The 280 000 km² Xingu indigenous lands and protected areas (ILPAs) corridor, inhabited by 24 indigenous peoples and about 215 riverine (ribeirinho) families, lies across active agriculture frontiers in some of the historically highest-deforestation regions of the Amazon. Much of the Xingu is anthropogenic landscape, densely inhabited and managed by indigenous populations over the past millennium. Indigenous and riverine peoples' historical management and use of these landscapes have enabled their long-term occupation and ultimately their protection. The corridor vividly demonstrates how ILPAs halt deforestation and why they may account for a large part of the 70 per cent reduction in Amazon deforestation below the 1996–2005 average since 2005. However, ongoing and planned dams, road paving, logging and mining, together with increasing demand for agricultural commodities, continued degradation of upper headwaters outside ILPA borders and climate change impacts may render these gains ephemeral. Local peoples will need new, bottom-up, forms of governance to gain recognition for the high social and biological diversity of these territories in development policy and planning, and finance commensurate with the value of their ecosystem services. Indigenous groups' reports of changing fire and rainfall regimes may themselves evidence climate change impacts, a new and serious threat.

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.

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Early Warning Systems (EWS) for near real-time detection of deforestation are a fundamental component of public policies focusing on the reduction in forest biomass loss and associated CO2 emissions. Most of the operational EWS are based on optical data, which are severely limited by the cloud cover in tropical environments. Synthetic Aperture Radar (SAR) data can help to overcome this observational gap. SAR measurements, however, can be altered by atmospheric effects on and variations in surface moisture. Different techniques of time series (TS) stabilization have been used to mitigate the instability of C-band SAR measurements. Here, we evaluate the performance of two different approaches to SAR TS stabilization, harmonic deseasonalization and spatial stabilization, as well as two deforestation detection techniques, Adaptive Linear Thresholding (ALT) and maximum likelihood classification (MLC). We set up a rigorous, Amazon-wide validation experiment using the Google Earth Engine platform to sample and process Sentinel-1A data of nearly 6000 locations in the whole Brazilian Amazonian basin, generating more than 8M processed samples. Half of those locations correspond to non-degraded forest areas, while the other half pertained to 2019 deforested areas. The detection results showed that the spatial stabilization algorithm improved the results of the MLC approach, reaching 94.36% global accuracy. The ALT detection algorithm performed better, reaching 95.91% global accuracy, regardless of the use of any stabilization method. The results of this experiment are being used to develop an operational EWS in the Brazilian Amazon.

To the Editor -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