hig.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Wildfire Hazard Mapping using GIS-MCDA and Frequency Ratio Models: A Case Study in Eight Counties of Norway
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences. University of Gävle.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Sustainable development
The essay/thesis is mainly on sustainable development according to the University's criteria
Abstract [en]

Abstract

A wildfire is an uncontrollable fire in an area of combustible fuel that occurs in the wild or countryside area. Wildfires are becoming a deadly and frequent event in Europe due to extreme weather conditions. In 2018, wildfires profoundly affected Sweden, Finland, and Norway, which were not big news before. In Norway, although there is well–organized fire detection, warning, and mitigation systems, mapping wildfire risk areas before the fire occurrence with georeferenced spatial information, are not yet well-practiced. At this moment, there are freely available remotely sensed spatial data and there is a good possibility that analysing wildfire hazard areas with geographical information systems together with multicriteria decision analysis (GIS–MCDA) and frequency ratio models in advance so that subsequent wildfire warning, mitigation, organizational and post resilience activities and preparations can be better planned. 

This project covers eight counties of Norway: Oslo, Akershus, Østfold, Vestfold, Telemark, Buskerud, Oppland, and Hedmark. These are the counties with the highest wildfire frequency for the last ten years in Norway. In this study, GIS-MCDA integrated with analytic hierarchy process (AHP), and frequency ratio models (FR) were used with selected sixteen–factor criteria based on their relative importance to wildfire ignition, fuel load, and other related characteristics. The produced factor maps were grouped under four main clusters (K): land use (K1), climate (K2), socioeconomic (K3), and topography (K4) for further analysis.

The final map was classified into no hazard, low, medium, and high hazard level rates. The comparison result showed that the frequency ratio model with MODIS satellite data had a prediction rate with 72% efficiency, followed by the same model with VIIRS data and 70% efficiency. The GIS-MCDA model result showed 67% efficiency with both MODIS and VIIRS data. Those results were interpreted in accordance with Yesilnacar’s classifications such as the frequency ratio model with MODIS data was considered a good predictor, whereas the GIS-MCDA model was an average predictor. When testing the model on the dependent data set, the frequency ratio model showed 72% with MODIS & VIIRS data, and the GIS-MCDA model showed 67% and 68% performance with MODIS and VIIRS data, respectively. In the hazard maps produced, the frequency ratio models for both MODIS and VIIRS showed that Hedmark and Akershus counties had the largest areas with the highest susceptibility to wildfires, while the GIS-MCDA method resulted to Østfold and Vestfold counties.

Through this study, the best independent wildfire predictor criteria were selected from the highest to the lowest of importance; wildfire constraint and criteria maps were produced; wildfire hazard maps with high-resolution georeferenced data using three models were produced and compared; and the best, reliable, robust, and applicable model alternative was selected and recommended. Therefore, the aims and specific objectives of this study should be considered and fulfilled.

Place, publisher, year, edition, pages
2019. , p. iii+44+appendixes
Keywords [en]
Wildfire, GIS, MCDA, Frequency Ratio, Hazard Map, Constraint Criteria, Factor Criteria, AUC
National Category
Forest Science Environmental Sciences related to Agriculture and Land-use Geosciences, Multidisciplinary Physical Geography Information Systems Remote Sensing
Identifiers
URN: urn:nbn:se:hig:diva-31369OAI: oai:DiVA.org:hig-31369DiVA, id: diva2:1382747
Subject / course
Geospatial information science
Educational program
Master Programme in Geospatial Information Science
Presentation
2019-08-30, 11:220, Högskolan i Gävle, Gävle, 13:00 (English)
Supervisors
Examiners
Available from: 2020-01-13 Created: 2020-01-05 Last updated: 2020-01-14Bibliographically approved

Open Access in DiVA

fulltext(3670 kB)47 downloads
File information
File name FULLTEXT01.pdfFile size 3670 kBChecksum SHA-512
5a2a17f0450ead8418ff84df0f1609417b28811d4b32284f58476bdf36b0116f4170d1f06471f66b79cd6179bd763b0528a51ac8aa26fd2b743973209427580e
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Zeleke, Walelegn Mengist
By organisation
Department of Computer and Geospatial Sciences
Forest ScienceEnvironmental Sciences related to Agriculture and Land-useGeosciences, MultidisciplinaryPhysical GeographyInformation SystemsRemote Sensing

Search outside of DiVA

GoogleGoogle Scholar
Total: 47 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 343 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf