iForest - Biogeosciences and Forestry


Contribution of anthropogenic, vegetation, and topographic features to forest fire occurrence in Poland

Mariusz Ciesielski (1)   , Radomir Balazy (2), Boleslaw Borkowski (3), Wieslaw Szczesny (4), Michal Zasada (5), Jan Kaczmarowski (6), Miroslaw Kwiatkowski (7), Ryszard Szczygiel (7), Slobodan Milanovic (8-9)

iForest - Biogeosciences and Forestry, Volume 15, Issue 4, Pages 307-314 (2022)
doi: https://doi.org/10.3832/ifor4052-015
Published: Aug 23, 2022 - Copyright © 2022 SISEF

Research Articles

Climate is one of the main causes of forest fires in Europe. In addition, forest fires are influenced by other factors, such as the reconstruction of tree stands with a uniform species composition and increasing human pressure. At the same time, the increasing number of fires is accompanied by a steady increase in the number and quality of spatial information collected, which affects the ability to conduct more accurate studies of forest fires. The appropriate use of spatial information systems (GIS) together with all the collected information on fires could provide new insights into their causes and, in further steps, allow the development of new, more accurate predictive models. The objectives of the study were: (i) to estimate the probability of fire occurrence in the period 2007-2016; (ii) to evaluate the performance of the developed model; (iii) to identify and quantify anthropogenic, topographic and stand factors affecting the probability of fire occurrence in forest areas in Poland. To achieve these objectives, a statistical model based on a logistic regression approach was built using the nationwide forest fire database for the period from 2007 to 2016. The information in the database was obtained from the Polish State Forest Information System (SILP). Then it was supplemented with spatial, topographic and socio-economic information from various spatial and statistical databases. The results showed that fire probability is significantly positively affected by population density and distance from buildings. In addition, the further the distance from roads and railways, watercourses and water objects or the edge of the forest, height above sea level, and steep slopes, the lower is the fire probability. Analysis of spatial, ecological and socio-economic factors provides new insights that contribute to a better understanding of fire occurrence in Poland.


Forest Fires, Logistic Regression, Variables Selection, Anthropogenic Factors

Authors’ address

Mariusz Ciesielski 0000-0002-1215-140X
Department of Geomatics, Forest Research Institute, Sekocin Stary ul. Braci Lesnej 3, 05090 Raszyn (Poland)
Radomir Balazy 0000-0003-1633-5115
Prevent Fires Foundation, Warszawa, ul. Drawska 29A/56, 02-202 Warszawa (Poland)
Boleslaw Borkowski 0000-0001-6073-6173
Department of Econometrics and Statistics, Institute of Economy and Finances, Warsaw University of Life Sciences - SGGW (Poland)
Wieslaw Szczesny 0000-0002-8083-4624
Department of Applied Informatics, Institute of Information Technology, Warsaw University of Life Sciences - SGGW (Poland)
Michal Zasada 0000-0002-4881-296X
Department of Forest Management, Dendrometry and Forest Economics, Institute of Forest Sciences, Warsaw University of Life Sciences - SGGW (Poland)
Jan Kaczmarowski 0000-0002-5205-2780
General Directorate of the State Forests, ul. Grójecka 127, 02-124 Warszawa (Poland)
Miroslaw Kwiatkowski 0000-0003-1661-9847
Ryszard Szczygiel 0000-0001-8008-7430
Forest Fire Protection Laboratory, Forest Research Institute, Sekocin Stary, ul. Braci Lesnej 3, 05-090 Raszyn (Poland)
Slobodan Milanovic 0000-0002-8260-999X
Chair of Forest Protection, University of Belgrade Faculty of Forestry, 11030 Belgrade (Serbia)
Slobodan Milanovic 0000-0002-8260-999X
Department of Forest Protection and Wildlife Management, Faculty of Forestry and Wood Technology, Mendel University, 61300 Brno (Czech Republic)

Corresponding author

Mariusz Ciesielski


Ciesielski M, Balazy R, Borkowski B, Szczesny W, Zasada M, Kaczmarowski J, Kwiatkowski M, Szczygiel R, Milanovic S (2022). Contribution of anthropogenic, vegetation, and topographic features to forest fire occurrence in Poland. iForest 15: 307-314. - doi: 10.3832/ifor4052-015

Academic Editor

Davide Ascoli

Paper history

Received: Dec 29, 2021
Accepted: Jun 19, 2022

First online: Aug 23, 2022
Publication Date: Aug 31, 2022
Publication Time: 2.17 months

