Improving the Usability of the Land Cover and Use Information System

of Spain (SIOSE): A Proposal to Distribute New Thematic Layers and

Predeﬁned Reclassiﬁcations

Benito Zaragoz

1 a

, Jos

e Tom

as Navarro-Carri

2 b

, Jes

us Javier Rodr

ıguez-Sala

3 c

Sergio Trilles

4 d

and Alfredo Ram

on-Morte

2 e

Departament de Geograﬁa, Universitat Rovira i Virgili, C/Joanot Martorell, Vilaseca, Spain

Instituto Interuniversitario de Geograf

ıa, Universidad de Alicante, C/ San Vicente s/n, Alicante, Spain

Centro de Investigaci

on Operativa, Universidad Miguel Hernandez de Elche, Av. de la Universidad, Elche, Spain

Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, Castell

on de la Plana, Spain

Keywords:

Land Use, Land Cover, Geodatabase, Object-oriented, Siose.

Abstract:

Information on land use and land cover (LULC) is fundamental in the study and planning of human activities.

In recent years, accessibility to quality geographical information has signiﬁcantly increased, and this is also

true for the case of LULC datasets. In Spain, the Land Cover and Use Information System of Spain (SIOSE) is

concerned with harmonising access to this type of information through an object-oriented model and a series

of technical speciﬁcations that regional administrations must follow. However, the information from SIOSE is

so rich and complex that there is a usability gap that makes this data not exploited to its full potential in some

contexts. In this communication, we analyse the context in which this usability gap occurs, its causes and

consequences. Among other possible improvements, we suggest that enriching the SIOSE database with new

thematic information would make its use more attractive and reduce the usability gap for less expert users. We

propose an extension to the SIOSE object-oriented data model that will make it possible to enrich the LULC

data with new data that are useful for various types of studies.

1 INTRODUCTION

The information on land use and land cover (LULC)

is essential for the planning of human activities. This

type of information has the virtue of agglutinating

biophysical information and socio-economic uses of

a territory. The design of LULC data warehouses is

more often related to the concept that geographers

have of the landscape conﬁguration as an object of

study (Antrop, 2006), which makes them suitable for

many landscape science studies. This approach has

been applied in numerous studies that show the close

relationships between LULC and essential ecosys-

tem services (Foster et al., 2003; Polasky et al.,

2011), which casts LULC data as a strategic source

https://orcid.org/0000-0003-2501-484X

https://orcid.org/0000-0002-8718-218X

https://orcid.org/0000-0002-3796-0692

https://orcid.org/0000-0003-0557-6319

https://orcid.org/0000-0002-9304-0719

of information for natural resource management and

land management (Valcarcel and Casta

no Fern

andez,

2013).

1.1 LULC Data Production in the EU

Land use information has been systematically col-

lected in Spain since the creation of the National Ge-

ographic Institute (IGN) in 1870, although initially

only as a fundamental source for the preparation of

the National Topographic Map. The development of

Geographic Information Technologies, in the second

part of the last century, promoted the creation of large

ofﬁcial repositories of digital geographic information

compiled through different means, such as: (1) the

digitisation of ofﬁcial cartographic information, (2)

the incorporation of GPS ﬁeld data, (3) the interpreta-

tion of aerial photography and (4) the development

of Remote Sensing. In a broader context, the Co-

ordination of Information on the Environment pro-

gramme (CORINE) was initiated in 1985 by the Eu-

294

Zaragozí, B., Navarro-Carrión, J., Rodríguez-Sala, J., Trilles, S. and Ramón-Morte, A.

Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers and Predeﬁned Reclassiﬁcations.

DOI: 10.5220/0009579502940301

In Proceedings of the 6th International Conference on Geographical Information Systems Theor y, Applications and Management (GISTAM 2020), pages 294-301

ISBN: 978-989-758-425-1

ropean Commission in order to dynamically collect,

coordinate and structure the inventory of environmen-

tal information of the European Union. The Corine

databases and several of its programmes were taken

over by the European Environment Agency (EEA).

Among these, there is the inventory of land cover at

a 1:100,000 scale for the whole European territory.

This “Land-Cover” project (CORINE Land-Cover;

CLC) has set a global precedent (B

uttner et al., 2002).

