An INSPIRE-compliant open-source GIS for fire-fighting management

Abstract

Every year, there are almost 50,000 forest fires in Europe (127/day), which have burned an area equal to more than 450,000 ha. An effective management of forest fires is therefore fundamental in order to reduce the number of the fires and, especially, the related burned areas, preserving the environment and saving human lives. However, some problems still exist in the structure of information and in the harmonization of data and fire management procedures among different European countries. Pursuing the same interoperability aims, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU (European Union) is currently working on developing ad hoc infrastructures for the safe management of forests and fires. Moving from this premises and following an analysis of the state of the art of information systems for forest fire-fighting, in the light of the end-user requirements, the paper presents the INSPIRE—compliant design of a geographical information system, implemented using open-source platforms.

Full text

ORIGINAL PAPER
An INSPIRE-compliant open-source GIS for fire-fighting
management
Nives Grasso
1
•Andrea Maria Lingua
1
•Maria Angela Musci
1
•
Francesca Noardo
1
•Marco Piras
1
Received: 10 July 2017 / Accepted: 6 October 2017 / Published online: 17 October 2017
ÓSpringer Science+Business Media B.V. 2017
Abstract Every year, there are almost 50,000 forest fires in Europe (127/day), which have
burned an area equal to more than 450,000 ha. An effective management of forest fires is
therefore fundamental in order to reduce the number of the fires and, especially, the related
burned areas, preserving the environment and saving human lives. However, some prob-
lems still exist in the structure of information and in the harmonization of data and fire
management procedures among different European countries. Pursuing the same interop-
erability aims, the European Union has invested in the development of the INSPIRE
Directive (Infrastructure for Spatial Information in Europe) to support environmental
policies. Furthermore, the EU (European Union) is currently working on developing ad hoc
infrastructures for the safe management of forests and fires. Moving from this premises and
following an analysis of the state of the art of information systems for forest fire-fighting,
in the light of the end-user requirements, the paper presents the INSPIRE—compliant
design of a geographical information system, implemented using open-source platforms.
Keywords Forest fire-fighting Decision support system Emergency
management INSPIRE data model GIS
1 Introduction
As a consequence of the global climate change and the interaction among several natural
and technological hazards, the number of natural disasters, including forest fires, is
increasing. They usually cause life losses and property and environmental damages (IPCC
2014). A recently published international document, the Sendai Framework for Disaster
Risk Reduction 2015–2030 (Sendai Framework 2015), identifies, considering the open
&Maria Angela Musci
mariaangela.musci@polito.it
1
Dipartimento di Ingegneria Dell’ambiente, Del Territorio E Delle Infrastrutture (DIATI),
Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
123
Nat Hazards (2018) 90:623–637
https://doi.org/10.1007/s11069-017-3059-0

problems, two critical issues: the standardization of the risk and danger factors information
and the necessity to optimize the employed capital in emergencies.
In the list of main natural and man-made disasters, the forest fires take up an important
role, for the seriousness of damages, and the consequent costs (Guha-Sapir et al. 2015).
This phenomenon is widely spread in Europe. The total European burned area only in the
year 2012 (most recent available official data) is 519,424 ha (European Commission—
Joint Research Centre 2012).
In order to be more effective, forest fires require both active and passive fighting
activity. In particular, the management of fire emergencies is organized in three steps
(Fig. 1).
Management, monitoring of fire risk areas and fire-fighting actions are very complex
operations, especially when dealing with ‘‘mega fires’’ (Pyne 2007). Some critical issues
have to be solved for effectively perform these activities by means of automatic and
integrated tools (Chuvieco and Salas 1996).
During each step, the operators must collect and analyse a lot of data, having various
nature (dynamic data, historic series, geometric or thematic data, and so on) (Chuvieco
et al. 2010). Private and public institutions produce and provide data in different and,
sometimes, proprietary formats (Zlatanova et al. 2010). This produces difficulties in the
information retrieval process, and the rapid and efficient interpretation and usage of the
data. It is therefore necessary to harmonize the involved data. For example, the national (or
even sub-national) digital maps are often structured following local specifications and are
shared through independent procedures, which vary in each case.
Finally, the coordination and collaboration among the various operators taking part in
the emergency intervention are essential: official forces (land, marine and air), volunteer
corps, resources coming from neighbouring countries, providing help, and so on. However,
a current difficulty is the coordination process, because it is ruled by different procedures
in each country.
These issues have to be taken into action for effectively realizing a unified European
supporting system for fire-fighting, possibly sharing and optimizing resources.
The foremost tasks to be realized are as follows: an early warning system and a support
for a risk management integrated approach, which allow controlling all the fire-fighting
steps (Fig. 1).
Considering these aspects, this study proposes a method to design and implement a
standard-based Geographic Information System (GIS) increasing the interoperability of
involved data, and producing an effective information for fire-fighting, allowing the pos-
sibility of an automatic and real-time data validation and integration. A possible good
solution could be the use of the European Directive INSPIRE (Information for spatial
information in Europe) (INSPIRE Directive 2007), which has the aim of transboundary
Fig. 1 Steps of the fire-fighting operations
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cartographic data harmonization and management for environmental protection and
common policies. Nevertheless, the data model proposed by INSPIRE is a complete and
international standard-compliant (e.g. with the ISO/TC211 standards) model, suitable to
represent cartographic entities even outside Europe. For its completeness and application
independence, it could be considered as an ontology.
In the implementation of the system, open-source software products were preferred,
because, firstly, they are recommended by programs such as ‘‘Interoperable Delivery of
European eGovernment Services to public Administrations, Business and Citizens’’, at
European level (European Commission 2004) or Digital Administration Code in Italy.
Secondly, open-source software were selected for their well-known features such as
cheapness, portability and customization potentiality.
Finally, a further contribution of this study is the introduction of automated processes
such as triggers and specific queries, which allow the system to be quickly consulted also
exploiting real-time data. Moreover, through similar processes, the system can fill-in
automatically, and in real time, some tables useful for the management of the resources in
fire-fighting activity (e.g. firefighters teams during emergencies).
2 The state of the art: GISs dealing with fires
Currently, in Europe, there are already several GIS platforms providing decision support
for fires issues. However, each system emphasizes only some specific functionality for the
stages of the fire management. In recent years, some examples of tailored forest fire
decision support systems based on GIS technology have been developed and used in
different regions, for example, France, Italy, Spain and the Alpine areas of Europe.
However, they do not implement the whole process, but only a part, or some parts of it.
In particular, GIS EMERCARTO (made by TRAGSA) (http://visores.tragsatec.es/)is
focused in the command and control of operations, as well as in the management and
allocation of fire-fighting resources and support in the decision-making process in real
time. Another GIS tool was developed by (Moreno et al. 2012), in which a dedicated
simulation was realized for a rapid organization of human resources and their equipment.
The capabilities of this tool allow the analysis of the impact of different fire-fighting
strategies considering different simulated scenarios of active operations on the field. In
Italy, the system called SIRIO was developed by (Losso et al. 2012). This system was
tested in Sanremo (Imperia Province, Italy) for monitoring the fire risk areas and giving an
early warning message (e-mail or text message) in real time, when the algorithms detect a
high risk of fire. Nowadays, the European Forest Fire Information System (European
Commission JRC 2000), INSPIRE-compliant, is one of the few GIS applications, aiming at
the retrieval of the data from the whole process of fire-fighting. EFFIS GIS acquires, at this
moment, only the data (e.g. Risk index, hotspots and size of fire) involved in the fire risk
forecasting, and the fires occurred in Europe, using and integrating these historic data to
support decisions. Another case of a fully integrated and interoperable system for fire-
fighting management is the result of ArcFUEL
TM
project (Bonazountas et al. 2012), which
adopts Global Technology standards at all operational layers (e.g. INSPIRE, OGC and
XML/GML). The aim would be providing and producing updated fuel maps to be used in
forest fire management operations and geoplatforms as ArcFIRE
TM
(Mitsopoulos et al.
