Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

2020 Pantanal's widespread fire: short- and long-term implications for biodiversity and conservation.

The Pantanal faced an unprecedented drought event in 2020. The hydrological year ended in July, 2020 had an annual average rainfall 26 % lower than the average from 1982 to 2020. Consequently, catastrophic wildfires burned out of control. Active fires during this year have also increased, and were 123 % higher than the 2002-2020 Pantanal's average. Approximately 95 % of these active fires occurred in natural land covers with 28 % of them occurring in areas classified as wetlands that likely dried out due to the drought. Therefore, the development of a special policy is needed to minimize the impact of this crisis on the biodiversity, conservation, and traditional people of the Pantanal.

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.

Author Correction: Benchmark maps of 33 years of secondary forest age for Brazil.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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

Effects of climate and land-use change scenarios on fire probability during the 21st century in the Brazilian Amazon.

The joint and relative effects of future land-use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land-use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041-2070) and late (2071-2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071-2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best-case land-use scenario ("Sustainability") alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse-case land-use scenario ("Fragmentation") causes a 73.2% increase. The optimistic climate-land-use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071-2100 compared to the baseline period. In contrast, the most pessimistic climate-land-use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land-use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land-use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

Persistent effects of fragmentation on tropical rainforest canopy structure after 20 yr of isolation.

Assessing the persistent impacts of fragmentation on aboveground structure of tropical forests is essential to understanding the consequences of land use change for carbon storage and other ecosystem functions. We investigated the influence of edge distance and fragment size on canopy structure, aboveground woody biomass (AGB), and AGB turnover in the Biological Dynamics of Forest Fragments Project (BDFFP) in central Amazon, Brazil, after 22+ yr of fragment isolation, by combining canopy variables collected with portable canopy profiling lidar and airborne laser scanning surveys with long-term forest inventories. Forest height decreased by 30% at edges of large fragments (>10 ha) and interiors of small fragments (<3 ha). In larger fragments, canopy height was reduced up to 40 m from edges. Leaf area density profiles differed near edges: the density of understory vegetation was higher and midstory vegetation lower, consistent with canopy reorganization via increased regeneration of pioneers following post-fragmentation mortality of large trees. However, canopy openness and leaf area index remained similar to control plots throughout fragments, while canopy spatial heterogeneity was generally lower at edges. AGB stocks and fluxes were positively related to canopy height and negatively related to spatial heterogeneity. Other forest structure variables typically used to assess the ecological impacts of fragmentation (basal area, density of individuals, and density of pioneer trees) were also related to lidar-derived canopy surface variables. Canopy reorganization through the replacement of edge-sensitive species by disturbance-tolerant ones may have mitigated the biomass loss effects due to fragmentation observed in the earlier years of BDFFP. Lidar technology offered novel insights and observational scales for analysis of the ecological impacts of fragmentation on forest structure and function, specifically aboveground biomass storage.

Improved tree height estimation of secondary forests in the Brazilian Amazon / Estimativa melhorada de altura de árvores em florestas secundárias da Amazônia brasileira

This paper presents a novel approach for estimating the height of individual trees in secondary forests at two study sites: Manaus (central Amazon) and Santarém (eastern Amazon) in the Brazilian Amazon region. The approach consists of adjusting tree height-diameter at breast height (H:DBH) models in each study site by ecological species groups: pioneers, early secondary, and late secondary. Overall, the DBH and corresponding height (H) of 1,178 individual trees were measured during two field campaigns: August 2014 in Manaus and September 2015 in Santarém. We tested the five most commonly used log-linear and nonlinear H:DBH models, as determined by the available literature. The hyperbolic model: H = a.DBH/(b+DBH) was found to present the best fit when evaluated using validation data. Significant differences in the fitted parameters were found between pioneer and secondary species from Manaus and Santarém by F-test, meaning that site-specific and also ecological-group H:DBH models should be used to more accurately predict H as a function of DBH. This novel approach provides specific equations to estimate height of secondary forest trees for particular sites and ecological species groups. The presented set of equations will allow better biomass and carbon stock estimates in secondary forests of the Brazilian Amazon.

Este trabalho apresenta uma nova abordagem para a estimativa de altura de árvores em florestas secundárias em duas áreas de estudo na Amazônia brasileira: Manaus (Amazônia central) e Santarém (Amazônia oriental). A abordagem consistiu em ajustar modelos hipsométricos separados por área de estudo e grupos ecológicos de espécies: pioneiras, secundárias iniciais e secundárias tardias. No total, 1178 árvores foram medidas em diâmetro e altura em duas etapas de campo: agosto de 2014 em Manaus e Setembro de 2015 em Santarém. Foram testados cinco modelos log-lineares e não lineares mais utilizados na literatura. O modelo hiperbólico: H = a.D/(b+D) foi o que apresentou o melhor ajuste quando avaliado com os dados de validação. Diferenças significativas nos parâmetros de ajuste foram observadas entre as espécies pioneiras e secundárias de Manaus e Santarém pelo teste F, significando que equações específicas por grupos ecológicos e área de estudo deveriam ser utilizadas para estimar a altura (H) a partir do diâmetro (D) com maior acurácia. Esta nova abordagem fornece equações específicas para localidade e grupo ecológico, para estimar a altura das árvores em florestas secundárias. O conjunto de equações desenvolvidas permitirá melhorar as estimativas de biomassa e a quantificação dos estoques de carbono nas florestas secundárias da Amazônia brasileira.

Climatic and anthropogenic drivers of northern Amazon fires during the 2015-2016 El Niño event.

The strong El Niño Southern Oscillation (ENSO) event that occurred in 2015-2016 caused extreme drought in the northern Brazilian Amazon, especially in the state of Roraima, increasing fire occurrence. Here we map the extent of precipitation and fire anomalies and quantify the effects of climatic and anthropogenic drivers on fire occurrence during the 2015-2016 dry season (from December 2015 to March 2016) in the state of Roraima. To achieve these objectives we first estimated the spatial pattern of precipitation anomalies, based on long-term data from the TRMM (Tropical Rainfall Measuring Mission), and the fire anomaly, based on MODIS (Moderate Resolution Imaging Spectroradiometer) active fire detections during the referred period. Then, we integrated climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) model to quantify fire probability, assessing (1) the model accuracy during the 2015-2016 and the 2016-2017 dry seasons; (2) the relative importance of each predictor variable on the model predictive performance; and (3) the response curves, showing how each environmental variable affects the fire probability. Approximately 59% (132,900 km2 ) of the study area was exposed to precipitation anomalies ≤-1 standard deviation (SD) in January and ~48% (~106,800 km2 ) in March. About 38% (86,200 km2 ) of the study area experienced fire anomalies ≥1 SD in at least one month between December 2015 and March 2016. The distance to roads and the direct ENSO effect on fire occurrence were the two most influential variables on model predictive performance. Despite the improvement of governmental actions of fire prevention and firefighting in Roraima since the last intense ENSO event (1997-1998), we show that fire still gets out of control in the state during extreme drought events. Our results indicate that if no prevention actions are undertaken, future road network expansion and a climate-induced increase in water stress will amplify fire occurrence in the northern Amazon, even in its humid dense forests. As an additional outcome of our analysis, we conclude that the model and the data we used may help to guide on-the-ground fire-prevention actions and firefighting planning and therefore minimize fire-related ecosystems degradation, economic losses and carbon emissions in Roraima.

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso, Brazilian Amazon.

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300 m × 300 m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000-2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300 m × 300 m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000-2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

BACKGROUND: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. RESULTS: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m-2) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m-2, 4 m-2, 2 m-2 and 1 m-2) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m-2, the bias in height estimates translated into errors of 80-125 Mg ha-1 in predicted aboveground biomass. CONCLUSIONS: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Disentangling the contribution of multiple land covers to fire-mediated carbon emissions in Amazonia during the 2010 drought.

