Multipurpose line for mapping coastal information using a data model ...

J Coast Conserv (2015) 19:461–474 DOI 10.1007/s11852-015-0400-1

Multipurpose line for mapping coastal information using a data model: the Andalusian coast (Spain) M. Fernandez-Nunez 1 & P. Díaz-Cuevas 1 & J. Ojeda 1 & A. Prieto 1 & N. Sánchez-Carnero 1,2

Received: 9 October 2014 / Revised: 7 June 2015 / Accepted: 19 June 2015 / Published online: 31 August 2015 # Springer Science+Business Media Dordrecht 2015

Abstract The concept of the coastline is key in coastal studies. However, its definition depends on the purpose and scale of the study. To determine the shoreline position, a set of criteria has to be chosen to define its position: the high tide mark, the base or top of a ridge, or cliff line, for example. Usually, these criteria are site-specific and often forgotten after digitisation. In this paper, a methodology is described for generating a multipurpose line within a spatial database. Here, the term ‘multipurpose line’ refers to a digital map structure holding diverse coastal data (natural and anthropogenic) digitised by a user and easily convertible into a single line after application of feature selection criteria. A data model is defined for the database design, which gives robustness to the database and facilitates the data entry, updates and data exploitation. This allows the generation of three shorelines (physiographic, erosion or simplified shoreline), which can be easily extracted from the spatial database. To illustrate the methodology, the development of the Andalusian multipurpose line (southern

* M. Fernandez-Nunez [email protected] P. Díaz-Cuevas [email protected] J. Ojeda [email protected] A. Prieto [email protected] N. Sánchez-Carnero [email protected] 1

Departamento de Geografía Física y Análisis Geográfico Regional, Universidad de Sevilla, C/ María de Padilla s/n, 41004 Seville, Spain

2

Grupo de Oceanografía Física, Facultad de Ciencias del Mar, Universidad de Vigo, Campus Lagoas Marcosende s/n, 36310 Vigo, Spain

Spain) is explained in detail. An example of data exploitation is also given, generating different environmental indicators (shoreline length by type, beach width, etc.) that may lead to further research or to assist coastal managers and decision makers at the coastal zone. Keywords Multipurpose line . Shoreline . Data model . Coastal indicators . Andalusia

Introduction The coastal zone is a very important transition environment due to its high ecological and social value, which provides a meaningful number of ecosystem services such as climate and flood regulation, coastal protection and recreational opportunities (Costanza et al. 1997; Millennium Ecosystem Assessment 2005; Turner et al. 2008). However, coastal systems worldwide are becoming highly sensitive due to numerous pressures such as human pressure and sea-level rise (Nicholls and Cazenave 2010) that threaten their survival. During the last few decades this has motivated numerous studies focused on coastal areas with different interests such as coastal population (Small and Nicholls 2003), tourism (Wong 1998; Tejada et al. 2009), coastal ecosystems (Cornwell et al. 1999; Burke et al. 2001), erosion (Mimura and Nunn 1998), coastal management (Krause and Glaser 2003; Cicin-Sain and Belfiore 2005), coastal vulnerability due to sea-level rise (Ojeda et al. 2009; Nageswara et al. 2009) or climatic change (Klein and Nicholls 1999; Moser and Tribbia 2006; Tibbetts and van Proosdij 2013). The concept of the coastline is a crucial point in most studies undertaken at the coastal zone and its definition can vary depending on the work scale and the study purpose. For example, Dolan et al. (1980) defined the coastline as the