2014). However, despite these attempts, the use of standards is not so widespread: data
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content and format are often not uniform, consistent, complete and compatible with the
available technologies.
Following a careful analysis of the existing tools, it is possible to notice that an inte-
grated central system is missing. Moreover, it should be underlined that the metadata of the
observed maps are almost never available, and it is impossible to determine the quality,
accuracy, last updating and further useful information about the data.
3 The design of a standard-compliant spatial data model for fire-fighting
The well-known rules for database modelling, as defined by the ANSI/X3/SPARC standard
(Laurini and Thompson 1992), were followed for designing the GIS.
An effective GIS has to consider all the phases of fire-fighting: prevention, prepared-
ness, fire response and recovery (Fig. 2; Neal 1997).
For fire prevention and preparedness aims, GIS should be used for risk analysis and for
supporting preventive activities and decisions. Considering these aspects, it is important to
integrate various data (e.g. past fires, fuel models and weather) and to simulate some
scenarios (Pausas and Ferna
´ndez-Mun
˜oz 2012) also useful for forecasting activities (Vi-
valda et al. 2017a).
During emergencies, the GIS should be able to help in real-time mission planning,
activities management for fire-fighting, and rescue operations. Being designed as a decision
support system, it should also collect and provide information about network infrastructure,
buildings, number of people in danger, available water supplies and further useful data
involved in the analysis.
For the recovery phase, the system is a useful platform, where to store data of the
mission and update the fire registry. These functionalities are very important for fire
damage assessment and reconstruction planning.
The GIS proposed is composed of three main categories of objects: the competent
authorities and actors in the fire-fighting process (command); the land, intended as objects
that are both to be protected and to be considered in the fire-fighting operations, for various
Fig. 2 Schema of the life cycle stages in forest fire management
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reasons (infrastructures where to move, water supply resources and so on); and a more
dynamic component describing the events (fire and hotspot) (Fig. 3).
The command entities are represented following the proposal of the project of having a
unique control centre (the command centre), which coordinates the human resources and
the performing operations. This would be a simplification of the actual state of things, since
the fire-fighting at present involves, for example in Italy, public and private authorities with
the hierarchical order in Fig. 4. Therefore, the proposal of a single command centre for the
coordination of activities and data processing is needed to optimize the management, the
data distribution, updating and use in emergency situations.
The command centre handles all the data from the local operations centres and is able to
direct both the teams moving in the air and the ones acting on the ground. The proposed
structure consists in a national unified command centre, that manages the forest moni-
toring, the fire emergencies and all the equipment for fire-fighting. It coordinates local
operating centres, which are responsible for the local resources management, and the
updating of the fires registry and the mission report. Finally, the operating teams handle
fire-fighting operations in the field (Fig. 5).
The INSPIRE data model provides fundamentals for completely defining the infor-
mation layers closely related to the land description (e.g. cadastral parcels, building,
exposed element and spot elevation), the event development (for instance, event registry
and event time) and the meteorological data (e.g. meteorological data and stations) (Burgan
et al. 1998; Han et al. 1992).
As previously mentioned, in Europe, a systematic fire model is missing, therefore an
approximation on the fuel models was realized, to improve our capacity of fire forecasting
and, consequentially, of fire-fighting management. It introduces some vegetation param-
eters (relative moisture, time lag and combustion heat) that are essential for modelling the
forest fire behaviour (Ager et al. 2011).
In the model, both static and dynamic entities, which are collected and produced during
the event (e.g. operating team locator or resources available), are included. These latter
entities allow the real-time data to be handled and the information to be uninterruptedly
updated. The tables are updated in the system through an SQL script, simulating the data
Fig. 3 The three objects categories for the external model development
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provided by external data sources. A considered option for a real-time acquisition and
communication of the data could be the use of JSON files and protocols provided by the
external sources and employed sensors (Sriparasa 2013).