In a global scope, LULC information is used for

strategic issues within the United Nations Framework

Convention on Climate Change (1992) and the revi-

sion of the Kyoto Protocol (1998). In this context,

many users are using these data for different interests,

which has led to the development of a speciﬁc ser-

vice in Europe to meet this heterogeneous demand,

the “Copernicus Land Monitoring Service” (CLMS),

within the Global Monitoring for Environment and

Security (GMES) initiative of the European Environ-

ment Agency (EEA), which in turn depends on the

Earth Observation Program “Copernicus” of the Eu-

ropean Space Agency (ESA).

In Spain, the Ministry of Development and Eco-

logical Transition addresses the general need for

LULC data, under the legal framework of the Euro-

pean Directive INSPIRE (2007/2/CE). The National

Geographic Institute (IGN) takes the role of coordi-

nator in the management and creation of data on land

occupation according to the National Territory Occu-

pation Plan (PNOT), yielding millions of downloads

on the open data platforms of this body and of the re-

gional spatial data infrastructures. The main users of

these data are the General State Administration itself

and the regional administrations, almost always for

issues regarding agrarian policy, environmental man-

agement, urban development or cartographic elabora-

tion. Applications to projects in University research

laboratories, research ofﬁcer centres and public and

private companies are also remarkable.

1.2 The Appearance of Object-oriented

LULC Classiﬁcations

Throughout the development of these programs to

produce quality LULC data, many technical and tech-

nological changes took place. In this sense, the

paradigm of hierarchical classiﬁcation of LULC used

at the end of the 20th century by the CORINE Pro-

gram – and by others in the same period, such as

the “land use and land cover classiﬁcation system” of

the United States Geological Survey (Anderson et al.,

1976) – showed inadequacies at the beginning of the

current century. The baseline of said limitations is

that mutually exclusive classes were often more ori-

ented to the realisation of maps than to the analy-

sis and diagnosis of reality. The need for managing

more complex and bulky data sets motivated the emer-

gence of trends aimed at applying an object-oriented

paradigm (Villa et al., 2008).

Following these trends, the EAGLE group within

the European Network of Information and Observa-

tion on the Environment (EIONET) was created to

deﬁne techniques that allow optimising the integra-

tion and harmonisation of LULC data from the of-

ﬁcial repositories of each country at pan-European

scale (Arnold et al., 2013). EAGLE proposes an

object-oriented data model (OODM) which takes into

account standards or reference code lists, such as

Corine LC and technical speciﬁcations driven by

INSPIRE (2007/2/CE) and ISO standard 19144-2

(LCML-Land Cover Meta Language). In 2005, the

object-oriented database of the Information System

on land occupation in Spain (SIOSE), emerged as

an integrated initiative in the EIONET Network (Del

Bosque Gonz

alez et al., 2005). SIOSE is developed

in Spain through the PNOT, under the coordination

of the IGN and the National Geographic Information

Center (CNIG). SIOSE is backed by a data model that

conforms to INSPIRE technical speciﬁcations, and its

design follows the indications of the EAGLE group,

ensuring compatibility and comparability with pre-

existing databases such as Corine CLC90, CLC00,

Murbandy/Moland or LCCS of the United Nations

FAO.

Since there are no mutually exclusive classes,

such an object-oriented model will not incur informa-

tion losses during the labelling process, thus making

feasible to store LULC statistical observations at lev-

els of detail that hierarchical classiﬁcation models are

not capable of due to its dichotomic nature (Omrani

et al., 2015). It has an impact on the economic sav-

ings in the production of data sets. The elements or

variables of the landscape are unique in their deﬁni-

tion, which allows the possibility of obtaining cus-

tom thematic outputs, according to user needs. As

a consequence, the object-oriented approach allows

generating dynamic and extensible classiﬁcations to

respond to future needs. In addition, new types of pa-

rameters can be included in different versions of the

database without conﬂict with the previous data (Val-

carcel et al., 2008).

