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GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

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2021
sensors-21-06520.pdf (3.300Mb)
Authors
Novković, Ivan
Marković, Goran B.
Lukić, Đorđe
Dragićević, Slavoljub
Milošević, Marko
Đurđić, Snežana
Samardžić, Ivan
Ležaić, Tijana
Tadić, Marija
Article (Published version)
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Abstract
The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional In...ternet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

Keywords:
fire outbreak occurrence / forest fire susceptibility / fuzzy analytic hierarchy process (fuzzy AHP) / GIS / IoT sensor networks / random forest (RF) / remote sensing / technique for order of preference by similarity to ideal solution (TOPSIS)
Source:
Sensors, 2021, 21, 19, 6520-
Publisher:
  • MDPI

DOI: 10.3390/s21196520

ISSN: 1424-8220

WoS: 000729173500001

Scopus: 2-s2.0-85115919274
[ Google Scholar ]
4
1
URI
https://gery.gef.bg.ac.rs/handle/123456789/1110
Collections
  • Radovi istraživača
Institution/Community
Geografski fakultet
TY  - JOUR
AU  - Novković, Ivan
AU  - Marković, Goran B.
AU  - Lukić, Đorđe
AU  - Dragićević, Slavoljub
AU  - Milošević, Marko
AU  - Đurđić, Snežana
AU  - Samardžić, Ivan
AU  - Ležaić, Tijana
AU  - Tadić, Marija
PY  - 2021
UR  - https://gery.gef.bg.ac.rs/handle/123456789/1110
AB  - The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.
PB  - MDPI
T2  - Sensors
T1  - GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia
VL  - 21
IS  - 19
SP  - 6520
DO  - 10.3390/s21196520
ER  - 
@article{
author = "Novković, Ivan and Marković, Goran B. and Lukić, Đorđe and Dragićević, Slavoljub and Milošević, Marko and Đurđić, Snežana and Samardžić, Ivan and Ležaić, Tijana and Tadić, Marija",
year = "2021",
abstract = "The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.",
publisher = "MDPI",
journal = "Sensors",
title = "GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia",
volume = "21",
number = "19",
pages = "6520",
doi = "10.3390/s21196520"
}
Novković, I., Marković, G. B., Lukić, Đ., Dragićević, S., Milošević, M., Đurđić, S., Samardžić, I., Ležaić, T.,& Tadić, M.. (2021). GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia. in Sensors
MDPI., 21(19), 6520.
https://doi.org/10.3390/s21196520
Novković I, Marković GB, Lukić Đ, Dragićević S, Milošević M, Đurđić S, Samardžić I, Ležaić T, Tadić M. GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia. in Sensors. 2021;21(19):6520.
doi:10.3390/s21196520 .
Novković, Ivan, Marković, Goran B., Lukić, Đorđe, Dragićević, Slavoljub, Milošević, Marko, Đurđić, Snežana, Samardžić, Ivan, Ležaić, Tijana, Tadić, Marija, "GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia" in Sensors, 21, no. 19 (2021):6520,
https://doi.org/10.3390/s21196520 . .

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