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Combining AVHRR and meteorological data for estimating live fuel moisture content

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Dokumenter

  • Garcia 2008

    Forlagets udgivne version, 1,16 MB, PDF-dokument

  • Mariano García
  • Emilio Chuvieco
  • Hector Nieto Solana
  • Inmaculada Aguado
Spatial assessment of fire risk is very important for reducing the impacts of wildland fires. Several variables related to fire ignition, propagation and its effects are included in fire risk analysis. Life Fuel Moisture Content (LFMC) is one such variable, which is highly related to fire ignition, and propagation. A wide variety of methods have been applied to estimate LFMC, including field sampling and meteorological indices. Given the limitations of these methods, satellite images are a sound alternative for estimating LFMC because of their capability to spatially and temporally monitor the vegetation status.

This paper aims to improve previous empirical models to estimate LFMC from satellite images, by considering meteorological information. The original models proposed by Chuvieco et al. [Chuvieco, E., Cocero, D., Riaño, D., Martin, M.P., Martinez-Vega, J., et al., (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92, 322–331] for grasslands and shrublands were used as starting point for this research. High over-estimation of LFMC values from those models was found when applied to dry years. Consequently, the new models proposed in this paper use a simple drought index to discriminate between dry and wet years at the beginning of the spring season. A different harmonic function was fitted to each group of hydrological years, to take into account the inter-annual variations in LFMC seasonal trends. Subsequently, two empirical models, one for grasslands and one for shrubs (Cistus ladanifer), were derived based on multivariate linear regression analysis of the data collected at Cabañeros National Park (Central Spain). Determination coefficients greater than 0.8 for grasslands and 0.7 for shrubs were found. The models showed good performance too when applied to other plots of grasslands (R2 = 0.76) and shrubland (R2 = 0.71) with similar environmental characteristics to the calibration site.
OriginalsprogEngelsk
TidsskriftRemote Sensing of Environment
Sider (fra-til)3618-3627
ISSN0034-4257
DOI
StatusUdgivet - 15 sep. 2008
Eksternt udgivetJa

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