4 The standard data model extension and automatic schema generation:
the Model Driven Architecture (MDA) approach
To respond to requirements of interoperability and demands of integration among existing
systems and the developed system, the Model Driven Architecture (MDA) approach was
developed by OMG (Object Management Group) in 2001. MDA is enabled to development
through existing specification such as UML and XMI (Cephas Consulting Corp 2006).
Indeed, the Model Driven Architecture (MDA) approach allows the automatic trans-
formation from an UML diagram to a conceptual data schema script (Lisboa-Filho et al.
2013). More specifically, it allows to translate an object-oriented UML model (Platform
Independent Model, PIM) into an object-relational database model in PostgreSQL/PostGIS
(Platform Specific Model, PSM).
Enterprise Architect (hereafter EA) by Sparx Systems is the Computer Aided Software
Engineering (CASE) tool used to support the GeoDB implementation by means of this
approach. This software product is able to automate the construction of a suitable UML
(Unified Modelling Language) diagram, permitting the reuse and extension of the already
available schemas, to effectively design the database and exchange it through applications.
Thus, to generate an INSPIRE-compliant UML class diagram with interoperable data
formats, the INSPIRE UML profile and the INSPIRE repository are imported in EA. The
INSPIRE UML profile is an XML file containing the definition of each element present in
the UML diagram, essential to interpret its meaning by humans or machines (Kutzner and
Donaubauer 2012).
Using the INSPIRE Repository, INSPIRE classes (e.g. Metereological Data, Event-
Time, CadastralParcels, etc.), highlighted in the conceptual model (Fig. 5), were extracted
and imported into a new model. In order to extend the standard with some tailored features
for fire-fighting management, the procedure stated by the OGC as the best practice for
Fig. 4 Involved subject in fire event stages
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extending the Open Geospatial Consortium (OGC) data model CityGML was used (Van
den Brink et al. 2012).
The physical structure of the DB was realized in a semi-automatic way, according to the
steps shown in Fig. 6.
The UML Class Model was converted in an XSD (XML Schema Definition) file through
the specific Enterprise Architect tool. It is suitable for being used as GML application
schema. Then, the XSD was imported and validated with Altova XMLSpy software (or
Fig. 5 UML conceptual model including the INSPIRE-compliant entities and their extension for fire-
fighting applications
Fig. 6 Procedure of implementation. The underlined passage (in grey) is the only manual step
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equivalent open-source ones, such as XPad). During the validation process, the main
problem was the recognition of some data types. Indeed, the semantics, in XML format, are
different from INSPIRE. For example, the notation of text data type in XML schema is
xs:string, while in INSPIRE application schema, the same data type is defined by Char-
acterString. The only solution, in this case, was the manual editing.
Besides validating the XML application schema, which structures XML data, a tool,
integrated in the Altova XMLSpy software, converts the XML schema in an SQL script
with a set of Data Definition Langauge (DDL) commands. This is a mandatory procedure
in order to generate a relational model-compliant DB in a common SQL-based DBMS
software.
The XML editor (in this case, Altova XMLSpy) automatically generates an Structured
Query Language (SQL) script that can be exported and used to automatically build the
structure of a conceptual model-compliant SQL database in a DBMS software.
5 GIS realization and filling-in in open-source software
As specified in the introduction, open-source software tools were preferred. In addition to
the above motivations, open-source software offers a major interoperability (since they can
easily employ open formats). Furthermore, they permit the access to the code and to the
connected libraries for the customization of some tools.
DBMS PosgreSQL, with its spatial extension PostGIS and the graphical interface
PgAdmin III, was used to manage the system (PostgreSQL 2016). Moreover, a connection
with Q-GIS was realized, with purpose to see the data.
PgAdmin III and SQL language allow the implementation of triggers and other func-
tions useful for data querying and analysing, and the realization of views for users and
different uses, and, the semi-automatic filling-in of the data.
To fill the database, the main difficulty is linked to the integration between national and
regional data structures and the data structure suggested by INSPIRE and used to create the
proposed GIS. Indeed, the provided data are often released as single shapefiles, and they
are not organized in a systematic database.
