A tropical rainforest clearing experiment by biomass burning in the state of Pará, Brazil

Araújo, Thaı́s Maia; Carvalho, João Andrade de; Higuchi, Níro; Brasil, Antônio; Mesquita, André Luiz Amarante

doi:10.1016/s1352-2310(98)00428-2

Cited by 41 publications

(35 citation statements)

References 10 publications

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“…The (small-size) biomass in these subplots is oven dried and weighed both pre-and post-fire to estimate the CC and to determine the FC. The consumption of larger-size material (diameter > 10 cm) is often estimated based on experimental observations of randomly selected trunks and branches that were identified before the fire (Araújo et al, 1999). The PIM is mainly applied in prescribed burns, and obtaining FC measurements for large wildfires is logistically more challenging but can be based on comparing burned and adjacent unburned patches.…”

Section: Measurementsmentioning

confidence: 99%

Biomass burning fuel consumption rates: a field measurement database

Leeuwen

Werf²,

Hoffmann³

et al. 2014

Biogeosciences

150

133

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Abstract. Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burned. Fuel consumption (FC) depends on the biomass available to burn and the fraction of the biomass that is actually combusted, and can be combined with estimates of area burned to assess emissions. While burned area can be detected from space and estimates are becoming more reliable due to improved algorithms and sensors, FC is usually modeled or taken selectively from the literature. We compiled the peerreviewed literature on FC for various biomes and fuel categories to understand FC and its variability better, and to provide a database that can be used to constrain biogeochemical models with fire modules. We compiled in total 77 studies covering 11 biomes including savanna (15 studies, average FC of 4.6 t DM (dry matter) ha −1 with a standard deviation of 2.2), tropical forest (n = 19, FC = 126 ± 77), temperate forest (n = 12, FC = 58 ± 72), boreal forest (n = 16, FC = 35 ± 24), pasture (n = 4, FC = 28 ± 9.3), shifting cultivation (n = 2, FC = 23, with a range of 4.0-43), crop residue (n = 4, FC = 6.5 ± 9.0), chaparral (n = 3, FC = 27 ± 19), tropical peatland (n = 4, FC = 314 ± 196), boreal peatland (n = 2, FC = 42 [42-43]), and tundra (n = 1, FC = 40). Within biomes the regional variability in the number of measurements was sometimes large, with e.g. only three measurement locations in boreal Russia and 35 sites in North America. Substantial regional differences in FC were found within the defined biomes: for example, FC of temperate pine forests in the USA was 37 % lower than Australian forests dominated by eucalypt trees. Besides showing the differences between biomes, FC estimates were also grouped into different fuel classes. Our results highlight the large variability in FC, not only between biomes but also within biomes and fuel classes. This implies that substantial uncertainties are associated with using biome-averaged values to represent FC for whole biomes. Comparing the compiled FC values with co-located Global Fire Emissions Database version 3 (GFED3) FC indicates that modeling studies that aim to represent variability in FC also within biomes, still require improvements as they have difficulty in representing the dynamics governing FC.

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Section: Measurementsmentioning

confidence: 99%

Biomass burning fuel consumption rates: a field measurement database

Leeuwen

Werf²,

Hoffmann³

et al. 2014

Biogeosciences

150

133

View full text Add to dashboard Cite

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“…1) is the fraction of biomass exposed to fire that is actually consumed (ARAÚJO et al, 1999). Combustion completeness depends on vegetation type, moisture content and meteorological conditions during the fire.…”

Section: Agb Ns =04299×(fp×t×l/365)mentioning

confidence: 99%

Integration of remotely sensed and ancillary data to assess the impacts of shifting cultivation / Integração de dados de detecção remota com dados complementares para a avaliação dos impates da agricultura de corte e queima

