.97 ha in 2005, 102539.04 ha in 2010, 96302.92 ha in 2015, and 115722.75 ha in 2019. The main driving factors of salt marsh change from 1985 to 2015 are reclamation, mudflat aquaculture, climate change, coastal zone erosion, invasion of alien species, and natural competition and succession among salt marshes species. The results can be used to quantitatively analyze the salt marshes carbon storage in space and time, and provide data support for the protection of salt marsh wetlands, the restoration of ecological functions and the implementation of "carbon neutral".
Article
Full-text available
Population estimates are widely used to underpin conservation decisions. However, determining accurate population estimates for migratory species is especially challenging, since they are often widespread and it is rarely possible to survey them throughout their full distribution. In the East Asian-Australasian Flyway (EAAF), this problem is compounded by its size (85 million square kilometres) and the number of migratory species it supports (nearly 500). Here, we provide analytical approaches for addressing this problem, presenting a revision of the EAAF population estimates for 37 migratory shorebird species protected under Australian national environmental legislation. Population estimates were generated by (i) summarising existing count data in the non-breeding range, (ii) spatially extrapolating across uncounted areas, and (iii) modelling abundance on the basis of estimates of breeding range and density. Expert review was used to adjust modelled estimates, particularly in under-counted areas. There were many gaps in shorebird monitoring data, necessitating substantial use of extrapolation and expert review, the extent of which varied among species. Spatial extrapolation to under-counted areas often produced estimates that were much higher than the observed data, and expert review was used to cross-check and adjust these where necessary. Estimates of population size obtained through analyses of breeding ranges and density indicated that 18 species were poorly represented by counts in the non-breeding season. It was difficult to determine independently the robustness of these estimates, but these breeding ground estimates were considered the best available data for ten species, that mostly use poorly-surveyed freshwater or pelagic habitats in the non-breeding season. We discuss the rationale and limitations of these approaches to population estimation, and how they could be modified for other applications. Data available for population estimates will vary in quality and extent among species, regions and migration stage, and approaches need to be flexible enough to provide useful information for conservation policy and planning.
Article
Mapping the topography of intertidal zones through in situ methods has many challenges associated with it and airborne techniques for determining the elevation profile are expensive. High temporal and spatial coverage provided by satellite remote sensing makes it highly efficient in assessing the extent and profile of intertidal zone even in remote and inaccessible areas. In this study, a semi-automated and scalable methodology is presented for generating the digital elevation model of the intertidal zone of Gulf of Kutch, India, thus bridging the existing gaps between terrestrial and marine elevation datasets. Time series multispectral data from the Landsat series are explored in the tidal height realm, and a median composting approach is used with NDWI and subsequent thresholding to effectively determine the extent of the intertidal zone. The median composite is used to handle the presence of noise or artefacts, thus ensuring the use of high-quality data and eradicating the problems associated with single-scene approaches previously used for such analysis. Using the capabilities of cloud-based computing platforms and openly accessible satellite data, this study aims to map the extent and 3D distribution of intertidal flats in a hostile coastal environment.
Technical Report
Full-text available
Seagrass meadows are important marine ecosystems, providing a range of services including carbon sequestration, nursery habitats for fish and coastal protection. They are suffering rapid global decline in the face of eutrophication and other pollution, damage caused by fishing activities, tourism and coastal development. Degradation and loss of seagrass meadows negatively impacts their ability to provide ecosystem services, which are often of vital importance to resource-poor communities such as local fishers who depend on seagrass ecosystems for sustenance and income. Community-based management (CBM) presents an opportunity for effective, efficient and socially just conservation of seagrass. Payments for Ecosystem Services (PES) has been used in other ecosystems as a model to support community-based conservation but its application to seagrass meadows is in a very early stage. Community-based PES involving seagrass meadows would involve parties (buyers) making payments to communities or their representatives in exchange for management measures (implementation, restriction or adaptation of certain activities) that can be shown to enhance or ensure the delivery of seagrass ecosystem services. Here, the opportunities and challenges associated with community-based seagrass conservation, particularly PES-based, are discussed; we draw on experience in community-based PES projects in similar settings, but with different ecosystems, such as mangroves, and use carbon as the exemplar service (since a global market exists for carbon trading). Recommendations are made on how community-based seagrass conservation is best facilitated through policy mechanisms and tools.
