Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard
Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/5014 |
| _version_ | 1797554625197375488 |
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| author | Stavros Sakellariou Pedro Cabral Mário Caetano Filiberto Pla Marco Painho Olga Christopoulou Athanassios Sfougaris Nicolas Dalezios Christos Vasilakos |
| author_facet | Stavros Sakellariou Pedro Cabral Mário Caetano Filiberto Pla Marco Painho Olga Christopoulou Athanassios Sfougaris Nicolas Dalezios Christos Vasilakos |
| author_sort | Stavros Sakellariou |
| collection | DOAJ |
| description | Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools. |
| first_indexed | 2024-03-10T16:34:44Z |
| format | Article |
| id | doaj.art-4223e8fd8af440019f3e40e4af69cf5d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:34:44Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4223e8fd8af440019f3e40e4af69cf5d2023-11-20T12:30:44ZengMDPI AGSensors1424-82202020-09-012017501410.3390/s20175014Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire HazardStavros Sakellariou0Pedro Cabral1Mário Caetano2Filiberto Pla3Marco Painho4Olga Christopoulou5Athanassios Sfougaris6Nicolas Dalezios7Christos Vasilakos8NOVA Information Management School, Universidade Nova de Lisboa, 1070-312 Lisbon, PortugalNOVA Information Management School, Universidade Nova de Lisboa, 1070-312 Lisbon, PortugalNOVA Information Management School, Universidade Nova de Lisboa, 1070-312 Lisbon, PortugalInstitute of New Imaging Technologies (INIT), Universitat Jaume I (UJI), 12071 Castellón, SpainNOVA Information Management School, Universidade Nova de Lisboa, 1070-312 Lisbon, PortugalDepartment of Planning and Regional Development, University of Thessaly, 38334 Volos, GreeceDepartment of Agriculture Crop Production and Rural Environment, University of Thessaly, 38446 Volos, GreeceDepartment of Civil Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Geography, University of the Aegean, University Hill, 81100 Mytilene, GreeceForest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.https://www.mdpi.com/1424-8220/20/17/5014forest fire hazardAnalytical Hierarchy Processfuzzy logicspatiotemporal analysisspatial variabilityremote sensing |
| spellingShingle | Stavros Sakellariou Pedro Cabral Mário Caetano Filiberto Pla Marco Painho Olga Christopoulou Athanassios Sfougaris Nicolas Dalezios Christos Vasilakos Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard Sensors forest fire hazard Analytical Hierarchy Process fuzzy logic spatiotemporal analysis spatial variability remote sensing |
| title | Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard |
| title_full | Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard |
| title_fullStr | Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard |
| title_full_unstemmed | Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard |
| title_short | Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard |
| title_sort | remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard |
| topic | forest fire hazard Analytical Hierarchy Process fuzzy logic spatiotemporal analysis spatial variability remote sensing |
| url | https://www.mdpi.com/1424-8220/20/17/5014 |
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