A method to produce a flexible and customized fuel models dataset
A.C.L. Sá,
A. Benali,
B.A. Aparicio,
C. Bruni,
C. Mota,
J.M.C. Pereira,
P.M. Fernandes
Affiliations
A.C.L. Sá
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal; ForestWISE - Laboratório Colaborativo para a Gestão Integrada da Floresta e do Fogo, Quinta de Prados, Vila Real 5001-801, Portugal; Corresponding author at: ForestWISE - Laboratório Colaborativo para a Gestão Integrada da Floresta e do Fogo, Quinta de Prados, Vila Real 5001-801, Portugal
A. Benali
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
B.A. Aparicio
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
C. Bruni
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
C. Mota
Autoridade Nacional de Emergência e Proteção Civil, Avenida do Forte, Carnaxide 2794-112, Portugal
J.M.C. Pereira
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
P.M. Fernandes
ForestWISE - Laboratório Colaborativo para a Gestão Integrada da Floresta e do Fogo, Quinta de Prados, Vila Real 5001-801, Portugal; Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, Vila Real 5000-801, Portugal
Simulation of vegetation fires very often resorts to fire-behavior models that need fuel models as input. The lack of fuel models is a common problem for researchers and fire managers because its quality depends on the quality/availability of data. In this study we present a method that combines expert- and research-based knowledge with several sources of data (e.g. satellite and fieldwork) to produce customized fuel models maps. Fuel model classes are assigned to land cover types to produce a basemap, which is then updated using empirical and user-defined rules. This method produces a map of surface fuel models as detailed as possible. It is reproducible, and its flexibility relies on juxtaposing independent spatial datasets, depending on their quality or availability. This method is developed in a ModelBuilder/ArcGis toolbox named FUMOD that integrates ten sub-models. FUMOD has been used to map the Portuguese annual fuel models grids since 2019, supporting regional fire risk assessments and suppression decisions. Datasets, models and supplementary files are available in a repository (https://github.com/anasa30/PT_FuelModels). • FUMOD is a flexible toolbox with ten sub-models included that maps updated Portuguese fuel models.