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border that coincides with the physical interface between the emerged and marine surface. Similar to this definition is the one adopted by the European Environment Agency (EEA), as ‘a line that separates a land surface from an ocean or sea’. These definitions are the most common for practical purposes at large scales, however, they are very general and not precise enough at finer spatial scales. When finer scales are used for coastline definition it is crucial to specify a library of digitisation criteria (Boak and Turner 2005) including all cases present in the study area. Thus, the larger and heterogeneous is the study area, the bigger the library will be. These criteria must be clear, precise and consistent along the coast and they are usually established based on the available information, digitising tools, spatial resolution and the study purpose (Fletcher et al. 2003; Boak and Turner 2005; Hughes et al. 2006). In this sense, several criteria have been applied in the literature: high tide line, wet-dry line, base or top of the ridge or cliff vegetation line, among others (Boak and Turner 2005). Coastline mapping has been usually based on the earth-water boundary interpretation and digitisation from aerial photographs based on specific criteria using Geographical Information System (GIS) techniques (e.g. Thieler and Danforth 1994; Brown 2006; Guariglia et al. 2006; Pian and Menier 2011). These techniques are based on subjective visual interpretation and at present they are the most common for coastline mapping. Other new techniques such as topographic data collection (e.g. with a laser scan such as LiDAR) and digital image-processing are currently making it possible to use objective coastline detection methods. However, these techniques do not allow the use of several criteria simultaneously. The utility of a digital coastline depends on the information quantity and quality that can be linked to this line as well as to how this information is organised for exploitation. In this sense, information storage methods such as geographical databases enable an optimal data management (Pian and Menier 2011; Vafeidis et al. 2008). Moreover, the use of an appropriate relational data model for the database design facilitates the digitisation and data entry processes as well as the data analysis. Thus, the data structure and organization is a key point in order to maximise the potential queries to the database and the use of this information in different applications such as coastal dynamic assessment processes and coastal environmental planning. The aim of this paper is to generate a multipurpose line of the Andalusian coast (Spain) at a fine scale (1:2500), including a library of digitalisation criteria and a database model design. The different nature of the information collected (natural and anthropogenic) and the associated database structure will allow many different applications after completion (e.g. generation of different types of indicators or erosion rate estimations). In addition and taking into account that INSPIRE (Infrastructure for Spatial Information in Europe) Directive calls European countries to have a good quality coastline (with specifications detailed in Data Specification on Sea regions –

M. Fernandez-Nunez et al.

Draft Technical Guidelines 2013), the use of GIS and relational data models like the methodology presented here could be a workable solution for those countries following the INSPIRE directive. Study area The Study area is the Andalusia coast (southern Spain) and extends from the Guadiana river mouth (border with Portugal) in the West (37°10’N 7° 24’ W) to the Murcia region in the East (37° 22 N 1° 37’ W), along around 900 km (Fig. 1). This coast is highly diverse in coastal types because it occupies the transition between the mid-latitudes and inter-tropical zones, and between the Atlantic Ocean and the Mediterranean Sea. It is subject to a complex coastal dynamic that together with other phenomena, such as its neotectonic macrostructure and past sea level changes, have enabled the formation and development of diverse coastal environments such as beaches, barrier islands, spits, saltmarshes, lagoons, estuaries, embayments, and rocky and sandy cliffs (Ojeda 2003). The evolution, dynamics and landscape forms of this coast have been well documented in several studies (Ojeda 1988; Goy et al. 1996; Ojeda 2003; Ojeda 2005; Zazo et al. 2008). Specifically, Andalusia has two distinct coasts: Atlantic and Mediterranean. The Atlantic coast is associated with low lying areas and is characterised by sandy coastal landforms (spits, beach barriers and tombolos) and large tidal ranges (mesotidal). Its topography and mesotidal character leads to the development of many large estuarine saltmarshes. In contrast, the Mediterranean coast has a microtidal range and is chiefly characterised by the steep Betic mountain range with many cliffs and beaches attached to narrow coastal plains, lagoons and river deltas. Due to the high diversity of coastal landforms in this area it is of great scientific interest to describe its coast using the proposed data model.

Material and methods Data source The data used for digitising the exposed shoreline were orthorectified photographs of 0.5 m spatial resolution acquired in 2011 by the Spanish National Plan of Aerial Photography (PNOA). The reference coordinate system of the data sources, ETRS89 UTM30N, was retained during the digitisation process. In addition, other aerial photographs were used to fulfil the digitisation criteria for estuaries and tidal creek networks: orthophotos taken between 1956 and 2009. All used photographs were provided by regional government composed on 1:10,000 sheets (228 covering the total study area) (Table 1). To complement the basic data sources, other information was used for implementing the spatial database:

Multipurpose line for mapping coastal information

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Fig. 1 Study area: the coastal zone of Andalusia (Spain)

&

&

&

&

Beach catalogue produced by the National Environmental Ministry. For all Andalusia beaches, this catalogue provides oblique aerial photographs and information such as beach access and sediment type, size and colour (this information is available on: http://www. magrama.gob.es/en/costas/servicios/guia-playas/ default.aspx) Geological maps (1:50,000) published by the Spanish institute of geology and mining. These maps were used for obtaining the substrate geological nature at the backshore limit Geomorphological maps (2007) published by the Andalusian Environmental Ministry (available on Environmental information network of Andalusia). These maps were used to obtain previous information about Andalusian coastal environments for establishing a previous coastal classification Topographic maps published by cartographic institute of Andalusia (available online on IDEAndalucia) and nautical charts published by the Spanish maritime institute.

