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). Differences in dry season length and severity throughout Brazil and the Amazon region delineate distinct periods of increased flammability across states and biomes. The timing and location of biomass burning events in the Amazon reflect seasonal variations in rainfall and the presence of human ignition sources ( Hoffmann et al. 2002 ; Hoffmann et al. 2003 ; Cardoso et al. 2003 ). Also, anomalous climate conditions such as El Niño/La Niña events may exacerbate dry season severity, creating favorable
). Differences in dry season length and severity throughout Brazil and the Amazon region delineate distinct periods of increased flammability across states and biomes. The timing and location of biomass burning events in the Amazon reflect seasonal variations in rainfall and the presence of human ignition sources ( Hoffmann et al. 2002 ; Hoffmann et al. 2003 ; Cardoso et al. 2003 ). Also, anomalous climate conditions such as El Niño/La Niña events may exacerbate dry season severity, creating favorable
in order to preserve the moistening and heating rates from the precipitation assimilation scheme. In contrast to the original PI scheme, Newtonian relaxation was not used here, because the regional model’s boundary conditions (i.e., the base field, which is part of the regional model total field) were updated each 6 h using reanalysis. This reduces any substantial drift due to moisture adjustment and avoids the strong damping introduced by nudging the spectral coefficients toward the coarser
in order to preserve the moistening and heating rates from the precipitation assimilation scheme. In contrast to the original PI scheme, Newtonian relaxation was not used here, because the regional model’s boundary conditions (i.e., the base field, which is part of the regional model total field) were updated each 6 h using reanalysis. This reduces any substantial drift due to moisture adjustment and avoids the strong damping introduced by nudging the spectral coefficients toward the coarser
1. Introduction Grassland wildfire exerts a strong control on the structure and functioning of semiarid ecosystems and leads to large economic losses when fire spreads into regions of development ( Dellasala et al. 2004 ). Fire is a defining characteristic of grasslands, helping to build complex mosaics of plant communities of various stages of succession ( Harrison et al. 2003 ). Fire conditions are influenced by topography and weather, but fuel quantity is the principal ecosystem structural
1. Introduction Grassland wildfire exerts a strong control on the structure and functioning of semiarid ecosystems and leads to large economic losses when fire spreads into regions of development ( Dellasala et al. 2004 ). Fire is a defining characteristic of grasslands, helping to build complex mosaics of plant communities of various stages of succession ( Harrison et al. 2003 ). Fire conditions are influenced by topography and weather, but fuel quantity is the principal ecosystem structural
pixels at class boundaries ( Wessels et al. 2004 ). Braswell et al. ( Braswell et al. 2003 ) also found that both the moderate-resolution MODIS and Multiangle Imaging Spectroradiometer (MISR) were able to accurately separate forest and nonforest classes, and, at 1.1-m resolution, subpixel classification using spectral unmixing was important for the proper characterization of nondominant classes such as deforestation. Spectral unmixing of higher-resolution MODIS data (250–500 m) proved useful for
pixels at class boundaries ( Wessels et al. 2004 ). Braswell et al. ( Braswell et al. 2003 ) also found that both the moderate-resolution MODIS and Multiangle Imaging Spectroradiometer (MISR) were able to accurately separate forest and nonforest classes, and, at 1.1-m resolution, subpixel classification using spectral unmixing was important for the proper characterization of nondominant classes such as deforestation. Spectral unmixing of higher-resolution MODIS data (250–500 m) proved useful for
, leaf area used was equal to 6 cm 2 . For A max and g s @ A max determinations (430 in total), conditions inside the chamber were controlled to maintain leaf temperature at 30°C, relative humidity around 80%, CO 2 concentrations at the sample cell at 360 mmol mol −1 , and saturating levels of photosynthetic active photon flux density (PPFD; 800 μ mol m −2 s −1 for understory plants and 1800 μ mol m −2 s −1 for mid- and top-canopy species). The biochemical photosynthesis model used in
