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

Autor: Djordje B. Lukic, Ivan Samardzic, Marija Tadic, Marko Milosevic, Snezana Djurdjic, Ivan Novković, Slavoljub Dragicevic, Tijana Lezaic, Goran Marković
Rok vydání: 2021
Předmět:
010504 meteorology & atmospheric sciences
Fire prevention
Endangered species
Climate change
TP1-1185
random forest (RF)
02 engineering and technology
Forests
01 natural sciences
Biochemistry
Fires
Article
Wildfires
Analytical Chemistry
Environmental data
forest fire susceptibility
remote sensing
0202 electrical engineering
electronic engineering
information engineering
Humans
technique for order of preference by similarity to ideal solution (TOPSIS)
Electrical and Electronic Engineering
Natural disaster
Instrumentation
Risk management
0105 earth and related environmental sciences
IoT sensor networks
Warning system
business.industry
Chemical technology
Environmental resource management
15. Life on land
GIS
Atomic and Molecular Physics
and Optics
High forest
Geography
13. Climate action
Geographic Information Systems
020201 artificial intelligence & image processing
fire outbreak occurrence
business
Serbia
fuzzy analytic hierarchy process (fuzzy AHP)
Zdroj: Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 19
Sensors, Vol 21, Iss 6520, p 6520 (2021)
ISSN: 1424-8220
Popis: 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.
Databáze: OpenAIRE
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