Table 1

&

These resources were used for obtaining information about the toponymy at the coastal zone Digital Elevation Models (DEMs) of Andalusia published by the Andalusian Environmental Ministry (available on Environmental information network of Andalusia). The DEMs (plus information derivate from topographic map contour lines) allowed determining cliff height within predefined height intervals (low: 30 m)

Methods The methodology used in this work was defined by the following steps: (i) criteria and shoreline definition, (ii) shoreline digitisation and validation, (iii) data model design and (iv) indicators construction. The workflow is presented in Fig. 2 and described in the following sections. i)

Criteria and shoreline definition

Aerial photographs available for the study area (‘B/W’ means black and white photography, and ’C’ colour photography)

Year

Resolution (m)

Type

Source

URL

1956/57 1977/83 1984/85 2001/02 2007 2008/09 2011

1 0.5 1 0.5 0.7 0.5 0.5

B/W B/W B/W B/W C C C

US Military Service SP National Photogrammetric flight SP National Photogrammetric flight SP National Photogrammetric flight SP National Photogrammetric flight PNOA PNOA

[1]/REDIAM_Ortofoto_Andalucia_1956 [1]/REDIAM_Ortofoto_BN_1977_83 [1]/REDIAM_Ortofoto_PAN_Andalucia_84_85 [2]//ortofoto2001 [2]//ortofoto2007 [2]//ortofoto2009 [2]//ortofoto2010

[1] http://www.juntadeandalucia.es/medioambiente/mapwms, [2] http://www.ideandalucia.es/wms

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Fig. 2 Flowchart of the methodology used

In this work, we will adhere to the shoreline and coastline definitions used by the INSPIRE Directive feature concept dictionary according to which Bshoreline^ refers to the line marking the limit between the sea and the land regardless of the criterion chosen for its representation; and Bcoastline^ refers to the shoreline at Mean High Water (MHW). Based on this shoreline general concept, many shorelines can be defined depending on the criteria defined by the digitiser. Because of limitations in aerial photography (photographs were taken at different tide moments), only visible shorelines were digitised for this work. The shoreline segments digitised used different criteria as shown in Table 2 and Fig. 3, defining different anthropogenic and natural coastal environments (coastal defends, creeks, beaches, coastal cliff, etc.). All this information has been stored within a single file that defines the geometry of a ‘multipurpose line’, referred to as a digital map structure holding all digitised shoreline segments. From this line, specific single shorelines can be extracted after application of feature selection criteria, making the most of the GIS capabilities to the database. In this sense, three shorelines have been assembled for this study: physiographic, erosion and simplified shoreline. The representation of each shoreline can be easily displayed through queries to the database. The physiographic shoreline is formed by shoreline segments at exposed beaches (following the swash limit criterion, see Table 2), rocky coasts, exposed coastal infrastructures and sheltered coasts (channels, creeks and infrastructures within the estuaries). The erosion shoreline is composed by the same shoreline segments than the physiographic shoreline, except at exposed beaches segments where they are defined as the border between the incipient dune and the backshore due to its stable character in the medium term. It makes this border suitable for estimating


erosion rates (Ojeda 2000; Stive et al. 2002; Pajack and Leatherman 2002). However, the physiographic shoreline length presents some problems when it comes to the computation of some statistics because in this shoreline all the coastal infrastructures and creeks are drawn. Thus the coastal length of the administrative departments (where intricate harbor structures or marshes exist) is greatly increased, having relevant management implications. Therefore, a simplification of the physiographic shoreline has been proposed: a third line type called ‘simplified shoreline’ (Fig. 4). In this line, only the shoreline segments located in exposed coasts are considered and the line that follows the shape of exposed coastal infrastructures has been simplified, drawing the original coast edge (looking at the oldest ortho-photography in 1956). This line has also been simplified in estuaries, channels and creeks where a single line segment connects both shores at the estuary mouths. This simplified shoreline will be very useful in calculating total coast length statistics for comparison studies for instance. ii)