2 USABILITY IN THE SIOSE

DATABASE

Regardless of the signiﬁcant advantages of applying

the object-oriented paradigm to SIOSE, the SIOSE

Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers

and Predeﬁned Reclassiﬁcations

295

Figure 1: Geometric comparison between a CORINE LC plot (orange limit) and a SIOSE plot (red limit) shows a clear

difference in size and scale (1:100.000 and 1:25.000). Furthermore, the classiﬁcation of the CORINE plot (Discontinuous

Urban Fabric) hides other realities such as those found in the SIOSE plot (different types of agriculture) or the cases indicated

by green circles: A (Industrial Park) or B (Opencast Mines).

Figure 2: The SIOSE Object-Oriented Model offers more wealth of information than the CORINE LC Classiﬁcation Model.

SIOSE provides a higher level of detail as to the types of crops and the area quantiﬁers. In this example, the plot is composed

of 2 covers, 80% of irrigated fruit trees and 20% of cultivated pastures, both regularly distributed).

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

296

data model has also certain drawbacks, and it is of-

ten difﬁcult to understand for non-expert users due to

differences in operation concerning traditional hierar-

chical classiﬁcations which may arguably be regarded

as more straightforward. Hierarchical data models,

such as CORINE LC, facilitate the interpretation of

information by reducing thematic resolution so that

directly observable data mostly ﬁt the model require-

ments. On the other hand, the object-oriented model

must be adapted to relational database management

systems with spatial capabilities, and database ad-

ministrators have to deal with this incompatibility at

the conceptual level. SIOSE is a case of the object-

relational impedance mismatch that has been clearly

identiﬁed in the literature as a problem of data struc-

ture due to paradigm differences (Ireland et al., 2009).

Another drawback derived from the complexity of the

system is the analysis of the evolution of Land Occu-

pancy and the detection of changes, which must be

carried out not only in geometry but also in seman-

tics (Valcarcel and Casta

no Fern

andez, 2013). These

types of problems have already been faced in differ-

ent studies, where the authors point to the usefulness

of the data collected by SIOSE, but also to the dif-

ﬁculty in handling those data. As for example, some

effort was necessary to use SIOSE LULC data in stud-

ies related to areas such as climate change (Ropero

et al., 2019; Olaya-Abril et al., 2017), ﬂood risk and

mapping of ﬂood areas (Morte et al., 2019), farm-

land abandonment (Pe

na-Angulo et al., 2019), Wild-

land–Urban Interface (Badia et al., 2014) or purely

cartographic studies (Garc

ıa-

Alvarez, 2018).

Nevertheless, one of the most outstanding aspects

of SIOSE goes beyond the object-oriented data model

and instead resides in the fact that it has led to the

development of a coordinated and participatory pro-

duction system that integrates data from all interested

public administrations. This results in an economy

of effort and an increase in the quality of the out-

comes which has earned it the 2013 UN Public Ser-

vice Award.

In this general context, the SIOSE-INNOVA

project (http://siose-innova.es/) aims to address both

issues, the technical problems related to the SIOSE

object-oriented data model and the usability draw-

backs derived from it. The main objectives of this

work are as follows:

1. To deﬁne the SIOSE usability gap by analysing

the ecosystem where these data are used (actors,

contexts and use cases).

2. To evaluate different solutions to overcome the us-

ability gap.

3. To propose a “soft” solution to increase the attrac-

tiveness of the SIOSE database, which comple-

ments other measures based on alternative tech-

nologies or mediating platforms.

3 THE SIOSE USABILITY GAP

In this work, usability is considered as a measure of

quality that evaluates how easily the SIOSE database

is used. The word usability also refers to methods to

improve usability during the design process. Among

the factors that determine the usability, we can men-

tion accessibility, readability, navigability, ease of

learning, speed of use, user efﬁciency and error rates

(Ben Ramadan et al., 2017). According to this deﬁ-

nition, there may be different actors and use cases in

which the SIOSE database could be more challeng-

ing to manage, query or update, so ﬁnding a gen-

eral solution for these many scenarios would require

a broader perspective from the design phase. Thank-

fully, the SIOSE object-oriented model favours exten-

sibility (Valcarcel et al., 2008).

Usability is also a characteristic of a system that is

intended to be used (1) by a speciﬁc type of user, (2)

to complete the task for which the system has been de-

signed, and (3) in the context in which the interaction

occurs (Ben Ramadan et al., 2017). In this sense, it

is essential to perform a detailed description of these

three components of the SIOSE environment of ac-

tors, applications and contexts. In this section, we

have attempted to do so.