6 Test site and available data
In order to test the functionality of the proposed GIS, the data of Sardinia (Italy) are used.
In particular, the area of the Park of Sulcis, in the south-west of Sardinia, is considered.
For this specific area, several data were collected, as information about forests, fuel
models, hydrographical sources, roads and technological networks, command centre,
operating centres, teams, meteorological data, hotspots and alarms. Other data (e.g. event)
were not referred to real cases of forest fires, but they were hypothesized for testing the
system.
Each data set was therefore converted according to the designed database structure.
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7 A specific TRIGGER function for early warning
In order to allow a real-time monitoring, a trigger system was developed and included in
the platform. The triggers are ad hoc procedures for the automatic manipulation (insertion,
modification and deletion) of the information related to an event (Perry 1990). In this
study, a dedicated trigger devoted to initiate the sending of a first operation team on the
field when receiving the alarm is proposed (Fig. 7).
The trigger was built in SQL code. Analysing the single phases (Fig. 8): the event starts
due to an alarm (1). The alarm is given when the command centre is contacted, and some
data are communicated and inserted into the system, among which the fire location
(geographic coordinates) (Fig. 9); the system requires some variables to be defined as a
reference for performing the following steps (2). The database selects the Command Centre
in charge based on the field ‘‘country’’ where the alarm is given (3). Through the com-
putation of minimum (linear) distance (‘‘min distance’’ function), developed based on
coordinates of the alarm, the nearest Operating Centre is selected (4). The Operating
Centre sends the first Operating Team in field (TIF0) (5).
In order to exploit the advantages of such a GIS, different queries can be performed
where the goal was the automatic calculation of the number of fire-fighters to be sent on the
field. The variables considered by the automatic query are as follows: the class of fire, the
teams that are already in the field and the availability of further fire-fighters in the nearest
operating centres. A further parameter to be considered for calculating the number of
needed fire-fighters is the extension of the fire. This can be assessed also in real time
through some recently proposed methods (Vivalda et al. 2017b). At present, it is an
automatic but independent task; however, future work could permit to fill-in the needed
variables in the here proposed query with the fire extension forecasting results. Moreover, a
network of physical sensors communicating with the built GIS could give real data about
the development and extension of the fire.
Another interesting issue to be considered for determining the number of needed fire-
fighters would be the presence on the territory of some additional risks or elements that
could intensify or influencing the fire development (e.g. gas stations, nuclear plants and
industries).
However, since the objective of this study was the development of an expeditious
procedure and tools for fire management considering the whole Europe, these aspects,
dealing with a major detail in the analysis, will be further developed in future researches.
Fig. 7 Flow chart for developing alarm trigger
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The query is composed by two iterative processes (the two loops), which finish only
when the number of men in the field is sufficient to fight the developing fire (and this last
one is no more increasing) (Fig. 10).
When the trigger in Fig. 7creates a new team (TIF0) after the alarm, a new event in
EventRegistry is inserted and the HotSpot coordinates are registered.
This query is used when a new update on EventTime table arrives to the Command
Centre. Generally, rises of Rate of Spread (ROS) and intensity of fire in EventTime
table produce a new request of fire-fighters (Andrews and Rothermel 1982). When the ROS
is constant or decreases the loop stops. To simplify the process, in this case, only the ROS
was considered. Therefore, the query takes into account the ROS of the Event and selects a
proper value of ‘‘class of Fire’’. The fires are classified considering the control problems on
Fig. 8 Alarm trigger SQL code
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each kind of fire (Cesti 2002). As a consequence, it is possible to define the number of the
needed fire-fighters (NF).
Once the query is performed, the first loop updates the class of fire and checks if the
number of fire-fighters on the field (TIFt) is sufficient, compared with the needed fire-
fighters (NF). If the result is negative, the second loop starts, verifying if the fire-fighters
availability, in the nearest Operating Centres, allows to cover the request of support, and so
on considering the Operating Centres in order of their (linear) distance from the fire. The
loop stops when the request of support is totally covered. The query, in this way, updates
Operating Centre Resources and allows to know who is on field, how many men, and
when, they are involved in the fire management. The time saved by means of such tool is
very precious in an emergency case.
