Urban Growth on Coastal Erosion Vulnerable Stretches - e-Geo

Journal of Coastal Research
SI 56
1567 - 1571
ICS2009 (Proceedings)
Portugal
ISSN 0749-0258
Urban Growth on Coastal Erosion Vulnerable Stretches
P. Pinto†, P. Cabral‡, M. Caetano‡ and M. F. Alves†
† CESAM – Centro Estudos do Ambiente e do Mar
Universidade de Aveiro, Aveiro
3810-193 Aveiro, Portugal
[email protected]; [email protected]
‡ ISEGI - Instituto Superior de Estatística e Gestão da Informação
Universidade Nova de Lisboa, Lisboa
1070-312, Portugal
[email protected]; [email protected]
ABSTRACT
PINTO, P., CABRAL, P., CAETANO, M. and ALVES, M.F., 2009. Urban growth on coastal erosion vulnerable
stretches. Journal of Coastal Research, SI 56 (Proceedings of the 10th International Coastal Symposium), 1567 –
1571. Lisbon, Portugal, ISSN 0749-0258.
Coastal zones are unique and irreversible ecosystems on a human scale. Its delicate balance is currently under
threat by the intensive use of coastal spaces, particularly by the artificial land uses in these coastal areas.
In addition to the artificialization of coastal zones, these areas face a range of additional problems of human and
biophysical nature. The most widespread problem is coastal erosion, which results from a combination of natural
factors and that is aggravated by human action.
From an integrated coastal zone management perspective, it is essential to frame the compatibilization of adverse
factors and interests so that the development model of these areas doesn’t compromise the use of coastal
resources by future generations. In this context, the application of predictive models that allow for the ex-ante
impact assessment of spatial planning policies and options is an important tool to support decisions as it reduces
the degree of uncertainty surrounding these decisions.
In this study we use satellite imagery and remote sensing techniques to evaluate, through an urban growth model,
the land use/cover changes occurred over the period 1996 to 2006. We also combine a coastal erosion
vulnerability model and an urban growth simulation model to assess the implications of coastal erosion on
artificial land uses in the Ovar-Mira (central Portugal) stretch by the year 2030.
ADITIONAL INDEX WORDS: Decision making support, Remote sensing, Coastal erosion, LUCC models
INTRODUCTION
In 1990 the Intergovernmental Panel on Climate Change
(IPCC) exposed, for the first time, coastal zones as particularly
vulnerable areas to the climatic changes, mainly in what refers to
the sea level rise (ALVES, 2006).
The intense population’s increase, the expansion of the
industrial spaces, the impacts of human activities made on river
basins and for coastal protection, the increase of beach tourism,
the climatic changes, among others factors, compose the main
problems of the coastal areas. According to report The Changing
Face of Europe's Coastal Areas (EEA, 2006), the speeding up use
of coastal spaces, threats the delicate balance of these ecosystems.
The population density of these areas is, on average, 10% more
than in the interior areas. This value could arrive at 50% in some
countries. More preoccupying is the fact that conversion rates of
natural coastal areas in artificial areas has been stronger than
population’s increase, reaching in some places, of which
Portuguese coast is an example, about 45% of the total area of this
stretch (ALVES et al., 2007).
The coastal erosion process is one of the most visible problems
that affect coastal zones. According to DIAS and PEREIRA (1994),
the generalized erosive process and the consequent shoreline
retreat it’s due to a wide range of factors. They have pointed out
the sea level rise, the decrease of sediments supplied to the coastal
stretch, the anthropogenic degradation of the natural structures and
the heavy works for coastal protection.
COELHO (2005) considers that evaluating energetic sea actions
exposure of coastal zones is not possible without a good forecast
capacity. However, it considers that the degree of knowledge and
modelling capacity for coastal phenomena’s is still limited.
Abstract representations (models) of land uses changes, mainly
those caused by urban land expansion, have been studied by a
wide number of scientists for the past years. The use of models is
essential for analysis and simulation of the urban evolution
dynamics (SILVA, 2002).
