FRAMEWORK SERVICE CONTRACT COPERNICUS - EMERGENCY MANAGEMENT SERVICE RISK & RECOVERY MAPPING
Contract No: 259811
Specific contract under the Framework service contract n. 259811:
“EMSN 041 - Forest fire risks assessment in Croatia”
FINAL REPORT
European Commission – Joint Research Centre Institute for the Protection and Security of the Citizen Global Security and Crisis Management Unit Ispra (VA), ITALY
October 2017
TECHNICAL REPORT
FWC FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK & RECOVERY MAPPING 09.10.2017
EMSN‐041: Forest fire risks assessment in Croatia
Table of Contents
1 THE SERVICE CONTEXT ...... 9
1.1 SERVICE DOMAIN OF APPLICATION ...... 10
1.2 THE AREA OF INTEREST ...... 11
2 OUTLINE OF TECHNICAL REALIZATION ISSUES ...... 19
2.1 THEMATIC DELIVERABLES ...... 20
2.1.1 AOIS, MAPS AND MAP SCALES ...... 20
2.1.2 OTHER DELIVERABLES ...... 22
2.1.3 PRODUCTS OVERVIEW ...... 23
2.1.4 TECHNICAL ISSUES ...... 24
2.2 REALIZATION PROBLEMS & SOLUTIONS ADOPTED ...... 26
2.2.1 MANAGEMENT ...... 26
2.2.2 TECHNICAL – FIRE RISK ...... 26
2.2.2.1 HAZARD ...... 26
2.2.2.2 VULNERABILITY ...... 26
2.2.2.3 RISK ASSESSMENT ...... 26
3 TECHNICAL METHODOLOGY ...... 27
3.1 DATA SOURCES ...... 28
3.1.1 SATELLITE DATA PROCUREMENT ...... 28
3.1.2 GEOSPATIAL DATA PROCUREMENT ...... 29
3.2 PROCESSING ...... 30
3.2.1 BASIC PROCESSING ...... 30
3.2.1.1 EO DATA PRE‐PROCESSING ...... 30
3.2.1.2 GEO‐SPATIAL DATA INTEGRATION ...... 30
3.2.1.3 GEO‐SPATIAL DATA GENERATION ...... 31
3.2.1.4 MAP COMPOSITION & SET UP ...... 33
3.2.1.5 DATA COMBINATION – SPATIAL ANALYSIS ...... 33
3.2.2 HAZARD SPECIFIC PROCESSING & RESULTS OBTAINED ...... 35
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3.2.2.1 FOREST FIRES ...... 35
3.2.2.2 FIRST RESPONSE || ACCESS & EVACUATION ...... 77
4 RISK PRODUCTS QUALITY ASSESSMENT ...... 79
4.1 GENERIC TECHNICAL QUALITY TARGETS ...... 80
4.1.1 APPLICABLE STANDARDS/ DOCUMENTS ...... 80
4.1.2 TOLERANCES FOR DELIVERABLES ...... 80
4.1.2.1 ORTHORECTIFID IMAGES ...... 80
4.1.2.2 THEMATIC PRODUCTS ...... 80
4.1.2.3 VECTOR GEOSPATIAL DATA ...... 81
4.2 QUALITY ASSESSMENT RESULTS ...... 82
4.2.1 RISK MAPS ...... 83
4.2.1.1 SUB AOI 1 ...... 83
4.2.1.2 SUB AOI 2 ...... 83
4.2.1.3 SUB AOI 3 ...... 84
4.2.1.4 SUB AOI 4 ...... 84
4.2.1.5 SUB AOI 5 ...... 84
4.2.1.6 SUB AOI 6 ...... 85
4.2.1.7 SUB AOI 7 ...... 85
4.2.1.8 SUB AOI 8 ...... 85
4.2.1.9 SUB AOI 9 ...... 85
4.2.1.10 SUB AOI 10 ...... 86
4.2.1.11 SUB AOI 11 ...... 86
4.2.1.12 SUB AOI 12 ...... 86
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List of figures
Figure 1: AOIs Location. Background: ESRI World Topographic Map...... 13 Figure 2: Footprint of a theropod dinosaur from the Ploce promontory at the SE of Veli Brijun ...... 13 Figure 3. Preview of the Vrana Lake ...... 14 Figure 4: Flora and fauna of the Plakenica Natural Park ...... 15 Figure 5. View of the Veli Rat protected landscape ...... 15 Figure 6. Stari Grad Plain. (a) the land parcel system and (b) the dry stone wall boundaries...... 16 Figure 7. Landscape on Lastovo – inner part of the island ...... 17 Figure 8. View of the Mljet National Park ...... 18 Figure 9. From Original ‐ 27 AOIs to Products ‐ Extended 14 AOIs & the 12 Overview Map Frames. Background: ESRI National Topographic Map...... 21 Figure 10. Satellite (MODIS Aqua and Terra) FRP retrievals (MW) for the years 2001‐2016. MODIS Active Fire Detections distributed by NASA FIRMS23...... 36 Figure 11. Evolution of fire occurrences and severity for the years 2001‐2016 (up). Monthly analysis (January‐ December) of FRP and fire detections (down)...... 36 Figure 12. The extent of the three priority zones for vegetation management ...... 40 Figure 13. Vegetation management ...... 40 Figure 14. Water storage tank with supply to fire hydrant ...... 40 Figure 15. Fire hydrant ...... 41 Figure 16. Strips of land without vegetation or roads within the forest...... 41 Figure 17. Forest road intercepts the fire from spreading ...... 42 Figure 18. Surveillance point for detection of forest fires in large remote locations ...... 42 Figure 19. FireHub Real‐time Fire Monitoring System in Greece ...... 42 Figure 20. Awareness raising campaigns for forest fires in Portugal ...... 43 Figure 21. Assets and Population Exposure Map for the subAOI01 ...... 48 Figure 22. Assets and Population Exposure maps legend ...... 48 Figure 23. Assets and Population Vulnerability map of the Island of Cres ...... 49 Figure 24. Assets and Population Vulnerability maps legend ...... 49 Figure 25. Transportation Network Vulnerability to disruption Map ...... 50 Figure 26. Transportation Network Vulnerability to disruption maps legend ...... 50 Figure 27. Forest Fire Risk Assessment map for the sub‐AOI 06...... 51 Figure 28. Forest Fire Risk Assessment map legend...... 51 Figure 29. Position of the random QC samples within the AOIs. Background: ESRI National Topographic Map...... 83
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List of tables
Table 1: Project AOIs & the number of map sheets (overview map series)...... 20 Table 3. Overview of the technical challenges of the specific activation and our approach for delivery...... 24 Table 3. EO data ordering and download dates ...... 28 Table 4. Sensor models that were used for this activation ...... 30 Table 5. List of Combination Information products ...... 33 Table 6. List of input layers, the conditions in spatial analysis and the expected outcome per case ...... 34 Table 7. List of input layers, the GIS conditions and the expected outcome per case ...... 34 Table 8. Risk lookup table used to combine hazard and vulnerabilty (Legend: Very Low: VL, Low: L, Medium: M, High: H, Very High: VH)...... 38
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Glossary
Abbreviation Corresponding Full Text AOI Area of Interest CCM Copernicus Contributing Missions CDDA Common Database on Designated Areas CFP Copernicus Focal Point CLC CORINE Land Cover CORDA Copernicus Reference Data Access CSC Copernicus Space Component CSCDA Copernicus Space Component Data Access DAP Data Access Portfolio DB Database DEM Digital Elevation Model DGU State Geodetic Administration of the Republic of Croatia DSM Digital Surface Model DTM Digital Terrain Model DUZS Državna uprava za zaštitu i spašavanje DZS Državni zavod za statistiku EEA European Environmental Agency EFFIS European Forest Fire Information System EMS Emergency Management Service EO Earth Observation EOLI Earthnet On‐Line Interactive ERCC Emergency Response Coordination Center ESA European Space Agency ESDAC European Soil Data Centre ETRS European Terrestrial Reference System EU European Union FTP File Transfer Protocol GCP Ground Control Point GEOAP Geoapikonisis SAPGE GIS Geographic Information Systems GMES Global Monitoring for Environment and Security GSD Ground Sample Distance Ha Hectares HR High Resolution HRVATSKE Croatian Waters VODE ICG Intergovernmental Coordination Group IWG‐SEM International Working Group on Satellite‐based Emergency Mapping
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Abbreviation Corresponding Full Text JRC Joint Research Center KML Keyhole Mark‐up Language KMZ Keyhole Mark‐up Language, Zipped LULC Land Use/ Land Cover ML Milestones MM Mitigation Measures MR Medium Resolution NA Non Applicable NDWI Normalized Difference Water Index NOA National Observatory of Athens NUTS Nomenclature of Territorial Units for Statistics OGC Open Geospatial Consortium OSM Open Street Map OTM Open Topo Map PAS Project Administrative Support PC Project Coordinator PM Preventive Measures PMIS Project Management Information System POIs Points Of Interest PPL Partners’ Project Leaders PRJ Projection PRN Principles PRR Priorities QA Quality Assurance QAC Quality Assurance Coordinator QAP Quality Assurance Plan QAR Quality Assurance Report QC Quality Check/Control QCR Quality Control Record QM Quality Manager QMP Quality Management Plan RDBMS Relational Database Management System RMS Root Mean Square RS Remote Sensing SDI Spatial Data Infrastructure SHP Shape file SMC Earth Observation Center SMCS Satellite Mapping Coordination System Sqkm Square Kilometres SRTM Shuttle Radar Topography Mission
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Abbreviation Corresponding Full Text TP‐FC Technical proposal ‐ Framework Contract UNISDR United Nations International Strategy for Disaster Reduction UNOSAT United Nations Operational Satellite Applications Programme USGS U.S. Geological Survey VHR Very High Resolution W3C World Wide Web Consortium WD Working Days WF Workflow WG Working Group WGL Working Group Leader WGS84 World Geodetic System 1984 WMS Web Map Service WMTS Web Map Tile Service WP Work Package WPL Work Package Leader WV WorldView
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1 THE SERVICE CONTEXT
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1.1 SERVICE DOMAIN OF APPLICATION The present activation, focused on pre‐disaster situation analysis concerning forest fires hazard in several (27) sub AOIs in Croatia. The specific activation focuses on the generation of:
Risk assessment products. Exposure, vulnerability and risk maps. Our deliverables, provide this information, adopting the following definitions: Hazard: A dangerous phenomenon, substance, human activity or condition that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage. In technical settings, hazards are described quantitatively by the likely frequency of occurrence of different intensities for different areas, as determined from historical data or scientific analysis. (UNISDR, 2009) Exposure: People, property, systems, or other elements present in hazard zones that are thereby subject to potential losses, (UNISDR 2009). it susceptible to the damaging effects of a hazard, (UNISDR 2009). Vulnerability: The characteristics and circumstances of a community, system or asset that makes it susceptible to the damaging effects of a hazard, (UNISDR 2009). Risk: Risk is the combination of the consequences of an event (hazard) and the associated likelihood/probability of its occurrence, (ISO 31010)
Mitigation measures proposal with respect to all evaluated risks, evaluating and proposing adequate measures according to the encountered circumstances
Crisis Management related information, with relevance to safe shelters, location of field hospitals, etc. with respect to the evaluated risk and accounting for the encountered circumstances
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1.2 THE AREA OF INTEREST The activation was triggered by the National Protection and Rescue Directorate on behalf of the Croatian Crisis Management Association (CCMA1) The goal of the association is to encourage, promote, facilitate and develop: the system of crisis management, activities of protection and rescue, principles of leadership and command, the assistance in disasters and any other areas of protection and rescue that require urgent and immediate intervention. CCMA brings together experts from the field of crisis management, improves and researches the system of civil protection, develops methodology of assessment and risk regulation and their accompanying tools, encourages the publication of research papers, cooperates with other associations and organizations in Croatia and abroad, along with other numerous activities. CCMA has, recently, doubled its efforts by initiating the development of occupational classifications of crisis management in accordance with good practice in EU member states more clearly defining sets of tasks and duties. In the above context all products and data that are generated in the context of the activation will contribute in enhancing risks assessment, in establishing the frame of crisis management priorities as well as in suggesting areas requiring urgent and immediate interventions. The Republic of Croatia is a country between Central and Southeast Europe, on the Adriatic Sea. Croatia is administratively divided into 20 counties and the City of Zagreb, which has the status of a county. For the purposes of the current proposal, the Areas Of Interest (AOIs) have been divided into five groups (Istarska, Primorsko‐Goranska, Zadarska, Splitsko‐Dalamatinska and Dubrovacko‐ Neretvanska), according to the county to which each AOI belongs to (Figure 1).
(a) (b)
1 http://hukm.hr/en/
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(c)
(d)
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(e)
Figure 1: AOIs Location. Background: ESRI World Topographic Map. Istarska County is the westernmost county of Croatia. The Istarska group of AOIs (Figure 7a) includes the three following AOIs: Istarska – IS01: This AOI contains most of the Brijuni islands. The entire AOI is characterized, by the Croatian Environmental Agency (AZO)2, as a protected National Park. There is a Safari Park on the island Olocic Veliki Brijuni, home to a variety of exotic animals, like the Asian elephant and zebu from India. The island has also four sites with over 200 dinosaur footprints from the last period of the Mesozoic Era. On the Brijuni islands there are some plant species that are among the endangered plant species of Istria (marine poppy, wild cucumber, some grass species etc.). The most important plant associations of Veliki Brijun are: Macchia, Meadows, Parks, Holm oak forests, Holm oak and laurel forests, Ancient Olive tree and Conifers. The local seas of Brijuni are important hatching grounds and representative Marine Park for the typical marine organisms of the northern Adriatic. The pen‐shell and the date‐shell marine organisms that are protected by the Law on Environmental Conservation have their home in the waters of Brijuni.
Figure 2: Footprint of a theropod dinosaur from the Ploce promontory at the SE of Veli Brijun3
2 http://www.azo.hr/ 3 Source: http://www.np‐brijuni.hr/en/documents/Setaliste‐dinosaura.pdf
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There are numerous and rich archaeological sites from different periods, from the Neolithic period to the Middle Ages. On the western coast of this AOI, in the bay of Verige, there is a Roman Villa constructed in the 1st century BC. Istarska – IS02: Includes almost entire the municipality of Marcana, as well as certain areas of the eastern Vodnjan, northern Liznjan, southern Barban and western Rasa municipalities. Istarska – IS03: It mainly contains the areas from the Medulin and Liznjan municipalities. At the SW of this AOI there is a forest park, another nationally designated protected area. Primorsko – Gorqanska County. This group of AOIs includes two AOIs (Figure 7b): Primorsko – Gorqanska – PG01: Contains the central part of the municipality of Cres. Primorsko – Gorqanska – PG02: Contains the southern part of the municipality of Cres. The AOI includes the Vrana Lake (Vransko Jezero), the biggest freshwater lake in Croatia. At the north‐ western part of the lake there is an Ornithological Reserve, which due to its biodiversity (110 species nest and 230 more species visit the park) and extraordinary scientific and ecological value it has been included in the list of Important Bird Areas in Europe.
Figure 3. Preview of the Vrana Lake4 Zadarska County. This group of AOIs includes the following nine AOIs (Figure 7c): Zadarska –ZD01: Covers the central and southern parts of the Silba Island. Zadarska –ZD02: Contains the central part of the Olib Island. Zadarska –ZD03: Includes the central areas of the municipality of Starigrad as well as the southern areas of the Gospic municipality. The AOI covers part of the protected Paklenica Natural Park (Figure 4 a). Numerous flora and fauna species are present in the aforementioned park. Specifically, there are 84 butterfly species, 31 amphibian and reptile species, 53 mammalian species (e.g swine, deer, brown bear, wolf, etc.) (Figure 4 b) and 254 bird species (e.g. falcon, eagle, hawk, owl, nuthatch, etc) (Figure 4 c). Bats have also been reported in the caves of the park (Figure 4 Error! Reference source not found.e). There is a variety of forest coverings (e.g. dwarf pine, black pine, different beech forests) (Figure 4 d) due to wide range of altitudes and geological footprints of the park. There is a large variety of plants, many of which are found in the Red List of Plant Species of the Republic of Croatia and on the IUCN Red List of Endangered Plant Species.