In ﬁgure 3, we show a use case diagram in which

we perform an analysis to identify, clarify, and organ-

ise system elements. Of course, this is only a model

that may not be applicable in some cases but, to the

best of our knowledge, this model represents most of

the uses of SIOSE present in the scientiﬁc literature

and the uses that we know from our own experience.

The SIOSE project must take into account three dif-

ferent phases that are needed for leveraging the use

of the LULC data. These phases are (1) the produc-

tion phase, (2) the data integration phase and (3) the

usage phase. In each of these phases, we can see dif-

ferent actors – individuals involved with some parts of

the system according to their roles – that accomplish

one or more tasks for the SIOSE environment. On

the bottom left-hand side of the diagram, there are the

main producers of SIOSE, who carry out their tasks

from an ofﬁcial request (see section 1). On the bottom

right-hand side, we have placed sources of Volunteer

Geographic Information, who are encouraged to con-

tribute in situ data to increase the SIOSE data qual-

ity. In the usage phase, we can distinguish those users

with little or no knowledge about spatial databases —

but who use data derived from SIOSE for their spe-

Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers

and Predeﬁned Reclassiﬁcations

297

Non-GIS

consultant

Usage

Reporting

<<include>>

Analysing

<<include>>

Transformation

<<include>>

Data

processing

Publish

processed data

(CLC, SIU, etc)

Mashups /

Infomediaries

GIS technician

GIS user

GIS analyst

GIS Devops GIS scientist

Provide tools

Data Integration

Publish general

purpose data

(SIOSE)

<<include>>

Integrate data

<<include>>

Define data

models

<<extend>>

Data

compilation/selection

<<extend>>

Define

standards

DB Manager /

SDI Manager

Standards organization

(e.g. OGC, ISO, etc)

Mapping agencies or

GIS departments

VGI

organizations

(e.g. OSM)

Data Production

Remote

Sensings

GPS

VGI

GIS producers

or contractors

Photointerpretation

<<extend>>

Figure 3: Use case diagram showing the actors, applications and contexts where the SIOSE is developed.

ciﬁc objectives — from other users with varying ex-

perience in exploding the SIOSE database.

The diagram displays actors who can perform dif-

ferent tasks related to their context, so they are more

or less attached to a particular phase. For example,

a GIS technician may be interested in processing or

transforming some SIOSE data — for mapping or an-

alytical purposes — but they usually will not have any

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

298

interest or resources for developing new tools. An-

other example would be that of the Non-GIS consul-

tant, who may be interested in reporting some infor-

mation. Still, they lack experience in working with

geospatial data — so they would need the collabora-

tion of a GIS analyst. However, once the database

manager publishes the general-purpose SIOSE data

sets or the required speciﬁc processed data, there are

no speciﬁc actors taking care of providing new tools

or facilitating the access to this information (e.g. in

the form of operating environments with custom re-

classiﬁcations and pre-conﬁgured equivalence maps).

This is represented as the Provide tools task being out

of the main production contexts.

The usability problems of SIOSE may require the

development of new tools that help less expert users

to convert LULC information to custom classiﬁca-

tions that are more useful in their ﬁelds of study.

Another possibility would be to strengthen those as-

pects of training that the different actors require in

each case. However, this would affect a signiﬁcant

number of users and does not seem necessary in a

context where most ofﬁcial GIS data is distributed

in a way that is most usable for users. To date, few

studies have attempted to solve this problem by us-

ing different approaches including the development

of new interoperable web services (Fern

andez Villar-

ino et al., 2012), the use of alternative database tech-

nologies (Navarro-Carri

on et al., 2016) or the design

of an ArcGIS extension for performing the reclassiﬁ-

cation of hierarchical LULC information (Fern

andez

Noguerol, 2017). Apart from these technical solu-

tions, other possibilities may involve a reinterpreta-

tion of the SIOSE data model to allow more speciﬁc

information to be attached to the database. From this

perspective, it would be necessary to (1) identify the

main applications that users make of the SIOSE data,

and (2) propose at least one mechanism for allowing

any user to add new thematic information – or useful

reclassiﬁcations – that can be reused by the commu-

nity. In this paper, we address the second question (an

extensibility mechanism) in a generic way, consider-

ing that users may have very different needs.