Shoreline digitisation, data entry and validation

The segmentation of the shoreline into independent components with associated information is the best option to characterise the coastal area (Stanchev et al. 2013) and has been used frequently at fine scales for planning and management purposes (Hoozemans et al. 1993; Mangor 2001; Vafeidis et al., 2008; Casal et al. 2010). In this work shorelines were digitised in homogeneous shoreline segments, each of them characterised by several attributes related to natural and anthropogenic features of the coast (e.g. coast type and coastal infrastructure type). The digitisation of these segments was carried out within an ESRI geo-database file using ArcMap (ArcGIS, 9.3) at a scale 1:2500. During the digitisation process, the shoreline segments were drawn using several tools depending on the nature of the object in the photograph (anthropogenic or natural). Straight lines for example are often used for clearly defined borders such as some coastal infrastructures, meanwhile tangent lines were used for positioning natural borders such as beaches and creeks. The geometry was validated through a topological analysis over the shoreline segments applying the following rules: connectivity among all segments except the first and the last vertex (must not have gaps), overlapping (must not overlap; must not self-overlap) and segment intersections (must not self-intersect). Alongside the digitisation process, the data entry into the ESRI geo-database was carried out by the digitiser chiefly through domains (code that limit the field values to the defined options) as the majority of stored data have been included into

Multipurpose line for mapping coastal information Table 2

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Criteria definition in different coastal environments for shoreline digitisation

Coastal environment Physiographic shoreline

Erosion shoreline

Beaches

Swash limit: Border between the foreshore and the backshore (red dash line, Fig. 3a)

Estuaries, channels and creeks

Same as physiographic shoreline Dune limit: Border between the backshore and the incipient dune. In those cases where there is no dune and there is a cliff or coastal infrastructure, the shoreline segments will follow the cliff bottom limit or the infrastructure outer limit (e.g. promenade) (blue dash line, Fig. 3a) Same as physiographic shoreline A single line segment connects both shores at the estuary mouth

The limit of the low lowest tide found among all the available ortho-photos (Fig. 3b). It should be the line closer to the hydrographic zero (which it is not available for the whole Andalusia). Here, only creeks wider than 15 m have been drawn The cliff base (bottom) or at the limit of Same as physiographic shoreline rock emergence (Fig. 3c) Same as physiographic shoreline The shoreline segments associated with coastal infrastructures facing the open ocean or sea is positioned at the external limit of the coastal infrastructure (Fig. 3d). The geometry of the ports, marina and dikes (aerial photograph date) have been drawn

Rocky coast Exposed coastal infrastructure

different typologies previously defined such as coast type, dune width, beach width, cliff height, infrastructure type, urban uses, urban proximity and source date (more details are

Simplified shoreline

Same as physiographic shoreline The natural coast edge looking at the oldest orthophotography (1956)

given in the next section). In this geo-database, the data were stored in a single table with different fields that facilitate the digitisation process.

Fig. 4 Andalusia multipurpose shoreline composed by three line types: physiographic shoreline, erosion shoreline and simplified shoreline

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Fig. 3 Criteria used for the shoreline digitisation: (a) beaches in the physiographic shoreline (red dash line) and erosion shoreline (blue dust line), (b) rocky coasts (red dash line), (c) estuaries: channel and creeks (red dash line), and (d) exposed coastal infrastructure (red dash line)

iii)

The conceptual data model for spatial database design

Apart from the precision and quality of the geometric objects (lines), the shoreline utility depends on the information linked to the shoreline segments and the data organisation. However, to organise data from different themes (geomorphological, ecological and tourism for instance) into a single geodatabase is not an easy task, and it can complicate the data exploitation if the information are not well structured. In this sense, for the geo-database design, it is essential to define a data model suitable to integrate different information and flexible to be easily exploited by different users. Hence, the data originally stored in an ESRIdatabase were imported into a normalised data model nested in a more powerful and flexible open code relational database manager: PostgreSQL/PostGIS (data importation into the PostGIS data model was programmed in Procedural Language/Structured Query Language; from now onwards PL/SQL). The data model is composed of relational tables and is shown in Fig. 5. From it, all the information required by technicians and researchers can be extracted from the PostGIS geo-database, which can be updated with newly collected data. This data model has been normalised to maximise the data analytical capabilities and to avoid redundancy. In the data model, the digitised shoreline is the main object. This object has a primary key (id_shoreline) and foreign keys (id_beach_catalog, id_shoreline_type, and id_typology_lv4) that enable the creation of relationships with other data tables. For example, the ‘id_typology_lv4’ links the shoreline object

to the shoreline hierarchy typology table (Fig. 5), which gives information about the nature of the shoreline at different hierarchy levels (Table 3). The information associated with the shoreline type (id_shoreline_type) is of special interest because it enables (by means of a specific code) the definition of a multipurpose line, the primary aim of this study. The three shorelines described in previous sections can be easily extracted here by means of simple queries to the database. iv)