Land use & cover change (LUCC) models have registered a fast
development in the scientific domain (PONTIUS and CHEN, 2006),
supported by an international project - named LUCC - that aimed
to promote knowledge exchange about land use/cover change
dynamics and its relationship with environmental changes at a
global level (IGBP and IHDP, 2005). In 1995, appears the
Geomod, a tool that belongs to dynamic models group which
operates over space dimension. Initially conceived to analyze the
impact of the deforestation on carbon emissions to atmosphere, the
model, based on a raster data structure, simulates the space pattern
of the alterations occurred between two land cover categories (e.g.
1=”urban” and 2=”non urban”). Geomod also allow going forward
and backward through time.
Paying attention to the enormous pressure that coastal
settlements have felt in the past recent years, definition of urban
growth sceneries for these areas is figured as an important
instrument to support decision making, in a way that these
methods allow the visualization of the planning policies impacts.
In this domain, geographic information systems (GIS), which are a
Journal of Coastal Research, Special Issue 56, 2009
1567
Urban growth on coastal territories
powerful tool for storage, editing, integration, extraction,
visualization and analysis of spatial data (CABRAL, 2001),
constitute an excellent method for develop and validate options in
order to decide conscientiously.
The present paper aims to achieve a confrontation scenario of
urban growth, with the definitions of vulnerability and risk of the
coastal zone to energetic sea actions. The main goal is to find
possible conflict situations, maintaining the actual land user/cover
change pattern till 2030.
STUDY AREA
The case study is situated in the central Portuguese coast,
specifically, the six coastal municipalities of the Ria de Aveiro
Municipality Association (AMRIA). This area (Figure 1) has a
shoreline extension of 65 km, going north to south: Ovar,
Murtosa, Aveiro, Ílhavo, Vagos and Mira.
Figure 1. Study area: Ovar-Mira stretch
The analysis of the resident population evolution shows an
increase trend. Between 1991 and 2006 the population grew
14,09%, between 2001 and 2006, 3,86%, corresponding to
191242, 210089 and 218189 inhabitants for each moment of time.
This coastal stretch, marked for a presence of a long and fragile
dune field, low height sand beaches and developed through deltas,
is considered one of the most dynamic type of coast (COELHO,
2005). Several scientific reports and official studies have assumed
this stretch as one of the most preoccupying in terms of coastal
erosion (BARBOSA, 2003; CEHIDRO and INAG, 1998; DIAS and
PEREIRA, 1994; HIDROTÉCNICA PORTUGUESA et al., 1998; and
VELOSO-GOMES et al., 2004).
Not newly, manifestations of coastal erosion in this stretch have
come, each time, more frequent and with burdensome
consequences for the population. The strong proliferation number
of hydroelectric dams in Douro’s River is pointed out as the main
cause for the raising shoreline retreat rates, involving, most of the
cases, an increase of coastal erosion events (SILVA et al., 2007).
METHODS
The methodology adopted in this work is based, generally, in
three stages. In the first phase, we derive land cover maps for the
years 1990 and 2006. Based on these maps, we aim to simulate
urban growth for 2030. On a second moment, coastal zone
vulnerability conditions to energetic sea actions are defined. In the
last phase, we set against information created in the previous two
steps. On one side, we enter information derived through urban
growth models and, on the other side we take in consideration
inherent coastal erosion vulnerability conditions. The main goal is
trying to understand the implications of urban development over
coastal erosion risk areas.
The built-up areas map for 1990 is based, integrally, in the
Carta de Ocupação do Solo 1990 (COS'90) at a scale 1:25000
within Minimum Mapping Unit (MMU) of 1ha. It matters refer
that the nomenclature adopted on this cartographic product
separates 860 land use/cover units, however, the reference
nomenclature in the Europe (for land planning proposes) is the
same as used in CORINE Land Cover (CLC 2000) program,
which separates 44 classes. As a result, we use a conversion
scheme in order to adapt and compare the maps of 1990 with the
most recent ones (i.e. 2000).
Extraction of land use/cover information from satellite imagery
is one of the most representative applications of remote sensing
techniques (GONZÁLEZ, 2001; JOTHIMANI, 1997; LU et al., 2006;
O' HARA et al., 2003; SETO et al., 2002; YANG and LIU, 2005). The
up growing use of these techniques in detriment of most
conventional ones (e.g. aerial photography, photo-interpretation or
data collecting directly in the field) is due to a wide number of
reasons: it’s cheaper than other methods, allows faster
cartographic production and makes possible the acquisition of
large and inaccessible areas (NUNES et al., 2007, SANTOS, 2003).
