@Article{NumataCochGalv:2011:AnImFr,
               author = "Numata, Izaya and Cochrane, Mark A. and Galvao, Lenio Soares",
          affiliation = "S Dakota State Univ, GIScCE, Brookings, SD 57007 USA and S Dakota 
                         State Univ, GIScCE, Brookings, SD 57007 USA and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Analyzing the Impacts of Frequency and Severity of Forest Fire on 
                         the Recovery of Disturbed Forest using Landsat Time Series and 
                         EO-1 Hyperion in the Southern Brazilian Amazon",
              journal = "Earth Interactions",
                 year = "2011",
               volume = "15",
               number = "13",
                pages = "1--17",
                month = "May",
             keywords = "Amazon, forest disturbance, Hyperspectral Remote Sensing, 
                         Hyperion, IMAGING SPECTROSCOPY, TROPICAL FORESTS, EASTERN AMAZON, 
                         INDEX, REFLECTANCE, CLASSIFICATION, DEFORESTATION, EFFICIENCY, 
                         LANDSCAPE, MODELS.",
             abstract = "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.",
                  doi = "10.1175/2010EI372.1",
                  url = "http://dx.doi.org/10.1175/2010EI372.1",
                 issn = "1087-3562",
                label = "lattes: 5507769922001047 3 NumataCochGalv:2011:AnImFr",
             language = "en",
                  url = "http://journals.ametsoc.org/doi/abs/10.1175/2010EI372.1",
        urlaccessdate = "05 jun. 2024"
}