Generation of a global fuel dataset using the Fuel Characteristic Classification System (original) (raw)
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Development and mapping of fuel characteristics and associated fire potentials for South America
International Journal of Wildland Fire, 2013
The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially across the landscape. This paper presents a process to generate fuel maps for large areas using remotely sensed information and ancillary fuel characteristic data. The Fuel Characteristic Classification System was used to build fuelbeds for South America and predict potential fire hazard using a set of default environmental variables. A land-cover map was combined with a biome map to define 98 fuelbeds, and their parameters were assigned based on information from global datasets and existing Fuel Characteristic Classification System fuelbeds or photo series. The indices of potential surface fire behaviour ranged from 1.32 to 9, whereas in...
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Generation and Mapping of Fuel Types for Fire Risk Assessment
Fire
Fuel mapping is key to fire propagation risk assessment and regeneration potential. Previous studies have mapped fuel types using remote sensing data, mainly at local-regional scales, while at smaller scales fuel mapping has been based on general-purpose global databases. This work aims to develop a methodology for producing fuel maps across European regions to improve wildland fire risk assessment. A methodology to map fuel types on a regional-continental scale is proposed, based on Sentinel-3 images, horizontal vegetation continuity, biogeographic regions, and biomass data. A vegetation map for the Iberian Peninsula and the Balearic Islands was generated with 85% overall accuracy (category errors between 3% and 28%). Two fuel maps were generated: (1) with 45 customized fuel types, and (2) with 19 fuel types adapted to the Fire Behaviour Fuel Types (FBFT) system. The mean biomass values of the final parameterized fuels show similarities with other fuel products, but the biomass val...
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2007
Abstract: We present an overview of the Fuel Characteristic Classification System (FCCS), a tool that enables land man-agers, regulators, and scientists to create and catalogue fuelbeds and to classify those fuelbeds for their capacity to support fire and consume fuels. The fuelbed characteristics and fire classification from this tool will provide inputs for current and future sophisticated models for the quantification of fire behavior, fire effects, and carbon accounting and enable assess-ment of fuel treatment effectiveness. The system was designed from requirements provided by land managers, scientists, and policy makers gathered through six regional workshops. The FCCS contains a set of fuelbeds representing the United States, which were compiled from scientific literature, fuels photo series, fuels data sets, and expert opinion. The system enables modification and enhancement of these fuelbeds to represent a particular scale of interest. The FCCS then reports assigned and cal...
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A surface fuel classification for estimating fire effects
International Journal of Wildland Fire, 2009
We present a classification of duff, litter, fine woody debris, and logs that can be used to stratify a project area into sites with fuel loading that yield significantly different emissions and maximum soil surface temperature. Total particulate matter smaller than 2.5 μm in diameter and maximum soil surface temperature were simulated using the First Order Fire Effects Model. Simulation results were clustered into 10 Effects Groups using an agglomerative routine where each Effects Group defined a unique range of soil temperature and emissions. Classification tree analysis was used to estimate the critical duff, litter, fine woody debris, and log loadings associated with the soil temperature and emissions of each Effects Group. The resulting 21 fuel classes are called Fuel Loading Models and classified the study dataset with an ~34% misclassification rate. The classification can be used to describe fuel loadings for a plot or stand, or as map units for mapping fuel loadings across l...
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