In this way, 2006 urban map is derived from spring and summer
IRS P6/LISS-III high space resolution satellite imagery, dated
from 26/05/2006 and 06/08/2006. The classification is reached
through the application of maximum likelihood classifier, at pixel
level over summer and spring images. An attempt to improve the
quality of the resulting classification, more precisely the
elimination of misclassification urban areas and a MMU 1ha
guarantee, we apply analysis masks over the “urban” and “nonurban” binary images. In order to evaluate the overall accuracy
(OA) of the classification, is launched a stratified random
sampling at a pixel level which shows an OA=83,3%. The
complexity associated to the urban areas classification could be
justified for the high spectral heterogenity inherent to this class
(SMALL, 2005).
A model can be understood as an abstract representation of an
object, phenomena or real process, which makes possible an
increase of knowledge through the experimentation (CLARKE,
2003). The diversity of LUCC models is undeniable. In fact, the
definition of urban growth scenario for 2030 is reached through a
model that simulates land use/cover changes between two classes
(i.e “urban” and “non-urban”): the Geomod. This model requires,
as input information, spatial raster data for the initial moment t=0
and the number of cells (surface) of both land use/cover classes at
the validation moment, t=1. Based on linear extrapolation, it
simulates a new raster image that represents both land use/cover
classes at the final moment, t=2.
In order to model transitions between land uses/cover classes,
it’s absolutely necessary a thorough knowledge of the factors that
induce this changes in a given time series. These variables
represent, most of the times, natural and socioeconomics
characteristics in a given area (ALMEIDA et al., 2005 and VERBURG
et al., 2004). In this way, suitability maps derivation is an
excellence vehicle to integrate information related to the urban
growth factors in a given region. Indeed, Geomod enables the
integration these maps which reflect the inherent suitability of a
given cell/pixel, to change from a land use/cover class just to
another. This suitability is expressed in a range of values – “0”
express low suitabilities while “255” express high suitabilities.
Journal of Coastal Research, Special Issue 56, 2009
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Pinto et al.
A land use/cover map derived from satellite imagery is used for
the model validation. According to PONTIUS and CHEN (2006), the
overall quality of maps must be assessed through the simultaneous
use of three statistical indicators. Comparing the 2006 simulated
map with a real 2006 map we achieve a Kappa=0,91, a
Klocation=0,91, and a Khisto=1,00, that shows us a high-level
proximity between simulation and reality. Going forward into the
2030 urban growth simulation we need to determine the expected
amount cells/pixels related to each one of the land use/cover types.
In the present work, follows the PONTIUS and CHEN (2006)
suggestion: a simple linear extrapolation based on the values of
the year 1990 and 2006.
Regarding the definition of the vulnerability conditions of this
coastal stretch to energetic sea actions, we follow the methodology
used in SECUR-Ria project (COELHO et al., 2006), which is based
on a model presented by COELHO (2005); COELHO and VELOSOGOMES (2005). This method determines the vulnerability of a
costal stretch through a weight combination of specific
environmental and man-made variables: vulnerability factors.
Analysing, as a whole, all the vulnerability factors is an
extremely complex task (COELHO and VELOSO-GOMES, 2005).
Indeed, the authors suggest an isolate assessment of each
parameter according to a scale of vulnerability: from “1”
representing “very low” vulnerabilities to “5” representing “very
high” vulnerabilities.
RESULTS
An analysis of land use/cover maps for the years 1990 and 2000
– based on the first level of the nomenclature of cartographic
database CLC1990 and CLC2000, defined at 1:100 000 mapping
scale and a MMU of 25ha – shows a positive increase rate on
“artificial areas” territories, going from 8,3% in 1990 to 10% of
the whole study area in 2000. This growth is mainly due to the
expansion of existing artificial areas and the coming out of new
ones, in the last case, over areas which belonged to “forest and
semi-natural” space class in 1990. Despite the effective loss of
surface, from 43% in 1990 to 41,7% in 2000, “forest and seminatural” areas still remain the most representative of this coastal
segment.
During the same period of time, but redirecting the analysis
towards the more detailed spaces classes which compose the
“artificial areas” category, we notice a growth trend based mainly
on “Industrial, commercial and transportation units” areas that
have increased 600 ha, and on “discontinuous urban fabrics” areas
with an extension of 510 ha.