8 Conclusion
In the paper, a standard-compliant and integrated GIS was proposed, as the core part of a
more complex system for ‘‘big fire prevention and management’’, an SDI able to support
prevention, preparedness, emergency response and recovery phases of the fire-fighting
process. It can support early warning systems, the integrated management of historic data
and dynamic information, which is a critical challenge, especially when considering the
intensification of fire phenomena, because of the climate changes, and when it comes to the
so-called mega-fire. It is aimed at filling the existing gap in the fire-fighting process, in the
integration of a very heterogeneous information (multiplicity of procedures, used data
formats, used data sources and so on). The cartographic and territorial data very often do
not comply with shared standards, so they suffer from a limited interoperability. This study
proposes a solution by means of the interoperability technologies, developed in a ‘‘smart’’
framework.
Fig. 9 Trigger in Q-GIS (the numbers refer to the trigger steps in Fig. 7, and the output of them is
underlined in the green squares): (1) input of the alarm position in the system, (4) selection of nearest
operating Centre based on ‘‘min distance’’ function, (5) creation of new operating team (TIF
0
). The TIF
0
position would be surveyed and mapped during the operations through navigation sensors communicating to
the system. The numbers refer to the SQL code described in Fig. 8
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A great advantage of the proposed system is the use of the available reference standards
for structuring the data and the metadata referring both to the cartographic side (land shape,
land coverage and use, network and infrastructures, etc.) and to some thematic environ-
mental or monitoring data. This makes the system completely compatible and
importable by states having INSPIRE-compliant digital maps, as should be in the near
future, for states having adopted the INSPIRE Directive (the whole Europe). Being the
INSPIRE data model structured in form of a very general and application-independent
ontology, it could be probably exported to further regions with effective results.
Fig. 10 Schema of the query workflow. The SQL code of the query is shared at the link http://areeweb.
polito.it/geomatics_lab/Download.html
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Furthermore, the inclusion of dynamic data, historic data registry and similar information
can be an effective support to advanced analysis, to be performed directly in the GIS
platforms.
Another contribution of the presented study is the effort to improve the automaticity in
the conversion of the (in this case extended) conceptual model into the internal model. In
this way, during the transformation from an object-oriented UML class diagram to the
object-relational PostgreSQL/PostGIS database, the data formats and semantics defined by
INSPIRE are preserved. A modelling phase in Enterprise Architect was employed for
defining the extension, which is already affirmed practice in the community. On the other
hand, while the use of an MDA approach is probably affirmed in the informatics field, for
geomatics and cartography management goals, it has to be refined and tested. Although
being an essential part of the generation of the now unavoidably standard-compliant
geodatabases, an optimized procedure implementing is not fully integrated in the software
yet.
Finally, an innovation of the system is linked to the specific application field. The more
critical limit is the lack of integrated and harmonized procedures, solutions and data for
common analysis and optimized management of the entire command chain in interventions
for fire-fighting. In the functioning of the GIS, a solution was hypothesized considering a
unique control centre holding the task of coordination and management, and distributing
responsibilities and mission instructions to decentred bases, able to act. A more definitive
solution should be obviously proposed by the entities directly involved in the fire-fighting
application field.
A great part of the system was based on the open-source system, but some tools are only
available under shareware software, which still limits sometimes the full understanding of
some processing.
In future work, the proposed platform should be tested in collaboration with the actual
operators intervening in the fire-fighting. Moreover, the implementation of automatic
procedures should be improved. A fundamental part of such automatic procedures should
regard the fire alarm, which could be given by automatic physical sensors positioned in
especially vulnerable areas (as evaluated by experts). In this way, an effective sensor
network architecture, following the Internet of Thing paradigm, could be exploited and
integrated in the proposed tool (Gubbi et al. 2013; Arco et al. 2016). A fundamental
development will be the publication of such system as webGIS or as part of a more
complex SDI.
