Floresta e Ambiente
https://floram.org/article/doi/10.1590/2179-8087-FLORAM-2021-0078
Floresta e Ambiente
Original Article Conservation of Nature

Burning Susceptibility Modeling to Reduce Wildfire Impacts: A GIS and Multivariate Statistics Approach

Vicente Paulo Santana Neto, Rodrigo Vieira Leite, Vitor Juste dos Santos, Sabrina do Carmo Alves, Jackeline de Siqueira Castro, Fillipe Tamiozzo Pereira Torres, Maria Lucia Calijuri

Downloads: 0
Views: 160

Abstract

Forest burning susceptibility mapping is a tool to mitigate wildfires, with several methods to develop them. This study aimed to compare the Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), and Random Forest (RF) methods for mapping. Several variables were used to generate the maps. For MLR and RF methods, fire frequency between 1990 and 2010 was used as the response variable in the models. To validate the methods (AHP, MLR and RF), fire data between 2011 and 2018 were used in four stages. RF was the best method employed. Correct and incorrect values for this method were 74% and 26% and AUC 0.66. The sensitivity and specificity for the highest risk class were 31% and 96%. The low sensitivity values can be attributed to the randomness attributed to anthropic fire. The high specificity values point to a good separation of the higher risk class compared to the others.

Keywords

Analytic Hierarchy Process; Burn Frequency; Fuzzy Logic; Portugal; Random Forest

References

  • Abedi Gheshlaghi H, Feizizadeh B, Blaschke T. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning and Management 2020; 63(3): 481-499.

  • Adab H. Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data. Natural Hazards 2017; 87(3): 1807-1825.

  • Aini A, Curt T, Bekdouche F. Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria). Environmental Monitoring and Assessment 2019; 191(12).

  • Akinola OV, Adegoke J. Assessment of forest fire vulnerability zones in Missouri, United States of America. International Journal of Sustainable Development and World Ecology 2019; 26(3): 251-257.

  • Albano R, Mancusi L, Adamowski J, Cantisani A, Sole A. A GIS tool for mapping dam-break flood hazards in Italy. ISPRS International Journal of Geo-Information 2019; 8(6).

  • Arabameri A, Roy J, Saha S, Blaschke T, Ghorbanzadeh O, Bui DT. Application of probabilistic and machine learning models for groundwater potentiality mapping in Damghan sedimentary plain, Iran. Remote Sensing 2019; 11(24).

  • Aximoff I, Rodrigues R De C. Histórico dos Incêndios Florestais no Parque Nacional do Itatiaia. Ciência Florestal 2011; 21(1): 83-92.

  • Bedia J, Herrera S, Camia A, Moreno JM, Gutiérrez JM. Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios. Climatic Change 2014; 122(1-2): 185-199.

  • Bernier PY, Gauthier S, Jean PO, Manka F, Boulanger Y, Beaudoin A, et al. Mapping local effects of forest properties on fire risk across Canada. Forests 2016; 7(8): 1-11,

  • Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 1997; 30(7): 1145-1159.

  • Breiman L. Random Forests. Machine Learning 2001; 45: 5-32. https://doi.org/10.1023/A:1010933404324
    » https://doi.org/https://doi.org/10.1023/A:1010933404324

  • Bui DT, Le KTT, Nguyen VC, Le HD, Revhaug I. Tropical forest fire susceptibility mapping at the Cat Ba National Park area, Hai Phong City, Vietnam, using GIS-based Kernel logistic regression. Remote Sensing 2016; 8(4): 1-15.

  • Busico G, Giuditta E, Kazakis N, Colombani N. A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability (Switzerland) 2019; 11(24).

  • Camargo ACL, Barrio ROL, De Camargo NF, MEndonça AF, Ribeiro JF, Rodrigues CMF, et al. Fire affects the occurrence of small mammals at distinct spatial scales in a neotropical savanna. European Journal of Wildlife Research 2018; 64(6).

  • Carmo M, Moreira F, Casimiro P, Vaz P. Land use and topography influences on wildfire occurrence in northern Portugal. Landscape and Urban Planning 2011; 100(1-2): 169-176.

  • Catry FX, Rego FC, Bação FL, Moreira F. Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire 2009; 18(8): 921-931.

  • CAúla RH, Oliveira-Júnior JF, Lyra GB, Delgado RC, Heilbron Filho PFL. Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011. Environmental Earth Sciences 2015; 74(2): 1497-1508.

  • Clark WA V, Hosking PL. Statistical Methods for Geographers. New York, NY: John Wiley & Sons, 1986.

