Abstract
Natural disasters are defined as negative events that affect the lives of all living things on earth with their various effects. Fires which are one of these natural disasters, are the partial or complete burning of vegetated areas caused by natural or man-made causes. Today, monitoring and managing fires with Remote Sensing (RS) and Geographical Information Systems (GIS) tools are considered an integral part of and after their occurrence. RS and GIS are regarded as extremely useful and modern methods for data analysis, querying, and visualization in disaster management. The aim of this study is to determine the local and general patterns of the fires in the European continent by examining them with spatial statistical methods and making a spatial analysis. In addition, it aims to examine the fires that occurred over time with temporal pattern analysis. In the study, Global Moran’s I, Getis-Ord Gi* Hot Spot Analysis, Anselin Local Moran’s I Cluster and Outlier, and Emerging Hot Spot Analysis for Space-Time Pattern Analysis were performed on the dataset created with the fires that occurred in the European continent between 2000 and 2020. According to the analysis, the region that is exposed to the most intense fire and has the highest fire risk has been determined as the northern region of Portugal. The Balkans, the south of Italy, and the Sicilian peninsula are also dense and risky regions in terms of fires.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Affan M, Syukri M, Wahtuna L, Sofyan H (2016) Spatial statistic analysis of earthquakes in Aceh province year 1921–2014: cluster seismicity. Aceh Int J Sci Technol 5:54–62. https://doi.org/10.13170/aijst.5.2.4878
Aftergood OSR, Flannigan MD (2022) Identifying and analyzing spatial and temporal patterns of lightning-ignited wildfires in Western Canada from 1981 to 2018. Can J For Res 13:1–13. https://doi.org/10.1139/cjfr-2021-0353
Al-Ahmadi K, Al-Amri A, See L (2013) A spatial statistical analysis of the occurence of earthquakes along the red sea floor spreading: clusters of seismicity. Arab J Geosci 7:2893–2904. https://doi.org/10.1007/s12517-013-0974-6
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Arca D, Hacısalihoğlu M, Kutoğlu H (2020) Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Nat Hazards 104:73–89. https://doi.org/10.1007/s11069-020-04158-7
Arslantürk N (2007) Effect of fire on vegetation. Selçuk Üniversitesi Fen Fakültesi Fen Derg 29:141–153
Bae G, Jung Y, Yoo H (2015) An analysis on the characteristics of spatial clustering distribution in the urban fire of Gyeongsangnam-do, Korea. In: ACRS 2015–36th Asian conference on remote sensing: fostering resilient growth in Asia, proceedings. Asian association on remote sensing (AARS), Quezon City, Metro Manila, Philippines
Barreal J, Loureiro ML (2015) Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain). For Syst 24:e022. https://doi.org/10.5424/fs/2015242-05713
Bone C, Wulder MA, White JC et al (2013) A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran’s I statistic. Appl Geogr 40:161–170. https://doi.org/10.1016/j.apgeog.2013.02.011
Busico G, Giuditta E, Kazakis N, Colombani N (2019) A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustain. https://doi.org/10.3390/su11247166
Cardil A, Salis M, Spano D et al (2014) Large wildland fires and extreme temperatures in Sardinia (Italy). IForest 7:162–169. https://doi.org/10.3832/ifor1090-007
Cetin M, Isik Pekkan Ö, Ozenen Kavlak M et al (2022) GIS-based forest fire risk determination for Milas district, Turkey. Nat Hazards. https://doi.org/10.1007/s11069-022-05601-7
Chappaz F, Ganteaume A (2022) Role of land-cover and WUI types on spatio-temporal dynamics of fires in the French mediterranean area. Risk Anal. https://doi.org/10.1111/risa.13979
Cheruiyot K (2022) Detecting spatial economic clusters using kernel density and global and local Moran’s I analysis in Ekurhuleni metropolitan municipality, South Africa. Reg Sci Policy Pract 14:307–327. https://doi.org/10.1111/rsp3.12526