Indicator construction

Simple SQL queries executed on the multipurpose line allow the fast computation of coastal indicators (environmental and anthropogenic ones). Variables such as length of beaches, estuaries or infrastructures for any spatial area are easily and quickly extracted from the database. All this information can be potentially used to generate environmental indicators at different spatial scales, from landscape to local (e.g. percentage of artificial beach, percentage of beaches with dunes, beach width and erosion rates). As an example, in this work two indicators were computed: i) current available beach width and ii) natural beach-dune. The current beach width availability on the one hand has been defined as the Euclidean distance between the erosion and the physiographic shorelines previously defined by tracing perpendicular transects (using DSAS version 4.2 for ArcGIS 9.3) between the erosion and the physiographic shorelines. This variable is essential for estimating the tourism


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Fig. 5 Geodatabase model design of the Andalusia shoreline. The subgroups represented into the model are: (1) variables from digitising process, (2) shoreline typology, (3) details of anthropogenic and natural features, (4) beach catalogue and (5) toponymy submodel

carrying capacity and the sedimentary stability of coastal sectors. Furthermore, the transects used for calculating the beach width is also useful for estimating erosion rates (as it has been stated in Ojeda Zújar et al. 2013), although these rates have not been computed for this paper. On the other hand, the natural beach-dune indicator is defined as the longitudinal percentage of beaches with foredune over the total beach length, and it has been estimated for each municipality. This indicator presents spatial information related to anthropogenic pressure over the coastal zone.

Results The main result of this work is the data model designed to fit its particular needs, which together with detailed criteria and a clear digitising methodology represents a useful tool not just for shoreline generation but also for shoreline information retrieval and analysis. In what follows we will show the results associated with this data model, starting with the description of the generated shorelines, followed by the data model itself and the indicators that have been computed for this work.

Multipurpose line A multipurpose line of the Andalusian coast is composed by 9723 segments between 2.5 and 2767 m in length (Table 4). From this line, physiographic, erosion and simplified shorelines were generated (Fig. 6). Differences among shoreline types can be observed by looking at the total length or length by coast type (Table 4). Here, lengths of the exposed beaches categories and other landforms (isolated rocks and small islands) are very similar in both the physiographic and the simplified shoreline. However, in the estuaries category there are greater differences between the simplified and the physiographic shoreline lengths. In the physiographic shoreline, estuaries comprise more than 1000 km (51.41 % of the total coast length), while in the simplified shoreline only 26 km (2.79 %). This is due to the large estuaries and creeks mainly located in the Andalusian Atlantic coast such as Guadalquivir, Tinto-Odiel, Piedras and Guadiana. It is worth highlighting that when the physiographic shoreline is used, the total percentages disaggregated by municipalities can be unexpected if the length of the estuaries or coastal infrastructures is large.

468 Table 3

M. Fernandez-Nunez et al. Hierarchy typology created for Andalusia coast based on natural and anthropogenic features

Level 1

Level 2

1. Exposed beach

1.1. Undefined

Level 3

Level 4

1.2. Neither foredune nor cliff 1.3. With cliff

1.3.1. Undefined 1.3.2. With shore-platform 1.3.3.With plunging cliff 1.4.1.Undefined

1.4.With foredune

1.4.2.With cliff 1.4.3.With shore-platform 1.5.With shore-platform 1.6.Associated with rocky coast

1.6.1.Undefined 1.6.2.With shore-platform

1.7.Associated with sedimentary genesis


2. Rocky coast

2.1.Undefined 2.2.Lower


2.3.Steep


3. Estuary and marsh creek

3.1.Undefined marsh border 3.2.Estuary

3.3.Marsh creek

4. Anthropogenic exposed coast

3.2.1.Natural 3.2.2Modified 3.2.3.Artificial 3.3.1.Natural 3.3.2.Modified 3.3.3.Artificial

4.1.Undefined anthropogenic coast 4.2.Port 4.3.Airport 4.4.Coastal infrastructures