Considering the binary maps of “urban” and “non-urban” land
for the years 1990 and 2006, we notice an effective increase on
urban areas that represent, in 2006, approximately 17% of the
entire surface of the study area – this same value didn’t reached
10% in 1990. If we assume a land use/cover trend as verified from
1990 to 2006, in 2030 urban areas will represent approximately
28% of the whole surface of this coastal stretch (Table 1).
Table 1: Urban/Non-urban surfaces in the study area.
Land use/cover
classes
Urban
Non-urban
Total
1990
ha
%
ha
%
ha
7592,6
9,7
70711,4
90,3
2006
13557,9
17,3
64746,1
82,7
78304
2030
22506,4
28,7
55797,6
71,3
Based in this information, it could be said that urban areas had
increase about 79% between 1990 and 2006. In the same way, it is
expected that from 2006 till 2030 these areas experience an
increased of 68%. The figure 2 shows the cartographic expression
of these values.
Figure 2. Urban land growth: 1990, 2006 and 2030
The application of SECUR-Ria model shows that about 80% of
the study area is defined as “very low” and “low” vulnerability
territories. The same model, only classifies 7% of this coastal
stretch as “high” and “very high” vulnerability areas. These spaces
could be found within a variable strip, at a maximum distance of
2000 meters measured from coastline.
Through the intersection of SECUR-Ria and land use/cover
2006 real map, the greatest proportion (25%) of urban areas is
over “high” vulnerability spaces.
This means that ¼ of these “high” vulnerable spaces represent
urban areas. Likewise, the expression of urban areas on “very
high” vulnerability territories is very small, about 3%. According
to the simulation for 2030, we notice a decrease in the amount of
urban cells/pixels over “high” vulnerable areas, as well as,
unpredictably, a significant increase of urban lands in “very low”
vulnerability areas, representing 34,8% of these spaces in 2030
while 18,5% in 2006 (Table 2).
Table 2: Urban lands over coastal erosion vulnerable areas.
2006
2030
Real map
Simulated map
Vul.
Level
Ur
N-Ur
Ur/S
Ur
N-Ur
Ur/S
(ha)
(ha)
(%)
(ha)
(ha)
(%)
8815
38937
18,5
16600
31152
34,8
VLow
2040
12841
13,7
2392
12489
16,1
Low
1421
8945
13,7
2585
7780
24,9
Mod
1267
3852
24,8
901
4219
17,6
High
2
69
2,9
6
65
8,4
VHigh
DISCUSSION AND CONCLUSION
In an integrated discussion perspective, coastal areas are unique
and non-renewable ecosystems at a human scale of time. The high
use of these areas threatens to destroy its delicate balance.
Population density of these areas is, on average, about 10% higher
than interior territories, fact which requires a much faster
conversion between natural areas and built-up spaces, in many
cases, stronger than population density increase. Portugal is no
exception to the current coastal urban development paradigm and,
Journal of Coastal Research, Special Issue 56, 2009
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Urban growth on coastal territories
in some places of the Portuguese coast the built-up areas could
reach 45% of the whole territory.
Taking into account the high constructive pressure that coastal
settlements have experienced in recent years, is crucial to
formulate scenarios of urban development which provide to
decision makers, well-founded information about the implications
of their planning options in terms of coastal areas management
(PINTO, 2008). Regardless urban development pressure, coastal
stretches suffer from other biophysics and man-made problems.
Due to the significant impacts on worldwide coastal areas, the
most significant problem is the coastal erosion, demanding
particularly attention from scientific community and politicians.
Coastal erosion is a process driven by the combination of
multiple natural factors, exasperated, directly or indirectly, by
human actions. The derivation of scenarios is essential to a correct
assessment of the exposure level of coastal zones to energetic sea
actions. Despite this fact the today’s knowledge on coastal erosion
processes is still far from perfect, being, for this reason, more
exposed to criticism. However, the application of SECUR-Ria
model to the Ovar-Mira stretch identify “very high” vulnerable
areas in small spaces along the coastline, representing a little part
of the study area.