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Adámek M, Bobek P, Hadincová V, Wild J, Kopecky M (2015)
Forest fires within a temperate landscape: a decadal and millennial perspective from a sandstone region in Central Europe. Forest Ecology and Management 336: 81-90.
CrossRef | Gscholar
Adámek M, Jankovská Z, Hadincová V, Wild J, Kula E (2018)
Drivers of forest fire occurrence in the cultural landscape of Central Europe. Landscape Ecology 33: 2031-2045.
CrossRef | Gscholar
Alexandrian D, Gouiran M (1990)
Les causes d’incendie: levons le voile [The causes of fire: let’s lift the veil]. Revue Forestière Français 42: 34-41. [in French]
Allison P (2012)
Logistic regression using SAS: theory and application (2nd edn). SAS Institute, Cary, USA, pp. 139.
Online | Gscholar
Borkowski B, Dudek H, Szczesny W (2003)
Ekonometria. Wybrane zagadnienia [Econometrics. Selected issues]. Wydawnictwo Naukowe PWN, Warszawa, Poland, pp. 212. [in Polish]
Brach M, Kaczmarowski J (2014)
Ocena mozliwosci wykorzystania modelu HSI do analizy rozprzestrzeniania sie pozaru lasu [Suitability of the HSI model for the analysis of the forest fire spread]. Sylwan 158 (10): 769-778. [in Polish]
CrossRef | Gscholar
Breiman L (2001)
Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science 16 (3): 199-231.
CrossRef | Gscholar
Catry F, Damasceno P, Silva J, Galante M, Moreira F (2007)
Spatial distribution patterns of wildfire ignitions in Portugal. In: Proceedings of the “4th International Wildland Fire Conference”. Seville (Spain), 13-17 May 2007, Ministerio de Medio Ambiente, Seville, Spain.
Catry F, Rego F, Silva J, Moreira F, Camia A, Ricotta C, Conedera M (2010)
Fire starts and human activities. In: “Towards Integrated Fire Management - Outcomes of the European Project Fire Paradox” (Silva J, Rego F, Fernandes P, Rigolot E eds). European Forest Institute, Joensuu, Finland, pp. 9-22.
Christensen R (1997)
Log-linear models and logistic regression. Springer, New York, USA, pp. 116-167.
Costafreda-Aumedes S, Comas C, Vega-Garcia C (2017)
Human-caused fire occurrence modelling in perspective: a review. International Journal of Wildland Fire 26: 983-998.
CrossRef | Gscholar
Curt T, Fréjaville T, Lahaye S (2016)
Modelling the spatial patterns of ignition causes and fire regime features in southern France: implications for fire prevention policy. International Journal of Wildland Fire 25 (7): 785-796.
CrossRef | Gscholar
Draper NR, Smith H (1998)
Applied Regression Analysis. John Wiley and Sons, New York, USA, pp. 200.
Galizia LF, Curt T, Barbero R, Rodrigues M (2021)
Understanding fire regimes in Europe. International Journal of Wildland Fire 31: 56-66.
CrossRef | Gscholar
Ganteaume A, Camia A, Jappiot M, San Miguel-Ayanz J, Long-Fournel M (2013)
A review of the main driving factors of forest fire ignition over Europe. Environmental Management 51 (3): 651-662.
CrossRef | Gscholar
Gerdzheva A (2014)
A comparative analysis of different wildfire risk assessment models (a case study for Smolyan District, Bulgaria). European Journal of Geography 5: 22-36.
Ghorbanzadeh O, Valizadeh Kamran K, Blaschke T, Aryal J, Naboureh A, Einali J, Bian J (2019)
Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire 2 (3): 43.
CrossRef | Gscholar
González JR, Trasobares A, Palahí M, Pukkala T (2007)
Predicting stand damage and tree survival in burned forests in Catalonia (North-East Spain). Annals of Forest Science 64 (7): 733-742.
CrossRef | Gscholar
Greene WH (2012)
Econometric analysis. Prentice Hall, Upper Saddle River, NJ, USA, pp. 464.
Gruszczynski M (2010)
Modele zmiennych jakosciowych dwumianowych [Models of qualitative binomial variables]. In: “Mikroekonometria. Modele i analizy danych indywidualnych” [Microeconometry. Models and analyzes of individual data] (Gruszczynski M ed). Wolters Kluwer Polska, Warszawa, Poland, pp. 298. [in Polish]
Jackowska B (2011)
Efekty interakcji miedzy zmiennymi objasniajacymi w modelu logitowym w analizie zróznicowania ryzyka zgonu [Interaction effects between predictor variables in a logistic model in an analysis of the diversity of death risk]. Przeglad Statystyczny 58 (1-2): 24-41. [in Polish]
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020)
A review of machine learning applications in wildfire science and management. Environmental Reviews 28 (4): 478-505.
CrossRef | Gscholar
Karwel A (2012)