4 AN EXTENSION TO THE SIOSE

DATABASE MODEL

Given its object-oriented design, the SIOSE data

model and other similar models mentioned in the in-

troduction are well prepared to add new elements to

a LULC description without the previous information

being affected.

The SIOSE data model uses the composite pat-

tern, which is a partitioning design pattern which

treats a group of objects in the same way as a sin-

gle instance of the same type of object (Gamma et al.,

1994). In ﬁgure 4 this is shown in the three central

classes (LandCoverComponent,LandcoverComposite

and LandCoverLeaf ) and their relationships. This

model can be easily extended through the Attribute

interface, which represents the land-use part of each

land cover component or unit.

We show our proposal in ﬁgure 4 and we can use

the SIOSE polygon described in ﬁgure 2 to set an ex-

ample on how to read the class diagram. That ﬁgure

is showing LULC information from the SIOSE-2011

database, which is the LandCoverDataSet in this ex-

ample. The red polygon can be seen as an instance of

the LandCoverUnit class, which inherits from a ge-

ometry object. A composite land cover composes this

polygon with an 80% of the surface covered by fruit

trees and a 20% of grassland. The composite pattern

would allow any of these land covers to be further

decomposed (e.g. 90% percent are old orange trees,

but a 10% is planted with young avocados, which are

more productive). As seen in ﬁgure 2, in the current

SIOSE data model, land use information (Attributes)

can only describe a full land cover (e.g. irrigation for

fruit trees) but, in our proposal a more precise descrip-

tion could be made, for example, to explain if differ-

ent irrigation systems coexist (Presence), which is the

predominant irrigation system (Percentage) or store

the exact length of the irrigation network (Countable).

In this model, it is not necessary that the geometry

be a polygon, we could also use other types of ge-

ometries (for example, points to represent trees, linear

chains for power lines, among other possibilities).

The main difference from the current data model

is the greater semantic richness of the attributes,

which have been so far less important according to

the production scale. This extension or similar modi-

ﬁcation is necessary to import or to combine new data

from other sources. For example, it would be possi-

ble to build some thematic information from Open-

StreetMap or save a custom reclassiﬁcation and dis-

tribute it without losing the link to SIOSE.

5 CONCLUSIONS AND FUTURE

APPLICATIONS

The SIOSE database is an important resource for per-

forming many different geographical studies. The

database is modelled using an object-oriented ap-

proach, which has many technical advantages but

also a few usability problems. This work has shown

that, despite the aforementioned usability problems of

Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers

and Predeﬁned Reclassiﬁcations

299

<<Class>>

LandCoverDataSet

+ name: Character String

+ validFrom: Date

+ ValidTo: Date

+ beginLifespanVersion: DateTime

+ endLifespanVersion: DateTime

<<Class>>

LandCoverUnit

+ inspireId: Identifier

+ beginLifespanVersion: DateTime [0..1]

+ endLifespanVersion: DateTime [0..1]

GM_Object

<<Class>>

LandCoverComponent

+ beginLifespanVersion: DateTime [0..1]

+ endLifespanVersion: DateTime [0..1]

+ getDescription(): Text

+ getRealArea(): Double

+ getPrevalentLandCover(): Text

+ getPrevalentLandCover(threshold): Text

+ IsValid(): Bool

LandCoverType

1..*

<<Class>>

LandCoverComposite

+ Pattern: SpatialPattern

+ elements: LandCoverComponent [0..*]

+ add(): Void

+ remove(): Void

+ getChild(): LandCoverComponent

<<Class>>

LandCoverLeaf

1..*

Child

Parent

<<Interface>>

Attribute

+ observationDate: Date

+ key: String

1..*

<<Enumerate>>

SpatialPatern

+ Association

+ Regular

+ Irregular

AttributeType

<<Class>>

Percentage

+ value: Numeric (0 - 100]

<<Class>>

Countable

+ value: Numeric

<<Class>>

Presence

+ value: Bool

AttributeType is a label tree (e.g.

protected space, urban planning, etc.)