Silva

2012

Ambiência

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Annual emissions of CO 2 from biomass burning in shifting cultivation systems in Brazil were estimated as the product of area burned, aboveground biomass, combustion completeness and emission factor. The total area of shifting cultivation was derived from the Global Land Cover 2000 map for South America, while the area cleared and burnt annually was obtained by multiplying the total area by the rotation cycle of shifting cultivation, calculated using cropping and fallow lengths reported in the literature. Aboveground biomass accumulation was estimated as a function of the duration and mean temperature of the growing season, soil texture type and length of the fallow period. Published combustion completeness and emission factor values were used. Two scenarios were set using minimum and maximum fallow lengths and emissions calculated. Results revealed that a reduction of emissions in shifting cultivation systems is not easily achieved by extending the length of the fallow period. Results also showed a good agreement between the spatial distribution of shifting cultivation in Brazil, obtained with remote sensing, and the location of studies describing fallow and cropping periods practiced in shifting cultivation systems.Key words: Shifting cultivation; biomass burning; atmospheric emissions; tropical forests; remote sensing. ResumoAs emissões anuais de CO 2 provenientes da queima de biomassa em sistemas de agricultura de corte e queima no Brasil foram estimadas como o produto da área queimada, da biomassa acima do solo, do fator de combustão e do fator de emissão. A área total afetada por este tipo de agricultura foi obtida a partir do mapa Global Land Cover 2000 da América do Sul, enquanto a área cortada e queimada anualmente foi obtida multiplicando a área total pelo ciclo de rotação, calculado Palavras-chave: Agricultura de corte e queima; queima de biomassa; emissões atmosféricas; florestas tropicais; sensoriamento remoto.

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“…The range of combustion completeness from field studies for the initial fire in deforested areas in Amazonia is 19.5% to 61.5% Kauffman et al, 1995;Guild et al, 1998;Araujo et al, 1999;Nepstad et al, 2001;Laurance et al, 2004), with the average of published estimates of 40.6%. Morton et al (2008) used low, average, and high combustion completeness from field studies to estimate the combustion completeness of the multi-year deforestation process in this region using satellite-based fire frequency information.…”

Section: Modelling Fire In Decafmentioning

confidence: 99%

Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling

Werf¹,

Morton

DeFries

et al. 2009

Biogeosciences

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Abstract. Tropical deforestation contributes to the build-up of atmospheric carbon dioxide in the atmosphere. Within the deforestation process, fire is frequently used to eliminate biomass in preparation for agricultural use. Quantifying these deforestation-induced fire emissions represents a challenge, and current estimates are only available at coarse spatial resolution with large uncertainty. Here we developed a biogeochemical model using remote sensing observations of plant productivity, fire activity, and deforestation rates to estimate emissions for the Brazilian state of Mato Grosso during [2001][2002][2003][2004][2005]. Our model of DEforestation CArbon Fluxes (DECAF) runs at 250-m spatial resolution with a monthly time step to capture spatial and temporal heterogeneity in fire dynamics in our study area within the "arc of deforestation", the southern and eastern fringe of the Amazon tropical forest where agricultural expansion is most concentrated. Fire emissions estimates from our modelling framework were on average 90 Tg C year −1 , mostly stemming from fires associated with deforestation (74%) with smaller contributions from fires from conversions of Cerrado or pastures to cropland (19%) and pasture fires (7%). In terms of carbon dynamics, about 80% of the aboveground living biomass and litter was combusted when forests were converted to pasture, and 89% when converted to cropland because of the highly mechanized nature of the deforestation process in Mato Grosso. The trajectory of land use change from forCorrespondence to: G. R. van der Werf (guido.van.der.werf@falw.vu.nl) est to other land uses often takes more than one year, and part of the biomass that was not burned in the dry season following deforestation burned in consecutive years. This led to a partial decoupling of annual deforestation rates and fire emissions, and lowered interannual variability in fire emissions. Interannual variability in the region was somewhat dampened as well because annual emissions from fires following deforestation and from maintenance fires did not covary, although the effect was small due to the minor contribution of maintenance fires. Our results demonstrate how the DECAF model can be used to model deforestation fire emissions at relatively high spatial and temporal resolutions. Detailed model output is suitable for policy applications concerned with annual emissions estimates distributed among post-clearing land uses and science applications in combination with atmospheric emissions modelling to provide constrained global deforestation fire emissions estimates. DE-CAF currently estimates emissions from fire; future efforts can incorporate other aspects of net carbon emissions from deforestation including soil respiration and regrowth.

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A tropical rainforest clearing experiment by biomass burning in the state of Pará, Brazil

Cited by 41 publications

References 10 publications

Biomass burning fuel consumption rates: a field measurement database

Biomass burning fuel consumption rates: a field measurement database

Integration of remotely sensed and ancillary data to assess the impacts of shifting cultivation / Integração de dados de detecção remota com dados complementares para a avaliação dos impates da agricultura de corte e queima

Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling

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