In less than 15 years, the Amazon region experienced three major droughts. Links between droughts and fires have been demonstrated for the 1997/1998, 2005, and 2010 droughts. In 2010, emissions of 510 ± 120 Tg C were associated to fire alone in Amazonia. Existing approaches have, however, not yet disentangled the proportional contribution of multiple land cover sources to this total. We develop a novel integration of multisensor and multitemporal satellite-derived data on land cover, active fires, and burned area and an empirical model of fire-induced biomass loss to quantify the extent of burned areas and resulting biomass loss for multiple land covers in Mato Grosso (MT) state, southern Amazonia-the 2010 drought most impacted region. We show that 10.77% (96,855 km2) of MT burned. We estimated a gross carbon emission of 56.21 ± 22.5 Tg C from direct combustion of biomass, with an additional 29.4 ± 10 Tg C committed to be emitted in the following years due to dead wood decay. It is estimated that old-growth forest fires in the whole Brazilian Legal Amazon (BLA) have contributed to 14.81 Tg of C (11.75 Tg C to 17.87 Tg C) emissions to the atmosphere during the 2010 fire season, with an affected area of 27,555 km2. Total C loss from the 2010 fires in MT state and old-growth forest fires in the BLA represent, respectively, 77% (47% to 107%) and 86% (68.2% to 103%) of Brazil's National Plan on Climate Change annual target for Amazonia C emission reductions from deforestation.

Size and frequency of natural forest disturbances and the Amazon forest carbon balance.

Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7 Pg C y(-1) over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2 Pg C y(-1), and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004 Pg C y(-1). Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.

Export-oriented deforestation in Mato Grosso: harbinger or exception for other tropical forests?

The Brazilian state of Mato Grosso was a global deforestation hotspot in the early 2000s. Deforested land is used predominantly to produce meat for distal consumption either through cattle ranching or soya bean for livestock feed. Deforestation declined dramatically in the latter part of the decade through a combination of market forces, policies, enforcement and improved monitoring. This study assesses how representative the national-level drivers underlying Mato Grosso's export-oriented deforestation are in other tropical forest countries based on agricultural exports, commercial agriculture and urbanization. We also assess how pervasive the governance and technical monitoring capacity that enabled Mato Grosso's decline in deforestation is in other countries. We find that between 41 and 54 per cent of 2000-2005 deforestation in tropical forest countries (other than Brazil) occurred in countries with drivers similar to Brazil. Very few countries had national-level governance and capacity similar to Brazil. Results suggest that the ecological, hydrological and social consequences of land-use change for export-oriented agriculture as discussed in this Theme Issue were applicable in about one-third of all tropical forest countries in 2000-2005. However, the feasibility of replicating Mato Grosso's success with controlling deforestation is more limited. Production landscapes to support distal consumption similar to Mato Grosso are likely to become more prevalent and are unlikely to follow a land-use transition model with increasing forest cover.

Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment.

Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) - remotely estimated from LiDAR - control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.

Decoupling of deforestation and soy production in the southern Amazon during the late 2000s.

From 2006 to 2010, deforestation in the Amazon frontier state of Mato Grosso decreased to 30% of its historical average (1996-2005) whereas agricultural production reached an all-time high. This study combines satellite data with government deforestation and production statistics to assess land-use transitions and potential market and policy drivers associated with these trends. In the forested region of the state, increased soy production from 2001 to 2005 was entirely due to cropland expansion into previously cleared pasture areas (74%) or forests (26%). From 2006 to 2010, 78% of production increases were due to expansion (22% to yield increases), with 91% on previously cleared land. Cropland expansion fell from 10 to 2% of deforestation between the two periods, with pasture expansion accounting for most remaining deforestation. Declining deforestation coincided with a collapse of commodity markets and implementation of policy measures to reduce deforestation. Soybean profitability has since increased to pre-2006 levels whereas deforestation continued to decline, suggesting that antideforestation measures may have influenced the agricultural sector. We found little evidence of direct leakage of soy expansion into cerrado in Mato Grosso during the late 2000s, although indirect land-use changes and leakage to more distant regions are possible. This study provides evidence that reduced deforestation and increased agricultural production can occur simultaneously in tropical forest frontiers, provided that land is available and policies promote the efficient use of already-cleared lands (intensification) while restricting deforestation. It remains uncertain whether government- and industry-led policies can contain deforestation if future market conditions favor another boom in agricultural expansion.