Ocena dokladnosci modelu SRTM-X na obszarze Polski [Estimation of the accuracy of the SRTM model in Poland]. Archiwum Fotogrametrii, Kartografii i Teledetekcji 23: 139-145. [in Polish]
Kolanek A, Szymanowski M, Raczyk A (2021)
Human activity affects forest fires: the impact of anthropogenic factors on the density of forest fires in Poland. Forests 12: 728.
CrossRef | Gscholar
KSIPL (2017)
Krajowy System Informacji o Pozarach Lasów [National Forest Fire Information System]. Web site. [in Polish]
Online | Gscholar
Kufel T (2004)
Ekonometria. Rozwiazywanie problemów z wykorzystaniem programu GRETL [Econometrics. Solving problems with the use of GRETL]. PWN, Warszawa, pp. 141. [in Polish]
Maddala G (2006)
Introduction to econometrics. John Wiley and Sons Ltd. New York, USA, pp. 381-383.
Mansoor S, Farooq I, Kachroo MM, Mahmoud AED, Fawzy M, Popescu SM, Alyemeni MN, Sonne C, Rinklebe J, Ahmad P (2022)
Elevation in wildfire frequencies with respect to the climate change. Journal of Environmental Management 301: 113769.
CrossRef | Gscholar
McWethy DB, Pauchard A, García RA, Holz A, González ME, Veblen TT, Stahl J, Currey B (2018)
Landscape drivers of recent fire activity (2001-2017) in south-central Chile. PloS One 13: e0201195.
CrossRef | Gscholar
Milanović SM, Marković NM, Pamučar D, Gigović L, Kostić PK, Milanović SD (2021)
Forest fire probability mapping in Eastern Serbia: logistic regression versus random forest method. Forests 12 (1): 5.
CrossRef | Gscholar
Rodrigues M, De la Riva J (2014)
An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environmental Modelling and Software 57: 192-201.
CrossRef | Gscholar
San-Miguel-Ayanz J, Rodrigues M, Santos De Oliveira S, Pecheco C, Moreira F, Duguy B, Camia A (2012)
Land cover change and fire regime in the European Mediterranean Region. In: “Post-Fire Management and Restoration of Southern European Forests” (Moreira F, Arianoutsou M, Corona P, De las Heras J eds). Springer, Netherlands, pp. 21-43.
San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Liberta G, Artes-Vivancos T, Oom D, Branco A, De Rigo D, Ferrari D, Pfeiffer H, Grecchi R, Nuijten D, Onida M, Loffler P (2021)
Forest fires in Europe, Middle East and North Africa. Publications Office of the European Union, Luxembourg, pp. 11.
Socha J (2008)
Effect of topography and geology on the site index of Picea abies in the West Carpathian, Poland. Scandinavian Journal of Forest Research 23: 203-213.
CrossRef | Gscholar
Statistical Yearbook of Forestry (2018)
Statistics. Warsaw, Poland, pp. 40. [in Polish]
Szczygiel R, Ubysz B, Kwiatkowski M, Piwnicki J (2009)
Forest fire hazard classification in Poland. Forest Research Papers 70 (2): 131-141.
CrossRef | Gscholar
Szczygiel R (2012)
Large-area forest fires in Poland. Bezpieczenstwo i Technika Pozarnicza 1: 67-78.
Szczygiel R, Kwiatkowski M, Kolakowski B, Piwnicki J (2020)
Dynamic forest fire risk evaluation in Poland. Folia Forestalia Polonica 62 (2): 139-144.
CrossRef | Gscholar
Szymura TH, Szymura M (2013)
Spatial variability more influential than soil pH and land relief on thermophilous vegetation in overgrown coppice oak forests. Acta Societatis Botanicorum Poloniae 82 (1): 5-11.
CrossRef | Gscholar
Wozniak E (2014)
Forest fire risk estimation in Poland using geoinformatics methods. Teledetekcja Srodowiska (2014/2): 5-55.
Xanthopoulos G, Calfapietra C, Fernandes P (2012)
Fire hazard and flammability of European forest types. In: “Post-Fire Management and Restoration of Southern European Forests” (Moreira F, Arianoutsou M, Corona P, De las Heras J eds). Managing Forest Ecosystems, vol. 24, Springer, Dordrecht, Netherlands, pp. 79-92.
CrossRef | Gscholar
Zajaczkowski G, Jabloski M, Jablonski T, Szmidla H, Kowalska A, Malachowska J, Piwnicki J (2021)
Raport o stanie lasów w Polsce [Forests in Poland]. Centrum Informacyjne Lasów Panstwowych, Warszawa, Poland, pp. 90. [in Polish]
Zamarreño-Aramendia G, Cristòfol FJ, De-San-Eugenio-Vela J, Ginesta X (2020)
Social-media analysis for disaster prevention: forest fire in Artenara and Valleseco, Canary Islands. Journal of Open Innovation: Technology, Market, and Complexity 6 (4): 169.
CrossRef | Gscholar
Zhu L, Lo K (2021)
Non-timber forest products as livelihood restoration in forest conservation: a restorative justice approach. Trees, Forests and People 6: 100130.
CrossRef | Gscholar

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