Identifying pyroregions in mainland Portugal with clustering methods using GIS

Wildfires occur unevenly in the territory, driven by different biophysical and social factors. Understanding the spatial and temporal distribution of wildfires can help identifying common characteristics and/or dissimilarities between regions. In this research, we use specific fire metrics, from historical fire data, to explore the possibility to identify groups of municipalities based on their pyrosimilarities. We apply a clustering model based on the method k means to identify and compare groups of municipalities (defining pyroregions) of mainland Portugal (n=277), using fire data from the last 22 years (between 2000 and 2021). The fire metrics used were: (a) cumulative percentage of total burned area, (b) cumulative percentage of burned area in the summer months, (c) mean annual number of fires and (d) GINI index applied for burned area over time. We used tools available in Geographic Information Systems (ArcGIS Pro) linked with python programming, to apply the cluster method and map the results. Our preliminary results divided the mainland in 5 clusters. CL1 (n=66) is seen in the west coast and is characterised by a burned area concentrated in a few years (high Gini index), but the fires occur mainly outside the summer months; CL2 (n=50) cover municipalities in the northeast and is characterised by a high mean number of fires dispersed over the years (low GINI index); CL3 (n=26) located in the central Portugal has a high percentage of cumulative burned area throughout the years, but with low number of fires, concentrated in time; CL4 (n=63) covers the municipalities in the southwest and south and shows a low mean number of fires but  these occur mainly in the summer season and, CL5 (n=55) appears throughout the country, but is more concentrated in the west and is characterised by intermediate values in all analysed metrics. The extreme wildfires that occurred in 2017 in Portugal influence the clustering; for example, CL1 occurs on the west central coast, where the consolidated maritime pine forest has burned in October 2017, outside the summer months. The next steps of this analysis are: (i) apply other clustering methods to compare with these clusters identified with k means and their characteristics and (ii) analyse the explanatory variables that influence these fire patterns.

 

This work was funded by FCT, I.P.: BB and AG in the scope of PhD projects [2022.12095.BD], [2020.07651.BD], SO under the contract ‘2020.03873.CEECIND′, Centre for Geographical Studies—University of Lisbon and FCT under Grant number [UIDB/00295/2020 + UIDP/ 00295/2020].