Journal
EARTH INTERACTIONS
Volume 15, Issue -, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1175/2010EI372.1
Keywords
Forest disturbance and recovery; Hyperspectral sensor; Landsat time series; Amazon
Categories
Funding
- Earth Science Division of the NASA Science Mission Directorate [NNX07AF16G]
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Estimation of fire impacts and forest recovery using remote sensing is difficult because of the heterogeneity of fire history (frequency, severity, and time since last fire) across burned forest landscapes. The authors analyzed impacts of fire frequency and severity within recovering forests in the Amazon region using remote sensing. A multispectral Landsat time series dataset was used to reconstruct the fire history from 1990 to 2002 in a portion of Mato Grosso, Brazil. Five narrowband vegetation indices were then calculated from a hyperspectral Earth Observing One (EO-1) Hyperion image for spectral analysis of physiological characteristics of fire-disturbed forests and their recovery. A total of 30% of the forests burned during the study period, with 72% burned once, 24% burned twice, and less than 4% burned three times. In terms of severity, 70% of burned forest was lightly burned, 21.1% was moderately burned, and 9.1% was severely burned. Analyses of spectral indices [normalized difference vegetation index (NDVI), carotenoid reflectance index (CRI), and photochemical reflectance index (PRI)] showed that those related to canopy greenness and pigment contents can discriminate between burned forests and undisturbed forest for the first 3 years after forest fire, whereas the effectiveness of canopy water content indices [normalized difference water index (NDWI) and normalized difference infrared index (NDII)] varied from 1 to 3 years, depending on the fire severity. Despite the relatively low signal-to-noise ratios of Hyperion imagery, we show that narrowband-derived indices provide useful information for monitoring degraded forests beyond what is currently possible with Landsat. This illustrates the great potential for environmental monitoring using satellite-borne hyperspectral sensors, such as the Hyperspectral Infrared Imager (HyspIRI), which have better signal-to-noise ratios.
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