Modelo de riesgo de fuego para la ecorregión de los llanos colombo-venezolanos

dc.contributorArmenteras, Dolors
dc.contributorEcología del paisaje y modelación de ecosistemas
dc.creatorBarreto Rivera, Joan Sebastian
dc.date.accessioned2021-02-08T17:06:50Z
dc.date.available2021-02-08T17:06:50Z
dc.date.created2021-02-08T17:06:50Z
dc.date.issued2020-10-29
dc.identifierBarreto Rivera, J. S. (2020). Modelo de riesgo de fuego para la ecorregión de los llanos colombo-venezolanos [Tesis de maestría, Universidad Nacional de Colombia]. Repositorio Institucional.
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/79128
dc.description.abstractWhile fire has been part of the natural history of ecosystems, those that go out of control often represent significant threats to public safety, infrastructure, biodiversity and forest resources (Martell, 2007), and are considered one of the most important disturbance factors, especially in tropical and subtropical areas (G. R. Van Der Werf et al., 2010). The ecoregion of the Colombian-Venezuelan plains is characterized by a constant presence of fires (Chacón et al., 2015) caused mainly by human action, derived from land management and preparation practices that include slashing and burning (Armenteras et al., 2005; Leal et al., 2019). This work evaluated fire risk in the region of the colombian-v.00enezuelan plains, based on the probability of occurrence (hazard) and vulnerability at the ecological level. In order to model the probability of occurrence, the automatic learning model Random Forest was implemented, fed with variables associated with topography, climate, vegetation and human presence. To evaluate ecological vulnerability, information regarding biodiversity, conservation and fragmentation in the study area was used. The results of the probability of occurrence model indicate that the most important variable is the NDWI (Normalized Difference Water Index), an index that has been shown to offer better results for estimating the moisture content of living fuel and predicting the risk of fire occurrence in the case of savannah ecosystems (Cheng et al., 2006; Verbesselt et al., 2006, 2007). Finally, both subindices were integrated into a total risk index in order to identify those areas where the occurrence of this type of event is most likely to result in significant ecological damage. The results show that the high and very high probability of occurrence zoning is represented by 544,498 and 499,740 ha respectively, while the high and very high vulnerability zoning is less than 64,500 and 2,298 ha. It was found that in some special management figures, such as El Tuparro National Park (Colombia), Cinaruco Integrated Management District (Colombia) and Cinaruco-Capanaparo National Park (Venezuela), very high probability zoning predominates, which for these areas represents 47.7%, 56.9% and 37.8% of the total area of each park. Evaluating fire risk is a key process within the context of controlling and managing this type of event and represents an important tool for planning on a regional scale. This evaluation allows, among other things, to assess the suitability of landscape protection measures and different types of coverage (Costa et al., 2011), to support planning and protection of forest areas, to take surveillance measures in high-risk areas, to reorganize slash-and-burn practices and to strategically allocate resources to deal with this type of disaster (You et al., 2017).
dc.description.abstractSi bien el fuego ha sido parte de la historia natural de los ecosistemas, aquellos que se salen de control suelen representar importantes amenazas para la seguridad pública, la infraestructura, la biodiversidad y los recursos forestales (Martell, 2007), considerándose uno de los factores de disturbio más importante, especialmente en zonas tropicales y subtropicales (Van Der Werf et al., 2010). La ecorregión de los llanos colombo-venezolanos se caracteriza por una constante presencia de fuegos (Chacón et al., 2015) causados principalmente por acción humana, derivados de las prácticas de manejo y preparación del suelo que incluyen la tala y quema (Armenteras et al., 2005; Leal et al., 2019). Este trabajo evaluó el riesgo de fuego en la región de los llanos colombo-venezolanos, a partir de la probabilidad de ocurrencia (peligro) y la vulnerabilidad a nivel ecológico. Con el fin de modelar la probabilidad de ocurrencia se implementó el modelo de aprendizaje automático Random Forest, alimentado con variables asociadas a topografía, clima, vegetación y presencia humana. Para evaluar la vulnerabilidad ecológica se empleó información referente a biodiversidad, conservación y fragmentación en el área de estudio. Los resultados del modelo de probabilidad de ocurrencia indican que la variable más importante es el índice NDWI (Normalized Difference Water Index: índice diferencial de agua normalizado), índice que se ha demostrado ofrece mejores resultados para estimar el contenido de humedad del combustible vivo y predecir el riesgo de ocurrencia de fuegos en el caso de ecosistemas de sabana (Cheng et al., 2006; Verbesselt et al., 2006, 2007). Finalmente ambos subíndices se integraron en un índice de riesgo total con el fin de identificar aquellas áreas dónde es más probable la ocurrencia de este tipo de eventos y que derive en importantes afectaciones ecológicas. Los resultados muestran que la zonificación de probabilidad de ocurrencia alta y muy alta están representadas en 544498 y 499740 ha respectivamente, mientras que la zonificación de vulnerabilidad alta y muy alta tiene una extensión menor a 64500 y 2298 ha. Se encontró que, en algunas figuras de manejo especial, como en el caso del Parque Nacional El Tuparro (Colombia), el Distrito de Manejo integrado Cinaruco (Colombia) y el Parque Nacional Cinaruco-Capanaparo (Venezuela) predomina la zonificación de probabilidad de ocurrencia muy alta, que para estás áreas representan el 47.7%, el 56.9% y el 37.8% del área total de cada parque. Evaluar el riesgo de fuego es un proceso clave dentro del contexto del control y gestión de este tipo eventos y representa una importante herramienta para la planeación a escala regional. Esta evaluación, permite entre otras cosas evaluar la idoneidad de las medidas de protección del paisaje y de los diferentes tipos de coberturas (Costa et al., 2011), apoyar en la planificación y protección de áreas forestales, tomar medidas de vigilancia de zonas de alto riesgo, reorganizar prácticas de tala y quema y asignar estratégicamente los recursos para la atención de este tipo de desastres (You et al., 2017)
dc.languagespa
dc.publisherBogotá - Ciencias - Maestría en Ciencias - Biología
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relationAdab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3), 1723–1743. https://doi.org/10.1007/s11069-012-0450-8
dc.relationAdab, H., Kanniah, K. D., Solaimani, K., & Sallehuddin, R. (2015). Modelling static fire hazard in a semi-arid region using frequency analysis. International Journal of Wildland Fire, 24(6), 763–777. https://doi.org/10.1071/WF13113
dc.relationAkagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., & Wennberg, P. O. (2011). Emission factors for open and domestic biomass burning for use in atmospheric models. Atmospheric Chemistry and Physics, 11(9), 4039–4072. https://doi.org/10.5194/acp-11-4039-2011
dc.relationAkinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics, 05(07), 754–767. https://doi.org/10.4236/ojs.2015.57075
dc.relationAldersley, A., Murray, S. J., & Cornell, S. (2011). Global and regional analysis of climate and human drivers of wildfire. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2011.05.032
dc.relationAlmendros, G., Kgathi, D., Sekhwela, M., Zancada, M. C., Tinoco, P., & Pardo, M. T. (2003). Biogeochemical assessment of resilient humus formations from virgin and cultivated northern Botswana soils. Journal of Agricultural and Food Chemistry, 51(15), 4321–4330. https://doi.org/10.1021/jf034006u
dc.relationAlvarado, S. T., Fornazari, T., Cóstola, A., Morellato, L. P., & Silva, T. S. F. (2017). Drivers of fire occurrence in a mountainous Brazilian cerrado savanna: Tracking long-term fire regimes using remote sensing. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2017.02.037
dc.relationAndreae, M. O., & Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles, 15(4), 955–966. https://doi.org/10.1029/2000GB001382
dc.relationArchibald, S., Roy, D. P., van Wilgen, B. W., & Scholes, R. J. (2009). What limits fire? An examination of drivers of burnt area in Southern Africa. Global Change Biology. https://doi.org/10.1111/j.1365-2486.2008.01754.x
dc.relationArmenteras, D., Barreto, J., Tabor, K., Molowny, R., & Retana, J. (2017). Changing patterns of fire occurrence in proximity to forest edges. Biogeosciences Discuss. Biogeosciences Discuss, 5194(10), 2016–2532. https://doi.org/10.5194/bg-2016-532
dc.relationArmenteras, D., Bernal, F., González, F., Morales, M., Pabón, J., Páramo, G., González Alonso, F., Morales, M., Pabón Caicedo, J., Páramo Rocha, G., & Parra Lara, Á. (2011). Incendios de la cobertura vegetal en Colombia (Á. Parra (ed.); Primera Ed). Universidad Autónoma de Occidente.
dc.relationArmenteras, D., Gibbes, C., Vivacqua, C., Espinosa, J., Duleba, W., Goncalves, F., & Castro, C. (2016). Interactions between climate, land use and vegetation fire occurrences in El Salvador. Atmosphere, 7(2), 0–14. https://doi.org/10.3390/atmos7020026
dc.relationArmenteras, D., Gonzalez, F., & Franco, C. (2009). Distribución geográfica y temporal de incendios en Colombia utilizando datos de anomalías térmicas. Caldasia, 31(2), 303–318.
dc.relationArmenteras, D., González, T., & Retana, J. (2013). Forest fragmentation and edge influence on fire occurrence and intensity under different management types in Amazon forests. Biological Conservation. https://doi.org/10.1016/j.biocon.2012.10.026
dc.relationArmenteras, D., Retana, J., Molowny, R., Roman, R., Gonzalez, F., & Morales, M. (2011). Characterising fire spatial pattern interactions with climate and vegetation in Colombia. Agricultural and Forest Meteorology, 151(3), 279–289. https://doi.org/10.1016/j.agrformet.2010.11.002
dc.relationArmenteras, D., Romero, M., & Galindo, G. (2005). Vegetation fire in the savannas of The Llanos Orientales of Colombia. World Resource Review, 17(4), 531–543.