Urban growth models, considered a variant of traditional land
use/cover change models (LUCC), are a powerful tool to analyse
the causes and consequences of changes, and also capable to
produce important support scenarios to decision makers. In the
present work, Geomod model, that was started with 1990 and
validated with 2006 maps, allows the projection of two land
use/cover classes – “urban” and “non-urban” – up to 2030. Based
on this simulation, we note a general increase of urban lands,
which represents in 2030 about 30% of the whole territory,
corresponding at a 66% growth between 2006 and 2030, and
196% between 1990 and 2030.
Overlapping the growth simulation of urban lands and the
vulnerability conditions of this stretch to coastal erosion, it’s
possible to draw some interesting conclusions. Between 2006 and
2030, the expected surface of urban land increases at all different
levels of vulnerability. However, the most significant increase
takes place into “very high” vulnerability areas, i.e., very close to
shoreline. On the other hand, is amongst “very low” and “low”
areas that we can find the largest proportion of urban lands in
2030.
Consequently, we emphasize the necessity and importance of a
strong intervention by decision makers, in an attempt to assess and
redirect their options and main policies on coastal areas
management, particularly through restricting the increase of builtup spaces over “high” and “very high” vulnerability territories.
Thus, we intend to collaborate to change this actual trend that
could put citizens and their goods in an imminent risk situation.
LITERATURE CITED
ALMEIDA, C.M.; MONTEIRO, A.M.V. and CÂMARA, G., 2005.
Modelos de Simulação e Prognósticos de Mudanças de Uso do
Solo Urbano: Instrumento para o Subsídio de Ações e
Políticas Públicas Urbanas. XI Encontro Nacional da
Associação Nacional de Pesquisa em Planejamento Urbano e
Regional - ANPUR (Bahia - Salvador).
ALVES, F., 2006. Gestão sustentável da zona costeira: contributos
para um modelo de avaliação. Aveiro, Portugal. Universidade
de Aveiro PhD thesis, 255p.
ALVES F; SILVA C. and PINTO P., 2007. The assessment of the
coastal zone development at a regional level – the case study
of Portugal central area. Journal of Coastal Research, No 50,
pp. 72-76
BARBOSA, J.L.P., 2003. Aplicação dos Sistemas de Informação
Geográfica na Zona Costeira. Porto, Portugal. Faculdade de
Engenharia, Universidade do Porto, Master thesis.
CABRAL, P., 2001. Sistemas Espaciais de Apoio à Decisão: O
sistema de apoio ao licenciamento da Direcção Regional do
Ambiente do Alentejo. Lisboa, Portugal. Instituto Superior
Técnico, Universidade Técnica de Lisboa, Master thesis.
CEHIDRO and INAG, 1998. Carta de Risco do Litoral. trecho 2:
Foz do Douro-Nazaré. Notícia Explicativa. p. 37 (Lisboa:
INAG).
CLARKE, K.C., 2003. The Limits of Simplicity: Toward
Geocomputational Honesty in Urban Modeling. 7th
International
Conference
on
GeoComputation,
GeoComputation (Ed.) (University of Southampton, UK:
GeoComputation), [CD-ROM ].
COELHO, C., 2005. Riscos de Exposição de Frentes Urbanas para
Diferentes Intervenções de Defesa Costeira. Aveiro, Portugal.
Universidade de Aveiro, PhD thesis.
COELHO, C.; ALVES, F.; COELHO, C. and PINTO, P., 2006. Definição
das condições de Risco de Cheia, Incêndios Florestais, Erosão
Costeira e Industriais na área de Intervenção da AMRia –
Relatório Final: Erosão Costeira. p. 48 (Aveiro:
UA(dAO)/AMRia).
COELHO, C. and VELOSO-GOMES, F., 2005. Classificação de
Vulnerabilidades e Riscos como Contributo no Planeamento
das Zonas Costeiras. III Congresso sobre Planeamento e
Gestão das Zonas Costeiras de Países de Expressão
Portuguesa, Maputo, Moçambique, [CD-ROM].
DIAS, J.M.A. and FERREIRA, Ó., 1994. Estudo Sintético de
Diagnóstico da Geomorfologia e da Dinâmica Sedimentar dos
Troços Costeiros entre Espinho e Nazaré (Universidade do
Algarve).
EEA, 2006. The changing faces of Europe's coastal areas. E.
Report (Ed.), p. 107 (Copenhagen: European Environment
Agency).
