A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region

Lozano, F. J. de la Cruz; Suárez‐Seoane, Susana; Kelly, Maggi; Luís, E.

doi:10.1016/j.rse.2007.06.006

Cited by 95 publications

(62 citation statements)

References 58 publications

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“…For examples Bayesian networks have been used e.g., in Swaziland to analyze the impacts of climate change and variability on short term historical fire occurrence and predict 2000-2008 fire occurrence [131]. Classification and regression trees have been used in Spain to project 1991-2002 fire occurrence [132]. Correlation analysis was used, e.g., in the USA, to analyze the 1985-2002 fire occurrences [133].…”

Section: Modeling Fire Ignition and Occurrence: Empirical Modelsmentioning

confidence: 99%

Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

Herawati

Wijaya

Martius

et al. 2015

Forests

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Abstract:Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, determines species composition and influences landscape structure. However, wildfires may: have undesirable effects on biodiversity and vegetation coverage; produce carbon emissions to the atmosphere; release smoke affecting human health; and cause loss of lives and property. There have been increasing concerns about the potential impacts of climate variability and change on forest fires. Climate change can alter factors that influence the occurrence of fire ignitions, fuel availability and fuel flammability. This review paper aims to identify tools and methods used for gathering information about the impacts of climate variability and change on forest fires, forest fuels and the probability of fires. Tools to assess the impacts of climate variability and change on forest fires include: remote sensing, dynamic global vegetation and landscape models, integrated fire-vegetation models, fire danger rating systems, empirical models and fire behavior models. This review outlines each tool in terms of its characteristics, spatial and temporal resolution, limitations and applicability of the results. To enhance and improve tool performance, each must be continuously tested in all types of forest ecosystems.

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Section: Modeling Fire Ignition and Occurrence: Empirical Modelsmentioning

confidence: 99%

Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

Herawati

Wijaya

Martius

et al. 2015

Forests

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“…CART approaches have frequently been used in the environmental remote sensing community for classification and mapping [77][78][79] for modeling [80][81][82] and for forest characterization [83]. In the estimation of forest structural parameters with HSR satellite imagery, decision trees have been applied in diverse environments: Chubey et al [37] used CART for analysis of percent species composition, crown closure, stand height, and age with IKONOS imagery based on analysis of objects in Alberta, Canada, obtaining the best estimations for species composition and crown closure.…”

Section: Decision Treementioning

confidence: 99%

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Gómez

Wulder

Montes³

et al. 2012

Remote Sensing

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Abstract:Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR) imagery and related techniques to model structural parameters at the stand level (n = 490) in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively) served to model structural parameters. Classification and Regression Tree Analysis (CART) was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD) (R 2 = 0.8; RMSE = 0.13 m) with an average error of 17% while basal area (BA) models produced an average error of 22% (RMSE = 5.79 m 2 /ha). OPEN ACCESS Remote Sens. 2012, 4 136When the measured number of trees per unit area (N) was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from HSR imagery captured from space-borne platforms have capacity to inform on local structural parameters of Mediterranean pines. The nascent program for annual national coverages of HSR imagery over Spain offers unique opportunities for forest structural attribute estimation; whereby, depletions can be readily captured and successive annual collections of data can support or enable refinement of attributes. Further, HSR imagery and associated attribute estimation techniques can be used in conjunction, not necessarily in competition to, more traditional forest inventory with synergies available through provision of data within an inventory cycle and the capture of forest disturbance or depletions.

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“…In the past decade, many different statistical methods have been applied to identify fire driving factors and establish fire prediction models by considering all possible environmental, topographic, climatic and infrastructure factors. These include the artificial neural network [7], the maxent algorithm [8], the autoregressive model [9], classification trees [10], global logistic regression [11][12][13][14][15][16][17][18][19], multiple linear regression and random forest [20][21][22], of which logistic regression is the most commonly used tool.…”

Section: Introductionmentioning

confidence: 99%

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Guo

Tigabu

et al. 2017

Forests

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Fires in urban-forest ecosystems (UFEs) are frequent with complex causes, posing a serious hazard to human lives and infrastructure. Thus, quantifying wildfire risks in UFEs and their spatial pattern is quintessential to develop appropriate fire management strategies. The aim of this study was to explore spatial (geographically weighted logistic regression, GWLR) versus non-spatial (logistic regression, LR) modelling approaches to determine the relationship between forest fire occurrence and driving factors in Yichun, a typical urban-forest ecosystem in China. As drivers of fire, 13 factors related to topographic, vegetation, infrastructure, meteorological and socio-economy were considered and regressed against fire occurrence data from 1980 to 2010. Results demonstrate the superiority of GWLR models over LR in terms of prediction accuracy, goodness of fit and model residuals. The GWLR model further captured the spatial variability of driving factors over a broad study area, and the fire likelihood maps identified areas with different zones of fire risk in the study area.In conclusion, the study demonstrates quantitatively and spatially the importance of accounting for local variation in drivers of fires, thereby improving fire management and prevention strategies. The findings also contribute to the emerged field of fire management and fire risk assessment in UFEs.

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A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region

Cited by 95 publications

References 58 publications

Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

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