4.4.1.Undefined 4.4.2.Transversal

4.4.3.Longitudinal

4.4.4.Mixed

5.Other landforms

4.4.2.1.Undefined 4.4.2.2.Dike 4.4.2.3.Jetty 4.4.2.4.Pier 4.4.3.1.Undefined 4.4.3.2 Seawall 4.4.3.3.Dike-Riprap 4.4.3.4 Beach promenade 4.4.4.1.Undefined 4.4.4.2.T-shape jetty 4.4.4.3.Jetty-Riprap

4.5.Anthropogenic filling 4.6.Anthropogenic extraction 5.1.Island

Conceptual data model in a PostGIS database The conceptual data model is an important result of this work. Its design and construction ensure the automation of processes

and data flow in future updates as well as geometrical and topological data. Furthermore, the data model is repeatable and applicable to new study areas where it will allow obtaining a geometric multipurpose line that will support

Multipurpose line for mapping coastal information Table 4 Total length (km), number of segments and disaggregated length (km) by hierarchical typology (level 1) of the physiographic, simplified and erosion coastline

Physiographic Shoreline

Simplified Shoreline

Erosion Shoreline

(km)

(%)

(km)

(%)

(km)

(%)

Total length

2401.42

896.92

2408.88

Segments (count) Exposed Beaches

5770 623.61

25.96

4252 623.20

69.48

5825 648.82

Rocky coast

153.51

6.39

153.39

17.01

153.37

6.36

Estuary and creeks Exposed coastal infrastructure

1232.99 377.37

51.34 15.74

20.95 93.88

2.33 10.46

1238.99 359.87

51.43 14.93

Others landforms

7.75

0.32

1.92

0.21

7.74

0.32

different shoreline definitions as the three proposed here, thus maximising and optimising the analytical data capacity. Taking advantage of the data model, users can directly calculate variables (such as beach width, dune width or infrastructure type) over the whole area or disaggregated by administrative units, simply using SQL queries. Moreover, users can obtain also graphical data for maps generation as results of their queries, not just numerical outputs. In addition, a Fig. 6 Hierarchical landform typology level 1 on the physiographic shoreline and simplified shoreline

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26.34

correctly structured data model allows to be connected/ integrated with information from other available administrative and environmental data models (from government, NGOs, etc.). Environmental indicators of coastal areas The first indicator computed for this work was the beach width. In this paper, the results are only shown for a beach

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sector (Fig. 7) due to the difficulty of displaying this indicator for the whole Andalusia. The results highlight the variability of this indicator by shoreline segment and the potential for integrated management strategies at the coastal zone. The beach width is representative of a temporal moment of the beach profile, which can vary between seasons and years due to the dynamism of these environments. However, it provides information of the current state, and its evolution if compared with other years during the same season. This indicator can also give information about the carrying capacity of a beach. Although as it is stated in Silva et al. (2007) and Ribeiro et al. (2011), this concept is complex and it can not only be defined according to the sand area of a certain beach but other factors are also involved (e.g. car parking space). The second one is the natural beach-dune indicator. The foredune is defined as the dune that works as a sediment supply for maintaining the beach dynamic equilibrium. The sediment flow between the beach and the foredune is critical in the dune-beach profile. This profile is the basic sandsharing system, and each component episodically stores and releases sand in an exchange of sediments depending on


variations in energy level (Psuty 2008). In this sense, those beaches without adjacent dunes are potentially more sensitive to erosion processes. Hence, it is important to determine their location for future management strategies. This information combined with the beach width can be used as an anthropogenic pressure indicator. In Andalusia, the second indicator shows a clear difference between Mediterranean and Atlantic coasts (Fig. 8). In the Mediterranean zone, values of natural beach-dune indicator are much lower than in the Atlantic one. It can be explained by the huge increment in urban areas and coastal infrastructures in the coastal zone during the last few decades (Villar 2013). In some areas, foredunes have been strongly impacted or destroyed due to urbanisation over the foredune (Environmental European Agency 2005; Greenpeace 2010) and/or to modification in the coastal sediment budget balance. Nevertheless, some foredunes have been relatively well preserved to the intense urbanisation process in the Mediterranean coast due to conservation designations (e.g. Cabo de Gata and Nijar Natural Park in Almeria; Punta Entinas-Sabinar, Artola Dunes in Marbella). In the case of the Atlantic coast, characterised by large beach-dune systems, larger extension of

Fig. 7 Beach width in 2011; each dot represents the width of each segment of shoreline coded as beach


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Fig. 8 Natural beach-dune indicator represented by municipality (environmental indicator related to the percentage of beach with adjacent foredune)

foredune has been preserved maintaining the beach dynamic equilibrium. It is due to the urbanisation process has been less intense in this zone and the numerous protection measurements during this process (e.g. Donana National Park and Odiel salt marshes in Huelva; Natural Park and Bolonia foredune in Cadiz).