Article
Full-text available
Decreases in shorebird populations are increasingly evident worldwide, especially in the East Asian–Australasian Flyway (EAAF). To arrest these declines, it is important to understand the scale of both the problem and the solutions. We analysed an expansive Australian citizen-science dataset, spanning the period 1973 to 2014, to explore factors related to differences in trends among shorebird populations in wetlands throughout Australia. Of seven resident Australian shorebird species, the four inland species exhibited continental decreases, whereas the three coastal species did not. Decreases in inland resident shorebirds were related to changes in availability of water at non-tidal wetlands, suggesting that degradation of wetlands in Australia’s interior is playing a role in these declines. For migratory shorebirds, the analyses revealed continental decreases in abundance in 12 of 19 species, and decreases in 17 of 19 in the southern half of Australia over the past 15 years. Many trends were strongly associated with continental gradients in latitude or longitude, suggesting some large-scale patterns in the decreases, with steeper declines often evident in southern Australia. After accounting for this effect, local variables did not explain variation in migratory shorebird trends between sites. Our results are consistent with other studies indicating that decreases in migratory shorebird populations in the EAAF are most likely being driven primarily by factors outside Australia. This reinforces the need for urgent overseas conservation actions. However, substantially heterogeneous trends within Australia, combined with declines of inland resident shorebirds indicate effective management of Australian shorebird habitat remains important.
Article
Full-text available
The tidal flats of the Kimberley coast support the largest populations of migratory shorebirds in Australia. In this paper we review and discuss population sizes of all 41 shorebird species occurring on the Kimberley coastlines, and summarise the importance of the region in an international context. The Kimberley coastline is used by c. 3.7 million shorebirds, including c. 635,000 migrants from the northern hemisphere and c. 16,000 Australian-bred resident shorebirds which forage on the tidal flats of the Kimberley coast. A further c. 3.06 million migratory shorebirds from near-coastal grasslands (Oriental Plover, Little Curlew and Oriental Pratincoles) use roosts on the Kimberley coast at times. Most coast-dependent shorebirds of the Kimberley are concentrated in a small number of sites. Eighty-mile Beach and Roebuck Bay are the most important two sites; they have the highest numbers of birds, and the greatest diversity of species occurring in internationally significant numbers. Internationally important numbers of several species occur on some offshore islands (Adele Island, Ashmore Reef and the Lacepedes), including several species (e.g. Lesser Sand Plover, Grey Plover, Grey-tailed Tattler and Ruddy Turnstone) which are disproportionately abundant on offshore islands when compared to the mainland. Although most of the key shorebird sites on the Kimberley coast are remote and have not been greatly affected by humans, there are indications that populations of many migratory species on the Kimberley coast are declining, probably because of habitat loss in the east Asian areas where they stage on migration. Continued and enhanced monitoring of shorebirds in the Kimberley that contributes strategically to the conservation management of this group is strongly recommended.
Article
Full-text available
Tidal flats provide ecosystem services to billions of people worldwide, yet their changing status is largely unknown. In the Yellow Sea region of East Asia, tidal flats are the principal coastal ecosystem fringing more than 4000 km of the coastlines of China, North Korea and South Korea. However, widespread loss of areal extent, increasing frequency of algal blooms, hypoxic dead zones and jellyfish blooms, and declines of commercial fisheries and migratory bird populations suggest that this ecosystem is degraded and declining. Here, we apply the International Union for Conservation of Nature Red List of Ecosystems criteria to the Yellow Sea tidal flat ecosystem and determine that its status is endangered. Comparison of standardized remotely sensed habitat data and historic topographic map data indicated that in the last 50 years, a decline of more than 50% but less than 80% of tidal flat extent has occurred (criterion A1). Although restricted to a narrow band along the coastline, Yellow Sea tidal flats are sufficiently broadly distributed to be classified as least concern under criterion B. However, widespread pollution, algal blooms and declines of invertebrate and vertebrate fauna across the region result in a classification of endangered (C1, D1). Owing to the lack of long-term monitoring data and the unknown impacts of severe biotic and abiotic change, the ecosystem was scored as data deficient for Criterion E and several subcriteria. Our assessment demonstrates an urgent need to arrest the decline of the Yellow Sea tidal flat ecosystem, which could be achieved by (i) improved coastal planning and management at regional and national levels, (ii) expansion of the coastal protected area network, and (iii) improved managed of existing protected areas to reduce illegal land reclamation and coastal exploitation.
Article
Full-text available
This paper examines the proposition that juvenile percentages of waders in Australia, as routinely monitored from cannon-netting catches, are good indices of breeding success. Simple demographic models are developed for Red-necked Stint and Curlew Sandpiper in Victoria. The models estimate the survival rate which maximises the correlation between the annual model predictions and population monitoring program counts since 1978/79 in Victoria. The overall correspondence is remarkably good although there are instances of substantial differences. Reasons for these differences are discussed. Overall, the results support the monitoring of juvenile percentages and the population monitoring program as effective methods to monitor wader populations in Australia. Comparison of true survival rates estimated in the model with apparent survival estimates obtained in 1995, and a sensitivity analysis, suggest that the long-term decline in Curlew Sandpiper numbers in Victoria is more likely to be due to reduced adult survival rates than to breeding failures or mortality between fledging and capture some six months later.