4 Source: https://www.pakostane.hr/en/good‐to‐know/nature‐parks
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(a) (b)
(c) (d)
(e)
Figure 4: Flora and fauna of the Plakenica Natural Park
Zadarska –ZD04: This AOI covers East Starigrad and certain western areas of Lovinac. Beeing nearby the ZD03 AOI, this AOI also covers another part of the covers a part of Paklenica Natural Park. Zadarska –ZD05: Includes the entire island of Sverinac, the northen part of the Dugi Island and large areas of the islands of Rivanj, Sestrung and South Veli Tun. The bay of Veli Rat ( Figure 5), at the NE of this AOI, is also a nationally designated protected zone.
Figure 5. View of the Veli Rat protected landscape5
5 Source: http://www.lighthouses‐croatia.com
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Zadarska –ZD06: Bounds the northern part of the Ugljan Island Zadarska –ZD07: Covers the central areas of the Ugljan Island. Zadarska –ZD08: Bounds the central part of the Dugi Island. Zadarska –ZD09: Is comprised by the southern part of the Dugi Island. Most of the covered by this AOI is a nationally designated protected area. There is a Natural Park and the area at the NE of the AOI corresponds to a special reserve zone. Splitsko‐Dalmatinska County: This group of AOIs includes the following seven AOIs (Figure 7d): Splitsko‐Dalmatinska – SD01: Covers the western part of the Brac Island. AT the SE of the AOI there is a unique monument of Croatian history, the Blaca Hermitage, once a famous hermitage of Glagolitic monks (built in the 16th century) and later an observatory. There is also a nature park, the Sutivan Nature Park, which shelters a wide variety of species (e.g. blue and white peafowl’s, pheasants, ostrich’s, wild boars, donkeys, horses, pony‐horses and different turtles. The botanical area of the park includes young olive trees, vegetables, aromatic and ornamental plants and aloe. Splitsko‐Dalmatinska – SD02: Bounds the central areas of the Brac Island. This AOI includes the Vidova gore, a landscape protected by law and covered by Dalmatian black pine (Pinus nigra). Brac is one of the Croatian resorts known for its windsurfing sites. Splitsko‐Dalmatinska – SD03: Contains the eastern areas of the island of Brac. Splitsko‐Dalmatinska – SD04: Covers the Hvar municipality of the homonymous Harv Island. The AOI aloso incudes the Sveti Klement island, the largest of the Paklinski island, which host low pine forest with black pine, and aleppo pine. The island is also a very popular tourist area. Splitsko‐Dalmatinska – SD05: Contains the municipality of Stari Grad, the eastern part of the Harv municipality and the western areas of the Jelsa municipality. The most ancient part of Stari Grad falls within the UNESCO Protected World Heritage Site of the Stari Grad Plain, while the entire municipality lies within the surrounding buffer zone. The Stari Grand Plain has an outstanding universal value as its land parcel system, which was set up by the ancient Greek colonists in the 4th century BC and remains in use even today, witnesses the dissemination of the Greek geometrical model for the dividing up of agricultural land in the Mediterranean world. The ancient layout has been preserved by careful maintenance of the dry stone walls over 24 centuries.
(a) (b)
Figure 6. Stari Grad Plain. (a) the land parcel system and (b) the dry stone wall boundaries6. 7
6 Source: UNESCO World Heritage list
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Splitsko‐Dalmatinska – SD06: Contains the eastern areas of the Jelsa municipality. Splitsko‐Dalmatinska – SD07: Bounds the municipality of Sucuraj. Dubrovacko‐Neretvanska County: This group of AOIs includes the following six AOIs (Figure 7e): Dubrovacko‐Neretvanska – DN01: Contains the western part of the Korčula Island. Contains the western part of the Korčula Island. Dubrovacko‐Neretvanska – DN02: Covers the central areas of the Korčula Island. Dubrovacko‐Neretvanska – DN03: Includes East Korčula Island and the western tip of the Pelješac peninsula. Four protected areas exist in this AOI: (i) a special reserve zone located in the Pelješac peninsula, (ii) a forest park south of the city of Korčula, (iii) a protected pine and oak trees forest as well as the 14th and 15th century’s Franciscan monastery which is an example of the Dalmatian Gothic style in the Badija island, and (iv) a special reserve zone east of the city of Brdo. Dubrovacko‐Neretvanska – DN04: : Covers the Lastovo Island. The entire island has been caracterised by AZO as a protected National Park. The Lastovo National Park habitats include 175 vertebrate species, of which 71 species are endangered at national level and 37 species endangered at European level. It is an important resting site for numerous migrant birds and birds of prey nest; prominent among them are the Peregrine Falcon and the Eleonora`s Falcon. The Park hosts two endangered bat species (Geoffroy’s Bat and Greater Horseshoe Bat). There are 810 flora species recorded, including endangered species, species that have extinct elsewhere, endemic and steno‐endemic species. The area is covered with maquis, holm oak and aleppo pines. Also, almond, lemon, orange, palm and carob trees, as well as medicinal plants (e.g sage, mint, milfoil, rosemary, lavender, fennel, chamomile) and cultivated plants are also present in the Lastovo Park (Figure 7).
Figure 7. Landscape on Lastovo – inner part of the island8
Dubrovacko‐Neretvanska – DN05: Covers the western half of the Mljet Island. The AOI covers the Mljet National Park (Figure 8), which includes a marine area of 500 meters from the coast,
7 whc.unesco.org/en/documents/114162 8 Source: http://www.lastovotravel.com/photos/lastovo‐church‐landscape2
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islands and cliffs. The two deep bays filled with seawater, known as Malo Jezero and Veliko Jezero (Small Lake and Great Lake) are considered an important geological and oceanographical phenomenon. The entire surface area of the park is extremely rich with life, and numerous endemic and endangered species are a testament of the importance of protecting it. Mljet is located in the migration path of birds, and is therefore an important habitat for birds to rest and feed at. Ninety bird species have been recorded so far in the Park area, amongst which is Audouin’s gull, the most endangered species of gulls in Croatia and one of the nesting birds with the lowest populations. The nesting population of the European honey buzzard has been marked as vulnerable, and Mljet is the only know nesting ground for this species in Dalmatia. There are 26 species of mammals in the Park, including fallow deer, chital, mouflon, the wild boar and 10 species of bats. All these species are strictly protected by law. Due to its specific geographic location and soil structure, Mljet has lush vegetation mostly consisting of the Aleppo pine and the holly oak9. There is also a 12th century monumental Benedictine monastery, located on an island in the middle of Veliko Jezero, which consists a significant touristic attraction.
Figure 8. View of the Mljet National Park10
Dubrovacko‐Neretvanska – DN06: Covers the eastern part of the Mljet Island.
All the AOIs contain sites designated under the Habitats Directive11 of the Natura 2000 ecological network. The following AOIs correspond also to sites designated under the Birds Directive12 of the Natura 2000 network: Istarska: IS01 and IS03, Primorsko – Gorqanska: PG01 and PG02, Zadarska: ZD01 –ZD05 and ZD09, Splitsko‐Dalmatinska: SD04 – SD07 Dubrovacko‐Neretvanska: DN01 and DN02
9 http://np‐mljet.hr 10 Source: http://www.travel‐file.com 11 http://ec.europa.eu/environment/nature/natura2000/sites_hab/index_en.htm 12 http://ec.europa.eu/environment/nature/natura2000/sites_birds/index_en.htm
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2 OUTLINE OF TECHNICAL REALIZATION ISSUES
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2.1 THEMATIC DELIVERABLES
2.1.1 AOIS, MAPS AND MAP SCALES
Only overview maps are delivered. Taking into account the AOI boundaries, the required data scale (10.000) and coordinate system (UTM33N, EPSG: 32633), as well as the DG JRC map templates (landscape & portrait versions), the proposed scale for the overview maps is 1:50.000.
Table 1: Project AOIs & the number of map sheets (overview map series).
ACTIVATION AOI AREA SQKM PRODUCTS AOI AREA_SQKM OVERVIEW MAP FRAME FF01 33.87 14 38.66 FF02 33.87 13 38.65 12 FF03 238.13 12 250.66 FF04 33.86 11 263.34 11 FF12 211.66 FF05 33.85 09 38.64 FF06 33.86 06 75.52 09 FF07 33.86 FF08 33.86 10 87.01 10 FF09 38.09 FF10 238.04 07 296.14 08 FF13 38.08 FF11 33.85 08 82.30 08 FF14 38.09 FF15 211.64 FF16 211.66 FF17 211.67 04 1,157.25 05, 06 FF18 238.06 FF19 238.09 FF20 238.12 02 495.68 03, 05 FF21 238.15 FF22 238.08 FF23 238.12 03 741.36 03, 04 FF24 238.15 FF27 152.39 05 162.44 02 FF28 238.19 01 497.29 01 FF29 238.23 4.003,55 4.224,93
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To generate ‘continuous’ products (GEOSPAT‐vector layers), the original ‐ 27 AOIs were merged into 14 AOIs. A buffer zone of 200m was applied to all the AOIs, resulting in a totally mapped area of 4.224 sqkm.
Products ‐ 14 AOIs displayed at 12 Overview Original ‐ Activation 27 AOIs Map Frames
Figure 9. From Original ‐ 27 AOIs to Products ‐ Extended 14 AOIs & the 12 Overview Map Frames. Background: ESRI National Topographic Map.
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2.1.2 OTHER DELIVERABLES
To comply with the mandatory requests of the activation, the following , deliverables are also foreseen :
[A] The present final report, includes:
A detailed description of the methodologies used. A table with the main elements concerning the image data: request, delivery, delays, justifications and quality related issues. Exact dates of all mentioned steps will be listed. A flyer explaining in an appealing way the activation using text, images and graphs is delivered (Annex A)
[B] Spatial data and tabular information appropriate for integration at an information system OGC and ArcGis compliant formats, containing also the defined legends).
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2.1.3 PRODUCTS OVERVIEW
EMSN041 – THEMATIC CONTENT OF OVERVIEW MAPS
HAZARD/EXPOSURE VULNERABILITY RISK MAPS RISKS POPULATION, ASSETS (POIs) & TN TRANSPORT ASSETS & POPULATION ASSETS (POIs) & POPULATION NETWORK (TN) Including mitigation measures (point, linear, polygon) Vulnerability of population and buildings. Forest fire hazard index overlaid to assets. Vulnerability of biomass fuel types. Population Vulnerability to Population, assets and transportation network Exposed elements (population, assets, LU/LC and assets (bridges, dams, ropeways, gas, oil, disruption related to at risk. Population, POIs, LU/LC elements and FIRE classes, transportation segments) at various electricity infrastructure & schools, etc.) at transportation network transportation segments at various risk levels hazard levels presented in tabular format. various vulnerability level presented in tabular capacity estimates presented in tabular format. format
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2.1.4 TECHNICAL ISSUES
The following table summarises some specific challenges pertinent to this particular activation, and the adopted technical & technological solution.
Table 2. Overview of the technical challenges of the specific activation and our approach for delivery
General Specific challenge Our solution
Setup of an Oracle Spatial DB that allows for simultaneous access with R/W privileges Geo‐database environment setup to accommodate for all registered users of the activation & using ESRI ArcGIS, Intergraph GeoMedia Pro, multiple users and diverse datasets Data ERDAS Imagine handling Data and information sharing among partners VPN over high‐speed lines to support all members of the consortium (satellite imagery, geospatial layers, etc.) Application of RPC coefficients, and mission based transformation models and Ortho rectification of imagery coming from various identification of GCPs using ortho photo imagery. Relevant JRC guidelines will be satellite missions Satellite data followed. Primarily use ERDAS IMAGINE COTS. processing Varying ortho rectification geometric accuracy due Images Co‐registration to the variation of Off‐Nadir Angle of input imagery
Different scale harmonization, i.e. spatial, geometry (scale) & thematic (attribute) Harmonization adjustments to match the 1:10,000 scale and thematic nomenclature harmonization Geospatial within the ESRI ArcGIS and/or Intergraph GeoMedia Pro and/or DB environment. data processing Absence of appropriate categorization for vector Standard GIS procedures & photo interpretation using the acquired VHR imagery for thematic layers and/or incomplete spatial and digitization of missing features and enhancement of the original vector layer thematic information Land use / land cover update and enhancement Use Corine Land Cover and update/ enhance LU/LC layer using the acquired VHR Thematic (higher detail of LC classes) imagery. information extraction Data Combination Standard GIS procedures and tables queries
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General Specific challenge Our solution
Forest fire index calculation: Analysis of historical datasets of recorded fires, Forest Fire hazard estimation climatological and surface properties. Depending on the type of 1) biomass fuel, 2) the buildings construction material, and 3) Modelling Forest Fire vulnerability estimation the proximity of population and assets to the most hazardous areas in terms of forest fire activity. Automatic risk estimation, Classification to distinct empirical segments Forest Fire Risk estimation Hazard x Exposure x Vulnerability Map synthesis JRC Copernicus EMS Mapping Guidelines and best practice Employ the Data Driven Pages framework within the ArcGIS to quickly and easily Optimization of the map production process Deliverable create a series of layout pages from a single map document. production Metadata creation INSPIRE COMPLIANT using GeoNetwork tool All vector data in ESRI shapefile format will be transformed and deliver at the OGC OGC compliance of vector data compliant format kml Adopt a four level approach for technical control points: ‐ QC applied by the specialist following the specification guidelines Quality assurance procedure ‐ Group leader QC Technical ‐ Cross‐group QC coordination ‐ Project manager QC Where possible, evaluation of the thematic accuracy using a sample dataset and Ensure accuracy of the thematic products estimation of kappa coefficient, user's and producer's accuracy. Maintain the native geometry accuracy of the input data.
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2.2 REALIZATION PROBLEMS & SOLUTIONS ADOPTED
2.2.1 MANAGEMENT
The challenges of the activation on a management level may be summarized as follows:
Supply of the necessary input data: As soon as the contact award notice was communicated, the Contractor started contacts with the Authorized User. The available data were supplied the 22.09.2017. Due to the timely order of the necessary EO data (before the contract signature date) the whole of the EO data were downloaded, one week and a half after the contract signature, thus affecting the delivery schedule, the least possible.
Demanding work load for the establishment of an integrated data base of the necessary geospatial data The requested buildings footprints were not available through the Authorized User. In this context footprints, available through the OSM “buildings” layer were used. To enrich the building footprints information, selective manual extraction of footprints of large/ important infrastructures was carried out within the sub AOIs. Moreover, the quality of the CLC data set over the AOI was not adequate for the level of analysis and thematic accuracy necessary for the fire risk modelling. In this context extended PI and delimitation of the relative LULC categories was undertaken.
2.2.2 TECHNICAL – FIRE RISK
2.2.2.1 HAZARD The resolution of the geomorphological factors (aspect, slope, elevation), used for the forest fire hazard estimation, is determined by the resolution of DEM. The climatological properties are imported in hazard modelling as stable conditions, due to the limitation of meteorological data in finer scale. 2.2.2.2 VULNERABILITY Vulnerability maps are derived by combining different data (e.g. census data, land cover maps, transportation network, biomass fuel) with different spatial resolution, accuracy and production years. The land cover maps strongly influence the final vulnerability products (and, in turn, the final risk maps). Therefore, inaccuracies in the land cover input file (e.g. misclassification errors) may generate errors in the final vulnerability products. The scale and date of census data may also affect the vulnerability results. The assignation/definition of vulnerability levels (that, in turn, affect the final risk levels) strongly relies on the expert judgment and experience. As general principle, it was adopted a precautionary approach in order to not underestimate the final vulnerability and risk level. 2.2.2.3 RISK ASSESSMENT The Fire Risk assessment is based on the combination of Hazard, Exposure and Vulnerability information. Thus, possible inaccuracies, due to limitations of the available input data, in the aforementioned products also affect the final risk product.
