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Jan Mandel - One of the best experts on this subject based on the ideXlab platform.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    International Journal of Wildland Fire, 2016
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behaviour of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-Fire code WRF–SFire on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    arXiv: Atmospheric and Oceanic Physics, 2014
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behavior of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content observations from remote automated weather stations (RAWS) into a time-lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of fuel moisture content. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time-lag fuel moisture model in the coupled weather-Fire code WRF-SFire on 10-hr fuel moisture content observations from Colorado RAWS in 2013. We show using leave-one-out testing that the TSM compares favorably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve fuel moisture content estimates in unobserved fuel classes.

Martin Vejmelka - One of the best experts on this subject based on the ideXlab platform.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    International Journal of Wildland Fire, 2016
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behaviour of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-Fire code WRF–SFire on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    arXiv: Atmospheric and Oceanic Physics, 2014
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behavior of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content observations from remote automated weather stations (RAWS) into a time-lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of fuel moisture content. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time-lag fuel moisture model in the coupled weather-Fire code WRF-SFire on 10-hr fuel moisture content observations from Colorado RAWS in 2013. We show using leave-one-out testing that the TSM compares favorably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve fuel moisture content estimates in unobserved fuel classes.

Emilio Chuvieco - One of the best experts on this subject based on the ideXlab platform.

  • Generation and Mapping of Fuel Types for Fire Risk Assessment
    'MDPI AG', 2021
    Co-Authors: Elena Aragoneses, Emilio Chuvieco
    Abstract:

    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 values do not present a strong correlation with them (maximum Spearman’s rank correlation: 0.45) because of the divergences in the existing products in terms of considering the forest overstory biomass or not

  • integrating geospatial information into Fire Risk Assessment
    International Journal of Wildland Fire, 2014
    Co-Authors: Emilio Chuvieco, Inmaculada Aguado, Sara Jurdao, M L Pettinari, Marta Yebra, Javier Salas
    Abstract:

    Fire Risk Assessment should take into account the most relevant components associated to Fire occurrence. To estimate when and where the Fire will produce undesired effects, we need to model both (a) Fire ignition and propagation potential and (b) Fire vulnerability. Following these ideas, a comprehensive Fire Risk Assessment system is proposed in this paper,whichmakesextensiveuseofgeographicinformationtechnologiestoofferaspatiallyexplicitevaluationofFireRisk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approachestomergethosevariablesintosyntheticRiskindicesandfinallythevalidationoftheoutputs.Themodelhasbeen applied at a national level for the whole Spanish Iberian territory at 1-km 2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-Risk and landscape resilience. Each input variable included a particular accuracy Assessment, whereas the synthetic indices were validated using the most recent Fire statistics available. Significant relations (P,0.001) with Fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

  • development of a framework for Fire Risk Assessment using remote sensing and geographic information system technologies
    Ecological Modelling, 2010
    Co-Authors: Emilio Chuvieco, Inmaculada Aguado, Marta Yebra, Javier Salas, Hector Nieto, Pilar M Martin, Lara Vilar, Javier Martinez Martinez, Susana Ramirez Martin
    Abstract:

    Forest Fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in Fire Risk estimation are vital to reduce the negative impacts of Fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-Fire treatments. This paper presents the methods to generate the input variables and the Risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland Fire Risk for several regions of Spain. After defining the conceptual scheme for Fire Risk Assessment, the paper describes the methods used to generate the Risk parameters, and presents proposals for their integration into synthetic Risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal Assessment of Risk conditions. All variables were mapped at 1 km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and Fire occurrence in the different study regions.

  • estimation of live fuel moisture content from modis images for Fire Risk Assessment
    Agricultural and Forest Meteorology, 2008
    Co-Authors: Marta Yebra, Emilio Chuvieco, David Riano
    Abstract:

    Abstract This paper presents a method to estimate fuel moisture content (FMC) of Mediterranean vegetation species from satellite images in the context of Fire Risk Assessment. The relationship between satellite images and field collected FMC data was based on two methodologies: empirical relations and statistical models based on simulated reflectances derived from radiative transfer models (RTM). Both models were applied to the same validation data set to compare their performance. FMC of grassland and shrublands were estimated using a 5-year time series (2001–2005) of Terra moderate resolution imaging spectroradiometer (MODIS) images. The simulated reflectances were based on the leaf level PROSPECT coupled with the canopy level SAILH RTM. The simulated spectra were generated for grasslands and shrublands according to their biophysical parameters traits and FMC range. Both models, empirical and statistical models based on RTM, offered similar accuracy with better determination coefficients for grasslands ( r 2  = 0.907, and 0.894, respectively) than for shrublands ( r 2  = 0.732 and 0.842, respectively). Although it is still necessary to test these equations in other areas with analogous types of vegetation, preliminary tests indicate that the adjustments based on simulated data offer similar results, but with greater robustness, than the empirical approach.