dc.relationArpaci, A., Malowerschnig, B., Sass, O., & Vacik, H. (2014). Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests. Applied Geography, 53, 258–270. https://doi.org/10.1016/j.apgeog.2014.05.015
dc.relationAvila, D., Pompa, M., Antonio, X., Rodriguez, D. A., Vargas, E., & Santillan, J. (2010). Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective. Chinese Geographical Science, 20(6), 491–497. https://doi.org/10.1007/s11769-010-0437-x
dc.relationAyanz, J. S., Gitas, I., Camia, A., & Oliveira, S. (2011). Advances in remote sensing and GIS applications in forest fire management: From local to global assessments. 8th International EARSeL FF-SIG Workshop, 20-21 October 2011, 288. https://www.researchgate.net/publication/233867629_Integration_of_image_processing_methods_for_fuel_mapping/file/79e4150c712bc778d0.pdf%5Cnpapers3://publication/uuid/09A9E0D1-A29E-402B-9AB1-B7B49ECEC951
dc.relationAybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). An R package for interacting with Google Earth Engine. Journal of Open Source Software, 2020.
dc.relationBachmann, A., & Allgöwer, B. (2001). A consistent wildland fire risk terminology is needed! Fire Management Today, 61(28–33).
dc.relationBannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120. https://doi.org/10.1080/02757259509532298
dc.relationBar Massada, A., Syphard, A. D., Stewart, S. I., & Radeloff, V. C. (2013). Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA. International Journal of Wildland Fire, 22(2), 174. https://doi.org/10.1071/WF11178
dc.relationBarreto, J., Gonzalez, T., & Armenteras, D. (2017). Dinámica espacio temporal de ocurrencia de incendios en zonas con diferentes tipos de manejo en el noroeste de la amazonia: ¿barrera efectiva? Revista Facultad de Ciencias Básicas, 13(1), 19–25. https://doi.org/10.18359/rfcb.2009
dc.relationBeall, F. C., & Eickner, H. W. (1970). Thermal degradation of wood components: A review of the literature. U.S.D.a. Forest Service Research Paper, May, 26.
dc.relationBevilacqua, M., Cárdenas, L., & Medina, D. (2006). Las Áreas Protegidas en Venezuela: Diagnóstico de su condición 1993/2004. ACOANA/UICN/FEP/CIVenezuela.
dc.relationBhabatosh, C. (2011). Digital Image Processing and Analysis. In P. L. P. Ltd (Ed.), Current Protocols in Essential Laboratory Techniques. PHI Learning Pvt. Ltd. https://doi.org/10.1002/9780470089941.eta03cs9
dc.relationBidwell, T. G., Masters, R. E., Weir, J. R., & Engle, D. M. (n.d.). Fire Effects in Native Plant Communities.
dc.relationBigler, C., Kulakowski, D., & Veblen, T. T. (2005). Multiple disturbance interactions and drought influence fire severity in rocky mountain subalpine forests. Ecology, 86(11), 3018–3029. https://doi.org/10.1890/05-0011
dc.relationBilbao, B. A., Leal, A. V, & Méndez, C. L. (2010). Indigenous Use of Fire and Forest Loss in Canaima National Park, Venezuela. Assessment of and Tools for Alternative Strategies of Fire Management in Pemón Indigenous Lands. Human Ecology, 38(5), 663–673. https://doi.org/10.1007/s10745-010-9344-0
dc.relationBisbal, F. (1988). Impacto humano sobre los habitat de Venezuela. In Interciencia (Vol. 13).
dc.relationBisquert, M., Caselles, E., Snchez, J. M., & Caselles, V. (2012). Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. International Journal of Wildland Fire, 21(8), 1025–1029. https://doi.org/10.1071/WF11105
dc.relationBisquert, M., Sánchez, J. M., & Caselles, V. (2011). Fire danger estimation from MODIS Enhanced Vegetation Index data: Application to Galicia region (north-west Spain). International Journal of Wildland Fire, 20(3), 465–473. https://doi.org/10.1071/WF10002
dc.relationBolstad, P. V., Swank, W., & Vose, J. (1998). Predicting Southern Appalachian overstory vegetation with digital terrain data. Landscape Ecology, 13(5), 271–283. https://doi.org/10.1023/A:1008060508762
dc.relationBond, W. J., Woodward, F. I., & Midgley, G. F. (2005). The Global Distribtuion of Ecosystems in a world without Fire. New Phytologist, 165(2), 525–538. https://doi.org/10.1111/j.1469-8137.2004.01252.x
dc.relationBond, W., & Keane, R. (2017). Fires, Ecological Effects of. Encyclopedia of Biodiversity: Second Edition, February 2016, 435–442. https://doi.org/10.1016/B978-0-12-384719-5.00053-8
dc.relationBowman, D. M. J. S. (2007). Fire ecology. Progress in Physical Geography, 31(5), 523–532. https://doi.org/10.1177/0309133307083298
dc.relationBowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W., Carlson, J., Cochrane, M., D’Antonio, C., DeFries, R., Doyle, J., Harrison, S., Johnston, F., Keeley, J., Krawchuk, M., Kull, C., Marston, J., Moritz, M., Prentice, I., Roos, C., Scott, A. C., … Pyne, S. (2009). Fire in the earth system. Science, 324(5926), 481–484. https://doi.org/10.1126/science.1163886
dc.relationBowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W. J., Cochrane, M., D’Antonio, C. M., Defries, R., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., Kull, C. A., Mack, M., Moritz, M. A., Pyne, S., Roos, C. I., Scott, A. C., Sodhi, N. S., & Swetnam, T. W. (2011). The human dimension of fire regimes on Earth. Journal of Biogeography, 38(12), 2223–2236. https://doi.org/10.1111/j.1365-2699.2011.02595.x
dc.relationBreiman, L. (2001). Random Forests. Machine Learning, 45(5), 32. https://doi.org/10.1023/A:1010933404324
dc.relationBryant, R., Doerr, S. H., & Helbig, M. (2005). Effect of oxygen deprivation on soil hydrophobicity during heating. International Journal of Wildland Fire, 14(4), 449–455. https://doi.org/10.1071/WF05035
dc.relationBurgess, R. (2011). Development of a spatial , dynamic , fuzzy fire risk model for Chitwan District , Nepal. University of Twente.
dc.relationCaccamo, G., Chisholm, L. A., Bradstock, R. A., Puotinen, M. L., & Pippen, B. G. (2012). Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. International Journal of Wildland Fire, 21(3), 257–269. https://doi.org/10.1071/WF11024
dc.relationCalviño-Cancela, M., Chas-Amil, M. L., García-Martínez, E. D., & Touza, J. (2016). Wildfire risk associated with different vegetation types within and outside wildland-urban interfaces. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2016.04.002
dc.relationCalviño-Cancela, M., Chas-Amil, M. L., García-Martínez, E. D., & Touza, J. (2017). Interacting effects of topography, vegetation, human activities and wildland-urban interfaces on wildfire ignition risk. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2017.04.033
dc.relationCalviño-Cancela, M., & Rubido-Bará, M. (2013). Invasive potential of Eucalyptus globulus: Seed dispersal, seedling recruitment and survival in habitats surrounding plantations. Forest Ecology and Management, 305(November 2017), 129–137. https://doi.org/10.1016/j.foreco.2013.05.037
dc.relationCastro, R., & Chuvieco, E. (1998). Modeling forest fire danger from geographic information systems. Geocarto International, 13(1), 15–23. https://doi.org/10.1080/10106049809354624
dc.relationCatry, F. X., Rego, F. C., Bação, F. L., & Moreira, F. (2009). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921–931. https://doi.org/10.1071/WF07123
dc.relationCFS. (1992). Aspects sociaux, economiques et culturels des incendies de forêts en Italie. Seminar on Forest Fire Prevention, Land Use and People.
dc.relationChacón, E., Ulloa, A., Llambí, L., Acevedo, D., & Utrera, A. (2015). Paisajes Y Ecosistemas Llaneros : Ecología Y Conservación. In R. Lopez, J. Hétier, D. López, R. Schargel, & A. Zinck (Eds.), Tierras Llaneras de Venezuela …tierras de buena esperanza (pp. 195–240). Consejo de Publicaciones de la Universidad de Los Andes.
dc.relationChang, Y., Zhu, Z., Bu, R., Chen, H., Feng, Y., Li, Y., Hu, Y., & Wang, Z. (2013). Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecology, 28(10), 1989–2004. https://doi.org/10.1007/s10980-013-9935-4
dc.relationChen, K., Blong, R., & Jacobson, C. (2001). MCE-RISK: Integrating multicriteria evaluation and GIS for risk decision-making in natural hazards. Environmental Modelling and Software, 16(4), 387–397. https://doi.org/10.1016/S1364-8152(01)00006-8
dc.relationChen, Y., Morton, D. C., Jin, Y., Gollatz, G. J., Kasibhatla, P. S., Van Der Werf, G. R., Defries, R. S., & Randerson, J. T. (2013). Long-term trends and interannual variability of forest, savanna and agricultural fires in South America. Carbon Management, 4(6), 617–638. https://doi.org/10.4155/cmt.13.61
dc.relationCheng, Y. Ben, Zarco-Tejada, P. J., Riaño, D., Rueda, C. A., & Ustin, S. L. (2006). Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment, 105(4), 354–366. https://doi.org/10.1016/j.rse.2006.07.005
dc.relationCho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55(10), 8028–8045. https://doi.org/10.1029/2019WR024892
dc.relationChowdhury, E. H., & Hassan, Q. K. (2015). Operational perspective of remote sensing-based forest fire danger forecasting systems. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 224–236. https://doi.org/10.1016/j.isprsjprs.2014.03.011
dc.relationChuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., Vilar, L., Martínez, J., Martín, S., Ibarra, P., de la Riva, J., Baeza, J., Rodríguez, F., Molina, J. R., Herrera, M. A., & Zamora, R. (2010). Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling, 221(1), 46–58. https://doi.org/10.1016/j.ecolmodel.2008.11.017
dc.relationChuvieco, E., Aguado, Jurdao, S., Pettinari, M. L., Yebra, M., Salas, J., Hantson, S., De La Riva, J., Ibarra, P., Rodrigues, M., Echeverría, M., Azqueta, D., Román, M. V., Bastarrika, A., Martínez, S., Recondo, C., Zapico, E., Martínez-Vega, F. J., Aguado, I., … Martínez-Vega, F. J. (2012). Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23(5), 606–619. https://doi.org/10.1071/WF12052
dc.relationChuvieco, E., Allgöwer, B., & Salas, F. (2003). Integration of Physical and Human Factors in Fire Danger Assessment. Wildland Fire Danger Estimation and Mapping, 197–218. https://doi.org/doi:10.1142/9789812791177_0007
dc.relationChuvieco, E., Cocero, D., Riaño, D., Martin, P., Martínez-Vega, J., De La Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322–331. https://doi.org/10.1016/j.rse.2004.01.019
dc.relationChuvieco, E., Martínez, S., Román, M. V., Hantson, S., & Pettinari, M. L. (2014). Integration of ecological and socio-economic factors to assess global vulnerability to wildfire. Global Ecology and Biogeography, 23(2), 245–258. https://doi.org/10.1111/geb.12095
dc.relationClarke, R. (2005). Ecological requirements of birds specialising in mallee habitats.