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3 TECHNICAL METHODOLOGY
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3.1 DATA SOURCES
3.1.1 SATELLITE DATA PROCUREMENT
A summary of the actual (multispectral) satellite data that were supplied and used for the generation of the necessary data sets and the maps production is listed in the following table. To optimize the ordering and provision/ upload schedule we have organized the order in 14 sub AOIs, instead of 27, through appropriately grouping neighboring (tender) AOIs. Data order for all the AOIs took place the 9/4/2017. In the table below the EO data ordering and download dates are displayed.
Table 3. EO data ordering and download dates EODATA ESA GEOAP ORDER AOIs IMAGE ID (INPUT) SENSOR ACQ DATE UPLOAD DOWNLOAD AOI PH1A_PHR_FUS_1A_20170731T095320_20170731T095327_TOU_1234_d5f4.DIMA Pleiades 31/07/17 9/6/17 9/6/17 FF28 & 01 PH1B_PHR_FUS_1A_20170506T100342_20170506T100350_TOU_1234_a340.DIMA Pleiades 5/6/17 9/6/17 9/6/17 FF29 PH1B_PHR_FUS_1A_20170506T100359_20170506T100407_TOU_1234_fa66.DIMA Pleiades 5/6/17 9/6/17 9/6/17 FF20 & 02 SP07_NAO_PMS_1A_20170807T091948_20170807T092006_TOU_1234_1fa7.DIMA SPOT7 8/7/17 9/7/17 9/7/17 FF21 PH1A_PHR_FUS_1A_20170807T094927_20170807T094932_TOU_1234_f366.DIMA Pleiades 8/7/17 9/6/17 9/7/17 FF22‐ 03 PH1A_PHR_FUS_1A_20170614T100308_20170614T100315_TOU_1234_85d2.DIMA Pleiades 14/06/17 9/6/17 9/7/17 FF24 PH1B_PHR_FUS_1A_20160526T100730_20160526T100733_TOU_1234_a24b.DIMA Pleiades 26/05/16 9/6/17 9/7/17 PH1A_PHR_FUS_1A_20170514T095213_20170514T095218_TOU_1234_2d1a.DIMA Pleiades 14/05/17 9/7/17 9/7/17 PH1A_PHR_FUS_1A_20170621T095925_20170621T095933_TOU_1234_cd20.DIMA Pleiades 21/06/17 9/7/17 9/7/17 FF15‐ 04 PH1A_PHR_FUS_1A_20170705T095226_20170705T095231_TOU_1234_6f7a.DIMA Pleiades 7/5/17 9/7/17 9/7/17 FF19 PH1A_PHR_FUS_1A_20170807T094915_20170807T094921_TOU_1234_19ce.DIMA Pleiades 8/7/17 9/7/17 9/8/17 PH1B_PHR_FUS_1A_20170522T094045_20170522T094053_TOU_1234_bb94.DIMA Pleiades 22/05/17 9/7/17 9/9/17 05 FF27 PH1B_PHR_FUS_1A_20160528T095318_20160528T095319_TOU_1234_d514.DIMA Pleiades 28/05/16 9/8/17 9/9/17 FF10 & 07 SP07_NAO_PMS_1A_20170621T093113_20170621T093123_TOU_1234_d367.DIMA SPOT7 9/11/17 9/11/17 9/11/17 FF13 FF11 & PH1A_PHR_FUS_1A_20170526T095855_20170526T095902_TOU_1234_97c0.DIMA Pleiades 26/05/17 9/11/17 9/11/17 08 FF14 PH1A_PHR_FUS_1A_20170621T095846_20170621T095851_TOU_1234_e174.DIMA Pleiades 21/06/17 9/11/17 9/11/17 13 FF02 PH1A_PHR_FUS_1A_20170626T101107_20170626T101113_TOU_1234_d78d.DIMA Pleiades 26/06/17 9/11/17 9/11/17 FF06 & 06 SP06_NAO_PMS_1A_20170329T092717_20170329T092741_TOU_1234_2a93.DIMA SPOT6 29/03/17 9/12/17 9/12/17 FF07 09 FF05 PH1A_PHR_FUS_1A_20170321T100637_20170321T100639_TOU_1234_dd3b.DIMA Pleiades 21/03/17 9/12/17 9/12/17 FF08 & 10 PH1B_PHR_FUS_1A_20160521T095627_20160521T095629_TOU_1234_17ca.DIMA Pleiades 21/05/16 9/12/17 9/12/17 FF09 FF04 & 11 PH1A_PHR_FUS_1A_20170526T095839_20170526T095848_TOU_1234_c865.DIMA Pleiades 26/05/17 13/9/17 13/9/17 FF12 12 FF03 SP07_NAO_PMS_1A_20170526T093108_20170526T093118_TOU_1234_b9d4.DIMA SPOT7 26/05/17 13/9/17 13/9/17 14 FF01 PH1A_PHR_FUS_1A_20170626T101040_20170626T101046_TOU_1234_1c8b.DIMA Pleiades 26/06/17 13/9/17 13/9/17
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3.1.2 GEOSPATIAL DATA PROCUREMENT
To accomplish the activation needs and the generation of the requested information/ products, a series of geospatial data were collected, harmonized, geo‐referenced, quality checked and duly corrected/ updated. National digital orthophotos and digital topographic maps, both accessed through WMS were also used. National Sources: 1 - Digital Elevation Model with 5m horizontal resolution|| DUZS – raster; used for the generation of the thematic products (isolines) 2 - Census data || DUZS ‐ DZS ‐ raster
100m Population GRID 100m Buildings GRID; Number of buildings 3 - Digital orthophotos, scale: 1:5.000, ref. year: 2011||DGU ‐ WMS 4 - Croatian Base Maps, scale: 1:5.000, ref. year: 1996 ‐ 2010||DGU ‐ WMS 5 - Topographic Maps, scale: 1:25.000, ref. year: 1996 ‐ 2010||DGU ‐ WMS Additional geospatial layers were used, extracted from “open” data bases and portals. Other Sources: 1 - EEA || CLC12 2 - Digital Elevation Model || SRTM (1 Arc‐Second Global) 3 - Administrative Boundaries || Administrative Boundaries || GADM 4 - OpenStreetMap 5 - Geo‐portals (Google Maps, WkiMapia, OpenTopoMaps, Google Earth etc)
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3.2 PROCESSING The processing necessary for delivering the products related to the activation portfolio are presented in this Section. Within the Risk Specific processing, we present the different methodologies we employed to deliver the diverse set of risk hazard products.
3.2.1 BASIC PROCESSING
The processing necessary for establishing the database for the further elaboration of the Risk Mapping and the setup of the reference map and the LU/LC map is presented in the following. 3.2.1.1 EO DATA PRE‐PROCESSING The image exterior orientation as provided through the Rational Polynomial Coefficient (RPC) data was used. The latter represents the ground‐to‐image geometry without requiring a typical sensor model. RPC data create a connection between image and ground points via ratios of cubic polynomials. The provided exterior orientation data was refined with the use of Ground Control Points (GCPs). As reference layer for the GCPs collection the National digital orthophotos 2011 (scale of 1:5.000) were used (DGU WMS). To perform the Ortho‐rectification, the SRTM DEM data set, was properly projected to the local projection system. The Total RMSE and the check point achieved RMSE < tolerance for geometric accuracy. Through a visual systematic inspection (edges match between adjacent images) in of all ortho‐rectified image area it is was confirmed the overall geometric quality of the orthorectification results. The Table below summarizes the sensor models that were used for the available satellite data.
Table 4. Sensor models that were used for this activation
Mission Category Model Supported Metadata Format
Pleiades Rational Functions Pleiades RPC *.XML
SPOT6‐7 Rational Functions SPOT6‐7 RPC *.XML
3.2.1.2 GEO‐SPATIAL DATA INTEGRATION Re‐projection of all geo‐spatial data layers to UTM33N (EPSG: 32633). Format harmonization. Transformation of different vector (shp, GeoDB, dxf etc) & raster (tiff, bil, ecw etc) to common file formats respectively. Overviews production for raster files. Environments. Different scale harmonization. The mapping scale is set to 1:10.000. As a result, all geo‐ spatial layers at a smaller scale (eg 1:100.000 or smaller), were harmonized as far as possible. This task refers to spatial, geometry (scale) & thematic (attribute) adjustments. Thematic adjustments: Thematic nomenclature harmonization; missing information, gaps, etc. For example: the road network nomenclature extracted from OSM had to be adjusted/ completed on the basis of the image data. Spatial & attribute conformance to products’ specifications. Additional layers design; Contour lines and height spots
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o Import all geo‐spatial layers to a common RDBMS. Feature Datasets production for vector files o Raster Catalog and/or Raster Datasets production for raster files. o Common Coordinate System Set up o XY tolerance set up Quality controls, according to a detailed QA established following the guidelines of Kapnias, Milenov & Kay, (2008)
3.2.1.3 GEO‐SPATIAL DATA GENERATION Using as main reference data set, the VHR ortho‐rectified EO data and through exhaustive photo‐ interpretation, the input layers were accordingly updated. Significant changes and a substantial number of updates were necessary to establish an adequate input for the risks mapping Setting up of the Photo/image‐interpretation working environment; Set up the GIS working environment (project) RDBMS design; Users’ set up, Pick lists, triggers & constrains Geometry & thematic updates: Track of road network changes & updates of the relevant layer(s) respectively. Filling up any other potential gaps/modifications in information content for all input layers used. Digitization from scratch, on top of the VHR satellite data any missing, changed (e.g. coast line, road network, etc), etc. Digitization of buildings footprints (large structures/ POIs) Enrich buildings information (use: education, governmental facilities, hospitals, etc) through visual inspection of Geo‐portals (Google Maps, WkiMapia, Google Earth). The following BFs categorization was used
BF || TYPE_L01 BF || TYPE_L02 CODE code_value CODE code_value 1 Transportation 101 Airport 2 Commercial, Public & Private Services 102 Port 3 Industry & Utilities 103 Marina 4 Place of worship 105 Bus station 201 Government Facilities 202 Fire station 203 Police 204 Hospital 205 Education 206 Military 207 Sports 208 Other
301 Industrial facilities 305 Water infrastructure 307 Electricity infrastructure
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Perform completeness, correctness, homogeneity, topology, etc quality controls
Road Network (RN) Utilities Network (UN) Attribute Field Value (s) code CLASS Primary 1 Electrical grid (Medium Voltage) Secondary ROAD_TYPE Tertiary Local and Service Track BRIDGE 1 (or 0) TUNNEL 2 (or 0) Primary: 650 Secondary: 500 ROAD_CAPACITY Tertiary: 400
Local and Service: 200 Track: 200 LULC LULC_CODE LULC_CLASS CLC Level 1 Artificial areas 1 1110 Continuous Urban Fabric 3 1120 Discontinuous urban fabric 3 1211 Commercial Public and Private Services 4 1212 Industry and Utilities 4 1221 Main roads and associated land 4 1230 Port areas 3 1240 Airports 3 1310 Mineral extraction sites 3 1320 Dump sites 3 1330 Construction sites 3 1340 Land without current use 3 1410 Green urban areas 3 1420 Sports and leisure facilities 3 2 Agricultural areas 1 2120 Permanently irrigated land 3 2210 Vineyards 3 2220 Fruit trees and berry plantations 3 2230 Olive groves 3 2310 Pastures 3 2410 Annual crops associated with permanent crops 3 2420 Complex cultivation patterns 3 Land principally occupied by agriculture with significant areas of natural 2430 3 vegetation 3 Forest & semi‐natural areas 1 3110 Broad‐leaved forest 3 3120 Coniferous forest 3 3130 Mixed forest 3 3210 Natural grassland 3 3220 Moors and heathland 3 3230 Sclerophyllous vegetation 3 3240 Transitional woodland/shrub 3 3310 Beaches dunes and sand planes 3 3320 Bare rock 3
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LULC LULC_CODE LULC_CLASS CLC Level 3330 Sparsely vegetated areas 3
4 Wetlands 1 4110 Inland marshes 3 4120 Peat bogs 3 5 Water bodies 1 5120 Water bodies 3 5210 Coastal lagoons 3 5230 Sea and ocean 3
3.2.1.4 MAP COMPOSITION & SET UP Map composition in the GIS production environment, using the relative templates per case. Map composition o Layers’ symbology o Annotation design o Map Refinement (overpass & underpass, points, etc) o Map tiles design o Legend design, using the relevant map templates o Grids, & Net lines, North Arrow, Scale bar, references, etc. Map production. Export files in digital format: o Digital printable maps o Digital Georeferenced maps Deliverables GIS vector files production o Geo‐DB and/ or Shp (export all geo‐spatial layers from Geo‐DB to shp files) Perform quality checks concerning the usability/ readability of the products Integration of JRC’s comments and feedback (internal QC)
3.2.1.5 DATA COMBINATION – SPATIAL ANALYSIS The information that was generated through the specific analysis was stored in the Project Data Base and was be used (selectively) for display at the Per Hazard products.
Table 5. List of Combination Information products
Product Process Format/Type Vector/Polygon or Population Density Spatial Analysis Raster/Grid Places for: o field hospitals o helicopter landing spots Vector/Points or Spatial Analysis o camps locations Polygons o food storage/ gasoline tank locations o Communication Systems locations
Safe shelters in the area Spatial Analysis Vector/Polygons
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Field hospitals, helicopter landing spots, camps & food storage/ gasoline tank locations In this case, Spatial Analysis techniques were used to identify the potential suitable locations (areas), for temporarily or permanent settlement of necessary first response infrastructure. The following table demonstrates the relative input layers and how those were used per case.
Table 6. List of input layers, the conditions in spatial analysis and the expected outcome per case
Input layer Condition/use Identification of flat areas: ‐ Best areas: gentle slope (2‐4%) DEM ‐ slope layer ‐ Accepted areas: threshold slope value < 10% ‐ Flat sites (slope: 0‐2%) rejected, due to presence of serious drainage problems of waste and storm water ‐ Estimated capacity (area of the spot) Populated places ‐ Distance from major towns Water resources Access to water Road network Access to existing road network Exclusion of internal water bodies Hydrographic network Exclusion of streams & rivers (50m buffering) Electrical power grids Access to high voltage network for power supply Protected areas Exclusion of protected areas ‐ Exclusion of highly dense vegetated areas LULC layer ‐ Inclusions of low dense vegetated areas (for shade, erosion/ dust reduction) ‐ Fire prevention (buffer of 30m from highly dense vegetated areas) National services Proximity to existing infrastructure (health, education, ports & airports) Safe shelters The definition of “shelters” in this case actually refers to temporary ‐ mass care shelters, where the affected part of the population can be temporarily settled, right after a disaster. Besides shelter, Public Authorities and Non‐Governmental Agencies can provide to victims in these places, food, essential relief supplies, emotional support and first aid health services. Existing government shelters (built for these purposes) other public facilities, schools & universities, churches, indoor stadiums, commercial places like malls, hotels, military infrastructures, recreation centers and large building in general, can be used for this purpose. Standard GIS procedures, like attribute ‐ spatial selection & Spatial Analysis were used for the identification of the potential safe shelters in the area.
Table 7. List of input layers, the GIS conditions and the expected outcome per case
Input layer Condition/use Estimated capacity: Populated places ‐ per populated place & ‐ per building (shelter) Building footprints Area of the building Schools & universities, churches, indoor stadiums, commercial places like Buildings typology & use malls, hotels, military infrastructures etc
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3.2.2 HAZARD SPECIFIC PROCESSING & RESULTS OBTAINED
3.2.2.1 FOREST FIRES
3.2.2.1.1 HAZARD The assessment of forest fires hazard is based on geomorphological characteristics (elevation, orientation and slope inclination), climatological properties, vegetation cover susceptibility to fire, as well as spatial distribution of historical fire events. Geomorphological factors, which significantly influence fire behaviour, are slope steepness, aspect, and elevation. Variations in topography can cause rapid fire spread and modify weather patterns. Elevation is related to weather conditions (precipitation, temperature). Areas in higher altitude are less probable to forest fire occurrences. Aspect is also an important factor related to the variations in the amount of solar radiation and wind behaviour. Areas with south and southwest aspect are more prone to forest fires, as in northern hemisphere the solar radiation is higher while the humidity is lower. Slope greatly influences the fire behaviour, as steep slopes contribute to the faster up‐slope spread of fire. Fire susceptibility index for each vegetation type is determined based on different levels of combustibility related to tree moisture and oiliness content. Concerning the vegetation type, the land use classes that appear more favourable to fire events are transitional woodland‐shrub, coniferous forest and sclerophyllous vegetation. This result is also confirmed by historical data records. Investigation of satellite (MODIS) data of Fire Radiative Power (FRP) during the years 2001‐2016 indicated a total of 11373 fire episodes in Croatia. Most of these are low intensity events and 96.5% of the detections have an FRP less than 100 MW. As shown in Figure 10, the coastal and the eastern parts of Croatia suffer the most from wildfire events with the most intense fires occurring along the Dalmatian coastline and islands during summer months. These fires along the Adriatic coast are most common in summer triggered by drought, hot and windy weather. They can have severe impacts on Croatia's tourist industry as they cut off main roads, power supplies and threat citizen’s properties and lives. The number and intensity of the fires seems to follow the natural variability and there is no evidence of an increasing or decreasing trend during the period 2001‐2016 (Figure 11). Summer months (June‐August) show the highest FRP values while the increased number of low FRP fires during spring months is probably related to agricultural activities.
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Figure 10. Satellite (MODIS Aqua and Terra) FRP retrievals (MW) for the years 2001‐2016. MODIS Active Fire Detections distributed by NASA FIRMS23.
Figure 11. Evolution of fire occurrences and severity for the years 2001‐2016 (up). Monthly analysis (January‐ December) of FRP and fire detections (down).
As a general conclusion, it is indicated that the forest fire hazard increases from north to south. Areas characterized by high level of hazard are located in the islands of Korčula (subAOI03), Brač (subAOI04) and Hvar (subAOI02). Also there are areas of high to very high risk on the islands Lastovo (subAOI05) and Mljet Island (subAOI01). These areas are prone to forest fires as the LU/LC classes of coniferous forest and sclerophyllous vegetation cover the intense slope with southern orientation.
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In the northern subAOIs, areas characterized by moderate and low hazard levels appear more frequently, due to the smoother slope inclinations combined with vegetation type classes less prone to fire. Forest fires hazard maps show the hazard levels in forest and semi natural areas, as well as certain heterogeneous agricultural areas. Urban environment and arable land classes are excluded from the forest fire hazard assessment.
3.2.2.1.2 EXPOSURE Forest fires may be uncontrolled with serious impact in population, assets and the natural environment by critically disturbing ecosystems (flora and fauna damages) and inducing biodiversity losses. The exposure product shows the population and assets of each area overlaid to the forest fire hazard levels. The assets examined are population, POIs, transportation infrastructures and land use/ land cover classes.
3.2.2.1.3 VULNERABILITY Vulnerability assessment of the exposed elements was based on specific criteria which make the urban areas and non‐urban areas, vulnerable to the impact of fire. Different approaches have been used for the assessment of fire vulnerability in urban and non‐urban areas. In the case of non‐urban areas vulnerability estimation was based on the type of biomass fuel. The CORINE land cover biomass types used are:
Broad‐leaved forest Coniferous forests Mixed forests Natural grasslands Moors and heatland Sclerophyllous vegetation Transitional woodland and shrubs Olive groves Vineyards Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural vegetation Pastures Sparsely vegetated areas In the case of urban areas, vulnerability estimation was based on:
the population density the building density the road capacity of the transportation network in the urban area the number of possible evacuation roads from each urban area the number of possible “no through” evacuation roads the proximity of population and assets to water bodies or fire prone areas Estimation of Vulnerability of disruption was based on::
Road capacity inferred by the respective taxonomy (highway, primary, secondary, etc.),
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Proximity to population places Population (residents)
3.2.2.1.4 RISK ASSESSMENT Forest fires risk assessment was based on the combination of forest fire hazard and vulnerability of the exposed elements. Forest fire risk estimation depends on the type of biomass fuel and the proximity of population and assets to the most hazardous areas in terms of forest fire activity. Transport network risk is estimated as the combined effect of hazard and vulnerability to disruption. The final qualitative risk index was obtained according to the following Look‐Up Table (Table 8).
Table 8. Risk lookup table used to combine hazard and vulnerabilty (Legend: Very Low: VL, Low: L, Medium: M, High: H, Very High: VH). CATEGORIAL VULNERABILITY Very Low Low Medium High Very High CLASSES
HAZARD NUMERICAL 1 2 3 5 7 CLASSES Very Low 1 VL VL L M M Low 2 VL L L M H Medium 3 L M M M H High 5 M M M H VH Very High 7 M H H VH VH
3.2.2.1.5 MITIGATION MEASURES Mitigation measures can generally be grouped into categories, based on the scale and the time range they cover with their application, extending from national scale to regional and site scale, and from long‐term to short‐term and even near‐real time. The measures are combined for the most effective mitigation of the forest fire risk, based on the particular landscape properties of Croatia.
National scale: Installation of an early warning satellite based system (FireHub13) that is able to detect fire hot spots in near‐real time and enables quick response of local authorities and population awareness. Awareness raising campaigns aiming at policies and actions that contribute to the preparedness and prevention of forest fires. Relocation and land use planning, taking into consideration the protection of vulnerable population at forest fire risk areas. Prohibition of certain land uses inside hazardous areas; examples of restricted land uses in areas associated with high risk are hospitals, as well as spaces where people congregate or storage of flammable materials such as gas stations. Regional scale: Fire protection plans in order to ensure that access routes and assembly points for firefighting equipment, as well as potential sources of water are accessible. Proper mapping of the forest‐road network in conjunction with the unique topographic elements of each area in order to suggest the most appropriate routes. Maintenance and upgrade, if necessary, of transportation networks and water supply systems. Establishment of surveillance points, as well as implementation of wildfire defensible zones and forest roads that allow quick emergency response in case of fire event.
13 http://beyond‐eocenter.eu/images/docs/publications/other/NOA‐FireHub.pdf
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Site scale: Creation of defensible space around vulnerable assets with use of fire resistant building materials (such as metal roofs and fire‐resistant decks), construction of building fences, maintenance of adequate water reservoirs / fire hydrants as well as vegetation management zones (i.e. selectively removing plants, dead underbrush, branches or flammable materials around the assets). The mitigation measures proposed for the case of the fire risk are presented (as in the following table) on the risk maps.
MITIGATION MEASURE TYPE SYMBOL
Vegetation management zones and use of Polygon fire resistant building materials
Forest roads / wildfire defensible zones Polyline
Surveillance point Point
Examples of mitigation measures for forest fires: Vegetation management zones and use of fire resistant building materials: As proposed in the Wildland Fire Risk Assessment and Mitigation of the Canadian Ministry of Natural Resources and Forestry, 201414, priority zones for vegetation management can be established at site‐ scale. The zones encompass lands up to 100 meters from each structure or proposed structure, which are divided into three concentric priority zones.
Priority zone 1 focuses on the area immediately adjacent to a proposed structure and extends outwards in all directions for a recommended minimum of 10 meters. Priority zone 2 begins at 10 meters from a proposed structure and extends out to 30 meters. Priority zone 3 is the area that begins at 30 meters from a proposed structure and extends to 100 meters or farther from it. Priority Zones 1 and 2 are the most crucial areas to apply vegetation management, where biomass fuel is most likely to ignite small new fires and where radiant heat is most likely to affect the structure. In the following figure, the three priority zones are illustrated. In accordance to this Guidebook, the relevant proposed mitigation measure for the case of Croatia covers the first two priority zones 1 and 2. However, due to the scale of the map products, the relevant 30 meters surfaces are not illustrated on the maps, but are delivered in the geodatabase.
14 Wildland Fire Risk Assessment and Mitigation: A Guidebook in support of the Provincial Policy Statement, 2014 – DRAFT GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 39/86
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Figure 12. The extent of the three priority zones for vegetation management15
Figure 13. Vegetation management16
Figure 14. Water storage tank with supply to fire hydrant17
15 Wildland Fire Risk Assessment and Mitigation: A Guidebook in support of the Provincial Policy Statement, 2014 – DRAFT 16 http://www.sbcfire.com/vegetation‐management/
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Figure 15. Fire hydrant18 Forest roads / wildfire defensible zones
Figure 16. Strips of land without vegetation or roads within the forest19
17 http://www.tank‐depot.com/productimages/firesuppresion_illustration.gif 18 http://jacksdrainagebrisbane.com.au/ourservices/valve‐and‐hydrants‐installation 19 http://www.panoramio.com/photo/73131588 GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 41/86
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Figure 17. Forest road intercepts the fire from spreading20 Surveillance points:
Figure 18. Surveillance point for detection of forest fires in large remote locations21 Early warning satellite based system:
Figure 19. FireHub Real‐time Fire Monitoring System in Greece 22
20 http://www.sattlers.org/mickey/travel/2002/colorado/wildfires.html 21 http://www.telegroup.rs/en/ict‐solutions‐and‐products/technical‐security‐systems/216/technical‐security‐systems/
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FireHub is able to detect fire hot spots in real time and enables quick response of local authorities and population awareness. Surveillance activities support prediction, early detection of fires and crisis management. These activities are based on the use of automated technologies and information systems to detect fire risks, remote sensing of active fire data and historical records. Several tools to monitor fire risk can be established through the usage of ground‐based visual systems, ground‐based non‐visual sensors, manned or unmanned aircraft and satellites23. Awareness raising campaigns:
Figure 20. Awareness raising campaigns for forest fires in Portugal24
22 http://beyond‐eocenter.eu/index.php/fires/fire‐monitoring‐system 23 Dominguez, M.T., Marosi‐Kiss, M., (2016), Forest Fires in the Carpathian Mountains ‐ Analysis of Policy Responses at the Regional and National Level, Regional Academy on the United Nations (http://www.ra‐ un.org/uploads/4/7/5/4/47544571/unep_final_research_project.pdf ) 24 http://slideplayer.com/slide/744470/
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3.2.2.1.6 FOREST FIRE PRODUCTS & INFORMATION CONTENT
Processing General Estimation Thematic Types of output Table Mockups Automation Product name categories methodology information products level Consequences within the AOI Assets & Hazard: Hybrid 1. Coloured polygons Assets and PROD C.1. 1. Population Analysis of (independent depicting Forest Fire population 1. Forest Fires Hazard Level exposure historical automatic Hazard levels: Very Forest Fires Risk Hazard Assessment: V. Low Low Medium High V. High datasets of processes, Low; Low; Medium; Assets & Population No of people recorded fires manual – High; Very High Population during the expert Commercial years 2001‐ 2. Points of Interest Exposure Public and assessment) No of corresponding assets 2016, indicating assets Private Services climatological (hospital, industrial Assets properties, facilities, electricity Industry No of corresponding assets geomorphologi and water and Utilities cal infrastructures etc.) characteristics etc. No of corresponding assets and vegetation 3. Physiography 2. cover 4. Utilities network LU/LC V. Low Low Medium High V. High susceptibility to fire 5. Populated places Category # 1 Km2 6. Points/Lines Category # 2 Km2 indication Category # N Km2 transportation network (Road hierarchy, Airport, Marina etc.)
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Processing General Estimation Thematic Types of output Table Mockups Automation Product name categories methodology information products level Consequences within the AOI Assets & Depending on Hybrid 1. Polygons with Assets (building PROD C.2 1. Population the type of (independent grading and population) 1. Forest Fire vulnerability to biomass fuel, automatic according to Vulnerability Risk Vulnerability Level forest fires building & population and processes, Assessment: population Biomass Fuel Assets & V. Low Low Medium High V. High building manual vulnerability Vulnerability Population Population No of people density, number expert and capacity of 2. Polygons with Vulnerability 2 assessment) Build‐up areas Km evacuation grading roads, according to Hospitals No of corresponding assets biomass fuel proximity of Fire vulnerability Assets No of corresponding assets population Brigade based on LU/LC and assets to etc. No of corresponding assets water bodies and fire prone 2. areas Biomass Fuel V. Low Low Medium High V. High Type # 1 Km2 Type # 2 Km2 Type # 3 Km2 Type # N Km2
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Processing General Estimation Thematic Types of output Table Mockups Automation Product name categories methodology information products level Consequences within the AOI Transportation Depending on Hybrid 1. Coloured lines Vulnerability to PROD C.2 2. network road capacity, (independent depicting disruption 2. Forest Fires Vulnerability Level vulnerability to proximity to automatic disruption level related to Risk disruption population vulnerability Assessment: Transport processes, transportation V. Low Low Medium High V. High places and 2. Points of Transportation network manual availability of Interest network Network Primary km alternative expert capacity indicating assets Vulnerability to Secondary km roads assessment) 3. Utilities disruption 4. Physiography Tertiary km 5. Populated Local and Service km places Track km Bridges No Tunnels No
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Processing Types of General Estimation Product Table Mockups Automation Thematic information output categories methodology name level products Consequences within the AOI Forest Fire Hazard x Automatic 1. Risk range: Very Low; Low; Population PROD C.3 1. risk Exposure x risk Medium; High; Very High and assets Forest Fire Risk Level Vulnerability estimation at risk 2. Coloured lines depicting Risk leading to an Manual LULC Assessment: V. Low Low Medium High V. High risk levels for transportation empirical mitigation elements at Population, Population No of people matrix network: Very Low; Low; and recovery risk Assets and Build‐up areas Km2 planning Medium; High; Very High Transportat Transportati 3. Reference information ion network on network Hospitals No of corresponding assets (populated places, at risk at risk Fire No of corresponding assets hydrography, utilities Assets Brigade network, etc.) Industry No of corresponding assets 4. First responder access etc. No of corresponding assets conditions (efficiency: Low, 2. Medium, High) and LU/LC V. Low Low Medium High V. High evacuation possibilities; Access roads to the city & Category # 1 Km2 hospitals, etc. Category # 2 Km2 5. First Response Category # N Km2 Infrastructure; Potential 3. places Transport V. Low Low Medium High V. High for field hospitals, helicopter network landing spots, Safe shelters, Primary km etc. Secondary km 6. Mitigation measures etc. km Railways km Bridges No Airfields No
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3.2.2.1.6.1 PRODUCTS SAMPLES Typical overview examples are shown in the next figures:
Forest Fire Risk Assessment: Assets & Population Exposure In the following figure, assets and population exposure map is presented for the Mljet Island (subAOI01).
Figure 21. Assets and Population Exposure Map for the subAOI01 The following figure shows the legend of the assets and population exposure maps.
Figure 22. Assets and Population Exposure maps legend
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Assets & Population vulnerability to forest fires In the following figure the Assets and Population Vulnerability to forest fires product for the Island of Cres (subAOI11) is presented.
Figure 23. Assets and Population Vulnerability map of the Island of Cres
The following figure shows the legend of the Assets and Population Vulnerability maps.
Figure 24. Assets and Population Vulnerability maps legend
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Road network Vulnerability to disruption The Transportation Network Vulnerability to disruption map for Marcana (subAOI12), Pula (subAOI13) and Brijuni (subAOI14) is presented here below.
Figure 25. Transportation Network Vulnerability to disruption Map The following figure shows the legend of the transportation’s network vulnerability, classified in five vulnerability levels. The map also includes the rest of the elements depicted in the legend.
Figure 26. Transportation Network Vulnerability to disruption maps legend
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Forest Fire Risk Assessment: Population, Assets and Transportation network at risk The following figure presents the population, assets and transportation network at risk for the subAOI06.
Figure 27. Forest Fire Risk Assessment map for the sub‐AOI 06. The following figure shows the legend of the Forest Fire Risk Assessment map.
Figure 28. Forest Fire Risk Assessment map legend.
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3.2.2.1.6.2 STATISTICAL DATA – CONSEQUENCES OVER THE AOI The following tables indicate the population, assets and LU/LC classes exposed to forest fires hazard for each (sub) Area of Interest.
Hazard Level Consequences within the subAOI01 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 3 212 116 58 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 4 2 ‐ Industry & Utilities ‐ ‐ 1 2 ‐ Place of worship ‐ ‐ 2 1 1 Other ‐ ‐ 20 9 2 LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.502 0.984 0.127 0.010 Coniferous forest ‐ 0.009 8.726 2.400 6.237 Land principally occupied by agriculture with significant areas of natural ‐ ‐ 0.632 0.433 0.022 vegetation Mixed forest ‐ 0.190 27.995 20.690 0.553 Olive groves ‐ 0.175 1.565 0.148 0.171 Sclerophyllous vegetation ‐ 0.004 0.168 1.048 3.333 Transitional woodland/shrub ‐ 0.049 1.079 6.719 12.371
Hazard Level Consequences within the subAOI02 Very Low Low Medium High Very High Population (Nr of Inhabitants) 22 41 132 64 28 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 4 1 1 Place of worship ‐ ‐ 1 ‐ ‐ Other 41 86 194 63 59 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ ‐ 0.073 0.006 0.003 Complex cultivation patterns 0.672 1.462 2.460 0.179 0.069 Coniferous forest 0.000 0.051 5.960 3.556 5.334 Land principally occupied by agriculture with significant areas of natural 0.046 5.079 14.193 1.595 0.166 vegetation Mixed forest ‐ 0.034 0.907 0.513 0.019 Natural grassland ‐ 0.003 0.114 0.112 0.300 Olive groves 0.006 1.073 2.110 0.515 0.146 Sclerophyllous vegetation 0.021 0.023 2.510 10.450 8.530 Sparsely vegetated areas 0.050 0.782 0.651 0.005 0.046 Transitional woodland/shrub 0.011 0.414 6.970 25.061 11.185 Vineyards 0.401 ‐ 0.427 0.033 0.029
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Hazard Level Consequences within the subAOI03 Very Low Low Medium High Very High Population (Nr of Inhabitants) 157 1351 2376 893 231 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 1 9 12 1 ‐ Industry & Utilities ‐ 10 26 2 1 Place of worship 1 2 7 2 2 Other 160 794 1942 424 147 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 0.534 4.842 3.083 0.100 0.055 Complex cultivation patterns 0.823 3.495 6.714 0.322 0.064 Coniferous forest 0.096 0.495 28.869 10.079 7.891 Land principally occupied by agriculture with significant areas of natural 0.125 11.073 32.571 14.036 0.275 vegetation Mixed forest 0.066 6.732 27.514 4.899 0.193 Natural grassland ‐ 0.078 0.064 ‐ ‐ Olive groves 0.065 5.569 16.456 6.006 0.170 Sclerophyllous vegetation 0.003 0.157 2.538 5.567 10.332 Sparsely vegetated areas ‐ 1.678 2.919 0.363 0.078 Transitional woodland/shrub 0.052 0.786 24.288 64.651 23.755 Vineyards 1.090 3.853 8.024 0.429 0.010
Hazard Level Consequences within the subAOI04 Very Low Low Medium High Very High Population (Nr of Inhabitants) 316 1724 2132 688 261 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 4 10 21 7 2 Industry & Utilities 8 4 13 6 1 Place of worship 1 14 36 12 4 Other 69 1025 1429 632 198 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.558 3.270 0.147 0.005 ‐ Complex cultivation patterns 4.744 19.961 10.230 0.661 0.253 Coniferous forest 0.061 4.764 44.287 8.484 1.451 Land principally occupied by agriculture with significant areas of natural 1.486 27.696 67.236 16.685 0.252 vegetation Mixed forest 0.339 10.357 23.774 4.753 0.041 Natural grassland ‐ 0.276 19.302 8.385 10.248 Olive groves 0.006 9.035 18.694 1.900 0.106 Pastures ‐ 4.865 3.313 1.593 ‐ Sclerophyllous vegetation 0.003 0.322 15.965 20.988 15.349 Sparsely vegetated areas ‐ 2.607 18.207 4.777 0.099 Transitional woodland/shrub 0.224 1.121 52.909 69.336 16.663 Vineyards 0.127 7.002 3.268 0.061 0.032
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Hazard Level Consequences within the subAOI05 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ ‐ 184 83 23 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 3 2 1 Place of worship ‐ ‐ 1 1 1 Other ‐ 1 1 2 2 LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.358 0.193 0.027 0.012 Coniferous forest ‐ 0.026 8.072 2.462 1.250 Mixed forest ‐ 0.023 2.486 1.568 0.035 Olive groves ‐ 0.219 0.827 0.318 0.064 Pastures ‐ 0.187 0.710 0.076 0.062 Sclerophyllous vegetation ‐ 0.110 1.822 7.866 3.952 Sparsely vegetated areas ‐ 1.641 2.483 0.075 0.020 Transitional woodland/shrub ‐ 0.010 0.329 4.069 1.749 Vineyards ‐ 0.037 0.055 ‐ ‐
Hazard Level Consequences within the subAOI06 Very Low Low Medium High Very High Population (Nr of Inhabitants) 1 ‐ ‐ 1 5 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 1 ‐ ‐ Place of worship ‐ 1 ‐ ‐ ‐ Other ‐ 9 18 4 3 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 7.048 4.193 4.360 0.414 0.004 Coniferous forest ‐ 0.848 3.656 1.266 ‐ Land principally occupied by agriculture with significant areas of natural ‐ 0.013 0.269 0.079 0.085 vegetation Mixed forest 0.882 1.976 4.667 1.296 0.006 Moors and heathland 1.936 0.229 1.062 ‐ ‐ Natural grassland 1.337 1.597 3.499 0.945 0.024 Pastures ‐ 0.314 0.022 0.014 0.015 Sclerophyllous vegetation 0.191 0.139 12.083 7.042 10.362 Sparsely vegetated areas 0.802 0.567 0.574 0.007 0.009 Transitional woodland/shrub ‐ 0.066 0.625 0.454 0.489
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Hazard Level Consequences within the subAOI07 Very Low Low Medium High Very High Population (Nr of Inhabitants) 124 268 238 41 ‐ Assets (Nr) Very Low Low Medium High Very High Marina ‐ 1 ‐ ‐ ‐ Commercial, Public & Private Services ‐ ‐ 1 1 ‐ Industry & Utilities 2 ‐ ‐ ‐ ‐ Place of worship 1 3 4 2 ‐ Other 8 41 31 30 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 0.195 0.469 ‐ ‐ Complex cultivation patterns 0.804 1.141 0.845 0.063 0.008 Coniferous forest ‐ 0.006 1.012 0.295 0.005 Land principally occupied by agriculture with significant areas of natural 0.026 2.001 2.874 0.582 0.021 vegetation Mixed forest 0.003 2.178 3.417 1.236 0.064 Olive groves 0.150 1.401 1.729 0.354 0.094 Sclerophyllous vegetation 0.038 0.096 4.171 14.991 10.222 Sparsely vegetated areas ‐ 0.518 0.716 0.019 0.027 Transitional woodland/shrub 0.001 0.169 5.044 11.633 6.492
Hazard Level Consequences within the subAOI08 Very Low Low Medium High Very High Population (Nr of Inhabitants) 67 48 76 31 2 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ ‐ ‐ 1 Industry & Utilities 1 ‐ 1 ‐ ‐ Place of worship ‐ 1 1 ‐ ‐ Other 11 14 33 10 9 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 0.491 1.851 4.443 0.069 0.072 Complex cultivation patterns 0.250 0.361 0.308 0.205 0.076 Coniferous forest ‐ 0.005 0.899 0.480 0.203 Land principally occupied by agriculture with significant areas of natural 0.022 0.815 0.922 0.158 0.006 vegetation Mixed forest 0.002 ‐ 0.006 0.016 ‐ Natural grassland ‐ 0.025 1.967 1.560 1.418 Olive groves 0.056 1.714 3.457 1.098 0.092 Pastures ‐ ‐ 0.006 0.024 ‐ Sclerophyllous vegetation 0.024 0.221 3.543 7.333 4.913 Sparsely vegetated areas ‐ ‐ 0.138 ‐ 0.005 Transitional woodland/shrub 0.002 0.215 1.412 1.378 3.000
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Hazard Level Consequences within the subAOI09 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐410 153 23 2 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 2 ‐ 1 Industry & Utilities ‐‐1 ‐ ‐ Place of worship ‐1 1 ‐ ‐ Other ‐12 19 1 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.172 0.095 ‐ ‐ Land principally occupied by agriculture with significant areas of natural ‐ 1.933 2.811 0.710 0.059 vegetation Mixed forest ‐1.349 2.040 0.944 0.102 Olive groves ‐2.096 2.665 0.136 0.047 Sclerophyllous vegetation ‐0.408 0.954 1.449 2.785 Transitional woodland/shrub ‐‐0.046 0.027 ‐
Hazard Level Consequences within the subAOI10 Very Low Low Medium High Very High Population (Nr of Inhabitants) 5 21 17 ‐ ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 1 ‐ 1 1 ‐ Industry & Utilities ‐ ‐ 2 ‐ ‐ Place of worship ‐ ‐ 2 ‐ ‐ Other 7 9 20 ‐ ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 3.730 2.792 3.240 0.052 ‐ Complex cultivation patterns 0.172 0.032 0.004 ‐ ‐ Coniferous forest ‐ ‐ 0.055 ‐ ‐ Land principally occupied by agriculture with significant areas of natural 0.121 7.356 ‐ vegetation 7.258 0.050 Mixed forest 0.056 0.160 0.066 0.016 ‐ Sclerophyllous vegetation ‐ 0.002 0.028 ‐ ‐ Transitional woodland/shrub 0.056 0.056 2.264 2.710 ‐
Hazard Level Consequences within the subAOI11 Very Low Low Medium High Very High Population (Nr of Inhabitants) 11 84 168 77 1 Assets (Nr) Very Low Low Medium High Very High Commercial Public and Private Services ‐ 2 ‐ ‐ ‐ Industry & Utilities ‐ ‐ 1 ‐ ‐ Place of worship 1 3 1 ‐ ‐ Other 5 19 13 6 9 LU/LC (sqkm) Very Low Low Medium High Very High
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Hazard Level Consequences within the subAOI11 Very Low Low Medium High Very High Broad‐leaved forest 4.481 8.923 15.110 0.352 0.081 Complex cultivation patterns 0.357 0.216 1.048 0.056 0.002 Coniferous forest 0.032 0.319 5.490 4.426 0.512 Land principally occupied by agriculture with significant areas of natural 0.137 10.353 2.991 0.103 vegetation 7.165 Mixed forest ‐ 0.008 1.126 3.194 0.006 Natural grassland 0.012 0.033 4.296 1.648 0.079 Olive groves 0.006 1.366 3.798 1.364 0.046 Pastures 14.278 4.318 4.695 0.228 0.015 Sclerophyllous vegetation 0.098 0.166 10.631 9.219 1.728 Sparsely vegetated areas 3.942 2.784 3.401 0.194 0.047 Transitional woodland/shrub 0.186 0.274 10.340 14.034 7.527
Hazard Level Consequences within the subAOI12 Very Low Low Medium High Very High Population (Nr of Inhabitants) 340 692 395 69 1 Assets (Nr) Very Low Low Medium High Very High Marina ‐ ‐ ‐ 1 ‐ Commercial, Public & Private Services ‐ ‐ 1 ‐ ‐ Place of worship ‐ ‐ 1 ‐ ‐ Other 46 68 54 3 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 9.288 35.384 29.388 0.370 0.004 Complex cultivation patterns 4.705 5.157 4.961 0.117 ‐ Coniferous forest ‐ 0.131 1.188 1.213 0.075 Fruit trees and berry plantations ‐ 0.045 0.272 ‐ ‐ Land principally occupied by agriculture with significant areas of natural 0.547 17.949 8.349 ‐ vegetation 0.168 Mixed forest 0.055 0.982 2.254 0.033 ‐ Natural grassland 0.016 0.028 2.516 0.692 0.868 Pastures 0.603 4.548 2.302 0.060 ‐ Sclerophyllous vegetation ‐ ‐ 0.111 1.620 ‐ Transitional woodland/shrub 0.360 0.866 7.683 7.450 0.413 Vineyards ‐ ‐ 0.053 ‐ ‐
Hazard Level Consequences within the subAOI13 Very Low Low Medium High Very High Population (Nr of Inhabitants) 90 463 409 20 ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 3 ‐ ‐ ‐ Place of worship ‐ 1 ‐ ‐ ‐ Other 2 82 103 1 ‐
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Hazard Level Consequences within the subAOI13 Very Low Low Medium High Very High LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.584 1.995 1.637 0.002 ‐ Complex cultivation patterns 0.590 1.946 1.481 0.019 ‐ Coniferous forest ‐ ‐ 0.266 0.027 ‐ Land principally occupied by agriculture with significant areas of natural 0.124 3.476 3.087 0.002 ‐ vegetation Mixed forest 0.012 0.476 0.411 ‐ ‐ Olive groves ‐ 0.016 0.029 0.024 ‐ Pastures 0.451 0.073 0.154 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ 0.042 ‐ Transitional woodland/shrub 0.020 0.035 0.590 0.935 ‐ Vineyards 0.148 ‐ 0.040 ‐ ‐
Consequences within the subAOI14 Hazard Level Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 3 ‐ 1 ‐ ‐ Place of worship 1 ‐ ‐ ‐ ‐ Other 39 3 8 ‐ ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.686 0.518 1.452 ‐ ‐ Fruit trees and berry plantations 0.009 ‐ 0.011 ‐ ‐ Mixed forest ‐ ‐ 0.015 ‐ ‐ Natural grassland 0.010 ‐ ‐ ‐ ‐ Pastures 0.871 0.118 0.389 ‐ ‐ Sclerophyllous vegetation ‐ ‐ 0.073 ‐ ‐ Transitional woodland/shrub ‐ 0.009 0.103 ‐ ‐
The following tables indicate the vulnerability of assets and population, for each (sub) Area of Interest
Vulnerability Level Consequences within the subAOI 01 Very Low Low Medium High Very High Population (Nr of Inhabitants) 129 38 165 139 99 Built‐up areas (sqkm) 0.719 0.073 0.203 0.364 0.018 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 2 2 10 22 1 Industry & Utilities ‐ ‐ 2 1 ‐ Place of worship ‐ 2 3 3 ‐ Other 4 16 36 101 10 LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 1.623 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 17.372 ‐
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Vulnerability Level Consequences within the subAOI 01 Very Low Low Medium High Very High Land principally occupied by agriculture with significant areas of natural ‐ ‐ 1.087 ‐ ‐ vegetation Mixed forest ‐ ‐ 49.428 ‐ ‐ Olive groves ‐ ‐ 2.060 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 4.553 Transitional woodland/shrub ‐ ‐ ‐ ‐ 20.218
Vulnerability Level Consequences within the subAOI 02 Very Low Low Medium High Very High Population (Nr of Inhabitants) 2 191 450 265 192 Built‐up areas (sqkm) 0.011 0.811 1.235 0.940 0.069 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 1 3 4 17 2 Place of worship ‐ 4 2 4 ‐ Other 7 342 668 423 68 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 0.092 ‐ ‐ ‐ Complex cultivation patterns ‐ 4.843 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 14.917 ‐
Land principally occupied by agriculture with significant areas of natural ‐ ‐ 21.117 ‐ vegetation
Mixed forest ‐ ‐ 1.473 ‐ Natural grassland ‐ ‐ 0.529 ‐ Olive groves ‐ ‐ 3.850 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 21.535 Sparsely vegetated areas ‐ 1.928 ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 43.650 Vineyards ‐ ‐ 0.889 ‐ ‐
Vulnerability Level Consequences within the subAOI 03 Very Low Low Medium High Very High Population (Nr of Inhabitants) 52 157 2021 734 553 Built‐up areas (sqkm) 0.417 0.757 3.392 2.101 0.755 Assets (Nr) Very Low Low Medium High Very High Marina ‐ ‐ ‐ 1 ‐ Commercial, Public & Private Services 19 37 51 39 20 Industry & Utilities 2 8 46 1 2 Place of worship 6 6 12 17 6 Other 123 319 7410 4209 1734