Discussion The work presented here introduces the concept of ‘multipurpose line’. This line has been developed through shoreline segments digitalisation based on different criteria and using a conceptual data model for spatial database design. The organisation and the architecture of the spatial database makes easier the generation and update of coastal data and indicators over a large expanse of coast, often required for public administrations. The generation of different shorelines (in our case physiographic, erosion and simplified shorelines), each of them digitised applying different criteria (meeting some of the INSPIRE specifications, e.g. hierarchical classification), with different characteristics and applications in mind, represents an advantage over most works in this field, that generally only focus on one shoreline concept at a time (e.g. Lafon et al. 2004; Maiti and Bhattacharya 2009). Multipurpose line also opens a wide range of applications, providing information of interest for different users. Shoreline detection and mapping presents the difficulty of addressing the dynamic nature of the land-water boundary in time and space at coastal areas. Different data sources and specific application determine the detection method and the type of shoreline criterion chosen (Boak and Turner 2005), leading to different shoreline concepts. The use of several shorelines enables to combine several of these concepts. For example, in beaches, the shoreline digitisation criteria used here follows

two proxies commonly used in the literature: dune vegetation line for the erosion shoreline (Crowell et al. 2005; Del Río and Gracia 2013; Ojeda 2000; Pian and Menier 2011) and wet-dry line for the physiographic shoreline (Pajak and Leatherman 2002). Shoreline variability decreases upwards on the beach profile, making upper shoreline features (e.g. stable dune vegetation line) more reliable than seaward proxies for analysing shoreline changes (e.g. erosion rates). Additionally, Leatherman (2003) reports that by using data acquired in summer and spring, natural variability can be minimised. Data source used in this work were always acquired during spring and summer seasons (when the beach profile is more stable), reducing short-term shoreline variability. The multipurpose line is supported by a relational data model, improving information storage and multiplying the number of potential applications (and users) (coastal management and planning; erosion rates, coastal vulnerability estimation, among others). The use of data models in shoreline digitisation provides a tool to link efficiently coastal information to shorelines. Thus, the lack of detailed information usually associated to digitised shorelines can be addressed through the use of spatial database and data models, which will maximise the analytical capabilities of the GIS (Pian and Menier 2011; Thieler and Danforth 1994; Brown 2006; Guariglia et al. 2006). For example, this problem was addressed by Sharples et al. (2009)in the Australian coast, where a Bsmartline^ was defined as a shoreline with associated information from 500 m buffer. Later, this model has been applied to the eastern coast of Rio de Janeiro State (Brazil) for assessing coastal vulnerability and social risk (Lins-de-Barros and Muehe 2013), showing its utility for different applications. The specific design of the data model in this work also makes the multipurpose line very flexible in terms of database queries. This flexibility permits the exploration of spatial

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relationship between natural and anthropogenic features, and that the information of interest can be extracted according to custom settings (for different coastal physiographic units or at different scales, for example). It converts the data model in a specific result that can be transferred to other study cases. However, in order to achieve an adequate data model, deep knowledge of the studied area (coastal zone and shoreline in this case) and the potential applications after completion is required. Previous knowledge on these subjects will reduce the design process time and avoid major modifications in the data model once the digitalisation process has started. Other than the difficulties specifically associated with the data model design, our approach presents the difficulties usually linked to digitisation processes (Alesheikh et al. 2007). The main of them are those associated to i) data source quality, ii) time requirements and iii) subjectivity and interpretation errors: i)

ii)

iii)

