Amazonian forest degradation must be incorporated into the COP26 agenda

Nations will reaffirm their commitment to reducing greenhouse gas (GHG) emissions during the 26th United Nations Climate Change Conference (COP26; www.ukcop26.org), in Glasgow, Scotland, in November 2021. Revision of the national commitments will play a key role in defining the future of Earth’s climate. In past conferences, the main target of Amazonian nations was to reduce emissions resulting from land-use change and land management by committing to decrease deforestation rates, a well-known and efficient strategy1,2. However, human-induced forest degradation caused by fires, selective logging, and edge effects can also result in large carbon dioxide (CO2) emissions1,2,3,4,5, which are not yet explicitly reported by Amazonian countries. Despite its considerable impact, forest degradation has been largely overlooked in previous policy discussions5. It is vital that forest degradation is considered in the upcoming COP26 discussions and incorporated into future commitments to reduce GHG emissions

Spatio-Temporal variation in dry season determines the Amazonian fire calendar

Fire is one of the main anthropogenic drivers that threatens the Amazon. Despite the clear link between rainfall and fire, the spatial and temporal relationship between these variables is still poorly understood in the Amazon. Here, we stratified the Amazon basin according to the dry season onset/end and investigated its relationship with the spatio-Temporal variation of fire. We used monthly time series of active fires from 2003 to 2019 to characterize the fire dynamics throughout the year and to identify the fire peak months. More than 50% (32 246) of the annual mean active fires occurred in the peak month. In 52% of the cells, the peaks occurred between August-September and in 48% between October-March, showing well-defined seasonal patterns related to spatio-Temporal variation of the dry season. Fire peaks occurred in the last two months of the dry season in 67% of the cells and in 20% in the first month of the rainy season. The shorter the dry season, the more concentrated was the occurrence of active fires in the peak month, with a predominance above 70% in cells with a dry season between one and three months. We defined a Critical Fire Period by identifying the consecutive months that concentrated at least 80% of active fires in the year. This period included two to three months between January and March in the northwest, and in the far north it lasted up to seven months, ending in March-April. In the south, it varied between two and three months, starting in August. In the northeast, it was three to four months, between August and December. By quantifying the role of the dry season in driving fire seasonality across the Amazon basin, we provide recommendations to monitor fire dynamics that can support decision makers in management policies and measures to avoid environmentally or socially harmful fires

Intercomparison of burned area products and its implication for carbon emission estimations in the amazon

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2 ), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2 ) and Fire_cci (14,924 km2 ). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2 ), followed by TREES (7985 km2 ), MCD64A1 (7181 km2) and Fire_cci (1745 km2 ). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

CO2 emissions in the Amazon: are bottom-up estimates from land use and cover datasets consistent with top-down estimates based on atmospheric measurements?

Amazon forests are the largest forests in the tropics and play a fundamental role for regional and global ecosystem service provision. However, they are under threat primarily from deforestation. Amazonia's carbon balance trend reflects the condition of its forests. There are different approaches to estimate large-scale carbon balances, including top-down (e.g., CO2 atmospheric measurements combined with atmospheric transport information) and bottom-up (e.g., land use and cover change (LUCC) data based on remote sensing methods). It is important to understand their similarities and differences. Here we provide bottom-up LUCC estimates and determine to what extent they are consistent with recent top-down flux estimates during 2010 to 2018 for the Brazilian Amazon. We combine LUCC datasets resulting in annual LUCC maps from 2010 to 2018 with emissions and removals for each LUCC, and compare the resulting CO2 estimates with top-down estimates based on atmospheric measurements. We take into account forest carbon stock maps for estimating loss processes, and carbon uptake of regenerating and mature forests. In the bottom-up approach total CO2 emissions (2010 to 2018), deforestation and degradation are the largest contributing processes accounting for 58% (4.3 PgCO2) and 37% (2.7 PgCO2) respectively. Looking at the total carbon uptake, primary forests play a dominant role accounting for 79% (−5.9 PgCO2) and secondary forest growth for 17% (−1.2 PgCO2). Overall, according to our bottom-up estimates the Brazilian Amazon is a carbon sink until 2014 and a source from 2015 to 2018. In contrast according to the top-down approach the Brazilian Amazon is a source during the entire period. Both approaches estimate largest emissions in 2016. During the period where flux signs are the same (2015–2018) top-down estimates are approximately 3 times larger in 2015–2016 than bottom-up estimates while in 2017–2018 there is closer agreement. There is some agreement between the approaches–notably that the Brazilian Amazon has been a source during 2015–2018 however there are also disagreements. Generally, emissions estimated by the bottom-up approach tend to be lower. Understanding the differences will help improve both approaches and our understanding of the Amazon carbon cycle under human pressure and climate change