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Vulnerability Level Consequences within the subAOI 03 Very Low Low Medium High Very High LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 8.613 ‐ ‐ ‐ Complex cultivation patterns ‐ ‐ 11.420 ‐ ‐ Coniferous forest ‐ ‐ ‐ 47.431 ‐ Land principally occupied by agriculture with significant areas of natural ‐ ‐ 58.081 ‐ ‐ vegetation Mixed forest ‐ ‐ 39.405 ‐ ‐ Natural grasssland ‐ ‐ ‐ 0.142 ‐ Olive groves ‐ ‐ 28.266 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 18.596 Sparsely vegetated areas ‐ 5.037 ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 113.532 Vineyards ‐ ‐ 13.406 ‐ ‐
Vulnerability Level Consequences within the subAOI 04 Very Low Low Medium High Very High Population (Nr of Inhabitants) 104 451 1965 891 487 Built‐up areas (sqkm) 1.244 2.503 4.499 3.377 0.124 Assets (Nr) Very Low Low Medium High Very High Airport 3 ‐ ‐ ‐ ‐ Commercial, Public & Private Services 30 51 86 69 7 Industry & Utilities 4 15 33 8 4 Place of worship 6 19 63 27 25 Other 252 2238 6199 6282 611 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 5.979 ‐ ‐ ‐ Complex cultivation patterns ‐ ‐ 35.849 ‐ ‐ Coniferous forest ‐ ‐ ‐ 59.048 ‐ Land principally occupied by agriculture with significant areas of natural ‐ ‐ 113.355 ‐ ‐ vegetation Mixed forest ‐ ‐ 39.264 ‐ ‐ Pastures ‐ 9.771 ‐ ‐ ‐ Natural grasslands ‐ ‐ ‐ 38.211 ‐ Olive groves ‐ ‐ 29.742 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 52.629 Sparsely vegetated areas ‐ 25.691 ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 140.254 Vineyards ‐ ‐ 10.490 ‐ ‐
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Vulnerability Level Consequences within the subAOI 05 Very Low Low Medium High Very High Population (Nr of Inhabitants) 74 5 78 96 83 Built‐up areas (sqkm) 0.262 0.034 0.264 0.279 0.041 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 1 10 4 6 Industry & Utilities ‐ ‐ ‐ 1 ‐ Place of worship 1 ‐ 1 5 2 Other ‐ 3 9 5 2 LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.590 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 11.809 ‐ Mixed forest ‐ ‐ 4.113 ‐ ‐ Olive groves ‐ ‐ 1.427 ‐ ‐ Pastures ‐ 1.023 0.011 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 13.756 Sparsely vegetated areas ‐ 4.219 ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 6.157 Vineyards ‐ ‐ 0.092 ‐ ‐
Vulnerability Level Consequences within the subAOI 06 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 2 10 5 10 Built‐up areas (sqkm) ‐ ‐ 0.001 0.004 ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 1 ‐ ‐ ‐ Place of worship ‐ 1 ‐ ‐ ‐ Other ‐ 14 4 2 14 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 16.019 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 5.770 ‐ Land principally occupied by agriculture with significant areas of natural ‐ ‐ 0.446 ‐ ‐ vegetation Mixed forest ‐ ‐ 8.828 ‐ ‐ Moors and heathland ‐ ‐ 3.228 ‐ Natural grassland ‐ ‐ ‐ 7.403 ‐ Pastures ‐ 0.364 ‐ ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 29.817 Sparsely vegetated areas ‐ 1.959 ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 1.634
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Vulnerability Level Consequences within the subAOI 07 Very Very Low Low Medium High High Population (Nr of Inhabitants) 1 297 401 52 135 Built‐up areas (sqkm) 0.006 0.778 1.024 0.126 ‐ Assets (Nr) Marina ‐ ‐ 1 ‐ ‐ Commercial, Public & Private Services ‐ 4 16 2 ‐ Industry & Utilities ‐ 2 ‐ ‐ ‐ Place of worship ‐ 1 14 ‐ 1 Other 2 73 308 11 26 LU/LC (sqkm) Broad‐leaved forest ‐ 0.664 ‐ ‐ ‐ Complex cultivation patterns ‐ 2.860 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 1.318 ‐
Land principally occupied by agriculture ‐ ‐ 5.504 ‐ ‐ with significant areas of natural vegetation
Mixed forest ‐ ‐ 6.900 ‐ ‐ Olive groves ‐ ‐ 3.728 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 29.518 Sparsely vegetated areas ‐ 1.280 ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 23.339
Vulnerability Level Consequences within the subAOI 08 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 62 134 9 52 Built‐up areas (sqkm) 0.017 0.495 0.924 0.022 0.006 Assets (Nr) Commercial, Public & Private Services ‐ 6 9 ‐ 1 Industry & Utilities ‐ ‐ 2 ‐ ‐ Place of worship ‐ ‐ 5 ‐ 1 Other ‐ 110 216 5 20 LU/LC (sqkm) Broad‐leaved forest ‐ 6.927 ‐ ‐ ‐ Complex cultivation patterns ‐ ‐ 1.200 ‐ ‐ Coniferous forest ‐ ‐ ‐ 1.587 ‐ Land principally occupied by agriculture ‐ ‐ 1.924 ‐ ‐ with significant areas of natural vegetation Mixed forest ‐ ‐ 0.025 ‐ ‐ Natural grassland ‐ ‐ ‐ 4.970 ‐ Olive groves ‐ ‐ 6.416 ‐ ‐ Pastures ‐ 0.030 ‐ ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 16.034 Sparsely vegetated areas ‐ 0.143 ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 6.007
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Vulnerability Level Consequences within the subAOI 09 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 162 3178 800 50 Built‐up areas (sqkm) ‐ 0.086 1.580 0.287 0.004 Assets (Nr) Very Low Low Medium High Very High Marina ‐ ‐ 1 ‐ ‐ Commercial, Public & Private Services ‐ 19 8 1 ‐ Industry & Utilities ‐ ‐ 1 ‐ ‐ Place of worship ‐ ‐ 11 2 ‐ Other ‐ 5 363 28 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.268 ‐ ‐ ‐ Land principally occupied by agriculture with significant areas of natural ‐ ‐ 5.514 ‐ ‐ vegetation Mixed forest ‐ ‐ 4.436 ‐ ‐ Olive groves ‐ ‐ 4.944 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 5.597 Transitional woodland/shrub ‐ ‐ ‐ ‐ 0.074
Vulnerability Level Consequences within the subAOI 10 Very Low Low Medium High Very High Population (Nr of Inhabitants) 6 140 92 4 4 Built‐up areas (sqkm) 0.009 1.061 0.047 0.008 0.003 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 6 ‐ ‐ 2 Industry & Utilities ‐ 1 ‐ ‐ 1 Place of worship ‐ 6 2 1 1 Other 6 342 30 1 1 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 9.814 ‐ ‐ ‐ Complex cultivation patterns ‐ 0.208 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 0.055 ‐ Land principally occupied by agriculture with significant areas of natural ‐ ‐ 14.786 ‐ ‐ vegetation Mixed forest ‐ ‐ 0.298 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 0.030 Transitional woodland/shrub ‐ ‐ ‐ ‐ 5.086
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Vulnerability Level Consequences within the subAOI 11 Very Low Low Medium High Very High Population (Nr of Inhabitants) 871 509 1382 1428 1417 Built‐up areas (sqkm) 0.934 0.425 0.462 0.186 0.079 Assets (Nr) Very Low Low Medium High Very High Commercial Public and Private Services 3 8 4 3 1 Industry & Utilities 1 ‐ 2 ‐ ‐ Place of worship ‐ 6 6 3 4 Other 16 196 245 181 658 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 28.946 ‐ ‐ ‐ Complex cultivation patterns ‐ ‐ 1.679 ‐ ‐ Coniferous forest ‐ ‐ ‐ 10.780 ‐ Land principally occupied by agriculture ‐ ‐ 20.750 ‐ ‐ with significant areas of natural vegetation Mixed forest ‐ ‐ 4.333 ‐ ‐ Natural grassland ‐ ‐ ‐ 6.067 ‐ Olive groves ‐ ‐ 6.581 ‐ ‐ Pastures ‐ 23.534 ‐ ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 21.841 Sparsely vegetated areas ‐ 10.367 ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ 32.360
Vulnerability Level Consequences within the subAOI 12 Very Low Low Medium High Very High Population (Nr of Inhabitants) 251 559 793 136 105 Built‐up areas (sqkm) 5.714 3.019 1.573 0.487 0.090 Assets (Nr) Airport 5 ‐ ‐ ‐ 1 Marina ‐ ‐ ‐ ‐ 1 Commercial, Public & Private Services 2 2 5 1 ‐ Industry & Utilities 3 2 ‐ ‐ ‐ Place of worship 1 2 6 ‐ ‐ Other 163 230 263 54 57 LU/LC (sqkm) Broad‐leaved forest ‐ 74.435 ‐ ‐ ‐ Complex cultivation patterns ‐ ‐ 14.940 ‐ ‐ Coniferous forest ‐ ‐ ‐ 2.607 ‐ Land principally occupied by agriculture ‐ ‐ 27.012 ‐ ‐ with significant areas of natural vegetation Mixed forest ‐ ‐ 3.324 ‐ ‐ Pastures ‐ 7.514 ‐ ‐ ‐ Natural grasslands ‐ ‐ ‐ 4.119 ‐ Olive groves ‐ ‐ ‐ ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 1.732
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Vulnerability Level Consequences within the subAOI 12 Very Low Low Medium High Very High Sparsely vegetated areas ‐ ‐ ‐ ‐ ‐ Transitional woodland/shrub ‐ ‐ ‐ ‐ 16.772 Vineyards ‐ ‐ 0.053 ‐ ‐
Vulnerability Level Consequences within the subAOI 13 Very Low Low Medium High Very High Population (Nr of Inhabitants) 37 1585 1766 3088 209 Built‐up areas (sqkm) 0.026 1.868 0.673 1.945 0.038 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 23 5 14 ‐ Place of worship ‐ 1 2 4 ‐ Other 25 321 546 1412 21 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 6.208 0.010 ‐ ‐ Complex cultivation patterns ‐ 4.035 ‐ ‐ ‐ Coniferous forest ‐ ‐ ‐ 0.294 ‐ Land principally occupied by agriculture ‐ ‐ 6.689 ‐ ‐ with significant areas of natural vegetation Mixed forest ‐ ‐ 0.898 ‐ ‐ Olive groves ‐ ‐ 0.069 ‐ ‐ Pastures ‐ 0.678 ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 0.043 Transitional woodland/shrub ‐ ‐ ‐ ‐ 1.581 Vineyards ‐ ‐ 0.188 ‐ ‐
Vulnerability Level Consequences within the subAOI 14 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ ‐ ‐ ‐ ‐ Built‐up areas (sqkm) ‐ 0.131 ‐ ‐ 0.007 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 10 ‐ ‐ ‐ Place of worship ‐ 1 ‐ ‐ ‐ Other 1 60 1 ‐ 3 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 4.655 ‐ ‐ ‐ Mixed forest ‐ ‐ 0.015 ‐ ‐ Natural grassland ‐ ‐ ‐ 0.010 ‐ Pastures ‐ 1.379 ‐ ‐ ‐ Sclerophyllous vegetation ‐ ‐ ‐ ‐ 0.073 Transitional woodland/shrub ‐ ‐ ‐ ‐ 0.113
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The following tables indicate the vulnerability to disruption of the transportation network, for each (sub) Area of Interest.
Vulnerability Level Consequences within the subAOI01 Very Low Low Medium High Very High Primary (km) 37.082 6.381 ‐ ‐ ‐ Road Tertiary (km) 13.800 10.505 2.974 3.214 ‐ type Local and service (km) 1.700 2.075 2.172 1.344 0.288 Track (km) 70.660 10.619 5.217 2.939 ‐
Vulnerability Level Consequences within the subAOI02 Very Low Low Medium High Very High Primary (km) 40.970 3.238 ‐ ‐ ‐ Road Tertiary (km) 19.617 1.748 0.408 ‐ ‐ type Local and service (km) 6.314 6.582 3.250 1.766 1.212 Track (km) 229.144 15.243 4.682 ‐ ‐
Vulnerability Level Consequences within the subAOI03 Very Low Low Medium High Very High Primary (km) 51.297 0.908 0.489 ‐ ‐ Secondary (km) 54.674 21.289 5.229 1.018 ‐ Road Tertiary (km) 70.044 6.631 3.708 1.621 0.452 type Local and service (km) 91.825 58.505 33.287 13.293 1.341 Track (km) 602.756 35.403 5.939 0.402 ‐ Bridges (Nr) 2 1 1 ‐ ‐
Vulnerability Level Consequences within the subAOI04 Very Low Low Medium High Very High Primary (km) 81.202 0.537 0.329 ‐ ‐ Secondary (km) 106.815 6.183 0.304 0.301 ‐ Tertiary (km) 82.916 10.844 1.259 0.457 ‐ Road Local and service (km) 142.817 70.530 34.966 7.166 1.157 type Track (km) 1323.788 18.002 1.685 0.249 ‐ Bridges (Nr) 19 ‐ ‐ ‐ ‐ Tunnels (Nr) 6 1 ‐ ‐ ‐
Vulnerability Level Consequences within the subAOI05 Very Low Low Medium High Very High Primary (km) 8.626 0.820 ‐ ‐ ‐ Secondary (km) ‐ ‐ 2.581 ‐ ‐ Road Tertiary (km) 15.912 6.748 2.188 0.924 ‐ type Local and service (km) 1.111 0.699 1.373 2.624 0.610 Track (km) 49.682 3.349 2.635 1.353 ‐ Bridges (Nr) 1 ‐ ‐ ‐ ‐
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Vulnerability Level Consequences within the subAOI06 Very Low Low Medium High Very High Primary (km) 0.120 ‐ ‐ ‐ ‐ Road Tertiary (km) 3.116 ‐ ‐ ‐ ‐ type Local and service (km) 0.163 ‐ ‐ ‐ ‐ Track (km) 26.947 0.338 ‐ ‐ ‐
Vulnerability Level Consequences within the subAOI07 Very Low Low Medium High Very High Primary (km) 23.347 4.280 ‐ ‐ ‐ Secondary (km) 1.692 ‐ ‐ ‐ ‐ Road Tertiary (km) 4.362 3.130 1.800 2.167 type Local and service (km) 7.930 15.850 7.951 7.604 3.988 Track (km) 41.532 11.011 3.686 1.742 0.444 Bridges (Nr) ‐ 1 ‐ ‐ ‐
Vulnerability Level Consequences within the subAOI08 Very Low Low Medium High Very High Primary (km) 15.056 0.334 0.274 ‐ ‐ Tertiary (km) 6.466 1.257 0.051 1.199 ‐ Road Local and service (km) 3.294 4.746 4.579 1.546 1.029 type Track (km) 69.567 3.203 1.994 0.251 ‐ Bridges (Nr) ‐ ‐ 1 ‐ ‐
Vulnerability Level Consequences within the subAOI09 Very Low Low Medium High Very High Primary (km) 7.434 0.996 0.740 ‐ ‐ Secondary (km) 0.850 0.298 ‐ ‐ ‐ Road Tertiary (km) 2.953 2.008 0.911 ‐ ‐ type Local and service (km) 8.653 10.271 10.299 5.286 0.549 Track (km) 58.099 2.999 ‐ ‐ ‐ Bridges (Nr) 2 ‐ ‐ ‐ ‐
Vulnerability Level Consequences within the subAOI10 Very Low Low Medium High Very High Road Local and service (km) 3.174 4.422 5.267 5.116 2.164 type Track (km) 18.065 10.392 1.090 0.832 ‐
Vulnerability Level Consequences within the subAOI11 Very Low Low Medium High Very High Primary (km) 30.485 ‐ ‐ ‐ ‐ Secondary (km) 11.340 ‐ ‐ ‐ ‐ Road Tertiary (km) 16.034 0.636 ‐ ‐ ‐ type Local and service (km) 19.837 8.005 1.797 0.735 0.559 Track (km) 118.610 4.817 ‐ ‐ ‐
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Vulnerability Level Consequences within the subAOI12 Very Low Low Medium High Very High Primary (km) 14.331 2.275 0.535 ‐ ‐ Secondary (km) 37.541 10.124 2.885 0.357 ‐ Road Tertiary (km) 16.007 7.119 4.575 2.077 ‐ type Local and service (km) 55.673 20.711 11.902 6.169 2.291 Track (km) 238.306 25.897 4.752 ‐ 0.144
Vulnerability Level Consequences within the subAOI13 Very Low Low Medium High Very High Secondary (km) 6.572 4.704 1.292 ‐ ‐ Road Tertiary (km) 2.376 1.003 2.095 0.581 ‐ type Local and service (km) 34.561 19.623 12.399 6.611 2.831 Track (km) 72.586 15.544 5.365 2.661 1.481
Vulnerability Level Consequences within the subAOI14 Very Low Low Medium High Very High Local and service (km) 22.025 ‐ ‐ ‐ ‐ Road Track (km) 12.292 ‐ ‐ ‐ ‐ type Bridges (Nr) 2 ‐ ‐ ‐ ‐ The following tables indicate the population, assets, LULC elements and transportation network at risk.