dc.relationCochrane, M., & Laurance, W. (2008). Synergisms among Fire, Land Use, and Climate Change in the Amazon. AMBIO: A Journal of the Human Environment, 37(7), 522–527. https://doi.org/10.1579/0044-7447-37.7.522
dc.relationCochrane, M., & Ryan, K. (1978). Fire and fire ecology: Concepts and principles. Canadian Journal of Forest Research, 8, 220–227. https://doi.org/10.1007/978-3-540-77381-8_2
dc.relationCosta, P., Castellnou, M., Larrañaga, A., Miralles, M., & Kraus, D. (2011). Prevention of Large Wildfires using the Fire Types Concept (Issue July 2014). https://www.researchgate.net/publication/263923019_Prevention_of_Large_Wildfires_using_the_Fire_Types_Concept
dc.relationCostafreda-Aumedes, S., Vega-Garcia, C., & Comas, C. (2018). Improving fire season definition by optimized temporal modelling of daily human-caused ignitions. Journal of Environmental Management, 217, 90–99. https://doi.org/10.1016/j.jenvman.2018.03.080
dc.relationCountryman, C. M. (1977). The nature of heat. Forest Service. U.S. Department of Agriculture.
dc.relationCutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002
dc.relationCutter, S. L., Ismail-Zadeh, A., Alcántara-Ayala, I., Altan, O., Baker, D., Briceño, S., Gupta, H., Holloway, A., Johnston, D., McBean, G. A., Ogawa, Y., Paton, D., Porio, E., Silbereisen, R., Takeuchi, K., Valsecchi, G., Vogel, C., & Wu, G. (2015). Global risks: Pool knowledge to stem losses from disasters. Nature, 522(7556), 277–279. https://doi.org/10.1038/522277a
dc.relationda Silva, I., & Pontes, A. (2011). Elaboração de um Fator de Risco de Incêndios Florestais utilizando Lógica Fuzzy. 21, 113–128.
dc.relationDale, V., Joyce, L., McNulty, S., Neilson, R., Ayres, M., Flannigan, M. D., Hanson, P., Irland, L., Lugo, A., Peterson, C., Simberloff, D., Swanson, F., Stocks, B., & Wotton, M. (2001). Climate Change and Forest Disturbances. BioScience, 51(9), 723. https://doi.org/10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2
dc.relationde Vicente, F. (2012). Diseño De Un Modelo De Riesgo Integral De Incendios Forestales Mediante Técnicas Multicriterio Y Su Automatización En Sistemas De Información Geográfica. Una Aplicación En La Comunidad Valenciana. Escuela Técnica Superior de Ingeniería Montes de Madrid.
dc.relationDenil, M., Matheson, D., & De Freitas, N. (2014). Narrowing the Gap: Random Forests In Theory and In Practice. Proceedings of The 31st International Conference on Machine Learning, 1998, 665–673. http://jmlr.org/proceedings/papers/v32/denil14.html
dc.relationDennison, P. E., Roberts, D. A., Peterson, S. H., & Rechel, J. (2005). Use of Normalized Difference Water Index for monitoring live fuel moisture. International Journal of Remote Sensing, 26(5), 1035–1042.
dc.relationDias, F. P. M., Hübner, R., Nunes, F. de J., Leandro, W. M., & Xavier, F. A. da S. (2019). Effects of land-use change on chemical attributes of a Ferralsol in Brazilian Cerrado. Catena, 177(September 2018), 180–188. https://doi.org/10.1016/j.catena.2019.02.016
dc.relationDíaz-Martín, D., Yerena, E., Martinez, Z., & Trabucco, J. (2007). Semáforo Conservacionista de Parques Nacionales de Venezuela. Vitalis.
dc.relationDickson, B. G., Prather, J. W., Xu, Y., Hampton, H. M., Aumack, E. N., & Sisk, T. D. (2006). Mapping the probability of large fire occurrence in northern Arizona, USA. Landscape Ecology, 21(5), 747–761. https://doi.org/10.1007/s10980-005-5475-x
dc.relationDidan, K. (2015). MOD13A1 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13A1.006
dc.relationDillon, G. K., Holden, Z. A., Morgan, P., Crimmins, M. A., Heyerdahl, E. K., & Luce, C. H. (2011). Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006. Ecosphere, 2(12), art130. https://doi.org/10.1890/ES11-00271.1
dc.relationDinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E., Jones, B., Barber, C., Hayes, R., Kormos, C., Martin, V., Crist, E., … Saleem, M. (2017). An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience, 67(6), 534–545. https://doi.org/10.1093/biosci/bix014
dc.relationDong, X., Li-min, D., Guo-fan, S., Lei, T., & Hui, W. (2005). Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research, 16(3), 169–174. https://doi.org/10.1007/BF02856809
dc.relationDormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., Mcclean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 027–046. https://doi.org/10.1111/j.1600-0587.2012.07348.x
dc.relationDuff, T. J., Keane, R. E., Penman, T. D., & Tolhurst, K. G. (2017). Revisiting wildland fire fuel quantification methods: The challenge of understanding a dynamic, biotic entity. In Forests. https://doi.org/10.3390/f8090351
dc.relationElizalde, G., Viloria, J., & Rosales, A. (2007). Geografía de suelos de Venezuela. In GeoVenezuela (Vol. 2).
dc.relationEnright, N. J., & Fontaine, J. B. (2014). Climate change and the management of fire-prone vegetation in southwest and southeast Australia. Geographical Research, 52(1), 34–44. https://doi.org/10.1111/1745-5871.12026
dc.relationErcanoglu, M., Weber, K., Langille, J., & Neves, R. (2006). Modeling Wildland Fire Susceptibility Using Fuzzy Systems. GIScience & Remote Sensing, 43(3), 268–282. https://doi.org/10.2747/1548-1603.43.3.268
dc.relationEskandari, S. (2017). A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. In Forestry Paper (Vol. 10, Issue 8). Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-017-2976-2
dc.relationEskandari, S., & Chuvieco, E. (2015). Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42, 57–64. https://doi.org/10.1016/j.jag.2015.05.006
dc.relationEugenio, 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. https://doi.org/10.1016/j.jenvman.2016.02.021
dc.relationFalk, D. A., Miller, C., McKenzie, D., & Black, A. E. (2007). Cross-Scale Analysis of Fire Regimes. Ecosystems, 10(5), 809–823. https://doi.org/10.1007/s10021-007-9070-7
dc.relationFan, L., Wigneron, J.-P., Xiao, Q., Al-Yaari, A., Wen, J., Martin-Stpaul, N., Dupuy, J.-L., Pimont, F., Al Bitar, A., Fernandez-Moran, R., & Kerr, Y. H. (2017). Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region. Remote Sensing of Environment, 205, 210–223. https://doi.org/10.1016/j.rse.2017.11.020
dc.relationFAO. (2006). Código de Manejo del Fuego. 48.
dc.relationFAO. (2007). Fire management - global assessment 2006. FAO Forestry Paper 151, 135 pp. http://www.fao.org/docrep/009/a0969e/a0969e00.htm
dc.relationFAO. (2010). Evacluación de los recursos forestales mundiales 2010: Términos y definiciones. https://www.researchgate.net/publication/263923019_Prevention_of_Large_Wildfires_using_the_Fire_Types_Concept
dc.relationFAO, IIASA, ISRIC, ISS-CAS, & JRC. (2012). Harmonized World Soil Database (version 1.2) (p. 50). FAO.
dc.relationFarr, T., Rosen, P., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Palller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2005), 1–33. https://doi.org/10.1029/2005RG000183.1.INTRODUCTION
dc.relationFick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
dc.relationFinney, M. A. (2005). The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management, 211(1–2), 97–108. https://doi.org/10.1016/j.foreco.2005.02.010
dc.relationFire and Rescue Service. (2013). Wildfire Operational Guidance. www.scotland.gov.uk.