Marta Yebra - One of the best experts on this subject based on the ideXlab platform.

  • integrating geospatial information into Fire Risk Assessment
    International Journal of Wildland Fire, 2014
    Co-Authors: Emilio Chuvieco, Inmaculada Aguado, Sara Jurdao, M L Pettinari, Marta Yebra, Javier Salas
    Abstract:

    Fire Risk Assessment should take into account the most relevant components associated to Fire occurrence. To estimate when and where the Fire will produce undesired effects, we need to model both (a) Fire ignition and propagation potential and (b) Fire vulnerability. Following these ideas, a comprehensive Fire Risk Assessment system is proposed in this paper,whichmakesextensiveuseofgeographicinformationtechnologiestoofferaspatiallyexplicitevaluationofFireRisk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approachestomergethosevariablesintosyntheticRiskindicesandfinallythevalidationoftheoutputs.Themodelhasbeen applied at a national level for the whole Spanish Iberian territory at 1-km 2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-Risk and landscape resilience. Each input variable included a particular accuracy Assessment, whereas the synthetic indices were validated using the most recent Fire statistics available. Significant relations (P,0.001) with Fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

  • development of a framework for Fire Risk Assessment using remote sensing and geographic information system technologies
    Ecological Modelling, 2010
    Co-Authors: Emilio Chuvieco, Inmaculada Aguado, Marta Yebra, Javier Salas, Hector Nieto, Pilar M Martin, Lara Vilar, Javier Martinez Martinez, Susana Ramirez Martin
    Abstract:

    Forest Fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in Fire Risk estimation are vital to reduce the negative impacts of Fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-Fire treatments. This paper presents the methods to generate the input variables and the Risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland Fire Risk for several regions of Spain. After defining the conceptual scheme for Fire Risk Assessment, the paper describes the methods used to generate the Risk parameters, and presents proposals for their integration into synthetic Risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal Assessment of Risk conditions. All variables were mapped at 1 km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and Fire occurrence in the different study regions.

  • estimation of live fuel moisture content from modis images for Fire Risk Assessment
    Agricultural and Forest Meteorology, 2008
    Co-Authors: Marta Yebra, Emilio Chuvieco, David Riano
    Abstract:

    Abstract This paper presents a method to estimate fuel moisture content (FMC) of Mediterranean vegetation species from satellite images in the context of Fire Risk Assessment. The relationship between satellite images and field collected FMC data was based on two methodologies: empirical relations and statistical models based on simulated reflectances derived from radiative transfer models (RTM). Both models were applied to the same validation data set to compare their performance. FMC of grassland and shrublands were estimated using a 5-year time series (2001–2005) of Terra moderate resolution imaging spectroradiometer (MODIS) images. The simulated reflectances were based on the leaf level PROSPECT coupled with the canopy level SAILH RTM. The simulated spectra were generated for grasslands and shrublands according to their biophysical parameters traits and FMC range. Both models, empirical and statistical models based on RTM, offered similar accuracy with better determination coefficients for grasslands ( r 2  = 0.907, and 0.894, respectively) than for shrublands ( r 2  = 0.732 and 0.842, respectively). Although it is still necessary to test these equations in other areas with analogous types of vegetation, preliminary tests indicate that the adjustments based on simulated data offer similar results, but with greater robustness, than the empirical approach.

Adam K Kochanski - One of the best experts on this subject based on the ideXlab platform.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    International Journal of Wildland Fire, 2016
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behaviour of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-Fire code WRF–SFire on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

  • data assimilation of dead fuel moisture observations from remote automated weather stations
    arXiv: Atmospheric and Oceanic Physics, 2014
    Co-Authors: Martin Vejmelka, Adam K Kochanski, Jan Mandel
    Abstract:

    Fuel moisture has a major influence on the behavior of wildland Fires and is an important underlying factor in Fire Risk Assessment. We propose a method to assimilate dead fuel moisture content observations from remote automated weather stations (RAWS) into a time-lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of fuel moisture content. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time-lag fuel moisture model in the coupled weather-Fire code WRF-SFire on 10-hr fuel moisture content observations from Colorado RAWS in 2013. We show using leave-one-out testing that the TSM compares favorably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve fuel moisture content estimates in unobserved fuel classes.