The growing recognition of biomass

The growing recognition of biomass burning as a widespread and
significant agent of change in the Earth system has led to an ongoing
need for long-term fire data at the regional, continental, and global
scale. In part this demand has been met with a substantial body of
satellite-based active fire observations made using a number of
coarse- and medium-resolution sensors, initially the Advanced Very
High Resolution Radiometer (AVHRR) (Dozier, 1981; Matson and
Dozier, 1981), followed by the Geostationary Operational Environmental
Satellite (GOES) Imager (Prins and Menzel, 1992), the Defense
Meteorological Satellite Program (DMSP) Operational Linescan System
(OLS) (Elvidge et al., 1996), the Along-Track Scanning Radiometer
(ATSR) (Arino and Rosaz,1999), the Visible and Infrared Scanner (VIRS)
(Giglio et al., 2000), the Moderate Resolution Imaging Spectroradiometer
(MODIS) (Justice et al., 2002), and the Spinning Enhanced
Visible and Infrared Imager (SEVIRI) (Roberts et al., 2005).
While active fire products capture information about the location
and timing of fires burning at the time of the satellite overpass, they
do not generally permit burned area to be reliably (or at least directly)
estimated (Scholes et al., 1996; Eva and Lambin, 1998b; Kasischke
et al., 2003; Giglio et al., 2006). Yet reliable, large-scale (usually global)
maps of burned area are essential for many applications, in particular
the estimation of pyrogenic gaseous and aerosol emissions. This need
has consequently prompted the development of numerous satellitebased
methods for mapping burned areas, the majority of which
operate without exploiting active fire information. Kasischke and
French (1995), for example, applied differencing to 15-day AVHRR
Normalized Difference Vegetation Index (NDVI) composite imagery to
detect burns in Alaskan boreal forests during 1990 and 1991.
Fernández et al. (1997) mapped large forest fires in Spain during
1993 and 1994 with 10-day AVHRR maximum-NDVI composites using
separate regression and differencing techniques. Eva and Lambin
(1998a) mapped burns in central Africa during the 1994–1995 dry
season using 1-km ATSR imagery by matching decreases in shortwave
infrared (SWIR) reflectance with increases in surface temperature.
Barbosa et al. (1999) used daily 5-km AVHRR imagery to map
burned areas in Africa based on changes occurring in reflectance,
brightness temperature, and a vegetation index (VI). Pereira et al.
(2000) used classification trees to map burned area in central Africa
and Iberia with AVHRR thermal, albedo, and VI imagery; Stroppiana
et al. (2003) applied a similar technique in Australian woodland
savannas using 10-day SPOT VEGETATION (VGT) composites. Fraser et
al. (2003) developed an approach for mapping burned boreal forest at
the continental scale using 10-day VGT VI composites and a logistic
regression model. The GLOBSCAR global burned area product (Simon
et al., 2004) was produced for the year 2000 using two different
algorithms, one contextual and one fixed-threshold, applied to ATSR-2
and AATSR imagery. The GBA-2000 global burned area product was
independently produced by Tansey et al. (2004) using a combination
Remote Sensing of Environment 113 (2009) 408–420
⁎ Corresponding author. Science Systems and Applications, Inc., Lanham, Maryland,
USA.
E-mail address: [e-mail] (L. Giglio).
0034-4257/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2008.10.006
Contents lists available at ScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
of nine different regional algorithms applied to 1-km VGT daily
surface reflectance imagery. Roy et al. (2002, 2005b) developed a
predictive bi-directional reflectance modeling approach to map
burned areas on a daily basis using 500-m MODIS imagery. Most
recently, Tansey et al. (2008) modified one of the regional GBA-2000
algorithms for global use to produce the L3JRC 1-km global burned
area product for 2000–2007.
Although the majority of existing burned-area mapping methods
do not exploit active fire information, a minority are hybrid algorithms
which supplement the “standard” remotely-sensed indicators used for
burn mapping (surface reflectance, surface temperature, NDVI, etc.)
with active fire maps. Roy et al. (1999), for example, used AVHRR data
to map savanna burns in southern Africa from a temporal composite of
the range of a spectral index. Burned and unburned pixels were
differentiated using a threshold based on the mean and standard
deviation of the range of this index for pixels where active fires were
detected. Similarly, in the Fraser et al. (2000) HANDS algorithm, which
was designed for mapping boreal forest burns with AVHRR data, the
expected change in successive 10-day NDVI composites for burned
pixels was derived using an AVHRR active fire mask. A similar method
was developed by Pu et al. (2004) for mapping burned areas in
California, again with AVHRR data. Georg

Results (Indonesian) 1:

Meningkatnya pengakuan biomassa membakar sebagai luas dansignifikan agen perubahan dalam sistem bumi telah menyebabkan berkelanjutanperlu untuk jangka panjang data api di daerah, kontinental, dan globalskala. Dalam bagian permintaan ini telah bertemu dengan tubuh yang besarapi aktif berbasis satelit pengamatan yang dibuat menggunakan sejumlahkasar dan media-resolusi sensor, awalnya maju sangatResolusi tinggi Radiometer (AVHRR) (Dozier, 1981; Matson danDozier, 1981), diikuti oleh lingkungan operasional geostasionerSatelit (berjalan) Imager (Prins dan Menzel, 1992), pertahananSistem Linescan Meteorological satelit Program (hari) operasional(OLS) (Elvidge et al., 1996), sepanjang jalur pemindaian Radiometer(ATSR) (Arino dan Rosaz, 1999), terlihat dan inframerah Scanner (VIRS)(Giglio et al., 2000), resolusi moderat Imaging SpectroRadiometer (Misr)(MODIS) (Keadilan et al., 2002), dan berputar ditingkatkanTerlihat dan inframerah Imager (SEVIRI) (Roberts et al, 2005).Sementara aktif api produk menangkap informasi tentang lokasidan waktu api pembakaran pada saat jembatan satelit, merekaumumnya tidak mengizinkan daerah dibakar menjadi dapat diandalkan (atau setidaknya langsung)perkiraan (Scholes et al, 1996; Eva dan Lambin, 1998b; Kasischkeet al., 2003; Giglio et al., 2006). Namun dapat diandalkan, besar-besaran (biasanya global)Peta wilayah terbakar penting untuk banyak aplikasi, khususnyaperkiraan pyrogenic emisi gas dan aerosol. Kebutuhan inihas consequently prompted the development of numerous satellitebasedmethods for mapping burned areas, the majority of whichoperate without exploiting active fire information. Kasischke andFrench (1995), for example, applied differencing to 15-day AVHRRNormalized Difference Vegetation Index (NDVI) composite imagery todetect burns in Alaskan boreal forests during 1990 and 1991.Fernández et al. (1997) mapped large forest fires in Spain during1993 and 1994 with 10-day AVHRR maximum-NDVI composites usingseparate regression and differencing techniques. Eva and Lambin(1998a) mapped burns in central Africa during the 1994–1995 dryseason using 1-km ATSR imagery by matching decreases in shortwaveinfrared (SWIR) reflectance with increases in surface temperature.Barbosa et al. (1999) used daily 5-km AVHRR imagery to mapburned areas in Africa based on changes occurring in reflectance,brightness temperature, and a vegetation index (VI). Pereira et al.(2000) used classification trees to map burned area in central Africaand Iberia with AVHRR thermal, albedo, and VI imagery; Stroppianaet al. (2003) applied a similar technique in Australian woodlandsavannas using 10-day SPOT VEGETATION (VGT) composites. Fraser etal. (2003) developed an approach for mapping burned boreal forest atthe continental scale using 10-day VGT VI composites and a logisticregression model. The GLOBSCAR global burned area product (Simonet al., 2004) was produced for the year 2000 using two differentalgorithms, one contextual and one fixed-threshold, applied to ATSR-2and AATSR imagery. The GBA-2000 global burned area product wasindependently produced by Tansey et al. (2004) using a combinationRemote Sensing of Environment 113 (2009) 408–420⁎ Corresponding author. Science Systems and Applications, Inc., Lanham, Maryland,USA.E-mail address: [e-mail] (L. Giglio).0034-4257/$ – see front matter © 2008 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2008.10.006Contents lists available at ScienceDirectRemote Sensing of Environmentjournal homepage: www.elsevier.com/locate/rseof nine different regional algorithms applied to 1-km VGT dailysurface reflectance imagery. Roy et al. (2002, 2005b) developed apredictive bi-directional reflectance modeling approach to mapburned areas on a daily basis using 500-m MODIS imagery. Mostrecently, Tansey et al. (2008) modified one of the regional GBA-2000algorithms for global use to produce the L3JRC 1-km global burnedarea product for 2000–2007.Although the majority of existing burned-area mapping methodsdo not exploit active fire information, a minority are hybrid algorithmswhich supplement the “standard” remotely-sensed indicators used forburn mapping (surface reflectance, surface temperature, NDVI, etc.)with active fire maps. Roy et al. (1999), for example, used AVHRR datato map savanna burns in southern Africa from a temporal composite ofthe range of a spectral index. Burned and unburned pixels weredifferentiated using a threshold based on the mean and standarddeviation of the range of this index for pixels where active fires weredetected. Similarly, in the Fraser et al. (2000) HANDS algorithm, whichwas designed for mapping boreal forest burns with AVHRR data, theexpected change in successive 10-day NDVI composites for burnedpixels was derived using an AVHRR active fire mask. A similar methodwas developed by Pu et al. (2004) for mapping burned areas inCalifornia, again with AVHRR data. Georg