dc.relationFlannigan, M. D., Cantin, A. S., De Groot, W. J., Wotton, M., Newbery, A., & Gowman, L. M. (2013). Global wildland fire season severity in the 21st century. Forest Ecology and Management, 294, 54–61. https://doi.org/10.1016/j.foreco.2012.10.022
dc.relationFlannigan, M. D., Kochtubajda, B., & Logan, K. A. (2008). Forest fires and climate change in the Northwest Territories. Cold Region Atmospheric and Hydrologic Studies. The Mackenzie GEWEX Experience: Volume 1: Atmospheric Dynamics, 403–417. https://doi.org/10.1007/978-3-540-73936-4_23
dc.relationFlannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, M., & Gowman, L. M. (2009). Implications of changing climate for global wildland fire. International Journal of Wildland Fire, 18(5), 483–507. https://doi.org/10.1071/WF08187
dc.relationGai, C., Weng, W., & Yuan, H. (2011). GIS-based forest fire risk assessment and mapping. Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011, 1240–1244. https://doi.org/10.1109/CSO.2011.140
dc.relationGaither, C. J., Poudyal, N. C., Goodrick, S., Bowker, J. M., Malone, S., & Gan, J. (2011). Wildland fire risk and social vulnerability in the Southeastern United States: An exploratory spatial data analysis approach. Forest Policy and Economics, 13(1), 24–36. https://doi.org/10.1016/j.forpol.2010.07.009
dc.relationGanteaume, A., Jappiot, M., Lampin, C., Guijarro, M., & Hernando, C. (2013). Flammability of some ornamental species in wildland-urban interfaces in southeastern France: Laboratory assessment at particle level. Environmental Management, 52(2), 467–480. https://doi.org/10.1007/s00267-013-0067-z
dc.relationGao, B.-C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sens. Environ, 58(April), 257–266.
dc.relationGarcía, M., Chuvieco, E., Nieto, H., & Aguado, I. (2008). Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sensing of Environment, 112(9), 3618–3627. https://doi.org/10.1016/j.rse.2008.05.002
dc.relationGBIF. (2020). Registros de ocurrencias [Dataset]. https://doi.org/10.15468/dl.xx7sq4
dc.relationGenuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014
dc.relationGiglio, L., Justice, C., Boschetti, L., & Roy, D. (2015). MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.006
dc.relationGitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
dc.relationGomez, C., Mangeas, M., Curt, T., Ibanez, T., Munzinger, J., Dumas, P., Jérémy, A., Despinoy, M., & Hély, C. (2015). Wildfire risk for main vegetation units in a biodiversity hotspot: Modeling approach in New Caledonia, South Pacific. Ecology and Evolution, 5(2), 377–390. https://doi.org/10.1002/ece3.1317
dc.relationGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202(2016), 18–27. https://doi.org/10.1016/j.rse.2017.06.031
dc.relationGould, J. S., Lachlan McCaw, W., & Phillip Cheney, N. (2011). Quantifying fine fuel dynamics and structure in dry eucalypt forest (Eucalyptus marginata) in Western Australia for fire management. Forest Ecology and Management, 262(3), 531–546. https://doi.org/10.1016/j.foreco.2011.04.022
dc.relationGuevara, L., Aguirre, L., & Maldonado, D. (2017). Zonificación del terriotrio del Parque Nacional Santos Luzardo. Novum Scientiarum, 2(6), 37–53.
dc.relationGuns, M., & Vanacker, V. (2012). Logistic regression applied to natural hazards: Rare event logistic regression with replications. Natural Hazards and Earth System Science, 12(6), 1937–1947. https://doi.org/10.5194/nhess-12-1937-2012
dc.relationGuo, F., Su, Z., Wang, G., Sun, L., Lin, F., & Liu, A. (2016). Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood. Applied Geography. https://doi.org/10.1016/j.apgeog.2015.11.014
dc.relationGuo, F., Su, Z., Wang, G., Sun, L., Tigabu, M., Yang, X., & Hu, H. (2017). Understanding fire drivers and relative impacts in different Chinese forest ecosystems. Science of the Total Environment, 605, 411–425. https://doi.org/10.1016/j.scitotenv.2017.06.219
dc.relationGuo, F., Wang, G., Su, Z., Liang, H., Wang, W., Lin, F., & Liu, A. (2016). What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. International Journal of Wildland Fire, 25(5), 505–519. https://doi.org/10.1071/WF15121
dc.relationGuyon, I. (2003). AnIntroductionToVariableAndFeautureSelection.pdf. 3, 1157–1182. https://doi.org/10.1016/j.aca.2011.07.027
dc.relationHaas, J. R., Calkin, D. E., & Thompson, M. P. (2013). A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States. Landscape and Urban Planning, 119, 44–53. https://doi.org/10.1016/j.landurbplan.2013.06.011
dc.relationHadi, D. P. (2008). a Rs / Gis – Based Multi-Criteria Approaches To Assess Forest Fire Hazard in Indonesia a Rs / Gis – Based Multi-Criteria Approaches. East.
dc.relationHall, J. R., & Sekizawa, A. (1991). Fire risk analysis: General conceptual framework for describing models. Fire Technology, 27(1), 33–53. https://doi.org/10.1007/BF01039526
dc.relationHanley, J., & McNeil, B. (1982). The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) curve. Radiology, 143(1), 29–36. https://doi.org/10.1080/02634938208400381
dc.relationHardesty, J., Myers, R., & Fulks, W. (2005). Fire, ecosystems and people: a preliminary assessment of fire as a global conservation issue. Fire Management, 22(4), 78–87. https://doi.org/10.2307/43597968
dc.relationHarris, L., & Taylor, A. H. (2017). Previous burns and topography limit and reinforce fire severity in a large wildfire. Ecosphere, 8(11). https://doi.org/10.1002/ecs2.2019
dc.relationHartemink, A. E., & Huting, J. (2008). Land cover, extent, and properties of Arenosols in Southern Africa. Arid Land Research and Management, 22(2), 134–147. https://doi.org/10.1080/15324980801957689
dc.relationHernandez-Leal, P. A., Arbelo, M., & Gonzalez-Calvo, A. (2006). Fire risk assessment using satellite data. Advances in Space Research, 37(4), 741–746. https://doi.org/10.1016/j.asr.2004.12.053
dc.relationHilton, J. E., Miller, C., Sharples, J. J., & Sullivan, A. L. (2016). Curvature effects in the dynamic propagation of wildfires. International Journal of Wildland Fire, 25(12), 1238–1251. https://doi.org/10.1071/WF16070
dc.relationHong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.-X., & Xu, C. (2018). Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of the Total Environment, 630, 1044–1056. https://doi.org/10.1016/j.scitotenv.2018.02.278
dc.relationHosmer, D., Lemeshow, S., & Sturdivant, R. (2013). Applied Logistic Regression (John Wiley & Sons (ed.); 3er Editio). https://doi.org/10.1074/jbc.272.33.20373
dc.relationHu, T., & Zhou, G. (2014). Drivers of lightning- and human-caused fire regimes in the Great Xing’an Mountains. Forest Ecology and Management, 329, 49–58. https://doi.org/10.1016/j.foreco.2014.05.047
dc.relationHuang, J., Chen, D., & Cosh, M. H. (2009). Sub-pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites. International Journal of Remote Sensing, 30(8), 2075–2104. https://doi.org/10.1080/01431160802549245
dc.relationHuertas Herrera, A., Baptiste Ballera, B. L. G., Toro Manríquez, M., & Huertas Ramírez, H. (2018). Manejo De La Quema De Pastizales De Sabana Inundable: Una Mirada Del Pueblo Originario Sáliva En Colombia. Chungará (Arica), ahead, 0–0. https://doi.org/10.4067/s0717-73562018005002401
dc.relationHuesca, M., Litago, J., Palacios-Orueta, A., Montes, F., Sebastián-López, A., & Escribano, P. (2009). Assessment of forest fire seasonality using MODIS fire potential: A time series approach. Agricultural and Forest Meteorology, 149(11), 1946–1955. https://doi.org/10.1016/j.agrformet.2009.06.022
dc.relationHuete, A., Didan, K., Miura, T., Rodrigeuz, E., Gao, X., & Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(195), 213. https://doi.org/10.1080/0965156x.2013.836857
dc.relationHurwitz, J., & Kirsch, D. (2018). Machine Learning for dummies. John Wiley & Sons, Inc.
dc.relationHutto, R. L. (2008). The ecological importance of severe wildfires: Some like it hot. Ecological Applications, 18(8), 1827–1834. https://doi.org/10.1890/08-0895.1
dc.relationIDEAM. (2011). Protocolo para la realización de mapas de zonificación de riesgos a incendios de la cobertura vegetal - Escala 1:100.000.
dc.relationIUSS Working Group WRB. (2006). World reference base for soil resources 2006. In Encyclopedia of Soil Science, Third Edition. https://doi.org/10.1081/e-ess3-120053850
dc.relationJaafari, A., Gholami, D. M., & Zenner, E. K. (2017). A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological Informatics, 39, 32–44. https://doi.org/10.1016/j.ecoinf.2017.03.003
dc.relationJaafari, A., Zenner, E. K., & Pham, B. T. (2018). Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2017.12.006
dc.relationJain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. http://arxiv.org/abs/2003.00646
dc.relationJaiswal, R. K., Krishnamurthy, J., & Mukherjee, S. (2005). Regional study for mapping the natural resources prospect & problem zones using remote sensing and GIS. Geocarto International, 20(3), 21–31. https://doi.org/10.1080/10106040508542352
dc.relationJaiswal, R. K., Mukherjee, S., Raju, K. D., & Saxena, R. (2002). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1), 1–10. https://doi.org/10.1016/S0303-2434(02)00006-5
dc.relationJenness, J. S. (2000). The effects of fire on Mexican spotted owls in Arizona and New Me (Issue May). Northern Arizona University.
dc.relationJiang, M., Hu, Z., Ding, Y., Fang, D., Li, Y., Wei, L., Guo, M., & Zhang, S. (2012). Estimation of vegetation water content based on MODIS: Application on forest fire risk assessment. Proceedings - 2012 20th International Conference on Geoinformatics, Geoinformatics 2012, 2007, 2–5. https://doi.org/10.1109/Geoinformatics.2012.6270322
dc.relationJohnston FH, Henderson SB, Chen Y, Randerson JT, Marlier M, D. R. (2012). Estimated global 120:695–701., mortality attributable to smoke from landscape fires. Environ Health Perspect. 2012, 120(5), 695–702. https://doi.org/10.1289/ehp.1104422
dc.relationJurdao, S., Yebra, M., Guerschman, J. P., & Chuvieco, E. (2013). Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sensing of Environment, 132, 59–70. https://doi.org/10.1016/j.rse.2013.01.004
dc.relationKantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Inc. http://ebookcentral.proquest.com/lib/cranfield/detail.action?docID=4708912.