, leaf area used was equal to 6 cm 2 . For A max and g s @ A max determinations (430 in total), conditions inside the chamber were controlled to maintain leaf temperature at 30°C, relative humidity around 80%, CO 2 concentrations at the sample cell at 360 mmol mol −1 , and saturating levels of photosynthetic active photon flux density (PPFD; 800 μ mol m −2 s −1 for understory plants and 1800 μ mol m −2 s −1 for mid- and top-canopy species). The biochemical photosynthesis model used in
biophysical impacts of land-cover change are found in Rondonia ( Figure 16 ). Areas once dominated by open lowland and submontane tropical forest ( Figure 5 ) have been converted to pastoral and agricultural land uses. Very high NPV and bare substrate values are found in these clearings, with values typically ranging from 40% to 90%. Note the apparent differences in NPV and bare substrate conditions when crossing the boundary between Landsat images, such as shown in Figures 16a,b . These interscene
biophysical impacts of land-cover change are found in Rondonia ( Figure 16 ). Areas once dominated by open lowland and submontane tropical forest ( Figure 5 ) have been converted to pastoral and agricultural land uses. Very high NPV and bare substrate values are found in these clearings, with values typically ranging from 40% to 90%. Note the apparent differences in NPV and bare substrate conditions when crossing the boundary between Landsat images, such as shown in Figures 16a,b . These interscene
System) was created within IBAMA and started to use—in a semioperational way—satellite fire detection information coming from National Oceanic and Atmospheric Administration (NOAA) AVHRR data generated by INPE. In 1985 the first fire detections with AVHRR were made as part of the NASA–INPE Atmospheric Boundary Layer Experiment (ABLE)-2A mission, resulting in the report of previously unknown biomass burning in the Amazon, with regional transport of emissions ( Andreae et al. 1988 ). In 1987 the first
System) was created within IBAMA and started to use—in a semioperational way—satellite fire detection information coming from National Oceanic and Atmospheric Administration (NOAA) AVHRR data generated by INPE. In 1985 the first fire detections with AVHRR were made as part of the NASA–INPE Atmospheric Boundary Layer Experiment (ABLE)-2A mission, resulting in the report of previously unknown biomass burning in the Amazon, with regional transport of emissions ( Andreae et al. 1988 ). In 1987 the first
the spatial distribution of cultivated land. Previous studies have demonstrated the impact of road access on land-use change ( Stone et al. 1991 ; Wilkie et al. 2000 ; Laurance et al. 2002 ; Alves 2002 ). How much land in Mato Grosso will ultimately be converted to mechanized agriculture depends on future economic, political, biophysical, and climatic conditions. However, current assessment of the likelihood of conversion to mechanized agriculture for different landscapes in Mato Grosso can
the spatial distribution of cultivated land. Previous studies have demonstrated the impact of road access on land-use change ( Stone et al. 1991 ; Wilkie et al. 2000 ; Laurance et al. 2002 ; Alves 2002 ). How much land in Mato Grosso will ultimately be converted to mechanized agriculture depends on future economic, political, biophysical, and climatic conditions. However, current assessment of the likelihood of conversion to mechanized agriculture for different landscapes in Mato Grosso can
, revealing the dominance of woody vegetation and full closure of the vegetation ≥150 cm height in the 12- to 14-yr-old forests. 4. Discussion 4.1. Forest structure development When actively grazed, the areas we studied that are now abandoned had 84% grass, 11% bare soil, 5% trunk, and <1% shrub cover ( Wright et al. 1992 ). The invading secondary vegetation rapidly altered previous pasture conditions, with the development of high tree stem densities and forest canopy cover ( Table 3 ; Figure 1 ). A
, revealing the dominance of woody vegetation and full closure of the vegetation ≥150 cm height in the 12- to 14-yr-old forests. 4. Discussion 4.1. Forest structure development When actively grazed, the areas we studied that are now abandoned had 84% grass, 11% bare soil, 5% trunk, and <1% shrub cover ( Wright et al. 1992 ). The invading secondary vegetation rapidly altered previous pasture conditions, with the development of high tree stem densities and forest canopy cover ( Table 3 ; Figure 1 ). A
