“Standing bipmass is a major, often poorly quantified determinate of carbon losses from land clearing. we analyzed maps from the 2001-2007 prodes deforestation time series with recent regional predeforestation aboveground biomass estimates to calculate carbon emission trends for the brazilian amazon basin. although the annual rate of deforestation has not changed ” significantly since the 1990s (anova, p = 0.3), the aboveground biomass lost per unit of forest cleared increased from 2001 to 2007 (183 to 201 mg c ha -1; slope of regression significant: p < 0.01). remaining unprotected forests harbor significantly higher aboveground biomass still, averaging 231 mg c ha-1. this difference is large enough that, even if the annual area deforested remains unchanged, future clearing will increase regional emissions by ∼0.04 pg c yr-1 – a ~25% increase over 2001-2007 annual carbon emissions. these results suggest increased climate risk from future deforestation, but highlight opportunities through reductions in deforestation and forest degradation.(redd) copyright 2009 by the american geophysical union.”

“The prominent role assumed by brazil in the global environmental agenda in recent years has prompted the creation of legal provisions and the formulation of environmental policies and instruments aimed at improved and sustainable territorial governance. within this context, the brazilian government implemented in 2016, with the support of the forest investment program (fip), the first systematic and official monitoring initiative focused on the detection of vegetation suppression (since 2000) in the cerrado, the second largest biome in brazil and a global biodiversity hotspot. in this study, we assess, based on sampling procedures and field work, the quality of the prodes cerrado data, which is being generated according to the well-known and consolidated prodes amazônia methodology. our sample-based validation indicated an overall accuracy of 93.17 ± 0.89% (in line with the four fieldworks findings) and that the anthropization process in the cerrado surpassed 50% of the biome in 2013, a situation identified by the prodes cerrado only in 2018. although this discrepancy, there is a very good convergence between the prodes cerrado data and the sample estimates in more recent years. in fact, the prodes cerrado is a product in process of continuous improvement, in which the quality of the detected deforestation increases over the years. therefore, the prodes cerrado monitoring system is adequate to be used in support of governance instruments capable of curbing or even zeroing deforestation in the biome, where, as our study revealed, there are ~104,000 km2 of abandoned land, which could be used for the expansion of the brazilian agricultural production in the coming years.”

“Secondary vegetation (sv) from land abandonment is a common transition phase between agricultural uses following tropical deforestation. the impact of sv on carbon sequestration and habitat fragmentation across tropical forest frontiers therefore depends on sv dynamics and demographics. here, we used time series of annual mapbiomas land cover data to generate the first estimates of sv extent, age, and net carbon uptake in the brazilian amazon between 1985 and 2017. sv increased over time, totaling 12 mha in 2017, 44% of which was ≤5 years old. between 1988 and 2017, 19.6 mha of sv was cleared, adding 45.5% to the area of primary deforestation detected by the brazilian monitoring system (prodes). rates of sv loss have exceeded prodes deforestation since 2011. based on the age and extent of gains and losses, sv was a small net carbon sink during this period (8.9 tg c yr-1). as sv is not formally protected by national environmental legislation or monitored by prodes, long-term benefits from sv in the brazilian amazon remain uncertain.”

“Scenario analyses of land-use and land-cover change in the amazon are necessary steps to support decisions that can avoid the emission of millions of tons of co2 into the atmosphere. it is important to evaluate models that aim to simulate future scenarios. the present study evaluated scenarios generated for the 2011-2017 period in roraima state, in northern amazonia. simulated deforestation was compared to prodes satellite data. the mapping for the evaluations comprised (i) a ‘silvopastoral use area’ (excluding indigenous lands, conservation units and non-forest areas) intersected with (ii) a grid of nine (9) 10,000-km2 (100 × 100-km) sub-areas. the 2013 scenario had the greatest similarity (55.2%) with the corresponding prodes map. despite divergences between simulated deforestation in the scenarios and prodes deforestation, the evaluations generally demonstrated the model’s validity and its ability to produce scenarios that realistically represent the deforestation that occurred in roraima state during the analyzed period.”

“The great success of the brazilian deforestation programme ‘prodes digital’ has shown the importance of annual deforestation information for understanding and mitigating deforestation and its consequences in brazil. however, there is a lack of similar information on deforestation for the 1990s and 1980s. such maps are essential to understand deforestation frontier development and related carbon emissions. this study aims at extending the deforestation mapping record backwards into the 1990s and 1980s for one of the major deforestation frontiers in the amazon. we use an image compositing approach to transform 2224 landsat images in a spatially continuous and cloud free annual time series of tasseled cap wetness metrics from 1984 to 2012. we then employ a random forest classifier to derive annual deforestation patterns. our final deforestation map has an overall accuracy of 85% with half of the overall deforestation being detected before the year 2000. the results show for the first time detailed patterns of the expanding deforestation frontier before the 2000s. the high degree of automatization exhibits the great potential for mapping the whole amazon biome using long-term and freely accessible remote sensing collections, such as the landsat archive and forthcoming sentinel-2 data.”

“Open global forest cover data can be a critical component for reducing emissions from deforestation and forest degradation (redd+) policies. in this work, we determine the best threshold, compatible with the official brazilian dataset, for establishing a forest mask cover within the amazon basin for the year 2000 using the tree canopy cover 2000 gfc product. we compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the brazilian amazon deforestation monitoring project (prodes) data, produced by the national institute for space research (inpe). we also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. we found that thresholds of 80% and 85% best matched with the prodes data. consequently, we recommend using an 80% threshold for the tree canopy cover 2000 data for assessing forest cover in the amazon basin.”

“The availability of remote-sensing multisource data from optical-based satellite sensors has created new opportunities and challenges for forest monitoring in the amazon biome. in particular, change-detection analysis has emerged in recent decades to monitor forest-change dynamics, supporting some brazilian governmental initiatives such as prodes and deter projects for biodiversity preservation in threatened areas. in recent years fully convolutional network architectures have witnessed numerous proposals adapted for the change-detection task. this paper comprehensively explores state-of-the-art fully convolutional networks such as u-net, resu-net, segnet, fc-densenet, and two deeplabv3+ variants on monitoring deforestation in the brazilian amazon. the networks’ performance is evaluated experimentally in terms of precision, recall, f1-score, and computational load using satellite images with different spatial and spectral resolution: landsat-8 and sentinel-2. we also include the results of an unprecedented auditing process performed by senior specialists to visually evaluate each deforestation polygon derived from the network with the highest accuracy results for both satellites. this assessment allowed estimation of the accuracy of these networks simulating a process ‘in nature’ and faithful to the prodes methodology. we conclude that the high resolution of sentinel-2 images improves the segmentation of deforestation polygons both quantitatively (in terms of f1-score) and qualitatively. moreover, the study also points to the potential of the operational use of deep learning (dl) mapping as products to be consumed in prodes.”