Risk Level Consequences within the subΑΟΙ 01 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 6 210 77 42 Built‐up areas (sqkm) ‐ 0.016 1.057 0.200 0.101 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 30 6 1 ‐ Industry & Utilities ‐ ‐ 3 ‐ ‐ Place of worship ‐ ‐ 7 ‐ 1 Other ‐ 130 30 7 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.507 1.099 0.016 0.001 Coniferous forest ‐ 0.001 8.736 8.606 0.018 Land principally occupied by agriculture with ‐ ‐ 1.067 0.013 0.007 significant areas of natural vegetation Mixed forest ‐ 0.189 48.783 0.263 0.179 Olive groves ‐ 0.176 1.715 0.102 0.065 Sclerophyllous vegetation ‐ 0.005 0.078 1.066 3.398 Transitional woodland/shrub ‐ 0.003 0.213 7.518 12.480 Transport Network (KM) Very Low Low Medium High Very High Primary 0.762 22.172 19.940 0.588 ‐ Secondary ‐ ‐ ‐ ‐ ‐ Tertiary 0.061 10.983 16.759 1.570 ‐
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Risk Level Consequences within the subΑΟΙ 01 Very Low Low Medium High Very High Local and service 0.081 2.379 4.441 0.196 0.482 Track 3.416 33.359 47.888 3.705 1.076
Risk Level Consequences within the subΑΟΙ 02 Very Low Low Medium High Very High Population (Nr of Inhabitants) 22 116 617 198 15 Build‐up areas (sqkm) 0.031 0.229 1.257 0.526 0.042 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 21 4 1 Place of worship ‐ ‐ 8 2 ‐ Other 80 272 871 254 36 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 0.010 ‐ 0.081 ‐ ‐ Complex cultivation patterns 0.212 1.200 3.256 0.172 0.003 Coniferous forest 0.002 0.002 5.925 8.803 0.187 Land principally occupied by agriculture with ‐ 2.351 18.069 0.653 0.043 significant areas of natural vegetation Mixed forest ‐ 0.002 1.414 0.045 0.011 Natural grassland ‐ 0.001 0.114 0.410 0.004 Olive groves ‐ 0.500 2.860 0.466 0.024 Sclerophyllous vegetation ‐ 0.004 0.065 12.866 8.600 Sparsely vegetated areas 0.263 0.969 0.680 0.001 0.014 Transitional woodland/shrub ‐ 0.014 0.450 32.129 11.057 Vineyards ‐ ‐ 0.429 0.458 0.002 Transport Network (KM) Very Low Low Medium High Very High Primary 11.413 21.718 5.257 1.114 ‐ Secondary ‐ ‐ ‐ ‐ ‐ Tertiary 1.505 9.669 10.599 ‐ ‐ Local and service 3.170 9.211 5.257 1.439 ‐ Other 36.804 9.211 96.293 4.547 ‐
RIsk Level Consequences within the subΑΟΙ 03 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 779 1671 186 352 Built‐up areas (sqkm) ‐ 2.251 3.893 0.406 0.764 Assets (Nr) Very Low Low Medium High Very High Marina ‐ 1 ‐ ‐ ‐ Commercial, Public & Private Services ‐ 55 100 4 7 Industry & Utilities ‐ 21 28 2 8 Place of worship ‐ 11 31 3 2 Other ‐ 4563 7393 559 1280 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 0.538 7.905 0.094 0.043 0.027
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RIsk Level Consequences within the subΑΟΙ 03 Very Low Low Medium High Very High Complex cultivation patterns ‐ 4.301 6.882 0.120 0.081 Coniferous forest ‐ 0.046 29.426 9.722 8.136 Land principally occupied by agriculture with 0.002 11.218 45.182 0.276 0.214 significant areas of natural vegetation Mixed forest 0.002 6.785 32.350 0.031 0.055 Natural grasssland ‐ 0.002 0.138 ‐ ‐ Olive groves ‐ 5.633 22.309 0.149 0.109 Sclerophyllous vegetation ‐ 0.024 0.084 2.536 15.715 Sparsely vegetated areas ‐ 4.623 0.320 0.064 0.028 Transitional woodland/shrub ‐ 0.165 0.784 23.606 88.653 Vineyards ‐ 4.943 8.180 0.060 0.029 Transport Network (KM) Very Low Low Medium High Very High Primary 13.220 23.361 16.084 ‐ ‐ Secondary 13.854 32.983 30.255 4.011 ‐ Tertiary 10.373 46.919 24.797 0.367 ‐ Local and service 44.834 95.212 50.723 5.182 0.666 Track 109.289 342.963 185.725 1.773 0.027 Bridges (Nr) ‐ 2 2 ‐ ‐
Risk Level Consequences within the subΑΟΙ 04 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 1093 1505 444 396 Built‐up areas (sqkm) ‐ 4.218 4.978 1.477 1.008 Assets (Nr) Very Low Low Medium High Very High Airport ‐ 2 ‐ 1 ‐ Commercial, Public & Private Services ‐ 89 116 19 19 Industry & Utilities ‐ 29 27 5 3 Place of worship ‐ 45 63 18 14 Other 3 6345 6100 1659 1475 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.561 3.386 0.004 0.016 0.002 Complex cultivation patterns ‐ 24.740 10.513 0.349 0.156 Coniferous forest ‐ 0.030 49.001 8.347 1.544 Land principally occupied by agriculture with 0.003 29.200 83.340 0.442 0.168 significant areas of natural vegetation Mixed forest ‐ 10.719 28.419 0.072 0.017 Pastures ‐ 8.160 1.537 0.052 0.002 Natural grasslands ‐ 0.012 19.557 8.216 10.370 Olive groves ‐ 9.044 20.510 0.105 0.034 Sclerophyllous vegetation 0.003 0.043 0.067 16.068 36.397 Sparsely vegetated areas ‐ 20.813 4.607 0.144 0.101 Transitional woodland/shrub 0.021 0.264 0.619 52.836 86.308 Vineyards ‐ 7.104 3.293 0.025 0.028
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Risk Level Consequences within the subΑΟΙ 04 Very Low Low Medium High Very High Transport Network (KM) Very Low Low Medium High Very High Primary 28.565 34.752 17.159 ‐ ‐ Secondary 25.990 62.185 24.655 0.718 ‐ Tertiary 32.154 45.217 16.340 0.339 ‐ Local and service 77.775 130.992 43.114 2.103 0.559 Track 358.023 681.147 301.425 1.331 ‐ Bridges (Nr) 10 11 ‐ ‐ ‐ Tunnels (Nr) 2 1 4 ‐ ‐
Risk Level Consequences within the subAOI 05 Very Low Low Medium High Very High Population (Nr of Inhabitants) ‐ 3 111 100 22 Built‐up areas (sqkm) ‐ 0.038 0.568 0.236 0.035 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 14 6 1 Industry & Utilities ‐ ‐ 1 ‐ ‐ Place of worship ‐ 1 5 3 ‐ Other ‐ 2 10 5 2 LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns ‐ 0.359 0.218 0.013 0.001 Coniferous forest ‐ 0.003 8.084 3.696 0.024 Mixed forest ‐ 0.023 4.032 0.046 0.010 Olive groves ‐ 0.219 1.073 0.119 0.017 Pastures ‐ 0.173 0.830 0.028 0.003 Sclerophyllous vegetation ‐ 0.003 0.078 9.712 3.953 Sparsely vegetated areas ‐ 1.399 2.794 0.018 0.007 Transitional woodland/shrub ‐ 0.001 0.024 4.381 1.745 Vineyards ‐ 0.012 0.080 0.000 ‐ Transport Network (KM) Very Low Low Medium High Very High Primary 0.887 5.702 2.857 ‐ ‐ Secondary ‐ 0.036 0.699 1.846 ‐ Tertiary 1.467 8.428 13.495 2.382 ‐ Local and service 0.663 1.156 1.710 2.132 0.756 Track 4.957 23.970 26.719 1.373 Bridges (Nr) 1 ‐ ‐ ‐ ‐
Risk Level Consequences within the subΑΟΙ 06 Very Low Low Medium High Very High Population (Nr of Inhabitants) 1 ‐ 1 ‐ 5 Build‐up areas (sqkm) ‐ ‐ 0.007 0.014 0.028 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ 1 ‐ ‐ Place of worship ‐ 1 ‐ ‐ ‐
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Risk Level Consequences within the subΑΟΙ 06 Very Low Low Medium High Very High Other ‐ 8 11 12 3 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 6.970 4.253 4.771 0.025 ‐ Coniferous forest ‐ 0.004 4.581 1.184 0.049 Land principally occupied by agriculture with ‐ ‐ 0.198 0.199 0.049 significant areas of natural vegetation Mixed forest 0.169 2.567 6.068 0.021 0.002 Moors and heathland 0.007 ‐ 3.221 ‐ ‐ Natural grassland 0.083 0.003 6.354 0.964 0.001 Pastures ‐ 0.165 0.151 0.032 0.015 Sclerophyllous vegetation 0.019 0.003 0.884 18.697 10.215 Sparsely vegetated areas 0.797 0.566 0.591 0.004 0.001 Transitional woodland/shrub ‐ 0.004 0.054 1.094 0.482 Transport Network (KM) Very Low Low Medium High Very High Primary ‐ ‐ 0.120 ‐ ‐ Tertiary ‐ 0.884 2.233 ‐ ‐ Local and service ‐ ‐ 0.163 ‐ ‐ Other 5.615 12.907 8.754 ‐
Risk Level Consequences within the subΑΟΙ 07 Very Low Low Medium High Very High Population (Nr of Inhabitants) 179 591 714 86 ‐ Build‐up areas (sqkm) 0.220 0.649 1.107 0.267 0.020 Assets (Nr) Very Low Low Medium High Very High Marina ‐ 1 ‐ ‐ ‐ Commercial, Public & Private Services 1 4 1 16 ‐ Industry & Utilities ‐ ‐ 2 ‐ ‐ Place of worship ‐ 4 9 3 ‐ Other 28 125 206 60 1 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest ‐ 0.194 0.470 ‐ ‐ Complex cultivation patterns 0.139 1.293 1.405 0.023 ‐ Coniferous forest ‐ 0.001 1.068 0.249 0.001 Land principally occupied by agriculture with ‐ 1.238 4.004 0.249 0.012 significant areas of natural vegetation Mixed forest ‐ 1.685 5.007 0.199 0.009 Olive groves ‐ 0.849 2.360 0.485 0.035 Sclerophyllous vegetation ‐ 0.007 0.087 19.259 10.165 Sparsely vegetated areas ‐ 0.520 0.757 0.003 0.001 Transitional woodland/shrub ‐ 0.002 0.055 16.821 6.461 Transport Network (KM) Very Low Low Medium High Very High Primary 7.954 9.763 9.992 ‐ ‐
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Risk Level Consequences within the subΑΟΙ 07 Very Low Low Medium High Very High Secondary 0.395 0.450 0.847 ‐ ‐ Tertiary 3.935 6.583 2.878 ‐ ‐ Local and service 7.632 23.999 21.522 4.142 0.407 Other 22.819 48.442 38.710 1.197 Bridges (Nr) ‐ 1 ‐ ‐ ‐
Risk Level Consequences within the subAOI 08 Very Low Low Medium High Very High Population (Nr of Inhabitants) 34 106 70 37 26 Built‐up areas (sqkm) 0.111 0.450 0.136 0.010 0.019 Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 1 13 1 ‐ 1 Industry & Utilities ‐ 1 1 ‐ ‐ Place of worship ‐ 4 1 1 ‐ Other 94 142 83 13 19 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 0.490 5.354 0.992 0.044 0.040 Complex cultivation patterns 0.004 0.638 0.475 0.070 0.012 Coniferous forest ‐ 0.001 0.874 0.491 0.214 Land principally occupied by agriculture with ‐ 0.843 1.065 0.006 0.009 significant areas of natural vegetation Mixed forest ‐ 0.003 0.022 ‐ ‐ Natural grassland ‐ 0.001 2.009 1.527 1.424 Olive groves 0.001 1.765 4.326 0.130 0.191 Pastures ‐ ‐ 0.029 ‐ 0.001 Sclerophyllous vegetation 0.006 0.051 0.287 3.417 12.249 Sparsely vegetated areas ‐ 0.140 ‐ ‐ 0.002 Transitional woodland/shrub ‐ 0.026 0.056 1.492 ‐ Transport Network (KM) Very Low Low Medium High Very High Primary 6.851 6.484 2.321 ‐ ‐ Secondary ‐ ‐ ‐ ‐ ‐ Tertiary 3.010 2.757 3.017 0.123 ‐ Local and service 4.346 6.357 3.804 0.646 ‐ Track 14.063 32.945 27.398 0.474 ‐
Risk Level Consequences within the sub AOI 09 Very Low Low Medium High Very High Population (Nr of Inhabitants) 2 2429 901 2 1 Build‐up areas (sqkm) 0.063 1.498 0.545 0.004 ‐ Assets (Nr) Very Low Low Medium High Very High Marina ‐ 1 ‐ ‐ ‐ Commercial, Public & Private Services 10 14 3 1 ‐ Industry & Utilities ‐ ‐ 1 ‐ ‐
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Place of worship ‐ 9 4 ‐ ‐ Other ‐ 297 99 ‐ ‐ LU/LC (sqkm) Very Low Low Medium High Very High Complex cultivation patterns 0.050 0.117 0.098 0.003 ‐ Land principally occupied by agriculture with ‐ 1.454 3.976 0.051 0.032 significant areas of natural vegetation Mixed forest ‐ 1.188 3.106 0.121 0.021 Olive groves ‐ 1.350 3.513 0.070 0.011 Sclerophyllous vegetation ‐ 0.007 0.266 2.535 2.789 Transitional woodland/shrub ‐ ‐ 0.001 0.073 ‐ Transport Network (KM) Very Low Low Medium High Very High Primary 8.536 7.384 2.418 ‐ ‐ Secondary 1.618 0.679 ‐ ‐ ‐ Tertiary 4.789 6.873 0.084 ‐ ‐ Local and service 22.178 34.734 12.870 0.306 ‐ Other 45.504 68.052 8.630 ‐ ‐ Bridges (Nr) 3 ‐ ‐ ‐ ‐
Risk Level Consequences within the subAOI 10 Very Low Low Medium High Very High Population (Nr of Inhabitants) 72 122 72 ‐ ‐ Built‐up areas (sqkm) 0.397 0.514 0.214 ‐ ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services 3 1 2 2 ‐ Industry & Utilities ‐ ‐ 1 1 ‐ Place of worship 2 3 4 ‐ ‐ Other 133 166 80 1 ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 3.437 3.083 3.260 0.022 ‐ Complex cultivation patterns ‐ 0.204 0.004 ‐ ‐ Coniferous forest ‐ ‐ 0.055 ‐ ‐ Land principally occupied by agriculture with 0.006 7.389 7.354 0.026 ‐ significant areas of natural vegetation
Mixed forest 0.005 0.208 0.084 0.001 ‐ Sclerophyllous vegetation 0.029 ‐ Transitional woodland/shrub 0.010 0.010 0.041 5.014 ‐ Transport Network (KM) Very Low Low Medium High Very High Primary ‐ ‐ ‐ ‐ ‐ Secondary ‐ ‐ ‐ ‐ ‐ Tertiary ‐ ‐ ‐ ‐ ‐ Local and service 4.442 6.640 8.071 0.991 ‐ Track 11.552 16.393 2.340 ‐
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Risk Level Consequences within the subAOI 11 Very Low Low Medium High Very High Population (Nr of Inhabitants) 11 167 182 23 18 Built‐up areas (sqkm) 0.533 0.573 0.497 0.023 0.020 Assets (Nr) Very Low Low Medium High Very High Commercial Public and Private Services 6 8 5 ‐ ‐ Industry & Utilities 1 1 ‐ ‐ ‐ Place of worship ‐ 9 10 ‐ ‐ Other 19 295 970 1 11 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 4.523 23.971 0.257 0.036 0.130 Complex cultivation patterns 0.004 0.586 1.083 0.002 0.003 Coniferous forest 0.004 0.035 5.834 4.358 0.541 Land principally occupied by agriculture with 0.012 7.376 13.198 0.110 0.048 significant areas of natural vegetation Mixed forest ‐ 0.012 4.301 0.009 0.005 Natural grassland ‐ 0.024 4.316 1.630 0.093 Olive groves 0.001 1.386 5.138 0.036 0.014 Pastures 14.349 8.965 0.063 0.070 0.082 Sclerophyllous vegetation 0.029 0.050 0.032 10.733 10.989 Sparsely vegetated areas 3.942 6.170 0.137 0.052 0.048 Transitional woodland/shrub 0.045 0.115 0.084 10.333 21.768 Transport Network (KM) Very Low Low Medium High Very High Primary 7.611 15.281 7.549 ‐ ‐ Secondary 2.230 7.067 2.042 ‐ ‐ Tertiary 7.296 6.222 3.152 ‐ ‐ Local and service 13.796 13.259 3.742 ‐ ‐ Track 41.928 58.794 22.704 ‐ ‐
Risk Level Consequences within the subAOI 12 Very Low Low Medium High Very High Population (Nr of Inhabitants) 34 1057 236 73 51 Built‐up areas (sqkm) 0.659 8.154 1.544 0.175 0.087 Assets (Nr) Very Low Low Medium High Very High Airport ‐ 5 ‐ ‐ ‐ Marina ‐ ‐ ‐ ‐ 1 Commercial, Public & Private Services ‐ 8 1 1 ‐ Industry & Utilities ‐ 4 ‐ ‐ 1 Place of worship ‐ 6 3 ‐ Other 1 516 180 69 1 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 9.169 63.681 0.328 0.056 0.081 Complex cultivation patterns 0.014 9.959 4.635 0.063 0.011 Coniferous forest ‐ 0.034 1.265 1.214 0.077