Çolak E, Sunar F (2020) Evaluation of forest fire risk in the Mediterranean turkish forests: a case study of Menderes region, Izmir. Int J Disaster Risk Reduct 45. https://doi.org/10.1016/j.ijdrr.2020.101479
Costa H, de Rigo D, Libertà G et al (2020) European wildfire danger and vulnerability in a changing climate: towards integrating risk dimensions. European Comission
Csiszar IA, Morisette JT, Giglio L (2006) Validation of active fire detection from moderate-resolution satellite sensors: the MODIS example in Northern Eurasia. IEEE Trans Geosci Remote Sens 44:1757–1764. https://doi.org/10.1109/TGRS.2006.875941
De Klerk H (2008) A pragmatic assessment of the usefulness of the MODIS (Terra and Aqua) 1-km active fire (MOD14A2 and MYD14A2) products for mapping fires in the fynbos biome. Int J Wildl Fire 17:166–178. https://doi.org/10.1071/WF06040
De Luís M, Francisca García-Cano M, Cortina J et al (2001) Climatic trends, disturbances and short-term vegetation dynamics in a Mediterranean shrubland. For Ecol Manage 147:25–37. https://doi.org/10.1016/S0378-1127(00)00438-2
de Rigo D, Liberta G, Houstan Durrant T et al (2017) Forest fire danger extremes in Europe under climate change: variability and uncertainty. European Commission’s Joint Research Centre
Donis J, Kitenberga M, Snepsts G et al (2017) The forest fire regime in Latvia during 1922–2014. Silva Fenn 51:1–15. https://doi.org/10.14214/sf.7746
Duran C (2014) Spatial analysis according to start point of forest fires in Mersin province, Turkey (2001–2013). Orman Araştırma Derg 1:38. https://doi.org/10.17568/oad.87328
Erdoğan S (2010) GIS applications in epidemiology: a comparison of spatial clustering Methods-Example of Meningococcal. Harit Teknol Elektr Derg 2:23–31
ESRI (2020) How Creating a space time cube works. In: Environ. Syst. Res. Inst. Redlands, CA, USA. https://pro.arcgis.com/en/pro-app/2.8/tool-reference/space-time-pattern-mining/learnmorecreatecube.htm
ESRI (2021) Europe NUTS 3 demographics and boundaries. https://www.arcgis.com/home/item.html?id=1b328981e4644f5a82f3ea995db92517
European Forest Institute (2023a) Mobilising local citizens for fire prevention case-D. In: Wildfire Prev. https://efi.int/sites/default/files/files/efimed/resources/Wildfire Prevention Factsheet_Case D.pdf. Accessed 4 Feb 2023
European Forest Institute (2023b) Preventive livestock grazing (Rapca programme) Case-E. In: Wildfire Prev. https://efi.int/sites/default/files/files/efimed/resources/Wildfire Prevention Factsheet_Case E.pdf. Accessed 4 Feb 2023
FAO (2022) The state of the World’s forests 2022. Food and agriculture organization of the United Nations
Feng Y, Chen X, Gao F, Liu Y (2018) Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE: a case study of the neon flying squid (Ommastrephes bartramii) in the northwest Pacific Ocean. Acta Oceanol Sin 37:67–76. https://doi.org/10.1007/s13131-018-1212-6
Fernandes PM, Barros AMG, Pinto A, Santos JA (2016) Characteristics and controls of extremely large wildfires in the western Mediterranean basin. J Geophys Res Biogeosciences 121:2141–2157. https://doi.org/10.1002/2016JG003389
Flannigan MD, Amiro BD, Logan KA et al (2006) Forest fires and climate change in the 21st century. Mitig Adapt Strateg Glob Chang 11:847–859. https://doi.org/10.1007/s11027-005-9020-7
Fornacca D, Ren G, Xiao W (2017) Performance of three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. Remote Sens 9:1–20. https://doi.org/10.3390/rs9111131
Founda D, Giannakopoulos C (2009) The exceptionally hot summer of 2007 in Athens, Greece - A typical summer in the future climate? Glob Planet Change 67:227–236. https://doi.org/10.1016/j.gloplacha.2009.03.013
Gajovic V, Todorovic B (2013) Spatial and temporal analysis of fires in Serbia for period 2000–2013. J Geogr Inst Jovan Cvijic SASA 63:297–312. https://doi.org/10.2298/ijgi1303297g
Gayır B, Arslan O (2018) GIS based spatial statistical analysis of forest fires: wildfires in Muğla forest district boundaries between 2011 and 2015. Anadolu Orman Araştırmaları Derg 4:44–60
Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens Environ 178:31–41. https://doi.org/10.1016/j.rse.2016.02.054