Acknowledgements The study was realized on the themes treated in the European project AF3 (Advanced
Forest Fire Fighting—www.af3project.eu). The authors would like to thank the CVVFF of Cagliari for their
availability and data sharing. Furthermore, they thank Dr. Raffaella Marzano from University of Torino for
her help about fuel model and forest type and Dr. Cesti for his availability.
References
AF3—Advanced Forest Fire Fighting. http://af3project.eu/. Accessed 27 Sept 2017
Ager AA, Vaillant NM, Finney MA (2011) Integrating fire behavior models and geospatial analysis for
wildland fire risk assessment and fuel management planning. J Combust 2011:1–19
Andrews PL, Rothermel RC (1982) Charts for interpreting wildland fire behavior characteristics. USDA,
Forest Service, Ogden, pp 1–21
Nat Hazards (2018) 90:623–637 635
123

Arco E, Boccardo P, Gandino F, Lingua AM, Noardo F, Rebaudengo M (2016) An integrated approach for
pollution monitoring: smart acquirement and smart information. ISPRS Ann Photogramm Remote Sens
Spat Inf Sci 3:67–74
Bonazountas M, Kallidromitou D, Astyakopoulos A (2012) ArcFIRE
TM
/ArcFUEL
TM
: forest fire manage-
ment geoplatform and fuel maps. In: Brebbia CA, Perona G (eds) Modelling, monitoring and man-
agement of forest fires III. Witt Press, Buffalo, pp 67–78
Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface
observations. Int J Wildland Fire 8:159–170
Cephas Consulting Corp (2006) The fast guide to model driven architecture. The basics of model driven
architecture. http://www.omg.org/mda/mda_files/Cephas_MDA_Fast_Guide.pdf. Accessed 27 Sept
2017
Cesti G (2002) Tipologie e comportamenti particolari del fuoco: risvolti nelle operazioni di estinzione. Il
fuoco in foresta: ecologia e controllo. In: Atti del XXXIX Corso di Cultura in Ecologia. Centro Studi
per l’Ambiente Alpino, S. Vito di Cadore, 2–6 settembre 2002. Universita
`di Padova, Padova,
pp 77–116
Chuvieco E, Salas J (1996) Mapping the spatial distribution of forest fire danger using GIS. Int J Geogr Inf
Syst 10:333–345
Chuvieco E et al (2010) Development of a framework for fire risk assessment using remote sensing and
geographic information system technologies. Ecol Model 221(1):46–58
European Commission (2004) IDABC programme—Interoperable Delivery of European eGovernment
Services to public Administrations, Business and Citizens. http://ec.europa.eu/idabc/en/document/
5707/3.html. Accessed 27 Sept 2017
European Commission—Joint Research Centre (2000) EFFIS—European forest fire information system.
European Commission, Luxembourg. http://forest.jrc.ec.europa.eu/effis/. Accessed 27 Sept 2017
European Commission—Joint Research Centre (2012) Forest fires in Europe, middle east and north Africa
2012. European Commission, Luxembourg
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architecture
elements and future directions. Future Gener Comput Syst 29:1645–1660
Guha-Sapir D, Hoyois P, Below R (2015) Annual disaster statistical review 2014: the numbers and trends.
CRED, Brussels. http://www.cred.be/sites/default/files/ADSR_2014.pdf. Accessed 27 Sept 2017
Han S et al (1992) The method for calculating forest fire behaviour index. Fire Saf Sci 1:77–82
INSPIRE Directive (2007) Luxembourg: Official Journal of the European Union. http://inspire.ec.europa.eu/
. Accessed 27 Sept 2017
IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Con-
tribution of working group II to the fifth assessment report of the intergovernmental panel on climate
change. Cambridge University Press, Cambridge, New York
ISO/TC211—geographic information/geomatics. https://committee.iso.org/home/tc211. Accessed 27 Sept
2017
Kutzner T, Donaubauer A (2012) Critical remarks on the use of conceptual schemas in geospatial data
modelling—a schema translation perspective. In: Gensel J et al (ed) Bridging the geographic infor-
mation sciences: international AGILE’2012 conference, Avignon (France), April 24–27, 2012.