  • Çolak E, Sunar F. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction 2020; 45(January): 101479.

  • Da Silva Junior CA, Teodoro PE, Delgado RC, Teodoro LPR, Lima M, De Andréa Pantaleão A, et al. Persistent fire foci in all biomes undermine the Paris Agreement in Brazil. Scientific Reports 2020; 10(1): 1-14.

  • De Araújo Carvalho G, Minnett PJ, Ebecken NFF, Landau L. Classification of oil slicks and look-alike slicks: A linear discriminant analysis of microwave, infrared, and optical satellite measurements. Remote Sensing 2020; 12(13).

  • DGTERRITÓRIO. Carta na escala 1:2 500 000. 2003. [cited 2020 jun 30] Available from: <http://www.dgterritorio.pt/cartografia_e_geodesia/cartografia/cartografia_de_base___topografica_e_topografica_de_imagem/serie_cartografica_1_2_500_000/>.
    » http://www.dgterritorio.pt/cartografia_e_geodesia/cartografia/cartografia_de_base___topografica_e_topografica_de_imagem/serie_cartografica_1_2_500_000/

  • DGTERRITÓRIO. Modelos de terreno e de superfície, 2017.

  • DGTERRITÓRIO. Especificações técnicas da Carta de Uso e Ocupação do Solo (COS) de Portugal Continental para 1995, 2007, 2010 e 2015. Relatório Técnico 2018.

  • DGTERRITÓRIO. Carta de Uso e Ocupação do Solo de Portugal Continental (COS). [cited 2020a jun 29] Available from: <Available from: http://www.dgterritorio.pt/dados_abertos/cos/ >.
    » http://www.dgterritorio.pt/dados_abertos/cos/

  • DGTERRITÓRIO. Especificações Técnicas da Carta de Uso e Ocupação do Solo (COS) de Portugal Continental para 2018. 2019b.

  • Duarte L, Teodoro AC. An easy, accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler. Journal of Forestry Research 2016; 27(6): 1361-1372.

  • Elia M, Giannico V, Lafortezza R, Sanesi G. Modeling fire ignition patterns in Mediterranean urban interfaces. Stochastic Environmental Research and Risk Assessment 2019; 33(1): 169-181.

  • Eskandari S. A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences 2017; 10(8).

  • Eugenio FC, Dos Santos AR, Fiedler NC, Ribeiro GA, Da Silva AG, Dos Santos ÁB, et al. Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. Journal of Environmental Management 2016; 173: 65-71.

  • Fernandes J, Malheiro R, De Fátima Castro M, Gervásio H, Silva SM, Mateus R. Thermal performance and comfort condition analysis in a vernacular building with a glazed balcony. Energies 2020; 13(3).

  • Ferreira L, Constantino MF, Borges JG, Garcia-Gonzalo J. Addressing wildfire risk in a landscape-level scheduling model: An application in Portugal. Forest Science 2015; 61(2): 266-277.

  • Francos M, Pereira P, Alcañiz M, Úbeda X. Post-wildfire management effects on short-term evolution of soil properties (Catalonia, Spain, SW-Europe). Science of the Total Environment 2018; 633: 285-292.

  • Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-FOurnel M, Lampin C. A review of the main driving factors of forest fire ignition over Europe. Environmental Management 2013; 51(3): 651-662.

  • Gholamnia K, Nachappa TG, Ghorbanzadeh O, Blaschke T. Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry 2020; 12(4): 1-20.

  • Gigović L, Pourghasemi HR, Drobnjak S, BAI S. Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests 2019; 10(5).

  • Grala K, Grala RK, Hussain A, Cooke WH, Varner JM. Impact of human factors on wildfire occurrence in Mississippi, United States. Forest Policy and Economics 2017; 81(October): 38-47.

  • Guglietta D, Migliozzi A, Ricotta C. A Multivariate Approach for Mapping Fire Ignition Risk: The Example of the National Park of Cilento (Southern Italy). Environmental Management 2015; 56(1): 157-164.

  • Harris L, Taylor AH. Previous burns and topography limit and reinforce fire severity in a large wildfire. Ecosphere 2017; 8(11).

  • ICNF. 8o Relatório provisório de incêndios rurais. Instituto da Conservação da Natureza e das Florestas 2019a.