GONZÁLEZ, O.R., 2001. Assessing Vegetation and land Cover
Changes in Northeastern Puerto Rico: 1978-1995. Caribbean
Journal of Science, 37, pp. 95-106.
HIDROTÉCNICA PORTUGUESA; INAG and MAOTDR, 1998. Plano
de Ordenamento da Orla Costeira: Ovar-Marinha Grande.
Estudos de base - Usos e funções do território, p. 291
(Lisboa).
IGBP and IHDP, 2005. LUCC - Land-Use and Land-Cover
Change (URL: http://www.geo.ucl.ac.be/LUCC/lucc.html,
acceded on 24 de December 2008).
JOTHIMANI, P., 1997. Operational Urban Sprawl Monitoring using
Satellite Remote Sensing: Excerts from the Studies of
Ahmedabad, Vadodara and Surat, India.18th Asian
Conference on Remote Sensing (ACRS) (Malaysia: Asian
Association
on
Remote
Sensing),
(URL:
http://www.gisdevelopment.net/aars/acrs/1997/ts8/ts8005pf.ht
m, acceded on 20 de Setembro de 2008).
LU, S.; SHEN, X. and ZOU, L., 2006. Land cover change in Ningbo
and its surrounding area of Zhejiang Province, 1987~2000.
Journal of Zhejiang University SCIENCE A, pp. 1862-1775.
NUNES, A.; NUNES, V. and CAETANO, M., 2007. Utilização de
imagens de satélite para produção de mapas multiescala de
cortes e novas plantações florestais. V Conferência Nacional
de Cartografia e Geodesia, Lidel, Lisboa, 197-206.
O'HARA, C.; KING, J.; CARTWRIGHT, J. and KING, R., 2003.
Multitemporal Land Use and Land Cover Classification of
Urbanized Areas within Sensitive Coastal Environments.
IEEE Transactions on Geoscience and Remote Sensing, 40, 9,
pp. 2005-2014.
Journal of Coastal Research, Special Issue 56, 2009
1570
Pinto et al.
PINTO, P., 2008. Sistema de Apoio à Gestão das Zonas Costeiras:
aplicação de um modelo para simulação do crescimento
urbano no trecho Ovar-Mira. Lisboa, Portugal. Instituto
Superior de Estatística e Gestão da Informação, Universidade
Nova de Lisboa, Master thesis.
PONTIUS, R. and CHEN, H., 2006. GEOMOD Modeling - LandUse and Cover Change Modeling. (Clark University).
SANTOS, T.A.G., 2003. Actualização de Cartografia Temática com
Imagens de Satélite. Lisboa, Portugal. Universidade Técnica
de Lisboa, Master thesis.
SETO, K.C.; WOODCOCK, C.E.; SONG, C.; HUANG, X.; LU, J. and
KAUFMANN, R.K., 2002. Monitoring landuse change in the
Pearl River Delta using Landsat TM. International Journal of
Remote Sensing, 23, 10, pp. 1985-2004.
SILVA, E.A., 2002. Cenários de Expansão Urbana na Área
Metropolitana de Lisboa. Revista de Estudos Regionais - INE,
p. 72.
SILVA, R.; COELHO, C.; VELOSO-GOMES, F. and TAVEIRA-PINTO, F.,
2007. Dynamic numerical simulation of medium term coastal
evolution of the west coast of Portugal. Journal of Coastal
Research, SI 50, pp. 263-267.
SMALL, C., 2005. A global analysis of urban reflectance.
International Journal of Remote Sensing, 26, 4, pp. 661-681.
YANG, X. and LIU, Z., 2005. Using satellite imagery and GIS for
land-use and land-cover change mapping in an estuarine
watershed. International Journal of Remote Sensing, 26, 23,
pp. 5275- 5296.
VELOSO-GOMES, F.; TAVEIRA-PINTO, F. and IHRH, 2004.
EUrosion Case Study: Vagueira-Mira (Portugal). EUrosion, p.
18 (Porto: Instituto de Hidráulica e Recursos Hídricos ).
VERBURG, P.; SCHOT, P.; DIJST, M. and VELDKAMP, A., 2004. Land
use change modelling: current practice and research priorities.
GeoJournal, 61, pp. 309-324.
Journal of Coastal Research, Special Issue 56, 2009
1571