dc.relationKasischke, E. S., French, N. H. F., O’Neill, K. P., Richter, D. D., Bourgeauchavez, L. L., & O’Harrell, P. A. (2000). Influence of Fire on Long-Term Patterns of Forest Succession in Alaskan Boreal Forests. Fire, Climate Change, and Carbon Cycling in the Boreal Forest, 214–234.
dc.relationKastamonu, O. K., & Kucuk, O. (2017). Effect of Phenolic Compounds on the Flammability in Forest Fires. May.
dc.relationKeane, R. E. (2015). Wildland fuel fundamentals and applications. http://public.eblib.com/choice/publicfullrecord.aspx?p=1965189
dc.relationKeane, R. E., Agee, J., Fule, P., Keeley, J. E., Key, C., Kitchen, S. G., Miller, R., & Schulte, L. (2008). Ecological Effects of Large Fires on North American Landscapes : Benefit or Catastrophe ? International Journal of Wildland Fire, 17, 696–712. https://doi.org/10.1071/WF07148
dc.relationKeeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116–126. https://doi.org/10.1071/WF07049
dc.relationKeith, H., Mackey, B. G., & Lindenmayer, D. B. (2009). Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proceedings of the National Academy of Sciences, 106(28), 11635–11640. https://doi.org/10.1073/pnas.0901970106
dc.relationKelly, L., & Standish, R. (2019). Managing fire for plant and animal conservation: Putting fire to work for conservation requires local knowledge. Austral Ecology, 44(1), 173–180. https://doi.org/10.1111/aec.12604
dc.relationKelly, R. E. J., Drake, N. A., & Barr, S. L. (2004). Spatial Modelling of the Terrestrial Environment. Spatial Modelling of the Terrestrial Environment, 276. https://doi.org/10.1002/0470094001
dc.relationKennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811–826. https://doi.org/10.1111/gcb.14549
dc.relationKirby, M., Naden, T., Burt, P., & Butcher, D. (1987). Computer Simulation in Physical Geography. In J. Wiley (Ed.), Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.3290140513
dc.relationKrebs, P., Pezzatti, G. B., Mazzoleni, S., Talbot, L. M., & Conedera, M. (2010). Fire regime: History and definition of a key concept in disturbance ecology. Theory in Biosciences, 129(1), 53–69. https://doi.org/10.1007/s12064-010-0082-z
dc.relationKusangaya, S., & Sithole, V. B. (2015). Remote sensing-based fire frequency mapping in a savannah rangeland. South African Journal of Geomatics, 4(1), 36–49.
dc.relationLapon, L., Ooms, K., & De Maeyer, P. (2020). The influence of map projections on people’s global-scale cognitive map: A worldwide study. ISPRS International Journal of Geo-Information, 9(4), 1–19. https://doi.org/10.3390/ijgi9040196
dc.relationLasso, C. A., S, U. J., F, T., & A, R. (2010). El fuego como parte de la dinámica natural de las sabanas de Los Llanos Orientales de Colombia. In C. Lasso, J. Usma, F. Trujillo, & A. Rial (Eds.), Biodiversidad en la cuenca del Orinoco: bases científicas para la identificación de áreas prioritarias para la conservación y uso sostenible de la biodiversidad (pp. 408–415). I.A.v.H./WWF Colombia/Fundación Omacha/Fundación La Salle/ Universidad Nacional de Colombia/ Conservación Internacional Colombia. https://doi.org/10.1017/CBO9781107415324.004
dc.relationhttps://doi.org/10.1017/CBO9781107415324.004 Leal, A., Gassón, R., Behling, H., & Sánchez, F. (2019). Human-made fires and forest clearance as evidence for late Holocene landscape domestication in the Orinoco Llanos (Venezuela). Vegetation History and Archaeobotany, 28(5), 545–557. https://doi.org/10.1007/s00334-019-00713-w
dc.relationLeblon, B., Bourgeau-Chavez, L., & San-Miguel-Ayanz, J. (2015). Use of Remote Sensing in Wildfire Management. In B. Leblon & M. Alexander (Eds.), Current International Perspectives on Wildland Fires, Mankind and the Environment (p. 271). Science Publishers Inc. https://doi.org/http://dx.doi.org/10.5772/57353
dc.relationLee, D., & Quigñey, T. (2014). The final Phase in the Development of the National Cohesive Wildland Fire Managment Strategy.
dc.relationLehner, B., Verdin, K., & Jarvis, A. (2008). New global hydrography derived from spaceborne elevation data. Eos, Transactions, American Geophysical Union. Eos, Transactions American Geophysical Union, 89(10), 89, 93–94.
dc.relationLeuenberger, M., Parente, J., Tonini, M., Pereira, M. G., & Kanevski, M. (2018). Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environmental Modelling and Software. https://doi.org/10.1016/j.envsoft.2017.12.019
dc.relationLiaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(December), 18–22. https://doi.org/10.1177/154405910408300516
dc.relationLin, F. J. (2008). Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Quality and Quantity, 42(3), 417–426. https://doi.org/10.1007/s11135-006-9055-1
dc.relationLiu, Y., Goodrick, S., & Heilman, W. (2014). Wildland fire emissions, carbon, and climate: Wildfire-climate interactions. Forest Ecology and Management, 317, 80–96. https://doi.org/10.1016/j.foreco.2013.02.020
dc.relationMachado, L. N., Loss, A., Zilli Bacic, I. L., Dortzbach, D., & Lalane, H. D. C. (2017). Characterization and mapping of soil classes of the Lajeado Pessegueiro watershed in Santa Catarina, Brazil. Acta Agronomica, 67(2), 289–296. https://doi.org/10.15446/acag.v67n2.66131
dc.relationMaki, M., Ishiahra, M., & Tamura, M. (2004). Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90(4), 441–450. https://doi.org/10.1016/j.rse.2004.02.002
dc.relationMalczewski, J. (1999). GIS and Multicriteria Decision Analysis. In GIS, Remote Sensing, & Cartography. https://doi.org/10.1353/geo.2002.0003
dc.relationMancini, L. D., Corona, P., & Salvati, L. (2018). Ranking the importance of Wildfires’ human drivers through a multi-model regression approach. Environmental Impact Assessment Review. https://doi.org/10.1016/j.eiar.2018.06.003
dc.relationMartell, D. (2007). Forest fire management, current practices and new challenges for operational researchers. In A. Weintraub, C. Romero, T. Bjorndal, & R. Epstein (Eds.), Handbook of operations research in natural resources, International Series in Operations Research & Management Science (Vol. 99, Issue 3, pp. 419–506). https://doi.org/10.1174/113564009787531226
dc.relationMartin, P. H., Canham, C. D., & Marks, P. L. (2009). Why forests appear resistant to exotic plant invasions: Intentional introductions, stand dynamics, and the role of shade tolerance. Frontiers in Ecology and the Environment, 7(3), 142–149. https://doi.org/10.1890/070096
dc.relationMartin, R. (1994). Assessing the flammability of domestic and wildland vegetation. 12th Conference on Fire and Forest Meteorology, At Jekyll Island, GA, USA, Volume: Pages 26-28, November, 26–28. https://doi.org/10.13140/RG.2.1.3999.3680
dc.relationMartinez, D. (2003). Protected Areas, Indigenous Peoples, and TheWestern Idea of Nature. Ecological Restoration, 21(4), 247–250. https://doi.org/10.3368/er.28.2.135
dc.relationMccune, B., & Grace, J. (2002). Analysis of ecological communities. MJM Software Design, Gleneden Beach, OR.
dc.relationMcKenzie, D., Gedalof, Z., Peterson, D., & Mote, P. (2004). Climatic Change, Wildfire, and Conservation. Conservation Biology, 18(1), 890–902.
dc.relationMedina, E., & Sarmiento, G. (1981). Ecosystèmes pâturés tropicaux du Venezuela; I: Etudes écophysiologiques dans les savanes à Trachypogon (Llanos du Centre). In Ecosystèmes pâturés tropicaux: Recherche sur les ressources naturelles (pp. 631–649). UNESCO, PNUD y FAO.
dc.relationMeijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., & Schipper, A. M. (2018). Global patterns of current and future road infrastructure. Environmental Research Letters, 13(6). https://doi.org/10.1088/1748-9326/aabd42
dc.relationMell, W., Jenkins, M. A., Gould, J., & Cheney, P. (2007). A physics-based approach to modelling grassland fires. International Journal of Wildland Fire, 16(1), 1–22. https://doi.org/10.1071/WF06002
dc.relationMeyn, A., White, P. S., Buhk, C., & Jentsch, A. (2007). Environmental drivers of large, infrequent wildfires: The emerging conceptual model. Progress in Physical Geography, 31(3), 287–312. https://doi.org/10.1177/0309133307079365
dc.relationMiller, C., & Ager, A. (2013). A review of recent advances in risk analysis for wildfire management. International Journal of Wildland Fire, 22(1), 1–14. https://doi.org/10.1071/WF11114
dc.relationMinisterio de Ambiente y Desarrollo Sostenible. (2016). Concepto técnico “propuesta de creación del Distrito Nacional de Manejo Integrado (DNMI) Cinaruco.” In IOSR Journal of Economics and Finance (Vol. 1, Issue 1). https://doi.org/https://doi.org/10.3929/ethz-b-000238666
dc.relationMinisterio del Ambiente y de los Recursos Naturales Renovables. (1985). Proyecto Orinoco-Apure: Información Ambiental y Ecológica sobre los Llanos del Río Orinoco (M. Acevedo & J. Silva (eds.)).