  • ICNF. Informação Geográfica. [cited 2019b jun 29] Available from: <Available from: https://geocatalogo.icnf.pt/catalogo.html >.
    » https://geocatalogo.icnf.pt/catalogo.html

  • INE. Estimativas de População Residente em Portugal. Lisboa - Portugal. [cited 2020a jun 29] Available from: <https://www.ine.pt/ngt_server/attachfileu.jsp?look_parentBoui=438658715&att_display=n&att_download=y>.
    » https://www.ine.pt/ngt_server/attachfileu.jsp?look_parentBoui=438658715&att_display=n&att_download=y

  • INE. Instituto Nacional de Estatística. [cited 2020b jun 29] Available from: <Available from: https://www.ine.pt/ >.
    » https://www.ine.pt/

  • Jafari Goldarag Y, Mohammadzadeh A, Ardakani AS. Fire Risk Assessment Using Neural Network and Logistic Regression. Journal of the Indian Society of Remote Sensing 2016; 44(6): 885-894.

  • Kayet N, Chakrabarty A, Pathak K, Sahoo S, Dutta T, Hatai BK. Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk (FFR) mapping in Melghat Tiger Reserve (MTR) forest. Journal of Forestry Research 2020; 31(2): 565-579.

  • Keyser A, Leroy Westerling A. Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States. Environmental Research Letters 2017; 12(6).

  • Kleinbaum DG, Kupper LL, Muller KE. Applied Regression Analysis and Other Multivariable Methods. 2. ed. Boston: PWS-KENT Publishing Company, 1988.

  • Kuhn M. Caret: Classification and Regression Training, 2019. Available from: <https://cran.r-project.org/package=caret>.
    » https://cran.r-project.org/package=caret

  • Lee DH, Kim YT, Lee SR. Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing 2020; 12(7).

  • Leuenberger M, Parente J, Tonini M, Pereira MG, Kanevski M. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environmental Modelling and Software 2018; 101: 194-203.

  • Li X, Zhao G, Yu X, Yu Q. A comparison of forest fire indices for predicting fire risk in contrasting climates in China. Natural Hazards 2014; 70(2): 1339-1356.

  • Lim CH, Kim YS, Won M, Kim SJ, Lee WK. Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea. Geomatics, Natural Hazards and Risk 2019; 10(1): 719-739.

  • Ma W, Feng Z, Cheng Z, Chen S, Wang F. Identifying forest fire driving factors and related impacts in china using random forest algorithm. Forests 2020; 11(5).

  • Mitsopoulos I, Chrysafi I, Bountis D, Mallinis G. Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem. Journal of Environmental Management 2019; 235(January): 266-275.

  • Moreira F, Vaz P, Catry F, Silva JS. Regional variations in wildfire susceptibility of land-cover types in Portugal: Implications for landscape management to minimize fire hazard. International Journal of Wildland Fire 2009; 18(5): 563-574.

  • Mota PHS, Rocha SJSS Da, Castro NLM De, Marcatti GE, França LC De J, Schettini BLS, et al. Forest fire hazard zoning in Mato Grosso State, Brazil. Land Use Policy 2019; 88(September): 104206.

  • Ngoc Thach N, Bao-Toan Ngo D, Xuan-Canh P, Hong-Thi N, Hang Thi B, Nhat-Duc H, et al. Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study. Ecological Informatics 2018; 46: 74-85.

  • Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management 2012; 275: 117-129.

  • Oliveira S, Zêzere JL, Queirós M, Pereira JM. Assessing the social context of wildfire-affected areas. The case of mainland Portugal. Applied Geography 2017; 88: 104-117,

  • Pan J, Wang W, Li J. Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China. Natural Hazards 2016; 81(3): 1879-1899,

  • Parente J, Pereira MG. Structural fire risk: The case of Portugal. Science of the Total Environment 2016; 573: 883-893.

  • Parente J, Pereira MG, Amraoui M, Tedim F. Negligent and intentional fires in Portugal: Spatial distribution characterization. Science of the Total Environment 2018; 624: 424-437.

  • Pourghasemi H Reza, Beheshtirad M, Pradhan B. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk 2016; 7(2): 861-885.

  • Pourghasemi HR, Gayen A, Lasaponara R, Tiefenbacher JP. Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. Environmental Research 2020; 184.

  • Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators 2016; 64: 72-84.

  • R CORE TEAM. R: A Language and Environment for Statistical ComputingVienna, Austria R Foundation for Statistical Computing, 2018. Available from: <https://www.r-project.org/>
    » https://www.r-project.org/

  • Razavi-Termeh SV, Sadeghi-Niaraki A, CHOI SM. Ubiquitous GIS-based forest fire susceptibility mapping using artificial intelligence methods. Remote Sensing 2020; 12(10).