Springer, Heidelberg, pp 43–60
Laurini R, Thompson D (1992) Fundamentals of spatial information systems, 1st edn. Academic, London
Lisboa-Filho J et al (2013) Domain and model driven geographic database design. In: Reinhartz-Berger I,
Sturm A, Clark T, Cohen S, Bettin J (eds) Domain engineering. Product lines, languages, and con-
ceptual models. Springer, Heidelberg, pp 375–399. http://www.dpi.ufv.br/*jugurta/papers/DE%
20book.pdf. Accessed 27 Sept 2017
Losso A et al (2012) SIRIO: an integrated forest fire monitoring, detection and decision support system—
performance and results of the installation in Sanremo (Italy). In: Brebbia CA, Perona G (eds)
Modelling, monitoring and management of forest fires III. Witt Press, Buffalo, pp 79–90
Mitsopoulos ID et al (2014) Accuracy assessment of a mediterranean fuel-type map for wildland fire
management at national scale: the cases of Greece and Portugal. In: Viegas DX (ed) Advances in forest
fire research. Imprensa da Universidade de Coimbra, pp 1615–1622
Moreno A et al (2012) Introducing GIS-based simulation tools to support rapid response in wildland fire
fighting. In: Brebbia CA, Perona G (eds) Modelling, monitoring and management of forest fires III.
Witt Press, Buffalo, pp 163–174
Neal D (1997) Reconsidering the phases of disaster. Int J Mass Emerg Disasters 15(2); 239–264. http://
www.ijmed.org/articles/335/download/. Accessed 27 Sept 2017
Pausas JG, Ferna
´ndez-Mun
˜oz S (2012) Fire regime changes in the Western Mediterranean Basin: from fuel-
limited to drought-driven fire regime. Clim Change 110(1):215–226
636 Nat Hazards (2018) 90:623–637
123

Perry DG (1990) Wildland firefighting: fire behavior, tactics, and command. Fire Publications, Bellflower
Pyne SJ (2007) Megaburning: the meaning of megafires and the means of the management. In: 4th inter-
national wildland fire conference proceedings, 13–17 May 2007, Sevilla, Spain [CD-ROM]. Madrid:
Organismo Auto
´nomo de Parques Nacionales, Ministerio de Medio Ambiente
Sendai Framework: Sendai Framework for Disaster Risk Reduction 2015–2030. http://www.preventionweb.
net/. Accessed 27 Sept 2017
Sriparasa SS (2013) JavaScript and JSON essentials. Packt Publishing Ltd, Birmingham
TRAGSA EMERCARTO webGIS. http://visores.tragsatec.es/. Accessed 27 Sept 2017
The PostgreSQL Global Development Group (2016) PostgreSQL 9.6.1 Documentation. https://www.
postgresql.org. Accessed 27 Sept 2017
Van den Brink L, Stoter JE, Zlatanova S (2012) Modelling an application domain extension of CityGML in
UML. In: ISPRS conference 7th international conference on 3D geoinformation, the international
archives on the photogrammetry, remote sensing and spatial information sciences, 16–17 May 2012,
Que
´bec, Canada, ISPRS
Vivalda C, Verda V, Carpignano A, Dell’Erba C, Ca-gliero D, Guelpa E (2017a) Forest fire risk analysis
methods and simulation tools. ESREL 2017 international conference, June 18–22, 2017. Portoroz,
Slovenia
Vivalda C, Musci MA, Grasso N, Guelpa E, Piras M, Verda V (2017b) Forest wildfire risk mapping and the
influence of the weather and geo-morphological input data. In: European safety and reliability con-
ference ESREL 2017, Portoroz, Slovenia, 18–22 June, 2017, pp 171–178
Zlatanova S et al (2010) Models of dynamic data for emergency response: a comparative study. In: Pro-
ceedings of the Gi4DM conference—geomatics for disaster management, February 2–4, 2010, Torino
Nat Hazards (2018) 90:623–637 637
123