Giglio L, Boschetti L, Roy DP et al (2018) The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens Environ 217:72–85. https://doi.org/10.1016/j.rse.2018.08.005
Giglio L, Schroeder W, Hall JV, Justice CO (2020) MODIS collection 6 active fire product. User’s guide revision C. NASA
Harris NL, Goldman E, Gabris C et al (2017) Using spatial statistics to identify emerging hot spots of forest loss. Environ Res Lett. https://doi.org/10.1088/1748-9326/aa5a2f
Hawbaker TJ, Radeloff VC, Syphard AD et al (2008) Detection rates of the MODIS active fire product in the United States. Remote Sens Environ 112:2656–2664. https://doi.org/10.1016/j.rse.2007.12.008
Iban MC, Sekertekin A (2022) Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: a case study of Adana and Mersin provinces, Turkey. Ecol Inf. https://doi.org/10.1016/j.ecoinf.2022.101647
Jaafari A, Rahmati O, Zenner EK, Mafi-Gholami D (2022) Anthropogenic activities amplify wildfire occurrence in the Zagros eco-region of western Iran. Nat Hazards 114:457–473. https://doi.org/10.1007/s11069-022-05397-6
Karabacak K, Türkşen Ö, Bayar R (2019) Spatial statistics analysis of forest fires in Antalya province. In: 1st Istanbul international geography congress proceedings book, pp. 615–630. https://doi.org/10.26650/pb/ps12.2019.002.060
Karali A, Hatzaki M, Giannakopoulos C et al (2014) Sensitivity and evaluation of current fire risk and future projections due to climate change: the case study of Greece. Nat Hazards Earth Syst Sci 14:143–153. https://doi.org/10.5194/nhess-14-143-2014
Lan Y, Wang J, Hu W et al (2022) Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data. Nat Hazards. https://doi.org/10.1007/s11069-022-05689-x
Ma C, Pu R, Downs J, Jin H (2022) Characterizing spatial patterns of amazon rainforest wildfires and driving factors by using remote sensing and GIS geospatial technologies. Geosciences. https://doi.org/10.3390/geosciences12060237
Mansoor S, Farooq I, Kachroo MM et al (2022) Elevation in wildfire frequencies with respect to the climate change. J Environ Manage 301:113769. https://doi.org/10.1016/j.jenvman.2021.113769
Marumbwa FM, Cho MA, Chirwa PW (2019) Analysis of spatio-temporal rainfall trends across southern african biomes between 1981 and 2016. Phys Chem Earth 114:102808. https://doi.org/10.1016/j.pce.2019.10.004
Moran P (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23. https://doi.org/10.2307/2332142
Morisette JT, Giglio L, Csiszar I et al (2005) Validation of MODIS active fire detection products derived from two algorithms. Earth Interact 9:1–25. https://doi.org/10.1175/EI141.1
Naqvi SAA, Sajjad M, Waseem LA et al (2021) Integrating spatial modelling and space–time pattern mining analytics for vector disease-related health perspectives: a case of dengue fever in Pakistan. Int J Environ Res Public Health 18:78–96. https://doi.org/10.3390/ijerph182212018
Nezval V, Andrášik R, Bíl M (2022) Vegetation fires along the Czech rail network. Fire Ecol. https://doi.org/10.1186/s42408-022-00141-8
Nojarov P, Nikolova M (2022) Heat waves and forest fires in Bulgaria. Nat Hazards 114:1879–1899. https://doi.org/10.1007/s11069-022-05451-3
Novo A, Fariñas-álvarez N, Martínez-Sánchez J et al (2020) Mapping forest fire risk—a case study in Galicia (Spain). Remote Sens 12:1–21. https://doi.org/10.3390/rs12223705
Nunes AN, Lourenço L, Meira ACC (2016) Exploring spatial patterns and drivers of forest fires in Portugal (1980–2014). Sci Total Environ 573:1190–1202. https://doi.org/10.1016/j.scitotenv.2016.03.121
Ohyama T, Hanyu K, Tani M, Nakae M (2022) Investigating crime harm index in the low and downward crime contexts: a spatio-temporal analysis of the Japanese crime harm index. Cities 130:103922. https://doi.org/10.1016/j.cities.2022.103922
Oom D, Pereira JMC (2013) Exploratory spatial data analysis of global MODIS active fire data. Int J Appl Earth Obs Geoinf 21:326–340. https://doi.org/10.1016/j.jag.2012.07.018
Reddy CS, Sarika N (2022) Monitoring trends in global vegetation fire hot spots using MODIS data. Spat Inf Res 30:617–632. https://doi.org/10.1007/s41324-022-00457-2
