Assessment of two methods on zoning wildfire propagation in Itacolomi State Park, Minas Gerais State, Brazil

Authors

DOI:

https://doi.org/10.4336/2023.pfb.43e202102227

Keywords:

Forest fires, Methodology, Geographical information systems

Abstract

This study aimed to assess the wild fire propagation risk to wildfires in the Itacolomi State Park, in Minas Gerais State, Brazil, using GIS and to compare the efficiency of the incident solar radiation over the aspect variable. The following variables were used: land cover/use (LCU), slope (SLP), slope curvature (CUR), aspect (ASP) and incident solar radiation (SOL). The weights of each variable were calculated from the ratio between the total area and the burned area of each class in order to generate the fire propagation risk maps. Fire data from 2016 to 2019 were used for validation. When the moderate risk class was considered susceptible, inadequate precision was observed for both methods (ASP and SOL). On the other hand, when the moderate class was considered non-susceptible to fire, the results presented moderate accuracy. Furthermore, the methods using SOL and ASP showed similar results. The results can guide fire mitigation actions on the park.

Downloads

Download data is not yet available.

Author Biographies

Vicente Paulo Santana Neto, Universidade Federal de Viçosa, Departamento de Engenharia Florestal

David Marques Soares, Universidade Federal de Ouro Preto, Departamento de Engenharia Ambiental

http://lattes.cnpq.br/7884588417689856

Thaís Camargos da Silva, Universidade Federal de Viçosa, Departamento de Engenharia Florestal

http://lattes.cnpq.br/3985365956323300

Fillipe Tamiozzo Pereira Torres, Universidade Federal de Viçosa, Departamento de Engenharia Florestal

http://lattes.cnpq.br/0584307291769294

References

Andrade, S. C. & Ferreira, A. F. Mapeamento geoecológico da susceptibilidade à ocorrência de incêndios no Parque Estadual da Serra da Concórdia - Valença RJ. Revista Eletrônica TECCEN, v. 12, n. 2, p. 45-58, 2019. http://dx.doi.org/10.21727/teccen.v12i2.1999.

Bacani, V. M. Geoprocessing applied to risk assessment of forest fires in the municipality of Bodoquena, Mato Grosso do Sul. Revista Árvore, v. 40, n. 6, p. 1003-1011, 2016. http://dx.doi.org/10.1590/0100-67622016000600005.

Bontempo, G. C. et al. Registro de Ocorrência de Incêndio (ROI): evolução, desafios e recomendações. Biodiversidade Brasileira, v. 1, n. 2, p. 247-263, 2011.

Bradley, A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, v. 30, n. 7, p. 1145-1159, 1997. http://dx.doi.org/10.1016/S0031-3203(96)00142-2.

Carmo, M. et al. Land use and topography influences on wildfire occurrence in northern Portugal. Landscape and Urban Planning, v. 100, n. 1-2, p. 169-176, 2011. http://dx.doi.org/10.1016/j.landurbplan.2010.11.017.

Chang, Y. et al. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecology, v. 28, n. 10, p. 1989-2004, 2013. http://dx.doi.org/10.1007/s10980-013-9935-4.

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, v. 45, p. 101479, 2020. http://dx.doi.org/10.1016/j.ijdrr.2020.101479.

Duarte, L. & Teododo, A. C. An easy, accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler. Journal of Forestry Research, v. 27, n. 6, p. 1361-1372, 2016. http://dx.doi.org/10.1007/s11676-016-0267-5.

Eastman, J. R. IDRISI Selva Manual. 17.01 ed. Clark University, 2012. 324 p.

Edwards, A. C. et al. A comparison and validation of satellite-derived fire severity mapping techniques in fire prone north Australian savannas: extreme fires and tree stem mortality. Remote Sensing of Environment, v. 206, p. 287-299, 2018. http://dx.doi.org/10.1016/j.rse.2017.12.038.

ESRI. ArcGIS Desktop: Release 10.8. Redlands, CA: Instituto de Pesquisa de Sistemas Ambientais, 2011.

Eugenio, F. C. et al. Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil. Journal of Environmental Management, v. 173, p. 65-71, 2016. http://dx.doi.org/10.1016/j.jenvman.2016.02.021.

Fernandes Filho, E. I. ; & Sá, M. M. F. Influência das variáveis do terreno na radiação solar. In: Simpósio Brasileiro de Sensoriamento Remoto, 13., 2007. Anais [...]. Florianópolis: INPE, 2007. p. 5751-5753.

Gholamnia, K. et al. Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry, v. 12, n. 4, p. 1-20, 2020. http://dx.doi.org/10.3390/SYM12040604.

Guglietta, D. et al. A Multivariate approach for mapping fire ignition risk: the example of the National Park of Cilento (Southern Italy). Environmental Management, v. 56, n. 1, p. 157-164, 2015. http://dx.doi.org/10.1007/s00267-015-0494-0.

ICMBio. Instituto Chico Mendes de Conservação da Biodiversidade. Incêndios em Unidades de Conservação Federais. Disponível em: https://dados.gov.br/dataset/incendios-em-ucs. Acesso em: 9 fev. 2021.

IEF. Instituto Estadual de Florestas. Plano de manejo: Parque Estadual do Itacolomi. Belo Horizonte: Secretaria de Meio Ambiente de Minas Gerais, 2007. Disponível em: http://www.ief.mg.gov.br/component/content/193?task=view.Acesso em: 03 mar. 2022.

INPE. Instituto Nacional de Pesquisas Espaciais. TOPODATA: Banco de Dados Geomorfométricos do Brasil. Disponível em: http://www.dsr.inpe.br/topodata/index.php. Acesso em: 8 nov. 2021.

Kayet, N. et al. 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, v. 31, n. 2, p. 565-579, 2020. http://dx.doi.org/10.1007/s11676-018-0826-z.

Ladislau, F. F. et al. Análise multicritério aplicada ao mapeamento de risco de incêndio na APA Sul RMBH. Caderno de Geografia, v. 31, n. 66, p. 667, 2021. http://dx.doi.org/10.5752/p.2318-2962.2021v31n66p667.

Leal, F. A. et al. Zoneamento de riscos de incêndios florestais em regiões hot spot de focos de calor no estado do Acre. Nativa, v. 7, n. 3, p. 274, 2019. http://dx.doi.org/10.31413/nativa.v7i3.6768.

Le Stradic, S. et al. Diversity of germination strategies and seed dormancy in herbaceous species of campo rupestre grasslands. Austral Ecology, v. 40, n. 5, p. 537-546, 2015. http://dx.doi.org/10.1111/aec.12221.

Leuenberger, M. et al. Wildfire susceptibility mapping: deterministic vs. stochastic approaches. Environmental Modelling and Software, v. 101, p. 194-203, 2018. http://dx.doi.org/10.1016/j.envsoft.2017.12.019.

Marchesan, J. et al. Risco de incêndios na Estação Ecológica do Taim, Rio Grande do Sul. Nativa, v. 8, n. 1, p. 112, 2020. http://dx.doi.org/10.31413/nativa.v8i1.8180.

Ngoc Thach, N. 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, v. 46, p. 74-85, 2018. http://dx.doi.org/10.1016/j.ecoinf.2018.05.009.

Nicolete, D. A. P. & Zimback, C. R. L. Zoneamento de risco de incêndios florestais para a fazenda experimental Edgardia - Botucatu (SP), através de sistemas de informações geográficas. Revista Agrogeoambiental, v. 5, n. 3, p. 55-62, 2013. http://dx.doi.org/10.18406/2316-1817v5n32013518.

Novo, A. et al. Automatic processing of aerial LiDAR data to detect vegetation continuity in the surroundings of roads. Remote Sensing, v. 12, n. 10, p. 1-14, 2020. http://dx.doi.org/10.3390/rs12101677.

Pedreira, G. & De Sousa, H. C. Comunidade arbórea de uma mancha florestal permanentemente alagada e de sua vegetação adjacente em Ouro Preto-MG, Brasil. Ciencia Florestal, v. 21, n. 4, p. 663-675, 2011. http://dx.doi.org/10.5902/198050984511.

Pourtaghi, Z. S. et al. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators, v. 64, p. 72-84, 2016. http://dx.doi.org/10.1016/j.ecolind.2015.12.030.

Rodrigues, M. et al. Geospatial modeling of containment probability for escaped wildfires in a Mediterranean Region. Risk Analysis, 2020. http://dx.doi.org/10.1111/risa.13524.

Santana Neto, V. P. et al. Burning susceptibility modeling to reduce wildfire impacts: a GIS and multivariate statistics approach. Floresta e Ambiente, v. 29, n. 1, p. 1-12, 2022. http://dx.doi.org/10.1590/2179-8087-FLORAM-2021-0078.

Sari, F. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: a comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, v. 480, p. 118644, 2020. http://dx.doi.org/10.1016/j.foreco.2020.118644.

Sarricolea, P. et al. Recent wildfires in Central Chile: detecting links between burned areas and population exposure in the wildland urban interface. Science of the Total Environment, v. 706, p. 135894, 2020. http://dx.doi.org/10.1016/j.scitotenv.2019.135894.

Soares Neto, G. B. et al. Riscos de incêndios florestais no parque nacional de Brasília, Brasil. Territorium, n. 23, p. 161-170, 2016. http://dx.doi.org/10.14195/1647-7723_23_13.

Soares, R. V. et al. Controle, efeitos e uso do fogo. 2. ed. Viçosa, MG: Produção Independente, 2017.

Tien Bui, D. et al. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Ecological Informatics, v. 48, p. 104-116, 2018. http://dx.doi.org/10.1016/j.ecoinf.2018.08.008.

Torres, F. T. P. et al. Mapeamento da suscetibilidade a ocorrências de incêndios em vegetação na área urbana de Ubá-MG. Revista Árvore, v. 38, n. 5, p. 811-817, 2014. http://dx.doi.org/10.1590/S0100-67622014000500005.

Torres, F. T. P. et al. Mapeamento do risco de incêndios florestais utilizando técnicas de geoprocessamento. Floresta e Ambiente, v. 24, 2017. http://dx.doi.org/10.1590/2179-8087.025615.

Downloads

Published

2023-06-21

How to Cite

SANTANA NETO, Vicente Paulo; SOARES, David Marques; SILVA, Thaís Camargos da; TORRES, Fillipe Tamiozzo Pereira. Assessment of two methods on zoning wildfire propagation in Itacolomi State Park, Minas Gerais State, Brazil. Pesquisa Florestal Brasileira, [S. l.], v. 43, 2023. DOI: 10.4336/2023.pfb.43e202102227. Disponível em: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/2227. Acesso em: 19 may. 2024.

Issue

Section

Articles

Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.