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of 159 waves and 60 levels in the vertical, including a well-resolved boundary layer and stratosphere ( Betts et al. 2003 ). 2.3.2. Station datasets 2.3.2.1. LBA and World Meteorological Organization radiosondes During the Wet Season Atmospheric Mesoscale Campaign/Large-Scale Biosphere–Atmosphere Experiment (WETAMC/LBA) radiosonde sites in Rondonia, Brazil, were subject to a quality control process based on visual inspection, plausibility, and spatial and physical consistency ( Longo et al. 2002
of 159 waves and 60 levels in the vertical, including a well-resolved boundary layer and stratosphere ( Betts et al. 2003 ). 2.3.2. Station datasets 2.3.2.1. LBA and World Meteorological Organization radiosondes During the Wet Season Atmospheric Mesoscale Campaign/Large-Scale Biosphere–Atmosphere Experiment (WETAMC/LBA) radiosonde sites in Rondonia, Brazil, were subject to a quality control process based on visual inspection, plausibility, and spatial and physical consistency ( Longo et al. 2002
resolution, were also evaluated for their potential to identify new deforestation. Following the technique proposed by Shimabukuro and Smith ( Shimabukuro and Smith 1995 ), blue, red, NIR, and MIR bands at 250-m resolution were used in a linear spectral mixing model to estimate the subpixel fraction of soil, vegetation, and shade ( Anderson et al. 2005a ). Nonideal quality data were excluded from analyses based on information contained in the quality control band. Six data layers from the MOD13 16-day
resolution, were also evaluated for their potential to identify new deforestation. Following the technique proposed by Shimabukuro and Smith ( Shimabukuro and Smith 1995 ), blue, red, NIR, and MIR bands at 250-m resolution were used in a linear spectral mixing model to estimate the subpixel fraction of soil, vegetation, and shade ( Anderson et al. 2005a ). Nonideal quality data were excluded from analyses based on information contained in the quality control band. Six data layers from the MOD13 16-day
similar to data with sensor errors. Since the rmse is an overall measure of SMA model fit, it is desirable to exclude pixels with a high rmse regardless of the underlying reasons (clouds, data errors, etc.). A threshold rmse of 0.18 (units of reflectance) was found to accurately separate cloud-compromised and usable pixels. This rmse value was selected by comparing rmse values for all pixels with the MODIS quality assurance bit and with raw image reflectance data. This approach resulted in greater
similar to data with sensor errors. Since the rmse is an overall measure of SMA model fit, it is desirable to exclude pixels with a high rmse regardless of the underlying reasons (clouds, data errors, etc.). A threshold rmse of 0.18 (units of reflectance) was found to accurately separate cloud-compromised and usable pixels. This rmse value was selected by comparing rmse values for all pixels with the MODIS quality assurance bit and with raw image reflectance data. This approach resulted in greater
). Therefore, we collected canopy spectra using the Earth Observing-1 ( EO-1 ) Hyperion sensor. Hyperion is the first spaceborne imaging spectrometer for environmental applications ( Ungar et al. 2003 ). We used Hyperion data from forest and woodland control sites established in 1999. The data were derived from more than 40 000 spectral observations made at 30-m spatial resolution (G. P. Asner 2005, unpublished manuscript), atmospherically corrected to apparent top-of-canopy reflectance using the
). Therefore, we collected canopy spectra using the Earth Observing-1 ( EO-1 ) Hyperion sensor. Hyperion is the first spaceborne imaging spectrometer for environmental applications ( Ungar et al. 2003 ). We used Hyperion data from forest and woodland control sites established in 1999. The data were derived from more than 40 000 spectral observations made at 30-m spatial resolution (G. P. Asner 2005, unpublished manuscript), atmospherically corrected to apparent top-of-canopy reflectance using the
and Rocha 1992 ), supervised classification ( Stone and Lefebvre 1998 ), soil fraction images obtained through spectral mixture analysis ( Souza and Barreto 2000 ; Monteiro et al. 2003 ), contextual clustering ( Sgrenzaroli et al. 2002 ), and decision tree classification ( Souza et al. 2003 ). Additionally, efforts have been made to link forest biophysical properties of selectively logged forests with remotely sensed data ( Asner et al. 2002 ; Asner et al. 2004 ). Burned forests have also been
and Rocha 1992 ), supervised classification ( Stone and Lefebvre 1998 ), soil fraction images obtained through spectral mixture analysis ( Souza and Barreto 2000 ; Monteiro et al. 2003 ), contextual clustering ( Sgrenzaroli et al. 2002 ), and decision tree classification ( Souza et al. 2003 ). Additionally, efforts have been made to link forest biophysical properties of selectively logged forests with remotely sensed data ( Asner et al. 2002 ; Asner et al. 2004 ). Burned forests have also been
1984 ). Undulating terrain characterizes the landscape with an elevation of 50–150 m above sea level. The plateau soil on which the study sites are located is classified as dystrophic, isohyperthermic, clayey kaolinitic, Hapludox ( latossolo amarelo according to the Brazilian soil classification system). Detailed soil carbon and nutrient data for each forest in this study were presented by Feldpausch et al. ( Feldpausch et al. 2004 ) ( appendix ). They reported calcium and phosphorus were low in
1984 ). Undulating terrain characterizes the landscape with an elevation of 50–150 m above sea level. The plateau soil on which the study sites are located is classified as dystrophic, isohyperthermic, clayey kaolinitic, Hapludox ( latossolo amarelo according to the Brazilian soil classification system). Detailed soil carbon and nutrient data for each forest in this study were presented by Feldpausch et al. ( Feldpausch et al. 2004 ) ( appendix ). They reported calcium and phosphorus were low in
