Risk Assessment For Environmental Degradation Caused By Fires Using Remote Sensing And GIS In A Mediterranean Region (south-Euboia, Central Greece)

Vliegher, B.M. De

doi:10.1109/igarss.1992.576622

Cited by 4 publications

(4 citation statements)

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“…Most of the bivariate and multivariate techniques are sensitive to the inclusion of the collinear variables, as these intercorrelated variables can significantly reduce the accuracy of the model [7]. Thus, before model building, the highly collinear variables should be identified and excluded from further procedures [7][8][9][10][11][12][13][14][15][16]. To examine possible collinearity among the variables used in this study, we computed the variance inflation factor (VIF) and tolerance and checked their critical values for all variables.…”

Section: Multicollinearity Assessmentmentioning

confidence: 99%

“…Given the investment of resources and time required to suppress wildfires, efficient and adaptable techniques are needed to rapidly estimate the likelihood of fire occurrence. The first extensive works on predicting wildfire probability date back to Chuvieco and Congalton [12] in Spain and De Vliegher [13] in Greece, which were significantly elaborated by recent works [14][15][16], demonstrating that the future wildfires tend to occur under similar local conditions that caused them in the past.…”

Section: Introductionmentioning

confidence: 98%

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Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

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Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes.

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Section: Multicollinearity Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

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“…In spite of this, the models commonly used till now entail a priori setting of the modeling parameters. This preliminary setting is often based on fire experts' knowledge [Abhineet et al, 1996;Chuvieco and Congalton, 1989;Gouma and Chronopoulou-Sereli, 1998;Salas and Chuvieco, 1994;Thompson et al, 2000;Vliegher, 1992] or on the implementation of regression analysis, where coefficients represent the weights of the considered predictor variables. Statistical models proposed in previous works to implement these analysis range from simple multiple linear regression [Chuvieco and Congalton, 1989] to logistic regression (LR) [Chou, 1992;Chuvieco et al, 1999a;Martell et al, 1987;Preisler et al, 2004;de Vasconcelos et al, 2001;Vega-García et al, 1995].…”

Section: Introductionmentioning

confidence: 99%

Assessing long‐term fire risk at local scale by means of decision tree technique

Amatulli

Rodrigues²,

Marco

et al. 2006

J. Geophys. Res.

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[1] The main problem encountered when applying remote sensing and geographic information systems techniques for wildfire risk assessment is the necessity to integrate different data sources. The methods applied so far are usually based on regression techniques or on coefficients relying on experts' knowledge. Hence fire managers are seeking an unbiased statistical model able to highlight the multivariate spatial relationships between the predictor variables, yielding understandable output readily accessible to end users. The present research aims to test the capability of classification and regression trees (CART) analysis to assess long-term fire risk at a local scale. The CART analysis is a nonparametric statistical technique which generates decision rules in the form of a binary tree, for a classification or a regression process. A fire-prone study area was selected in the southeast of Italy. Fire ignition points, relative to a 7 year period (1997)(1998)(1999)(2000)(2001)(2002)(2003), were used to derive a fire occurrence map through a kernel density approach. The resulting map was then used as input response variable for the CART analysis with fire danger variables used as predictors. The rules induced by the regression process allowed the definition of different risk levels, expressed as 30 management units, which is useful for producing a fire risk map. The result of the regression process (r = 0.77), the capability of the CART analysis to highlight the hierarchical relationships among the predictor variables, and the improved interpretability of the regression rules represent a possible tool useful for better approaching the problem of assessing and representing fire risk.Citation: Amatulli, G., M. J. Rodrigues, M. Trombetti, and R. Lovreglio (2006), Assessing long-term fire risk at local scale by means of decision tree technique,

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“…Actualmente, a pesar de que el concepto de sostenibilidad es ampliamente aceptado en Colombia y a nivel mundial, aún continúa planteando grandes retos, ya que el planeta entero continúa desangrándose debido al deseo indiscriminado de progreso por parte del hombre y su falta de concientización, los ríos se están secando, bosques se están talando, no hay espacio suficiente para la generación de millones de residuos diarios (ONU, 2011), (Taylor, 2009), la brecha que existe entre el poco porcentaje de población mundial que acumula la mayor parte de las riquezas y el mayor porcentaje de población que dependen del poco porcentaje económico restante continua en aumento (PNUD, 2014), en donde debido a los ciclos económicos, naciones enteras son capaces de lograr un desarrollo acelerado a costa del detrimento de otras poblaciones, -en la actualidad una de cada cuatro personas vive en pobreza absoluta- (OECD, 2008), y a esto sumado el continuo crecimiento exponencial de la población, en relación a la cantidad de personas y también a los recursos necesarios para suplir sus necesidades básicas sin limitarse y regularse, harán que finalmente todo el mundo sin excepción no tenga como que suplir las necesidades debido a la sobreexplotación de los recursos que son limitados (FAO, 2011) (De Vliegher, 1993.…”

Section: Realidad De La Sostenibilidad En Colombiaunclassified

Responsabilidad Social Corporativa: una mirada integral en América Latina

Quintero Garzón,

Sánchez Fernández

2018

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En el contexto actual de alta competitividad con mercados abiertos al comercio y la globalización mundial de la economía, es de vital importancia para la buena marcha de las organizaciones tener en cuenta factores tales como la creatividad y capacidad de mejora de los sistemas de producción, que inciden en la calidad de los productos y servicios que ofrecen. La integración entre lo social, lo económico y lo ambiental tiene cabida dentro de toda organización, puesto que ella tiene participación en los tres ámbitos. Hoy en día, las organizaciones además de contar con sistemas de gestión de forma integrada, se deben preocupar por la aplicación de principios de Responsabilidad Social, que evidencien su preocupación no sólo por ofrecer productos de buena calidad, sino que puedan demostrar su preocupación por la conservación del medio ambiente y por los derechos y el bienestar de sus trabajadores. Con el paso del tiempo, las empresas se dieron cuenta que, la Responsabilidad Social Corporativa generaba ganancias a aquellas que incursionaban en dichos temas como parte de su quehacer, puesto que esto implicaba una fuerte revisión de sus procesos y de cómo los estaban ejecutando, en bien no sólo de la generación de más beneficios económicos, sino del bienestar tanto de sus empleados como de la sociedad en general y de la repercusión en el medio ambiente. Según, Porter y Kramer (2002, 2006), existe una convergencia de intereses entre la filantropía pura y el negocio puro, que puede generar beneficio social y económico. Según ellos, la Responsabilidad Social Corporativa puede constituirse en una ventaja competitiva diferencial que ya están utilizando las empresas líderes y que, por el contrario, mantener unas malas prácticas puede suponer graves perjuicios en actividades empresariales con buena eficiencia económica. Escándalos financieros empresariales a nivel mundial, plantearon la necesidad de retomar la ética empresarial, para recuperar un valor importantísimo para las empresas, como es la confianza de sus clientes, inversionistas, proveedores y en general todo el que tenga que ver con ella, los grupos de interés o stakeholders. Estas situaciones que se han presentado a nivel empresarial, han generado la necesidad de volver la mirada al interior de las organizaciones, analizando no sólo sus valores sino su Ética Empresarial.

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Risk Assessment For Environmental Degradation Caused By Fires Using Remote Sensing And GIS In A Mediterranean Region (south-Euboia, Central Greece)

Cited by 4 publications

References 1 publication

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Assessing long‐term fire risk at local scale by means of decision tree technique

Responsabilidad Social Corporativa: una mirada integral en América Latina

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