Rodrigues M, de la Riva J, Fotheringham S (2014) Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weightedlogistic regression. Appl Geogr 48:52–63. https://doi.org/10.1016/j.apgeog.2014.01.011
Rossi F, Becker G (2019) Creating forest management units with hot spot analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires. Aust For 82:166–175. https://doi.org/10.1080/00049158.2019.1678714
Ruffault J, Moron V, Trigo RM, Curt T (2017) Daily synoptic conditions associated with large fire occurrence in Mediterranean France: evidence for a wind-driven fire regime. Int J Climatol 37:524–533. https://doi.org/10.1002/joc.4680
Sari F (2022) Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. J For Res. https://doi.org/10.1007/s11676-022-01502-4
Schroeder W, Prins E, Giglio L et al (2008) Validation of GOES and MODIS active fire detection products using ASTER and ETM + data. Remote Sens Environ 112:2711–2726. https://doi.org/10.1016/j.rse.2008.01.005
Sivrikaya F, Küçük Ö (2022) Modeling forest fire risk based on GIS based analytical hierarchy process and statistical analysis in Mediterranean region. Ecol Inf 78. https://doi.org/10.1016/j.ecoinf.2021.101537
Sousa J, Çinar C, Carmo M, Malagoli MAS (2022) Social and historical dimensions of wildfire research and the consideration given to practical knowledge: a systematic review. Nat Hazards 114:1103–1123. https://doi.org/10.1007/s11069-022-05460-2
Tariq S, ul-Haq Z, Mariam A et al (2022) Assessment of air quality during worst wildfires in Mugla and Antalya regions of Turkey. Nat Hazards. https://doi.org/10.1007/s11069-022-05592-5
Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240. https://doi.org/10.2307/143141
Visner M, Shirowzhan S, Pettit C (2021) Spatial analysis, interactive visualisation and GIS-based dashboard for monitoring spatio-temporal changes of hotspots of bushfires over 100 years in New South Wales, Australia. Buildings 11:1–27. https://doi.org/10.3390/buildings11020037
Yakar M (2011) Spatial analysis of Population distribution: a case of Afyonkarahisar Province. Uluslararası Sos Araştırmalar Derg 4:389–406
Yuan Y, Cave M, Zhang C (2018) Using local Moran’s I to identify contamination hotspots of rare earth elements in urban soils of London. Appl Geochem 88:167–178. https://doi.org/10.1016/j.apgeochem.2017.07.011
Zahran ESMM, Shams S, Said SN, matullah BM (2020) Validation of forest fire hotspot analysis in GIS using forest fire contributory factors. Syst Rev Pharm 11:249–255. https://doi.org/10.31838/srp.2020.12.40
Zerbe K, Polit C, McClain S, Cook T (2022) Optimized hot spot and directional distribution analyses characterize the Spatiotemporal variation of large wildfires in Washington, USA, 1970 – 2020. Int J Disaster Risk Sci 13:139–150. https://doi.org/10.1007/s13753-022-00396-4
Zhang L, Tao Z, Wang G (2022) Assessment and determination of earthquake casualty gathering area based on building damage state and spatial characteristics analysis. Int J Disaster Risk Reduct 67:102688. https://doi.org/10.1016/j.ijdrr.2021.102688
Zúñiga-Vásquez JM, Cisneros-González D, Pompa-García M et al (2017) Modelación espacial de incendios forestales en México: Una integración de dos bases de datos. Bosque 38:563–574. https://doi.org/10.4067/S0717-92002017000300014
Chen CY, Yang QH (2018) Hotspot analysis of the spatial and temporal distribution of fires. GISTAM 2018 - Proc 4th int conf Geogr Inf Syst Theory. Appl Manag 3:15–21. https://doi.org/10.5220/0006638600150021
Acknowledgements
The author would like to thank ESRI for providing the regional scale data of the European continent and NASA FIRMS service for providing fire point data with Terra and Aqua satellites of the MODIS platform.
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest to be declared.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Akyürek, Ö. Spatial and temporal analysis of vegetation fires in Europe. Nat Hazards 117, 1105–1124 (2023). https://doi.org/10.1007/s11069-023-05896-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-05896-0