The quality of data source is defined basically by spatial and spectral resolution of the remotely sensed images. For example, poor quality (inadequate spatial resolution; poor spectral resolution -panchromatic-) or errors in the photography (errors in geo-rectification processes) make the digitisation process more complex and the results less reliable. However it is not usually a free choice of the work team, as they can only choose among available data. In this sense, it is important to assess if the quality achievable justifies the work investment. In our case, the spatial resolution (0.5 m pixel size) allowed us to use a 1:2500 scale in the shoreline digitisation process and to link very detailed information to shoreline segments. The digitisation process is time consuming, which increases highly when large areas are digitised at fine scales. However, this effort is justified by the additional detailed information collected during this process. The procedure time is greatly reduced in updating processes. Visible coastal features commonly used to digitise shorelines (Casal et al. 2010; Pajak and Leatherman 2002) are not totally objective, which may result in some interpretation errors. For example, the landwater mark is not always an obvious feature in aerial photography, and thus it is subjected to the digitiser interpretation (Pajak and Leatherman 2002). Criteria libraries found in the literature and summarised by Boak and Turner (2005) show the inconsistency (in terminology) of visible shoreline criteria broadly used for coastal researchers, introducing further source of potential uncertainty. This inconsistency is usually associated with seaward proxies such as ‘high water level’ (e.g. Crowell et al. 1991 and Overton et al. 1999). Thus, although visibility criteria are broadly used for practical purposes, their interpretation and terminology

are limitations to be aware when criteria library are used for shoreline digitisation. Recent technologies such as airborne laser scanner (ALS) (Stockdon et al. 2002; Zhang et al. 2005) and Synthetic Aperture Radar (SAR) (Mason and Davenport 1996; Wang and Allen 2008) have shown to provide precise measurements for shoreline detection and change, reducing some of the errors associated to satellite images and aerial photography interpretation. However, their high costs and limitations associated to data collection and processing (temporally and spatially) make these technologies impractical for data acquisition over a large expanse of coast (Gómez et al. 2014). Thus, high spatial resolution satellite imagery (e.g. Quickbird) and aerial photography will provide a better solution for covering large areas. In order to reduce the time procedure and subjectivity in coastal mapping, different automatic and semiautomatic approaches have been applied for shoreline detection. Some examples are video imaging (Kroon et al. 2007; Uunk et al. 2010; Van Dongeren et al. 2008) and image-processing analysis (Alesheikh et al. 2007; Chen and Chang 2009; Gómez et al. 2014). Despite the benefits provided by these techniques in comparison with digitisation methods, some drawbacks are also found. For example, although video imaging offers temporally dense data, they are spatially limited (Boak and Turner 2005). In the case of image-processing analysis, shoreline indicator features can be detected based on spectral parameters (sometimes invisible to human eyes). However, the pixel resolution may be a limitation for precise shoreline positioning and detailed coastal information detection (e.g. dikes or seawalls) when very high spatial resolution (< 5 m) images are not available. Overall, automatic and semiautomatic techniques may provide a better approach for particular cases and frequent shoreline monitoring, covering the need of a specific user and application. However they are not usually focused on additional thematic data collection (anthropogenic and natural information) that requires data associated to homogeneous shoreline segments to characterise the complexity of coastal area. Here, the multipurpose line can be a useful approach to cover large coastal areas in those cases where detailed information is required for different uses, providing an efficient tool for integrated coastal management for instance.

Conclusions The use of an appropriate data model for geo-database design is a key tool in managing geographic data. It allows the association of information with each geometric feature that can be tagged appropriately during the digitisation process. This has been the goal of this work. In the case of the coastal zone,


where the concept of shoreline can vary depending of the perspective of the user, and the assumption of different criteria can result in significantly different shorelines, this methodology is especially useful. For example, different shorelines are obtained with only one single digitisation process that contains all information required for different purposes. In this work the three different shorelines digitised show differences bigger than 35 % in length among them. These differences could be crucial in defining management strategies along the coast. Moreover, the availability of associated information about these shorelines (managed through a data model) can be used to exploit the geographical database for further analysis and research (by means of computing, for instance, environmental indicators). In this sense, the availability of different shorelines following a data model structure becomes a very helpful tool in coastal management. Acknowledgments The current study has been developed within two research projects: one funded by the Spanish National Research Plan and European Regional Development Fund (ERDF) (BEspacialización y Difusión Web de Variables Demográficas, Turísticas y Ambientales para la Evaluación de la Vulnerabilidad Asociada a la Erosión de Playas en la Costa Andaluza^; CSO2010-15807) and the other one by Andalusia regional government (BEspacialización y Difusión Web de Datos de Urbanización, y Fitodiversidad para el Análisis de Vulnerabilidad ante los Procesos de Inundación Asociados a la Subida del Nivel del Mar. en la Costa Andaluz^; RNM-6207).

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