dc.relationMishra, N., & Young, K. (2014). Savannas and Grasslands. In Encyclopedia of Natural Resources : Land Savannas and Grasslands (Issue October). Taylor and Francis. https://doi.org/10.1081/E-ENRL-120047446
dc.relationMontoya, E., Rull, V., Stansell, N. D., Abbott, M. B., Nogué, S., Bird, B. W., & Díaz, W. A. (2011). Forest-savanna-morichal dynamics in relation to fire and human occupation in the southern Gran Sabana (SE Venezuela) during the last millennia. Quaternary Research, 76(3), 335–344. https://doi.org/10.1016/j.yqres.2011.06.014
dc.relationMoore, I. D., & Grayson, R. B. (1991). Terrain‐based catchment partitioning and runoff prediction using vector elevation data. Water Resources Research, 27(6), 1177–1191. https://doi.org/10.1029/91WR00090
dc.relationMoritz, M. A., Morais, M. E., Summerell, L. A., Carlson, J. M., & Doyle, J. (2005). Wildfires, complexity, and highly optimized tolerance. Proceedings of the National Academy of Sciences of the United States of America, 102(50), 17912–17917. https://doi.org/10.1073/pnas.0508985102
dc.relationMoritz, M. A., Parisien, M. A., Batllori, E., Krawchuk, M. A., Van Dorn, J., Ganz, D. J., & Hayhoe, K. (2012). Climate change and disruptions to global fire activity. Ecosphere, 3(6), art49. https://doi.org/10.1890/ES11-00345.1
dc.relationMukherjee, S., Mukherjee, S., Garg, R. D., Bhardwaj, A., & Raju, P. L. N. (2013). Evaluation of topographic index in relation to terrain roughness and DEM grid spacing. Journal of Earth System Science, 122(3), 869–886. https://doi.org/10.1007/s12040-013-0292-0
dc.relationMulder, C., Boit, A., Bonkowski, M., De Ruiter, P. C., Mancinelli, G., Van der Heijden, M. G. A., Van Wijnen, H. J., Vonk, J. A., & Rutgers, M. (2011). A Belowground Perspective on Dutch Agroecosystems: How Soil Organisms Interact to Support Ecosystem Services. In Advances in Ecological Research (1st ed., Vol. 44). Elsevier Ltd. https://doi.org/10.1016/B978-0-12-374794-5.00005-5
dc.relationMundo, I. A., Wiegand, T., Kanagaraj, R., & Kitzberger, T. (2013). Environmental drivers and spatial dependency in wildfire ignition patterns of northwestern Patagonia. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2013.03.011
dc.relationMustafa, S. A., Ramlee, R., & Kassim, S. (2017). Economic forces and Islamic stock market: Empirical evidence from Malaysia. Asian Journal of Business and Accounting, 10(1), 45–85.
dc.relationMyneni, R. B., Hall, F. G., Sellers, P. J., & Marshak, A. L. (1995). Interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481–486. https://doi.org/10.1109/36.377948
dc.relationNájera Díaz, A. (2013). El Fuego en la Naturaleza. La Colección Bordeando El Monte Es Una Publicación de La Secretaría de Medio Ambiente, 2, 12. http://www.sema.gob.mx/descargas/manuales/2_El fuego.pdf
dc.relationNami, M. H., Jaafari, A., Fallah, M., & Nabiuni, S. (2018). Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15(2), 373–384. https://doi.org/10.1007/s13762-017-1371-6
dc.relationNeary, D. G. ., Ryan, K. C. ., & DeBano, L. F. (2005). Wildland Fire in Ecosystems, effects of fire on soil and water. USDA-FS General Technical Report, 4(September), 250. https://doi.org/http://dx.doi.org/10.1111/j.1467-7717.2009.01106.x
dc.relationNepstad, D. C., Stickler, C. M., Soares-Filho, B., & Merry, F. (2008). Interactions among Amazon land use, forests and climate: Prospects for a near-term forest tipping point. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1498), 1737–1746. https://doi.org/10.1098/rstb.2007.0036
dc.relationNepstad, D., Lefebvre, P., Da Silva, U. L., Tomasella, J., Schlesinger, P., Solórzano, L., Moutinho, P., Ray, D., & Benito, J. G. (2004). Amazon drought and its implications for forest flammability and tree growth: A basin-wide analysis. Global Change Biology, 10(5), 704–717. https://doi.org/10.1111/j.1529-8817.2003.00772.x
dc.relationNgoc Thach, N., Bao-Toan Ngo, D., Xuan-Canh, P., Hong-Thi, N., Hang Thi, B., Nhat-Duc, H., & Tien Bui, D. (2018). 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. https://doi.org/10.1016/j.ecoinf.2018.05.009
dc.relationNieto, H., Aguado, I., García, M., & Chuvieco, E. (2012). Lightning-caused fires in Central Spain: development of a probability model of occurrence for two Spanish regions. Agricultural and Forest Meteorology, 35–43. https://doi.org/10.1016/j.agrformet.2012.04.002
dc.relationNunes, A., Lourenço, L., & Castro Meira, A. (2016). Exploring spatial patterns and drivers of forest fires in Portugal (1980-2014). Science of the Total Environment, The, 573, 1190–1202. https://doi.org/10.1016/j.scitotenv.2016.03.121
dc.relationOlaya-Abril, A., Obregón-Romero, R., Parras-Alcántara, L., & Lozano-García, B. (2018). MURASOC, A Metaanalysis to Test the Effects of Independent Variables on Soil Organic Carbon: Application to Mediterranean Areas. In Soil Management and Climate Change: Effects on Organic Carbon, Nitrogen Dynamics, and Greenhouse Gas Emissions. Elsevier Inc. https://doi.org/10.1016/B978-0-12-812128-3.00018-5
dc.relationOliveira, S., Félix, F., Nunes, A., Lourenço, L., Laneve, G., & Sebastián-López, A. (2018). Mapping wildfire vulnerability in Mediterranean Europe. Testing a stepwise approach for operational purposes. Journal of Environmental Management, 206, 158–169. https://doi.org/10.1016/j.jenvman.2017.10.003
dc.relationOliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. C. (2012). Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275, 117–129. https://doi.org/10.1016/j.foreco.2012.03.003
dc.relationOliveira, S., Zêzere, J., Queirós, M., & Pereira, J. (2017). Assessing the social context of wildfire-affected areas. The case of mainland Portugal. Applied Geography. https://doi.org/10.1016/j.apgeog.2017.09.004
dc.relationOliveira, U., Soares-Filho, B., Leitão, R. F. M. H., & Rodrigues, H. O. (2019). BioDinamica: A toolkit for analyses of biodiversity and biogeography on the Dinamica-EGO modelling platform. PeerJ, 2019(7). https://doi.org/10.7717/peerj.7213
dc.relationOlson, D., Dinerstein, E., Wikramanayake, E., Burgess, N., Powell, G., Underwood, E., D’amico, J., Itoua, I., Strand, H., Morrison, J., Loucks, C., Allnutt, T., Ricketts, T., Kura, Y., Lamoreux, J., Wettengel, W., Hedao, P., & Kassem, K. (2006). Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience, 51(11), 933. https://doi.org/10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2
dc.relationPan, J., Wang, W., & Li, J. (2016). Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China. Natural Hazards, 81(3), 1879–1899. https://doi.org/10.1007/s11069-016-2160-0
dc.relationParisien, M. A., Miller, C., Parks, A., DeLanvey, E., Robinne, F., & Flannigan, M. D. (2016). impact and future change Australian fire regimes The spatially varying in fl uence of humans on fi re probability in North America. Environmental Research Letters, 11(7), 1–18. https://doi.org/10.1088/1748-9326/11/7/075005
dc.relationParisien, M. A., & Moritz, M. A. (2009). Environmental controls on the distribution of wildfire at multiple spatial scales. Ecological Monographs, 79(1), 127–154. https://doi.org/10.1890/07-1289.1
dc.relationParisien, M. A., Snetsinger, S., Greenberg, J. A., Nelson, C. R., Schoennagel, T., Dobrowski, S. Z., & Moritz, M. A. (2012). Spatial variability in wildfire probability across the western United States. International Journal of Wildland Fire, 21(4), 313–327. https://doi.org/10.1071/WF11044
dc.relationPekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
dc.relationPereira, P., Mierauskas, P., Ubeda, X., Mataix-Solera, J., & Cerda, A. (2012). Fire in Protected Areas - the Effect of Protection and Importance of Fire Management. Environmental Research, Engineering and Management, 59(1). https://doi.org/10.5755/j01.erem.59.1.856
dc.relationPérez, E., & Bulla, L. (n.d.). Northern South America -- in Colombia and Venezuela | Ecoregions | WWF. Retrieved April 29, 2019, from https://www.worldwildlife.org/ecoregions/nt0709
dc.relationPeterson, S. H., Roberts, D. A., & Dennison, P. E. (2008). Mapping live fuel moisture with MODIS data: A multiple regression approach. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2008.07.012
dc.relationPourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996. https://doi.org/10.1007/s11069-012-0217-2
dc.relationPourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., & Semeraro, T. (2016). Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators, 64, 72–84. https://doi.org/10.1016/j.ecolind.2015.12.030
dc.relationPradhan, B., Suliman, M. D. H. Bin, & Awang, M. A. Bin. (2007). Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 16(3), 344–352. https://doi.org/10.1108/09653560710758297
dc.relationQi, Y., Dennison, P. E., Spencer, J., & Riaño, D. (2012). Monitoring live fuel moisture using soil moisture and remote sensing proxies. Fire Ecology, 8(3), 71–87. https://doi.org/10.4996/fireecology.0803071
dc.relationRíos-Pena, L., Kneib, T., Cadarso-Suárez, C., & Marey-Pérez, M. (2017). Predicting the occurrence of wildfires with binary structured additive regression models. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2016.11.044
dc.relationRippstein, G., Amézquita, E., Escobar, G., & Grollier, C. (2001). Condiciones Naturales de la Sabana. In G. Rippstein, G. Escobar, & F. Motta (Eds.), Agroecologia y biodiversidad en los Llanos Orientales de Colombia (p. 308). Centro Internacional de Agricultura Tropica.