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Risk Level Consequences within the subAOI 12 Very Low Low Medium High Very High Land principally occupied by agriculture with 0.031 18.432 7.825 0.074 0.041 significant areas of natural vegetation Mixed forest 1.071 2.239 0.002 0.000 Pastures 0.596 6.670 0.041 0.009 0.019 Natural grasslands 0.007 0.013 2.098 0.670 0.936 Sclerophyllous vegetation ‐ 0.003 0.001 0.108 1.615 Transitional woodland/shrub 0.036 0.182 0.400 7.712 ‐ Vineyards 0.001 0.052 ‐ ‐ Transport Network (KM) Very Low Low Medium High Very High Primary 7.333 9.435 0.360 ‐ ‐ Secondary 21.102 25.987 3.786 ‐ ‐ Tertiary 10.006 14.691 5.035 ‐ ‐ Local and service 41.783 41.471 11.336 1.908 ‐ Track 153.511 100.859 14.373 0.144 ‐
Risk Level Consequences within the subAOI 13 Very Low Low Medium High Very High Population (Nr of Inhabitants) 349 816 3146 21 ‐ Build‐up areas (sqkm) 0.297 1.835 2.627 0.094 ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ 24 18 ‐ ‐ Industry & Utilities ‐ ‐ ‐ ‐ ‐ Place of workship 1 ‐ 6 ‐ ‐ Other 31 459 1812 ‐ ‐ LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.814 1.8653 1.5284 0.010 ‐ Complex cultivation patterns 1.646 1.0477 1.3358 0.006 ‐ Coniferous forest ‐ ‐ 0.2666 0.027 ‐ Land principally occupied by agriculture with 0.067 3.4001 3.2212 0.001 ‐ significant areas of natural vegetation Mixed forest ‐ 0.3217 0.5765 ‐ ‐ Olive groves ‐ 0.0150 0.0540 ‐ ‐ Pastures 0.405 0.0126 0.2566 0.004 ‐ Sclerophyllous vegetation ‐ ‐ ‐ 0.011 ‐ Transitional woodland/shrub 0.008 0.0084 0.0982 1.467 ‐ Vineyards 0.188 ‐ ‐ ‐ ‐ Transport Network (KM) Very Low Low Medium High Very High Primary ‐ ‐ ‐ ‐ ‐ Secondary 6.788 5.256 0.688 ‐ ‐ Tertiary 2.369 2.250 1.459 ‐ ‐ Local and service 34.354 25.041 14.871 1.903 ‐ Other 44.693 42.507 9.411 1.206 ‐
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Risk Level Consequences within the subAOI 14 Very Low Low Medium High Very High Built‐up areas (sqkm) 0.127 0.002 0.007 ‐ ‐ Assets (Nr) Very Low Low Medium High Very High Commercial, Public & Private Services ‐ ‐ ‐ 10 ‐ Place of worship ‐ ‐ ‐ 1 ‐ Other 1 60 1 ‐ 3 LU/LC (sqkm) Very Low Low Medium High Very High Broad‐leaved forest 2.673 1.955 ‐ ‐ ‐ Mixed forest ‐ 0.014 0.000 ‐ ‐ Natural grassland 0.010 ‐ ‐ ‐ ‐ Pastures 0.868 0.507 ‐ ‐ ‐ Sclerophyllous vegetation ‐ 0.000 0.025 0.046 ‐ Transitional woodland/shrub ‐ ‐ ‐ 0.109 ‐ Transport Network (KM) Very Low Low Medium High Very High Local and service 26.365 10.229 21.665 ‐ ‐ Track 13.443 1.282 7.989 ‐ ‐ Bridges (Nr) 1 ‐ 1 ‐ ‐
3.2.2.2 FIRST RESPONSE || ACCESS & EVACUATION In every emergency situation, time and safety are the greatest challenges for first responders, who need to act effectively in order to access the victims and the affected areas quickly and safely, provide first aid and facilitate evacuation. Putting more complete and accurate information in the hands of scene commanders and first responders allows them to make better split‐second decisions to ensure the safety of personnel, civilians, infrastructure and property. In this direction, it is critical to identify the best access conditions and evacuation possibilities, so that all available resources can be used as effectively as possible throughout the response effort by the civil protection regional and local authorities. This should be done considering the specific types and natures of risks per AOI.
3.2.2.2.1 PROCESS 1: RISK SPECIFIC ANALYSIS A multi‐parametric integrated analysis was performed, through combining available infrastructure and geo‐spatial information so that the greatest possible impact is achieved in the most cost‐efficient way. The following products that were generated in the framework of this service activation are the main input for this process: The forest fire risk map City population estimates: per district / commune /block. The road network and all other related transport network, especially access roads to the city (alternative roads), access roads to hospitals etc. The location of hospitals, schools and government facilities. The location of critical infrastructure, such as for food and water distribution, for energy production and distribution, and for telecommunications
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In addition, to the above mentioned products, further useful information was taken into account: Potential safe shelters in the area. Potential locations for helicopter landing spots and emergency communication systems. Potential places for field hospitals and camps locations (taking into account terrain conditions, access to water and energy). Potential storage places for food and water and gasoline tank locations.
The process establishes specific criteria for each access/ evacuation infrastructure (road, port, helicopter landing spot, etc) and identified the most suitable (safe) solutions in terms of capacity, distance, low vulnerability, etc. Usually, at each infrastructure the response condition/ evacuation possibility a ranking is assigned, referring to the high, medium and low efficiency. The process indicates the best first responder access conditions and evacuation possibilities for the risk and is included (displayed) at the generated risk maps. Due to (a) the nature of the fire risk, dynamically afecting all the vegetated areas and (b) the lack of highways or primary roads crossing safe ‐ open spaces with litle or no vegetation, resulted to lack of available roads, appropriate for evacuation routes or alternative roads. Moreover, the activation AOIs refer mostly in islands with significant anaglyph and extensive (and dense) vegetation cover and populated places located along the coastline
To establish areas adequate for implementing risk management infrastructure, the analysis combined several data sources: EO data, geospatial data, and risk. The factors that are taken into consideration include: slope, LULC, population density and distance, access to water, power supply and road network, proximity to existing infrastructure, exclusion of hydrographic network, protected areas and high risk areas, as well as buildings categories. The Shelters and the First Aid Areas are illustrated in the risk maps. In each Area the symbols of the proposed infrastructures (camp location, field hospital, helicopter landing spot, food storage/ gasoline tank ) is included through selecting the most suitable ones for each infrastructure type . .
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4 RISK PRODUCTS QUALITY ASSESSMENT
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4.1 GENERIC TECHNICAL QUALITY TARGETS
4.1.1 APPLICABLE STANDARDS/ DOCUMENTS
(1) ISO 9001:2008, published by ISO/TC 176, sub‐committee 2 (2) Guidelines for Best Practice and Quality Checking of Ortho Imagery, v 3.0 (JRC/IPSC, 2008) (3) INSPIRE Data Specifications Annex I, II & III (4) INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 (2013) (5) Technical Specifications JRC/IPR/2014/G.2/0012/OC; Framework service contract for Copernicus Emergency Management Service Risk and Recovery Mapping (6) The Copernicus Regulation COM (2013)0312‐2013/0164(COD) (7) European Commission, “GIO‐EMS mapping in non‐rush mode product portfolio specifications”. Annex A to the Tender specifications for GIO – Emergency Management Service ‐ Mapping in non‐rush Mode (IPSC/2011/02/05/OC) (8) JRC Copernicus EMS Mapping Guidelines and best practice, 2013, Ed. JRC83027, doi: 10.2788/91765 (9) Broglia M., Corbane C., Carrion D., Lemoine G., Pesaresi M., Validation Protocol for Emergency Response Geo‐Information Products, JRC Scientific and technical report, EUR 24496 EN‐2010, ISBN 978‐92‐79‐16428‐6, ISSN 1018‐5593, European Union, 2010 (10) Harshness in image classification accuracy assessment, Foody, G. M., 2008. International Journal of Remote Sensing, 29, 3137‐3158
4.1.2 TOLERANCES FOR DELIVERABLES
4.1.2.1 ORTHORECTIFID IMAGES Geometric accuracy The overall RMSExy at Control Points level does not exceed the 1‐1.5 pixels for confidence level 95%, in order to conform to the cartographic scale of 1:10.000. Radiometric accuracy Image saturation does not exceed 0.5% at each tail of the histogram. Cloud cover figure (over the AOI) does not exceed the 5%. Similar types of objects do not vary more than 10% in DN value (average).
4.1.2.2 THEMATIC PRODUCTS Thematic accuracy The thematic accuracy of all the products, e.g. Vulnerability & Risk grading and delineation, correct and complete location and representation of the exposed to the risk elements against the reference data used for validation (User Accuracy, Producer Accuracy, Overall Accuracy, k‐coefficient), and the thematic confidence interval at 95% statistical confidence level should be greater than 85%.
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Readability The exhaustive visual check for map readability and thematic appearance errors does return zeroed errors. Consistency Errors in consistency between the symbols in the map and the legend, and consistency between attributes (compliance with the type of data, the tabulated values provided in the value fields, population of fields, etc), the consistency between the declared scale and the scale of representation, the consistency in reference to missing information, is zeroed.
4.1.2.3 VECTOR GEOSPATIAL DATA Geometric accuracy The overall accuracy does not exceed the error of 2.5 m for confidence level 95% that is fully compliant with the cartographic scale of 1:10.000. Topological accuracy Topological consistency errors in particular for vector files are zero. Matching and completeness accuracy The exhaustive visual check for element (features) matching over the entire map by inspecting the map at the printable scale on a big screen return zero errors.
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4.2 QUALITY ASSESSMENT RESULTS The methodology for quality control consisted of: Random Selection of cells (500 x 500 m) within the AOI (representing the 5% of each sub AOI) On each cell, an independent expert (not involved in production) reviewed all elements (polygons, lines, etc) from the point of view of the thematic information.
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Figure 29. Position of the random QC samples within the AOIs. Background: ESRI National Topographic Map.
4.2.1 RISK MAPS
4.2.1.1 SUB AOI 1
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 1 20 Medium 0 21 4 High & Very High 0 0 37 Total number of areas: 65 Total number of areas where modelling and Expert opinion coincide: 59 Overall Accuracy: 90,8%
4.2.1.2 SUB AOI 2
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 4 20 Medium 1 17 6 High & Very High 0 0 45
GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 83/86
FWC FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK & RECOVERY MAPPING 09.10.2017
EMSN‐041: Forest fire risks assessment in Croatia
Total number of areas: 75 Total number of areas where modelling and Expert opinion coincide: 66 Overall Accuracy: 88,0%
4.2.1.3 SUB AOI 3
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 31 90 Medium 5 90 4 High & Very High 0 1 93 Total number of areas: 233 Total number of areas where modelling and Expert opinion coincide: 214 Overall Accuracy: 91,8%
4.2.1.4 SUB AOI 4
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 70 80 Medium 3 108 13 High & Very High 0 0 128 Total number of areas: 330 Total number of areas where modelling and Expert opinion coincide: 306 Overall Accuracy: 92,7%
4.2.1.5 SUB AOI 5
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 2 10 Medium 1 11 2 High & Very High 0 0 23 Total number of areas: 40 Total number of areas where modelling and Expert opinion coincide: 36 Overall Accuracy: 90,0%
GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 84/86
FWC FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK & RECOVERY MAPPING 09.10.2017
EMSN‐041: Forest fire risks assessment in Croatia
4.2.1.6 SUB AOI 6
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 11 20 Medium 0 10 3 High & Very High 0 0 14 Total number of areas: 39 Total number of areas where modelling and Expert opinion coincide: 34 Overall Accuracy: 87,2%
4.2.1.7 SUB AOI 7
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 3 00 Medium 0 10 3 High & Very High 0 0 29 Total number of areas: 45 Total number of areas where modelling and Expert opinion coincide: 42 Overall Accuracy: 93,3%
4.2.1.8 SUB AOI 8
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 14 20 Medium 1 13 3 High & Very High 0 0 23 Total number of areas: 56 Total number of areas where modelling and Expert opinion coincide: 50 Overall Accuracy: 89,3%
4.2.1.9 SUB AOI 9
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 4 00 Medium 0 4 1 High & Very High 0 0 3
GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 85/86
FWC FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK & RECOVERY MAPPING 09.10.2017
EMSN‐041: Forest fire risks assessment in Croatia
Total number of areas: 12 Total number of areas where modelling and Expert opinion coincide: 11 Overall Accuracy: 91,7%
4.2.1.10 SUB AOI 10
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 13 20 Medium 0 7 1 High & Very High 0 0 1 Total number of areas: 24 Total number of areas where modelling and Expert opinion coincide: 21 Overall Accuracy: 87,5%
4.2.1.11 SUB AOI 11
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 53 31 Medium 3 22 4 High & Very High 0 1 45 Total number of areas: 132 Total number of areas where modelling and Expert opinion coincide: 120 Overall Accuracy: 90,9%
4.2.1.12 SUB AOI 12
Expert Evaluation Modelling Results Range Very Low & Low Medium High & Nery High Very Low & Low 56 40 Medium 3 23 3 High & Very High 0 0 24 Total number of areas: 113 Total number of areas where modelling and Expert opinion coincide: 103 Overall Accuracy: 91,1%
GEOAPIKONISIS – NOA – CIMA – TRE ALTAMIRA TECH REPORT ‐ Page 86/86