Monitoring land cover in Acre State, western Brazilian Amazonia, using multitemporal remote sensing data

Shimabukuro, Yosio Edemir; Duarte, Valdete; Arai, Egídio; Freitas, Ramon Morais de; Martini, Paulo Roberto; Lima, André

doi:10.1080/19479832.2010.505177

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…The area of different land covers within Mato Grosso state was derived from the Panamazônia II project at a scale of 1:500,000 [Shimabukuro et al, 2010;Martini et al, 2012]. The methodology uses data from Landsat Multispectral Scanner System of 1980s, Landsat Thematic Mapper of 1990s, Landsat Enhanced Thematic Mapper Plus of 2000s, and the 2009 and 2010 data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) based on a multiresolution and multitemporal methodology [Shimabukuro et al, 2010].…”

Section: Data 221 Land Cover Mapmentioning

confidence: 99%

Disentangling the contribution of multiple land covers to fire‐mediated carbon emissions in Amazonia during the 2010 drought

Anderson

Aragão

Gloor

et al. 2015

Global Biogeochemical Cycles

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In less than 15 years, the Amazon region experienced three major droughts. Links between droughts and fires have been demonstrated for the 1997/1998, 2005, and 2010 droughts. In 2010, emissions of 510 ± 120 Tg C were associated to fire alone in Amazonia. Existing approaches have, however, not yet disentangled the proportional contribution of multiple land cover sources to this total. We develop a novel integration of multisensor and multitemporal satellite-derived data on land cover, active fires, and burned area and an empirical model of fire-induced biomass loss to quantify the extent of burned areas and resulting biomass loss for multiple land covers in Mato Grosso (MT) state, southern Amazonia-the 2010 drought most impacted region. We show that 10.77% (96,855 km 2 ) of MT burned. We estimated a gross carbon emission of 56.21 ± 22.5 Tg C from direct combustion of biomass, with an additional 29.4 ± 10 Tg C committed to be emitted in the following years due to dead wood decay. It is estimated that old-growth forest fires in the whole Brazilian Legal Amazon (BLA) have contributed to 14.81 Tg of C (11.75 Tg C to 17.87 Tg C) emissions to the atmosphere during the 2010 fire season, with an affected area of 27,555 km 2 . Total C loss from the 2010 fires in MT state and old-growth forest fires in the BLA represent, respectively, 77% (47% to 107%) and 86% (68.2% to 103%) of Brazil's National Plan on Climate Change annual target for Amazonia C emission reductions from deforestation.

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Section: Data 221 Land Cover Mapmentioning

confidence: 99%

Disentangling the contribution of multiple land covers to fire‐mediated carbon emissions in Amazonia during the 2010 drought

Anderson

Aragão

Gloor

et al. 2015

Global Biogeochemical Cycles

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“…We first developed for all Landsat images linear spectral mixture models [33] to decompose each pixel into three components: soil, vegetation and shade. This procedure aimed to enhance the contrast between unburned and burned areas in the shade component, due to low reflectance of burned targets in all spectral bands [34,35]. In these images, the burned areas were also mapped based on visual interpretation.…”

Section: Burned Area Mappingmentioning

confidence: 99%

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Sato

Gomes²,

Shimabukuro

et al. 2016

Remote Sensing

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Abstract:Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia's large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR) stands out as a technology capable of retrieving direct measurements of vegetation vertical arrangement, which can be directly associated with aboveground biomass. This work aims, for the first time, to quantify post-fire changes in forest canopy height and biomass using airborne LiDAR in western Amazonia. For this, the present study evaluated four areas located in the state of Acre, called Rio Branco, Humaitá, Bonal and Talismã. Rio Branco and Humaitá burned in 2005 and Bonal and Talismã burned in 2010. In these areas, we inventoried a total of 25 plots (0.25 ha each) in 2014. Humaitá and Talismã are located in an open forest with bamboo and Bonal and Rio Branco are located in a dense forest. Our results showed that even ten years after the fire event, there was no complete recovery of the height and biomass of the burned areas (p < 0.05). The percentage difference in height between control and burned sites was 2.23% for Rio Branco, 9.26% for Humaitá, 10.03% for Talismã and 20.25% for Bonal. All burned sites had significantly lower biomass values than control sites. In Rio Branco (ten years after fire), Humaitá (nine years after fire), Bonal (four years after fire) and Talismã (five years after fire) biomass was 6.71%, 13.66%, 17.89% and 22.69% lower than control sites, respectively. The total amount of biomass lost for the studied sites was 16,706.3 Mg, with an average loss of 4176.6 Mg for sites burned in 2005 and 2890 Mg for sites burned in 2010, with an average loss of 3615 Mg. Fire impact associated with tree mortality was clearly detected using LiDAR data up to ten years after the fire event. This study indicates that fire disturbance in the Amazon region can cause persistent above-ground biomass loss and subsequent reduction of forest carbon stocks. Continuous monitoring of burned forests is required for depicting the long-term recovery trajectory of fire-affected Amazonian forests.

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“…Attribution of fire type to mapped burned pixels remains a research issue and has mainly been concerned with inferring the spatial extent of understory fires (Alencar, Nepstad, and Diaz 2006;Shimabukuro et al 2010;Morton et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

Roy

Kumar

2016

International Journal of Digital Earth

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The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km 2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user's and producer's classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature. ARTICLE HISTORY

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Monitoring land cover in Acre State, western Brazilian Amazonia, using multitemporal remote sensing data

Cited by 9 publications

References 16 publications

Disentangling the contribution of multiple land covers to fire‐mediated carbon emissions in Amazonia during the 2010 drought

Disentangling the contribution of multiple land covers to fire‐mediated carbon emissions in Amazonia during the 2010 drought

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

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