To define the coverages every

LCComponent will has:

key = observedLC

key = RemoteSensedLC

Figure 4: Class diagram for extending the SIOSE data model.

SIOSE, the object-oriented model (a composite pat-

tern), can be easily extended to enrich the LULC

database further.

We have proposed a simple solution for extending

the model, but other possibilities could be evaluated.

We consider it attractive not to lose sight of this pos-

sibility since the fact of enriching the database would

generate certain positive effects: (1) it would com-

pensate even more the effort to work with SIOSE, and

(2) it would open new possibilities in the distribution

of geographic information (e.g. distribute thematic

reclassiﬁcations of interest in some ﬁelds). In other

words, it would be possible to produce and distribute

speciﬁc data sets for different applications (e.g. ﬁre

risk, planning, tourism, etc.).

ACKNOWLEDGEMENTS

This work was supported by grants from the Spanish

Ministry of Economy and Competitiveness, project

SIOSE-INNOVA (CSO2016-79420-R AEI/FEDER

UE). Sergio Trilles has been funded by the post-

doctoral programme PINV2018-Universitat Jaume I

(POSDOC-B/2018/12) and research stays programme

PINV2019-Universitat Jaume I (E-2019-31)

REFERENCES

Anderson, B. J. R., Hardy, E. E., Roach, J. T., and Witmer,

R. E. (1976). A Land Use and Land Cover Classiﬁca-

tion System for Use with Remote Sensor Data. Tech-

nical report, U.S. Geological Survey, Washington.

Antrop, M. (2006). Sustainable landscapes: contradiction,

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

300

ﬁction or utopia? Landscape and Urban Planning,

75(June 2005):187–197.

Arnold, S., Kosztra, B., Banko, G., Smith, G., Hazeu, G.,

and Bock, M. (2013). The EAGLE concept – A vi-

sion of a future European Land Monitoring Frame-

work. In EARSeL Symposium proceedings 2013, To-

wards Horizon 2020, pages 551–568.

Badia, A., P

elachs, A., Vera, A., Tulla, A. F., and Soriano,

J. M. (2014). Land use and land cover change and the

effects on vulnerability to forest ﬁre of counties in the

mountains of Catalonia : from managing the land to

managing a threat. Pirineos. Revista de Ecolog

ıa de

Monta

na, 169:1–13.

Ben Ramadan, A. A., Jackson-Thompson, J., and Boren,

S. A. (2017). Geographic Information Systems: Us-

ability, Perception, and Preferences of Public Health

Professionals. Online Journal of Public Health Infor-

matics, 9(2).

uttner, G., Feranec, J., and Jaffrain, G. (2002). Corine

land cover update 2000. Technical report, European

Environment Agency, Copenhagen.

Del Bosque Gonz

alez, I., Arozarena Villar, A., Villa

Alc

azar, G., and Valc

arcel Sanz, N. (2005). Creaci

de un Sistema de Informaci

on Geogr

aﬁco de Ocu-

paci

on del Suelo en Espa

na. PROYECTO SIOSE. In

XI Congreso Nacional de Teledetecci

on, pages 255–

262, Puerto de la Cruz, Tenerife. Universidad de La

Laguna.

Fern

andez Noguerol, S. (2017). Desarrollo de herramien-

tas para el tratamiento de la informaci

on y el an

alisis

con SIG de los usos del suelo utilizando el SIOSE.

Una aproximaci

on al caso de Asturias. GeoFocus Re-

vista Internacional de Ciencia y Tecnolog

ıa de la In-

formaci

on Geogr

aﬁca, 20(20):233–251.

Fern

andez Villarino, X., Delgado Hern

andez, J., Valc

arcel

Sanz, N., Caballero, M. E., Benito Saz, M. A., and

Porcuna Fern

andez-Monasterio, A. (2012). Geoser-

vicios web SIOSE: un sistema de informaci

on como

servicio p

ublico. In VI Jornadas de SIG Libre, page 8,

Girona. Servei de Sistemes d’Informaci

o Geogr

aﬁca i

Teledetecci

o (SIGTE). Universitat de Girona.

