Open AccessImproving burning efficiency estimates through satellite assessment of fuel moisture content
Author(s) -
Chuvieco Emilio,
Cocero David,
Aguado Inmaculada,
Palacios Alicia,
Prado Elena
Publication year - 2004
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2003jd003467
Subject(s) - environmental science , remote sensing , radiance , shrubland , satellite , vegetation (pathology) , advanced very high resolution radiometer , water content , correlation coefficient , radiometer , enhanced vegetation index , atmospheric sciences , radiometry , meteorology , leaf area index , normalized difference vegetation index , vegetation index , geology , geography , physics , ecosystem , mathematics , statistics , medicine , ecology , geotechnical engineering , pathology , astronomy , biology
The assessment of burning efficiency (BE) is a critical parameter for estimating gas emissions derived from biomass burning. Several authors have proven a strong dependence of BE on moisture conditions of the fuel. This paper presents an empirical study where the relationships between fuel moisture content (FMC) and satellite‐derived variables are evidenced. The study was conducted in Mediterranean ecosystems, using both high‐ and low‐resolution satellite images (Landsat‐TM, SPOT‐Vegetation and NOAA‐advanced very high resolution radiometer). First, theoretical relationships between FMC and reflected or emitted radiance are discussed. Second, multitemporal trends of vegetation indices and surface temperatures are compared with field measurements of FMC for Mediterranean grasslands and shrublands. Pearson r correlation coefficients were computed for a 4‐year series of field measurements of FMC and satellite images. Pearson r values show a good correlation between FMC and shortwave infrared (SWIR; 1.6–2 μm) reflectance, for both grasslands and shrublands, although the relations improve when near‐infrared (NIR) and SWIR reflectances are combined. “Traditional” spectral vegetation indices (based on the red and NIR reflectances) only work reasonably well with grasslands but not with shrublands. For shrublands a synthetic index mixing vegetation indices and surface temperature improves determination coefficients to estimate FMC. Finally, on the basis of these findings, multivariate fittings were computed and validated using AVHRR images. The assessment sample also provided high determination coefficients (r 2 > 0.75) in estimating FMC, both for the study site and other control sites.