  • Rodrigues M, Alcasena F, Gelabert P, Vega-García C. Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region. Risk Analysis 2020; 40(9): 1762-1779

  • Rodrigues M, De La Riva J. An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environmental Modelling and Software 2014; 57: 192-201.

  • Saaty TL. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 1977; 15(3): 234-281.

  • Sakellariou S, Tampekis S, Samara F, Flannigan M, Jaeger D, Christopoulou O, et al. Determination of fire risk to assist fire management for insular areas: the case of a small Greek island. Journal of Forestry Research 2019; 30(2): 589-601.

  • San-Miguel-Ayanz J, Moreno JM, Camia A. Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management 2013; 294: 11-22.

  • Sannigrahi S, Pilla F, Basu B, Basu AS, Sarkar K, Chakraborti S, et al. Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches. Science of the Total Environment 2020; 725(March): 138331.

  • Shang C, Wulder MA, Coops NC, White JC. Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data. Canadian Journal of Remote Sensing 2020; 0(0): 1-17.

  • Sivrikaya F, Sa-lam B, Akay AE, Bozali N. Evaluation of forest fire risk with GIS. Polish Journal of Environmental Studies 2014; 23(1): 187-194.

  • SNIRH. Sistema Nacional de Informação de Recursos Hídricos. [cited 2020 jun 29] Available from: <Available from: https://snirh.apambiente.pt/index.php?idMain= >.
    » https://snirh.apambiente.pt/index.php?idMain=

  • Sousa PM, Trigo RM, Pereira MG, Bedia J, Gutiérrez JM. Different approaches to model future burnt area in the Iberian Peninsula. Agricultural and Forest Meteorology 2015; 202: 11-25.

  • Stephens SL, Westerling ALR, Hurteau MD, Peery MZ, Schultz CA, Thompson S. Fire and climate change: conserving seasonally dry forests is still possible. Frontiers in Ecology and the Environment 2020; 18(6): 354-360.

  • Su Z, Hu H, Wang G, Ma Y, Yang X, Guo F. Using GIS and random forests to identify fire drivers in a forest city, Yichun, China. Geomatics, Natural Hazards and Risk 2018; 9(1): 1207-1229.

  • Tien Bui D, Le H Van, Hoang N-D. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Ecological Informatics 2018; 48: 104-116.

  • Tonini M, D’andrea M, Biondi G, Esposti SD, Trucchia A, Fiorucci P. A machine learning-based approach for wildfire susceptibility mapping. The case study of the liguria region in italy. Geosciences (Switzerland) 2020; 10(3): 1-18.

  • Tonini M, Pereira MG, Parente J, Vega Orozco C. Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps. Natural Hazards 2017; 85(3): 1489-1510.

  • Torres P, Ferreira J, Monteiro A, Costa S, Pereira MC, Madureira J, et al. Air pollution: A public health approach for Portugal. Science of the Total Environment 2018; 643(135): 1041-1053.

  • Tosic I, Mladjan D, Gavrilov MB, Zivanović S, Radaković MG, Putniković S, et al. Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017. Open Geosciences 2019; 11(1): 414-425.

  • Turkman KF, Turkman MAA, Pereira P, Sá A, Pereira JMC. Generating annual fire risk maps using bayesian hierarchical models. Journal of Statistical Theory and Practice 2014; 8(3): 509-533.

  • Vallejo-Villalta I, Rodríguez-Navas E, Márquez-Pérez J. Mapping forest fire risk at a local scale-A case study in Andalusia (Spain). Environments - MDPI 2019; 6(3),

  • Verde JC, Zêzere JL. Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Science 2010; 10(3): 485-497.

  • Whitman E, Parisien MA, Thompson DK, Hall RJ, Skakun RS, Flannigan MD. Variability and drivers of burn severity in the northwestern Canadian boreal forest: Ecosphere 2018; 9(2).

  • Yin S, Wang X, Guo M, Santoso H, Guan H. The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015. Atmospheric Research 2020; 243: 105027.

  • Zadeh LA. Fuzzy sets. Information and Control 1965; 8(3): 338-353.

  • Živanović S, Ivanović R, Nikolić M, Đokić M, Tošić I. Influence of air temperature and precipitation on the risk of forest fires in Serbia. Meteorology and Atmospheric Physics 2020; 132(6): 869-883.


Submitted date:
09/24/2021

Accepted date:
12/25/2021

621655d5a953957fd42f93f4 floram Articles

FLORAM

Share this page
Page Sections