Susceptibility to vegetation cover fires

an evaluation using multi-criteria and radio frequency methods (Cotacachi Cantón, Ecuador)

Authors

DOI:

https://doi.org/10.18172/cig.5867

Keywords:

Cotacachi, fire, multi-criteria analysis, radio frequency, vegetation cover

Abstract

In Ecuador, around 11688.88 hectares of vegetation cover were lost in 2023 due to 1495 registered vegetation cover fires (ICV).. Therefore, this research aimed to determine areas susceptible to ICV for the Cotacachi cantón in Ecuador and its two differentiated zones. To evaluate the susceptibility to ICV in a GIS environment, the multi-criteria methods of Analytic Hierarchy Process (AHP) and Radio Frequency (RF) were applied. For this purpose, 11 factors were established classified into topographic (altitude, slope, terrain orientation), climatic (precipitation, temperature, potential evapotranspiration, water deficit and wind speed) and anthropic (land cover, proximity to roads and proximity to agricultural areas). Afterwards, spatially explicit models were obtained, and the results were validated with the ROC curve and the area under the curve (AUC). The results show that around 47% of the territory is at extreme risk of ICV according to the AHP multi-criteria method and 53% of the canton according to the RF method, with a higher concentration in the subtropical zone than in the Andean zone. The performance values show that after comparing the models with heat spot information from the FIRMS-NASA system for the period 2000-2020, an AUC of 0.824 was obtained for the AHP model and an AUC value of 0.902 for the RF model. While, when compared with historical fires from the period 2018-2020, an AUC of 0.748 was obtained for the AHP model and an AUC value of 0.755 for the RF model. Finally, it is concluded that the AHP and RF multi-criteria models presented similar results and performances with minimal differences.

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References

Abedi Gheshlaghi, H., 2019. Using GIS to Develop a Model for Forest Fire Risk Mapping. Journal of the Indian Society of Remote Sensing 47(7), 1173-1185. https://doi.org/10.1007/s12524-019-00981-z

Abedi Gheshlaghi, H., Feizizadeh, B., Blaschke, T., 2020. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning and Management 63(3), 481-499. https://doi.org/10.1080/09640568.2019.1594726

Anrango, S., Chingal, M., Arias-Muñoz, P., 2020. Zonificación de Cobertura Vegetal Propensa a Incendios en el Cantón Ibarra: Una Mirada al Centro Poblado Más Grande de la Cuenca del Río Mira. En: P. Aguirre (Ed.). Riesgos Naturales en la cuenca del río Mira. Variabilidad del clima, deslizamientos, incendios y vulnerabilidad volcánica, pp. 57-74. Cuvillier Verlag. https://sustentabilidadyambiente.files.wordpress.com/2020/12/riesgos-naturales-en-la-cuenca-del-rio-mira.pdf

Arias-Muñoz, P., Encarnación, G., Díaz, A., Herrera, F., 2020. Zonificación de Áreas Propensas a Incendios de Cobertura Vegetal en la Subcuenca del Río Mataquí ubicada en la Provincia Imbabura. En P. Aguirre (Ed.). Riesgos Naturales en la cuenca del río Mira. Variabilidad del clima, deslizamientos, incendios y vulnerabilidad volcánica, pp. 41-56. Cuvillier Verlag. https://sustentabilidadyambiente.files.wordpress.com/2020/12/riesgos-naturales-en-la-cuenca-del-rio-mira.pdf

Bargali, H., Calderon, L. P. P., Sundriyal, R., Bhatt, D., 2022. Impact of forest fire frequency on floristic diversity in the forests of Uttarakhand, western Himalaya. Trees, Forests and People 9, 100300. https://doi.org/10.1016/j.tfp.2022.100300

Bonora, L., Claudio Conese, C., Marchi, E., Tesi, E., Montorselli, N. B., 2013. Wildfire Occurrence: Integrated Model for Risk Analysis and Operative Suppression Aspects Management. American Journal of Plant Sciences 04 (03), 705-710. https://doi.org/10.4236/ajps.2013.43A089

Bradstock, R. A., Williams, R. J., Gill, A. M., 2012. Flammable Australia: Fire regimes, biodiversity and ecosystems in a changing world. CSIRO publishing.

Buytaert, W., Célleri, R., De Bièvre, B., Cisneros, F., 2006. Hidrología del páramo andino: Propiedades, importancia y vulnerabilidad. Cuenca. Recuperado: http://www.paramo.org/files/hidrologia_paramo.pdf

Casado, A. L., Gil, V., 2006. Consecuencias de la variación de la disponibilidad hídrica en la cuenca del arroyo El Belisario, Buenos Aires, Argentina. https://repo.unlpam.edu.ar/handle/unlpam/2561

Cheng, Y., Luo, P., Yang, H., Li, H., Luo, C., Jia, H., Huang, Y., 2023. Fire effects on soil carbon cycling pools in forest ecosystems: A global meta-analysis. Science of The Total Environment 895, 165001. https://doi.org/10.1016/j.scitotenv.2023.165001

Cohen, J., 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37-46. https://doi.org/10.1177/001316446002000104

de Santana, R. O., Delgado, R. C., Schiavetti, A., 2021. Modelling susceptibility to forest fires in the Central Corridor of the Atlantic Forest using the frequency ratio method. Journal of Environmental Management 296, 113343. https://doi.org/10.1016/j.jenvman.2021.113343

del Campo Parra-Lara, Á., Bernal-Toro, F. H., 2010. Incendios de cobertura vegetal y biodiversidad: Una mirada a los impactos y efectos ecológicos potenciales sobre la diversidad vegetal. El hombre y la máquina 35, 67-81. https://www.redalyc.org/pdf/478/47817140008.pdf

Doerr, S. H., Shakesby, R. A., 2006. Forest fire impacts on catchment hydrology: A critical review. Forest Ecology and Management 234, S161. https://doi.org/10.1016/j.foreco.2006.08.212

Estacio, J., Narváez, N., 2012. Incendios forestales en el Distrito Metropolitano de Quito (DMQ): Conocimiento e intervención pública del riesgo. Letras Verdes: Revista Latinoamericana de Estudios Socioambientales 11, 27-52. https://dialnet.unirioja.es/servlet/articulo?codigo=5444128

Eugenio, F. C., Dos Santos, A. R., Fiedler, N. C., Ribeiro, G. A., Da Silva, A. G., Dos Santos, Á. B., Paneto, G. G., Schettino, V. R., 2016. Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. Journal of Environmental Management 173, 65-71. https://doi.org/10.1016/j.jenvman.2016.02.021

Fries, A., Rollenbeck, R., Göttlicher, D., Nauß, T., Homeier, J., Peters, T., Bendix, J., 2009. Thermal structure of a megadiverse Andean Mountain ecosystem in southern Ecuador and its regionalization. ERDKUNDE 63(4), 321-335. https://doi.org/10.3112/erdkunde.2009.04.03

García Leyton, L. A., Baldasano Recio, J. M., 2004. Aplicación del análisis multicriterio en la evaluación de impactos ambientales [Tesis de Doctorado, Universitat Politècnica de Catalunya]. https://doi.org/10.5821/dissertation-2117-94140

Garreaud, R. D., Vuille, M., Compagnucci, R., Marengo, J., 2009. Present-day South American climate. Palaeogeography, Palaeoclimatology, Palaeoecology 281(3-4), 180-195. https://doi.org/10.1016/j.palaeo.2007.10.032

Gobierno Autónomo Descentralizado de Cotacachi, 2015. Plan de desarrollo y ordenamiento territorial. Cantón Cotacachi. GAD Cotacachi. https://www.imbabura.gob.ec/ phocadownloadpap/K-Planes-programas/PDOT/Cantonal/PDOT%20COTACACHI.pdf

He, H. S., Mladenoff, D. J., Gustafson, E. J., 2002. Study of landscape change under forest harvesting and climate warming-induced fire disturbance. Forest Ecology and Management 155(1-3), 257-270. https://doi.org/10.1016/S0378-1127(01)00563-1

Hong, H., Naghibi, S. A., Moradi Dashtpagerdi, M., Pourghasemi, H. R., Chen, W., 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences 10(7), 167. https://doi.org/10.1007/s12517-017-2905-4

Huang, I. B., Keisler, J., Linkov, I., 2011. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment 409(19), 3578-3594. https://doi.org/10.1016/j.scitotenv.2011.06.022

Instituto Nacional de Estadísticas y Censos, 2023. Censo Ecuador. https://www.censoecuador.gob.ec/

Jaafari, A., Mafi Gholami, D., 2017. Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon’s entropy. Iranian Journal of Forest and Poplar Research 25(2). https://doi.org/10.22092/ijfpr.2017.111758

Johnston, K., Ver Hoef, J. M., Krivoruchko, K., Lucas, N., 2001. Using ArcGIS geostatistical analyst (Vol. 380). Esri Redlands.

Kane, V. R., Lutz, J. A., Alina Cansler, C., Povak, N. A., Churchill, D. J., Smith, D. F., Kane, J. T., North, M. P., 2015. Water balance and topography predict fire and forest structure patterns. Forest Ecology and Management 338, 1-13. https://doi.org/10.1016/j.foreco.2014.10.038

Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., Brumby, S. P., 2021. Global land use/land cover with Sentinel 2 and deep learning. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 4704-4707. https://doi.org/10.1109/IGARSS47720.2021.9553499

Landis, J. R., Koch, G. G., 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159. https://doi.org/10.2307/2529310

Liao, X., Carin, L., 2009. Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets with Application to UXO Sensing. IEEE Transactions on Geoscience and Remote Sensing 47(5), 1454-1466. https://doi.org/10.1109/TGRS.2008.2005268

Linkov, I., Satterstrom, F. K., Kiker, G., Batchelor, C., Bridges, T., Ferguson, E., 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management: Recent developments and applications. Environment International 32(8), 1072-1093. https://doi.org/10.1016/j.envint.2006.06.013

Maingi, J. K., Henry, M. C., 2007. Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire 16(1), 23. https://doi.org/10.1071/WF06007

Martelo-Jiménez, N., Vargas Ríos, O., 2022. Evaluación del riesgo a incendios de la cobertura vegetal del Santuario de Fauna y Flora Iguaque (Boyacá, Colombia). Cadalsia 44 (2), 380-393. https://doi.org/10.15446/caldasia.v44n2.91115

Morante-Carballo, F., Bravo-Montero, Lady, Carrión-Mero, P., Velastegui-Montoya, A., Berrezueta, E., 2022. Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador. Remote Sensing 14(8), 1783. https://doi.org/10.3390/rs14081783

Naderpour, M., Rizeei, H. M., Ramezani, F., 2021. Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework. Remote Sensing 13(13), 2513. https://doi.org/10.3390/rs13132513

Pazmiño, D., 2019. Peligro de incendios forestales asociado a factores climáticos en Ecuador. FIGEMPA: Investigación y Desarrollo 1(1), 10-18. https://doi.org/10.29166/revfig.v1i1.1800

Reyes-Bueno, F., Loján-Córdova, J., 2022. Assessment of Three Machine Learning Techniques with Open-Access Geographic Data for Forest Fire Susceptibility Monitoring-Evidence from Southern Ecuador. Forests 13(3), 474. https://doi.org/10.3390/f13030474

Rodrigues, M., Jiménez-Ruano, A., Peña-Angulo, D., De La Riva, J., 2018. A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using Geographically Weighted Logistic Regression. Journal of Environmental Management 225, 177-192. https://doi.org/10.1016/j.jenvman.2018.07.098

Saaty, T. L., 1980. The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill, New York London.

Servicio Nacional de Gestión de Riesgos y Emergencias, 2022. Informe de Situación No. 10 de Incendios Forestales a nivel Nacional 2022. https://www.gestionderiesgos.gob.ec/wp-content/uploads/downloads/2022/10/SITREP-No-10-Incendios-Forestales-01012022-a-31102022.pdf

Sivrikaya, F., Küçük, Ö., 2022. Modelling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics 68, 101537. https://doi.org/10.1016/j.ecoinf.2021.101537

Tebbutt, C. A., Devisscher, T., Obando‐Cabrera, L., Gutiérrez García, G. A., Meza Elizalde, M. C., Armenteras, D., Oliveras Menor, I., 2021. Participatory mapping reveals socioeconomic drivers of forest fires in protected areas of the post‐conflict Colombian Amazon. People and Nature 3(4), 811-826. https://doi.org/10.1002/pan3.10222

Tehrany, M. S., Pradhan, B., Jebur, M. N., 2015. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment 29(4), 1149-1165. https://doi.org/10.1007/s00477-015-1021-9

Thorhnwaite, C., Matter, J., 1955. The water balance, publication in climatology. Centerton. Drexel Institute of Technology.

Thornthwaite, C. W., 1948. An approach toward a rational classification of climate. Geographical Review 38(1), 55-94.

Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V., Turubanova, S., Parker, D., Zalles, V., Lima, A., Kommareddy, I., Song, X.-P., Wang, L., Harris, N., 2022. Global Trends of Forest Loss Due to Fire From 2001 to 2019. Frontiers in Remote Sensing 3, 825190. https://doi.org/10.3389/frsen.2022.825190

Úbeda, X., Sarricolea, P., 2016. Wildfires in Chile: A review. Global and Planetary Change 146, 152-161. https://doi.org/10.1016/j.gloplacha.2016.10.004

Vélez Muñoz, R., 2000. Las quemas incontroladas como causa de incendios forestales. Cuadernos de la Sociedad Española de Ciencias Forestales 9, 13-26. https://doi.org/10.31167/csef.v0i9.9179

Zambon, I., Cerdà, A., Cudlin, P., Serra, P., Pili, S., Salvati, L., 2019. Road Network and the Spatial Distribution of Wildfires in the Valencian Community (1993–2015). Agriculture 9(5), 100. https://doi.org/10.3390/agriculture9050100

Zhao, P., Zhang, F., Lin, H., Xu, S., 2021. GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing 13(18), 3704. https://doi.org/10.3390/rs13183704

Published

18-03-2024

How to Cite

1.
Arias-Muñoz P, Chuma-Pomasqui L, Coronado Cacuango P, Jácome-Aguirre G. Susceptibility to vegetation cover fires: an evaluation using multi-criteria and radio frequency methods (Cotacachi Cantón, Ecuador). CIG [Internet]. 2024 Mar. 18 [cited 2024 May 24];. Available from: https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/5867

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