Carbon emissions from deforestation and forest fragmentation in the Brazilian Amazon

Numata, Izaya; Cochrane, Mark A.; Souza, Carlos M.; Sales, Márcio

doi:10.1088/1748-9326/6/4/044003

Cited by 64 publications

(54 citation statements)

References 36 publications

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“…Primary forest in Amazonia is becoming increasingly fragmented because of high levels of forest loss and subsequent regeneration of secondary forest, with a staggering 53,000 km of forest edges created each year (Numata et al 2011). This boundary between primary and secondary forest (hereafter "the interface") may present a barrier to movement, but propensity to cross a barrier that regenerated in the absence of fire, and a Vismia-dominated community that regenerated after burning .…”

Section: Methodsmentioning

confidence: 99%

Recovery of understory bird movement across the interface of primary and secondary Amazon rainforest

Powell

Stouffer

Johnson

2013

The Auk

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Section: Methodsmentioning

confidence: 99%

Recovery of understory bird movement across the interface of primary and secondary Amazon rainforest

Powell

Stouffer

Johnson

2013

The Auk

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“…Forests provide many important regional to global ecosystem services including biodiversity [1,2], water supply [3], climate regulation [4], and carbon storage [5][6][7]. Monitoring forest cover and associated changes over time has therefore become a key component of many environmental management strategies, including Reduced Emission from Deforestation and Forest Degradation (REDD+).…”

Section: Introductionmentioning

confidence: 99%

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

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“…The Brazilian Tropical Moist Forest Biome (BTMFB) supports the world's largest contiguous area of tropical forest and has experienced high rates of deforestation over the last few decades (Fearnside 2007;Numata et al 2011) and extensive annual burning (Setzer and Pereira 1991;Giglio et al 2006;Chen et al 2011;Morton et al 2013). Fire is used as the primary tool for forest and agricultural land clearing and the majority of fires are thought to be anthropogenic.…”

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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Carbon emissions from deforestation and forest fragmentation in the Brazilian Amazon

Cited by 64 publications

References 36 publications

Recovery of understory bird movement across the interface of primary and secondary Amazon rainforest

Recovery of understory bird movement across the interface of primary and secondary Amazon rainforest

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

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

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