dc.relationRippstein, G., Lascano, C., & Decaëns, T. (1967). La production fourragère dans les savannes d`Amérique du Sud intertropicale. Fourrages (France), (145):33-52
dc.relationRoberts, D. A., Dennison, P. E., Peterson, S., Sweeney, S., & Rechel, J. (2006). Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. Journal of Geophysical Research: Biogeosciences, 111(4), 1–16. https://doi.org/10.1029/2005JG000113
dc.relationRoberts, S. J. (1970). Forest fuel ignitibility. Progress in Physical Geography, 6(4), 312–319. https://doi.org/10.1007/BF02588932
dc.relationRoberts, S. J. (2001). Tropical fire ecology. Progress in Physical Geography, 25(2), 286–291. https://doi.org/10.1177/030913330102500209
dc.relationRodrigues, M., De La Riva, J., & Fotheringham, S. (2014). Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Applied Geography, 48, 52–63. https://doi.org/10.1016/j.apgeog.2014.01.011
dc.relationRodríguez, I. (2007). Pemon perspectives of fire management in Canaima National Park, Southeastern Venezuela. Human Ecology, 35(3), 331–343. https://doi.org/10.1007/s10745-006-9064-7
dc.relationRomero-Ruiz, M., Flantua, S., Tansey, K., & Berrio, F. (2012). Landscape transformations in savannas of northern South America: Land use/cover changes since 1987 in the Llanos Orientales of Colombia. Applied Geography, 32(2), 766–776.
dc.relationRomme, W. H., & Knight, D. H. (1981). Fire Frequency and Subalpine Forest Succession Along a Topographic Gradient in Wyoming. Source: Ecology, 62(2), 319–326.
dc.relationRothermel, R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Research Paper INT USA, INT-115, 40. https://doi.org/http://www.snap.uaf.edu/webshared/JenNorthway/AKFireModelingWorkshop/AKFireModelingWkshp/FSPro Analysis Guide References/Rothermel 1972 INT-115.pdf
dc.relationRowlands, D., Frame, D., Ackerley, D., Aina, T., Booth, B., Christensen, C., Collins, M., Faull, N., Forest, C., Grandey, B., Gryspeerdt, E., Highwood, E., Ingram, W., Knight, S., Lopez, A., Massey, N., McNamara, F., Meinshausen, N., Piani, C., … Allen, M. (2012). Broad range of 2050 warming from an observationally constrained large climate model ensemble. Nature Geoscience, 5(4), 256–260. https://doi.org/10.1038/ngeo1430
dc.relationRunning, S. (1990). Estimating Terrestrial Primary Productivity by Combining Remote Sensing and Ecosystem Simulation. In R. J. Hobbs & H. A. Mooney (Eds.), Remote Sensing of Biosphere Functioning (pp. 65–86). Springer New York. https://doi.org/10.1007/978-1-4612-3302-2_4
dc.relationRunning, S., Mu, Q., & Zhao, M. (2015). MOD17A3H MODIS/Terra Net Primary Production Yearly L4 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org//10.5067/MODIS/MOD17A3H.006
dc.relationSakr, G. E., Elhajj, I. H., Mitri, G., & Wejinya, U. C. (2010). Artificial intelligence for forest fire prediction. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 1311–1316. https://doi.org/10.1109/AIM.2010.5695809
dc.relationSankaran, M., Hanan, N. P., Scholes, R. J., Ratnam, J., Augustine, D. J., Cade, B. S., Gignoux, J., Higgins, S. I., Le Roux, X., Ludwig, F., Ardo, J., Banyikwa, F., Bronn, A., Bucini, G., Caylor, K. K., Coughenour, M. B., Diouf, A., Ekaya, W., Feral, C. J., … Zambatis, N. (2005). Determinants of woody cover in African savannas. Nature, 438(7069), 846–849. https://doi.org/10.1038/nature04070
dc.relationSantín, C., & Doerr, S. H. (2016). Fire effects on soils: The human dimension. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1696), 28–34. https://doi.org/10.1098/rstb.2015.0171
dc.relationSarmiento, G., & Cabido, M. (1996). Biodiversidad y funcionamiento de pastizales y sabanas en América Latina. CYTED y CIELAT. https://books.google.com.co/books?id=ShcWAAAACAAJ
dc.relationSatir, O., Berberoglu, S., & Donmez, C. (2016). Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk. https://doi.org/10.1080/19475705.2015.1084541
dc.relationSayad, Y., Mousannif, H., & Al Moatassime, H. (2019). Predictive modeling of wildfires: A new dataset and machine learning approach. Fire Safety Journal, 104(September 2018), 130–146. https://doi.org/10.1016/j.firesaf.2019.01.006
dc.relationScott, A. C. (2000). The Pre-Quaternary history of fire. Palaeogeography, Palaeoclimatology, Palaeoecology, 164(1–4), 281–329. https://doi.org/10.1016/S0031-0182(00)00192-9
dc.relationScott, A. C., Bownman, D., Bond, W., Pyne, S., & Alexander, M. (2014). Fire on Earth: An Introduction. John Wiley and Sons Ltd.
dc.relationSecretariat of the Convention on Biological Diversity. (2001). Impacts of human-caused fires on biodiversity and ecosystem functioning, and their causes in tropical, temperate and boreal forest biomes. In Impacts of human-caused fires on biodiversity and ecosystem functioning, and their causes in tropical, temperate and boreal forest biomes (Vol. 5). https://doi.org/10.1016/j.ecss.2007.02.030
dc.relationShimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., & Lucas, R. (2014). New global forest/non-forest maps from ALOS PALSAR data (2007-2010). Remote Sensing of Environment, 155, 13–31. https://doi.org/10.1016/j.rse.2014.04.014
dc.relationSiegel, P., & Alwang, J. (2001). Paul B. Siegel Jeffrey Alwang, and Steen L. Jorgensen. 0115, 1–36.
dc.relationSilva, J. (2003). Sabanas. In M. Aguilera, A. Azócar, & E. González-Jiménez (Eds.), Biodiversidad de Venezuela (pp. 678–695). CONICIT & Fundación Polar.
dc.relationSimon, J., Rick, S., Dale, G., Luke, T., Angie, H., Michael, F., & Andrew, F. (2012). Effects of time since fire on birds: How informative are generalized fire response curves for conservation management? Ecological Applications, 22(2), 685–696. http://ejournals.ebsco.com/direct.asp?ArticleID=453BBA51D247DB94126F
dc.relationSletto, B., & Rodriguez, I. (2013). Burning, fire prevention and landscape productions among the Pemon, Gran Sabana, Venezuela: Toward an intercultural approach to wildland fire management in Neotropical Savannas. Journal of Environmental Management, 115, 155–166. https://doi.org/10.1016/j.jenvman.2012.10.041
dc.relationSolomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences, 106(6), 1704–1709. https://doi.org/10.1073/pnas.0812721106
dc.relationSommers, W. T., Loehman, R. A., & Hardy, C. C. (2014). Forest Ecology and Management Wildland fire emissions , carbon , and climate : Science overview and knowledge needs. Forest Ecology and Management, 317, 1–8. https://doi.org/10.1016/j.foreco.2013.12.014
dc.relationSpracklen, D. V., Mickley, L. J., Logan, J. A., Hudman, R. C., Yevich, R., Flannigan, M. D., & Westerling, A. L. (2009). Impacts of climate change from 2000 to 2050 on wildfire activity and carbonaceous aerosol concentrations in the western United States. Journal of Geophysical Research Atmospheres, 114(20), 1–17. https://doi.org/10.1029/2008JDO10966
dc.relationSRA. (2015). Society of Risk Analysis Glossary. Society for Risk Analysis. Committee on Foundations of Risk Analysis, 1–16.
dc.relationStoof, C. (2011). Fire effects on soil and hydrology. Wageningen University.
dc.relationStow, D., & Niphadkar, M. (2007). Stability, normalization and accuracy of MODIS-derived estimates of live fuel moisture for southern California chaparral. International Journal of Remote Sensing, 28(22), 5175–5182. https://doi.org/10.1080/01431160701616129
dc.relationStow, D., Niphadkar, M., & Kaiser, J. (2005). MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. International Journal of Remote Sensing, 26(17), 3867–3873. https://doi.org/10.1080/01431160500185342
dc.relationStow, D., Niphadkar, M., & Kaiser, J. (2006). Time series of chaparral live fuel moisture maps derived from MODIS satellite data. International Journal of Wildland Fire, 15(3), 347–360. https://doi.org/10.1071/WF05060
dc.relationSugihara, N. G. N. G., van Wagtendonk, J. W., Fites-Kaufmann, J., Fites-Kaufman, J., Wagtendonk, J. W. Van, & Fites-Kaufmann, J. (2006). Fire as an ecological process. Fire in California’s Ecosystems, 1916, 58–74. https://doi.org/10.1525/california/9780520246058.003.0004
dc.relationSulla-Menashe, D., & Friedl, M. A. (2018). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product. Figure 1, 1–18. https://doi.org/10.5067/MODIS/MCD12Q1
dc.relationSullivan, A. L. (2017). Inside the Inferno: Fundamental Processes of Wildland Fire Behaviour: Part 1: Combustion Chemistry and Heat Release. Current Forestry Reports, 3(2), 132–149. https://doi.org/10.1007/s40725-017-0057-0
dc.relationTabor, N. J., Montañez, I. P., Kelso, K. A., Currie, B., Shipman, T., & Colombi, C. (2006). A Late Triassic soil catena: Landscape and climate controls on paleosol morphology and chemistry across the Carnian-age Ischigualasto-Villa Union basin, northwestern Argentina. Special Paper of the Geological Society of America, 416(January), 17–41. https://doi.org/10.1130/2006.2416(02)
dc.relationTabor, N. J., Montanez, I. P., Zierenberg, R., & Currie, B. S. (2004). Mineralogical and geochemical evolution of a basalt-hosted fossil soil (Late Triassic, Ischigualasto Formation, northwest Argentina): Potential for paleoenvironmental reconstruction. Bulletin of the Geological Society of America, 116(9–10), 1280–1293. https://doi.org/10.1130/B25222.1
dc.relationTaubenböck, H., Post, J., Roth, A., Zosseder, K., Strunz, G., & Dech, S. (2008). A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing. Natural Hazards and Earth System Science, 8(3), 409–420. https://doi.org/10.5194/nhess-8-409-2008
dc.relationTaylor, S. W., & Alexander, M. E. (2006). Science, technology, and human factors in fire danger rating: The Canadian experience. International Journal of Wildland Fire, 15(1), 121–135. https://doi.org/10.1071/WF05021
dc.relationTeke, M. (2016). Satellite Image Processing Workflow for Rasat and. Journal of Aeronautics And Space Technologies, 9(1), 1–13.