Article
Full-text available
Evidence of long-term declines in migratory shorebird populations is reported at two areas in north-east Tasmania.In north-east Tasmania, both George Town Reserve and Cape Portland have featured in National Wader Countssince 1981, although observations go back to the early 1970’s. Compared with the extreme north-west of Tasmania and with many mainland study sites, wader numbers in north-east Tasmania are never large, whichmakes for relatively easier counting. At George Town, count data indicate long-term population declines from1974 to 2011 in Eastern Curlew, ( Numenius madagascariensis ), Ruddy Turnstone ( Arenaria interpres ), CurlewSandpiper ( Calidris ferruginea ), and Bar-tailed Godwit ( Limosa lapponica ). George Town has also seen adecrease in the number of migratory shorebird species recorded each year, a drop on average from nine to seven,while Cape Portland has seen a larger drop in migratory shorebird species richness from eleven to six. CapePortland has also experienced long-term declines from 1981 to 2011 in Ruddy Turnstone and Curlew Sandpiper.The reduction in species richness in both areas relates to historically uncommon species no longer being recordedsuch as Red Knot ( Calidris canutus ), Lesser Sand Plover ( Charadrius mongolus ), Greater Sand Plover ( Charadrius leschenaultia ), Grey-tailed Tattler ( Tringa brevipes ), Terek Sandpiper ( Xenus cinereus ) and GreyPlover ( Pluvialis squatarola ). Trends derived from these two north-east Tasmanian areas are similar to those beingreported more widely in Australia, with growing numbers of migratory shorebirds showing evidence of long-term population declines. Threats to the foraging areas of both study sites, which have the potential to compromise their viability, are outlined. The volume of data available from these areas will allow for more detailed analyses infuture
Article
Full-text available
In the Yellow Sea region of East Asia, tidal wetlands are the frontline ecosystem protecting a coastal population of more than 60 million people from storms and sea-level rise. However, unprecedented coastal development has led to growing concern about the status of these ecosystems. We developed a remote-sensing method to assess change over ~4000 km of the Yellow Sea coastline and discovered extensive losses of the region’s principal coastal ecosystem – tidal flats – associated with urban, industrial, and agricultural land reclamations. Our analysis revealed that 28% of tidal flats existing in the 1980s had disappeared by the late 2000s (1.2% annually). Moreover, reference to historical maps suggests that up to 65% of tidal flats were lost over the past five decades. With the region forecast to be a global hotspot of urban expansion, development of the Yellow Sea coastline should pursue a course that minimizes the loss of remaining coastal ecosystems.
Article
Summarizes the present state of knowledge regarding the relationship between grazing wildfowl and their food supply, and considers the complex interactions between shorebirds and their food supplies by reviewing the factors that affect the ability to meet energy requirements and the relationship between bird density and food supply. Finally, considers our inability to predict the effects of intertidal reclamation on the numbers of waders.-from Authors
Chapter
Adequate understanding of the validity of information contained in coral reef remote sensing products is required to support research and management decisions. This chapter introduces accuracy measures commonly applied to two types of coral related maps: discrete (e.g., benthic cover type) and continuous (e.g., percent coral cover). A critical review of 80 coral reef remote sensing mapping publications presents the approaches and metrics commonly used to measure accuracy. The literature review shows that few studies report accuracy information at all, and when obtained, ‘overall accuracy’ was the most commonly used accuracy measure. Variations in accuracy levels were not only a result of actual differences in map accuracy, but are likely also due to: spatial complexity of benthic features present in the study area; distribution of the calibration and validation samples relative to each other; and the level of detail measured for each sample. As a result, accuracy measures from different studies should be compared with caution and with due attention to how the measures were derived. This chapter enables scientist and managers to understand, design and interpret validation procedures for image-based maps of coral reef environments.
Chapter
This chapter introduces visible and infrared remote sensing, specifically photographic, multispectral and hyperspectral imaging systems (Chaps. 2–4), and the situations in which they do and don’t work for mapping and monitoring coral reefs. Spectral dimensions of imaging sensors are explained, along with their fundamental control on the amount and type of information able to be mapped on coral reefs from airborne and satellite sensors. A specific set of coral reef biophysical environmental variables capable of being mapped by visible and infrared imaging systems is also defined. Examples are provided of image processing approaches that deliver science and management relevant data for monitoring coral reefs.
Article
China's position as the world's second largest economy is largely due to its rapid economic growth in the coastal region, which composes only 13% of China's total land area, yet contributes 60% of the gross domestic product (GDP). To create extra land for the rapidly growing economy, coastal wetlands have been enclosed by thousands of kilometers of seawalls, whose length exceeds that of China's famous ancient “Great Wall” (see photos and map). This new “Great Wall,” covering 60% of the total length of coast-line along mainland China ( 1 ), caused a dramatic decline in internationally shared biodiversity and associated ecosystem services and will threaten regional ecological security and sustainable development. Here, we outline these problems, analyze the drivers behind wetland reclamation, and propose measures for effective wetland management.