## 5 Discussion  
The International Strategy for Disaster Reduction [100] encourages researchers to promote the protection of the environment in order to reduce vulnerability to disasters. Previous research has shown that protected areas can play a very important role in preventing or mitigating natural disasters [101; 102]. Assessment and zoning of natural disaster susceptibility, vulnerability, hazard, and risk are among the most important parts of natural disaster management and mitigation. Therefore, it is necessary to constantly improve necessary methods and to find new data sources that could be used for that purpose. Climate change trends are such that the forest fire risk is likely to increase globally in the near future. Most of the world countries will be affected that way. Many of them that are not considered as wealthy and will have problems in dealing with this disaster because of lack of funding for data and equipment, among other things. That is why it is important to consider different possibilities, methods, and datasets, which will be more affordable to most parts of the world. The consequences of forest fires in protected natural areas, due to their importance and characteristics, are the reason why forest fire management and mitigation must be at the top of the priority list. The application of GIS is indispensable for this type of research, because only with its help can an adequate and complete spatial analysis be performed. All the necessary parameters that are important for forest fire susceptibility assessment can be taken into account. Together with remote sensing, GIS represents a very powerful tool that enables the use of various methods and multi-criteria decision analysis. The data and software that were used in this paper for forest fire susceptibility assessment and zonation are completely open source. That kind of research could help communities that have a low budget when it comes to nature protection and forest fire risk management. This paper also shows that it is possible to perform suitable forest fire susceptibility zonation even if historical data of forest fires that occurred in a certain area and relevant meteorological data are not available. Of course, for comprehensive forest fire risk assessment, more accurate datasets than those used for this research and also historical forest fire occurrence, climate, and weather data should be considered. Not only that it would provide more detailed results of forest fire risk assessment and zonation, even before setting up a sensor network, but it would also allow more advanced methods to be used, such as the application of artificial intelligence. As already stated in the Introduction, the most significant contribution of this research is that the fuzzy AHP method was used for the first time for forest fire susceptibility assessment in Serbia. The fact that it was used on the territory of one of the most important protected natural areas amplifies that significance. Additionally, it compares results of the index-based method and fuzzy AHP using the TOPSIS method. The application of the fuzzy AHP method is more adequate, because it is more objective. Although at first glance there were no significant differences in the results of the applied methods, these differences are still significant for the further design of the sensor network based on the obtained results, as well as for the overall management of this disaster. For future research and improvement of methods and results, using other techniques that give adequate results, such as the fuzzy analytical network process (ANP) [15], or hybrid approaches [103], should be considered. The results obtained thanks to such research should be an integral part of spatial plans and other planning documents. They should help those who manage protected natural assets, such as the Nature Park Golija, to reduce the forest fire risk, as well as to prevent possible consequences.