dc.relationThomas, D., Butry, D., Gilbert, S., Webb, D., & Fung, J. (2017). The Costs and Losses of Wildfires: A Literature Review. NIST Special Publication 1215, 72. https://doi.org/10.6028/NIST.SP.1215
dc.relationThomas, S. G., Tabor, N. J., Yang, W., Myers, T. S., Yang, Y., & Wang, D. (2011). Palaeosol stratigraphy across the Permian-Triassic boundary, Bogda Mountains, NW China: Implications for palaeoenvironmental transition through earth’s largest mass extinction. Palaeogeography, Palaeoclimatology, Palaeoecology, 308(1–2), 41–64. https://doi.org/10.1016/j.palaeo.2010.10.037
dc.relationThompson, M. P., & Calkin, D. E. (2011). Uncertainty and risk in wildland fire management: A review. Journal of Environmental Management, 92(8), 1895–1909. https://doi.org/10.1016/j.jenvman.2011.03.015
dc.relationThompson, M. P., Zimmerman, T., Mindar, D., & Taber, M. (2016). Risk Terminology Primer: Basic Principles and a Glossary for the Wildland Fire Management Community. General Technical Report RMRS-GTR-349, May, 13.
dc.relationTien Bui, D., Bui, Q. T., Nguyen, Q. P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32–44. https://doi.org/10.1016/j.agrformet.2016.11.002
dc.relationTien Bui, D., Hung Le, V., Hoang, N.-D., Dieu, T. B., & Hung, V. (2018). GIS-Based Spatial Prediction of Tropical Forest Fire Danger Using a New Hybrid Machine Learning Metho. https://doi.org/10.1016/j.ecoinf.2018.08.008
dc.relationTien Bui, D., Le, K. T. T., Nguyen, V. C., Le, H. D., & Revhaug, I. (2016). Tropical forest fire susceptibility mapping at the Cat Ba National Park area, Hai Phong City, Vietnam, using GIS-based Kernel logistic regression. Remote Sensing, 8(4), 1–15. https://doi.org/10.3390/rs8040347
dc.relationTien Bui, D., Tuan, T., Hoang, N. D., Thanh, N. Q., Nguyen, D. B., Van Liem, N., & Pradhan, B. (2017). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447–458. https://doi.org/10.1007/s10346-016-0711-9
dc.relationTien Bui, D., Tuan, T., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361–378. https://doi.org/10.1007/s10346-015-0557-6
dc.relationTonini, M., D’Andrea, M., Biondi, G., Degli Esposti, S., Trucchia, A., & Fiorucci, P. (2020). A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences, 10(3), 105. https://doi.org/10.3390/geosciences10030105
dc.relationUNISDR. (2015). Sendai Framework for Disaster Risk Reduction 2015 - 2030.
dc.relationUNISDR. (2017). Words into Action Guidelines: National Disaster Risk Assessment. Wildfire Hazard and Risk Assessment. 1–9.
dc.relationVadrevu, K., Eaturu, A., & Badarinath, K. V. S. (2010). Fire risk evaluation using multicriteria analysis-a case study. Environmental Monitoring and Assessment, 166(1–4), 223–239. https://doi.org/10.1007/s10661-009-0997-3
dc.relationVaillant, N. M., Kolden, C. A., & Smith, A. M. S. (2016). Assessing Landscape Vulnerability to Wildfire in the USA. Current Forestry Reports, 2(3), 201–213. https://doi.org/10.1007/s40725-016-0040-1
dc.relationValdez, M. C., Chang, K.-T., Chen, C.-F., Chiang, S.-H., & Santos, J. L. (2017). Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems. Geomatics, Natural Hazards and Risk, 8(2), 876–892. https://doi.org/10.1080/19475705.2016.1278404
dc.relationVallejo-Villalta, I., Rodríguez-Navas, E., & Márquez-Pérez, J. (2019). Mapping forest fire risk at a local scale—A case study in Andalusia (Spain). Environments - MDPI, 6(3). https://doi.org/10.3390/environments6030030
dc.relationVan Der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., Defries, R. S., Jin, Y., & Van Leeuwen, T. T. (2010). Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmospheric Chemistry and Physics, 10(23), 11707–11735. https://doi.org/10.5194/acp-10-11707-2010
dc.relationVan Der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N., & Dolman, A. J. (2008). Climate controls on the variability of fires in the tropics and subtropics. Global Biogeochemical Cycles, 22(3), 1–13. https://doi.org/10.1029/2007GB003122
dc.relationVan Wagner, C. (2007). Age-class distribution and the forest fire cycle. Journal of Experimental Psychology: General, 136(1), 23–42.
dc.relationVélez, R. (2002). Causes of forest fires in the Mediterranean basin. EFI Proceedings, No.45, 35–42. http://www.efi.int/portal/virtual_library/publications/proceedings/
dc.relationVerbesselt, J., Jönsson, P., Lhermitte, S., Van Aardt, J., & Coppin, P. (2006). Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1622–1632. https://doi.org/10.1109/TGRS.2005.862262
dc.relationVerbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., & Coppin, P. (2007). Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment, 108(4), 357–368. https://doi.org/10.1016/j.rse.2006.11.019
dc.relationVogt, P., Riitters, K. H., Estreguil, C., Kozak, J., Wade, T. G., & Wickham, J. D. (2007). Mapping spatial patterns with morphological image processing. Landscape Ecology, 22(2), 171–177. https://doi.org/10.1007/s10980-006-9013-2
dc.relationVogt, P., Riitters, K. H., Iwanowski, M., Estreguil, C., Kozak, J., & Soille, P. (2007). Mapping landscape corridors. Ecological Indicators, 7(2), 481–488. https://doi.org/10.1016/j.ecolind.2006.11.001
dc.relationWang, L., Hunt, E. R., Qu, J. J., Hao, X., & Daughtry, C. S. T. (2013). Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sensing of Environment, 129, 103–110. https://doi.org/10.1016/j.rse.2012.10.027
dc.relationWebb, N. R. (1998). The traditional management of European heathlands. Journal of Applied Ecology, 35(6), 987–990. https://doi.org/10.1111/j.1365-2664.1998.tb00020.x
dc.relationWeinstein, D. A., & Woodbury, P. B. (2010). Review of methods for developing probabilistic risk assessments. part 1: modeling fire. Advances in Threat Assessment and Their Application to Forest and Rangeland Management, 2(1), 285–302. file:///C:/Users/jcronan.000/Documents/UW/JFSP_ASF/ASF_Info_LibraryPye_etal_2010_ThreatAssessment-1783596298/Pye_etal_2010_ThreatAssessment.pdf%5Cnhttp://0841764864590106624clipping.bmp%5Cn%3CGo to ISI%3E://BCI:BCI201100057494
dc.relationWhelan, R., Rodgerson, L., Dickman, C., & Sutherland, E. (2001). Critical life cycles of plants and animals: developing a process-based understanding of population changes in fire prone landscapes. In Flammable Australia: the fire regimes and biodiversity of a continent.
dc.relationWu, W., & Zhang, L. (2013). Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in north-eastern Puerto Rico. Applied Geography, 37(1), 52–62. https://doi.org/10.1016/j.apgeog.2012.10.012
dc.relationXin, J., & Huang, C. (2013). Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management. Fire Safety Journal, 62(PART A), 72–78. https://doi.org/10.1016/j.firesaf.2013.09.022
dc.relationYebra, M., Chuvieco, E., & Riaño, D. (2008). Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, 148(4), 523–536. https://doi.org/10.1016/j.agrformet.2007.12.005
dc.relationYehia, A., Elhifnawy, H., & Safy, M. (2019). Effect of Different Spatial Resolutions of Multi-temporal Satellite Images Change Detection Application. Proceedings of 2019 International Conference on Innovative Trends in Computer Engineering, ITCE 2019, November 2018, 41–46. https://doi.org/10.1109/ITCE.2019.8646510
dc.relationYou, W., Lin, L., Wu, L., Ji, Z., Yu, J., Zhu, J., Fan, Y., & He, D. (2017). Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2017.01.042
dc.relationZhang, J. H., Yao, F. M., Liu, C., Yang, L. M., & Boken, V. K. (2011). Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades-An overview. In International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph8083156
dc.relationZhang, X., Kondragunta, S., & Roy, D. (2014). Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011. Journal of Geophysical Research, 119, 1–13. https://doi.org/10.1002/2013JG002573.Received
dc.relationZizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., Farooq, H., Herdean, A., Ariza, M., Scharn, R., Svantesson, S., Wengström, N., Zizka, V., & Antonelli, A. (2019). CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution, 10(5), 744–751. https://doi.org/10.1111/2041-210X.13152
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleModelo de riesgo de fuego para la ecorregión de los llanos colombo-venezolanos
dc.typeOtro


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