Foster, D., Swanson, F., Aber, J., Burke, I., Brokaw, N.,

Tilman, D., and Knapp, A. (2003). The Importance

of Land-Use Legacies to Ecology and Conservation.

BioScience, 53(1).

Gamma, E., Helm, R., Johnson, R., and Vlissides, J. M.

(1994). Design Patterns: Elements of Reusable

Object-Oriented Software. Addison-Wesley Profes-

sional, Boston, 1 edition.

Garc

ıa-

Alvarez, D. (2018). The Inﬂuence of Scale in

LULC Modeling. A Comparison Between Two Dif-

ferent LULC Maps (SIOSE and CORINE). In Ca-

macho Olmedo, M., Paegelow, M., Mas, J., and Es-

cobar, F., editors, Geomatic Approaches for Modeling

Land Change Scenarios. Lecture Notes in Geoinfor-

mation and Cartography, chapter 10, pages 187–213.

Springer, Cham.

Ireland, C., Bowers, D., Newton, M., and Waugh,

K. (2009). A classiﬁcation of object-relational

impedance mismatch. Proceedings - 2009 1st Interna-

tional Conference on Advances in Databases, Knowl-

edge, and Data Applications, DBKDA 2009, pages

36–43.

Morte, A. R., Carri

on, J. T. N., and Botella, E. G. (2019).

Objective assessment of land use in hydrographical

studies. WIT Transactions on Ecology and the En-

vironment, 234(2019):41–51.

Navarro-Carri

on, J. T., Zaragoz

ı, B., Ram

on-Morte, A., and

Valc

arcel-Sanz, N. (2016). Should EU land use and

land cover data be managed with a NOSQL document

store? International Journal of Design & Nature and

Ecodynamics, 11(3):438–446.

Olaya-Abril, A., Parras-Alc

antara, L., Lozano-Garc

ıa, B.,

and Obreg

on-Romero, R. (2017). Soil organic carbon

distribution in Mediterranean areas under a climate

change scenario via multiple linear regression analy-

sis. Science of the Total Environment, 592:134–143.

Omrani, H., Abdallah, F., Charif, O., and Longford, N. T.

(2015). Multi-label class assignment in land-use mod-

elling. International Journal of Geographical Infor-

mation Science, 29(April):1023–1041.

na-Angulo, D., Khorchani, M., Errea, P., Lasanta, T.,

Mart

ınez-Arn

aiz, M., and Nadal-Romero, E. (2019).

Factors explaining the diversity of land cover in

abandoned ﬁelds in a Mediterranean mountain area.

Catena, 181(May):104064.

Polasky, S., Nelson, E., Pennington, D., and Johnson, K. A.

(2011). The Impact of Land-Use Change on Ecosys-

tem Services , Biodiversity and Returns to Landown-

ers : A Case Study in the State of Minnesota. Envi-

ronmental and Resource Economics, 48:219–242.

Ropero, R. F., Rum

ı, R., and Aguilera, P. A. (2019).

Bayesian networks for evaluating climate change in-

ﬂuence in olive crops in Andalusia, Spain. Natural

Resource Modeling, 32(1):1–18.

Valcarcel, N. and Casta

no Fern

andez, S., editors (2013).

Cartograf

ıa de Ocupaci

on del Suelo en Espa

na.

Proyecto SIOSE. Number May 2014. Centro Nacional

de Informaci

on Geogr

aﬁca (CNIG), Madrid.

Valcarcel, N., Villa, G., Arozarena, A., Garcia-Asensio,

L., Caballlero, M., Porcuna, A., Domenech, E., and

Peces, J. (2008). SIOSE , a successful test bench to-

wards harmonization and integration of land cover/use

information as environmental reference data. In Re-

mote Sensing and Spatial Information Sciences, vol-

ume XXXVII, pages 1159–1164, Beijing.

Villa, G., Valcarcel, N., Caballero, M. E., Porcuna, A.,

Domenech, E., and Peces, J. J. (2008). Land cover

classiﬁcations: an obsolete paradigm. In ISPRS

Archives – Volume XXXVII Part B4, pages 619–614,

Beijing.

Improving the Usability of the Land Cover and Use Information System of Spain (SIOSE): A Proposal to Distribute New Thematic Layers

and Predeﬁned Reclassiﬁcations

301