Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data

Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year^-1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year^-1 Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.

Benchmark maps of 33 years of secondary forest age for Brazil

The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986-2018 period. Land-use and land-cover maps from MapBiomas Project (Collection 4.1) were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement.