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The Brazilian Cerrado is one of the most biodiverse savannas in the world, yet 46% of its original cover has been cleared to make way for crops and pastures. These extensive land-use transitions (LUTs) are expected to influence regional climate by reducing evapotranspiration (ET), increasing land surface temperature (LST), and ultimately reducing precipitation. Here, we quantify the impacts of LUTs on ET and LST in the Cerrado by combining MODIS satellite data with annual land use and land cover maps from 2006 to 2019. We performed regression analyses to quantify the effects of six common LUTs on ET and LST across the entire gradient of Cerrado landscapes. Results indicate that clearing forests for cropland or pasture increased average LST by ~3.5°C and reduced mean annual ET by 44% and 39%, respectively. Transitions from woody savannas to cropland or pasture increased average LST by 1.9°C and reduced mean annual ET by 27% and 21%, respectively. Converting native grasslands to cropland or pasture increased average LST by 0.9 and 0.6°C, respectively. Conversely, grassland-to-pasture transitions increased mean annual ET by 15%. To date, land changes have caused a 10% reduction in water recycled to the atmosphere annually and a 0.9°C increase in average LST across the biome, compared to the historic baseline under native vegetation. Global climate changes from increased atmospheric greenhouse gas concentrations will only exacerbate these effects. Considering potential future scenarios, we found that abandoning deforestation control policies or allowing legal deforestation to continue (at least 28.4 Mha) would further reduce yearly ET (by -9% and -3%, respectively) and increase average LST (by +0.7 and +0.3°C, respectively) by 2050. In contrast, policies encouraging zero deforestation and restoration of the 5.2 Mha of illegally deforested areas would partially offset the warming and drying impacts of land-use change.O Cerrado brasileiro é uma das savanas mais biodiversas do mundo. Apesar disso, 46% da sua cobertura original foi desmatada para dar lugar a cultivos agrícolas e pastos. Estas extensas transições de uso do solo (LUT) têm o potencial de influenciar o clima regional, reduzindo a evapotranspiração (ET), aumentando a temperatura da superfície terrestre (LST) e por fim reduzindo a precipitação. O objetivo deste estudo foi quantificar os impactos de LUTs sobre ET e LST no Cerrado, combinando dados do satélite MODIS com mapas anuais de uso e cobertura do solo de 2006-2019. Foram realizadas análises de regressão para quantificar os efeitos de seis LUTs usuais sobre ET e LST, ao longo de todo o gradiente de paisagens do Cerrado. Os resultados indicaram que a retirada de florestas para dar lugar à agricultura ou pastagem aumentou a LST média em ~3.5°C e reduziu a ET média anual em 44% e 39%, respectivamente. Transições de formações savânicas para agricultura ou pastagem aumentaram a LST média em 1.9°C e reduziram a ET média anual em 27% e 21%, respectivamente. A conversão de campos nativos para agricultura ou pastagem aumentou a LST média em 0.9 e 0.6°C, respectivamente. Em contrapartida, transições de formações campestres nativas para pastagens aumentaram a ET média anual em 15%. Até o momento, as mudanças de uso do solo causaram redução de 10% da água reciclada para a atmosfera anualmente e aumento de 0.9°C da LST média ao longo do bioma, em comparação com a linha de base histórica sob vegetação nativa. As mudanças climáticas globais decorrentes do aumento das concentrações atmosféricas de gases do efeito estufa irão exacerbar esses efeitos. Considerando potenciais cenários futuros, observou-se que o abandono das políticas de controle do desmatamento ou o avanço do desmatamento legal (ao menos 28.4 Mha) reduziriam a ET anual (em −9% e −3%, respectivamente) e aumentariam a LST média (em +0.7 e +0.3ºC, respectivamente) até 2050. Por outro lado, políticas que promovam desmatamento zero e restauração dos 5.2 Mha de áreas ilegalmente desmatadas compensariam parte dos impactos de aquecimento e seca causados por alterações de uso do solo.

Climate change is a global phenomenon that affects biophysical systems and human well-being. The Paris Agreement of the United Nations Framework Convention on Climate Change entered into force in 2016 with the objective of strengthening the global response to climate change by keeping global temperature rise this century well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 °C. The agreement requires all Parties to submit their “nationally determined contributions” (NDCs) and to strengthen these efforts in the years ahead. Reducing carbon emissions from deforestation and forest degradation is an important strategy for mitigating climate change, particularly in developing countries with large forests. Extensive tropical forest loss and degradation have increased awareness at the international level of the need to undertake large-scale ecological restoration, highlighting the need to identify cases in which restoration strategies can contribute to mitigation and adaptation. Here we consider Brazil as a case study to evaluate the benefits and challenges of implementing large-scale restoration programs in developing countries. The Brazilian NDC included the target of restoring and reforesting 12 million hectares of forests for multiple uses by 2030. Restoration of native vegetation is one of the foundations of sustainable rural development in Brazil and should consider multiple purposes, from biodiversity and ecosystem services conservation to social and economic development. However, ecological restoration still presents substantial challenges for tropical and mega-diverse countries, including the need to develop plans that are technically and financially feasible, as well as public policies and monitoring instruments that can assess effectiveness. The planning, execution, and monitoring of restoration efforts strongly depend on the context and the diagnosis of the area with respect to reference ecosystems (e.g., forests, savannas, grasslands, wetlands). In addition, poor integration of climate change policies at the national and subnational levels and with other sectorial policies constrains the large-scale implementation of restoration programs. The case of Brazil shows that slowing deforestation is possible; however, this analysis highlights the need for increased national commitment and international support for actions that require large-scale transformations of the forest sector regarding ecosystem restoration efforts. Scaling up the ambitions and actions of the Paris Agreement implies the need for a global framework that recognizes landscape restoration as a cost-effective nature-based solution and that supports countries in addressing their remaining needs, challenges, and barriers.

The Brazilian Cerrado consists of a highly heterogeneous tropical savanna, and its biomass stocks are highly variable. Mapping and monitoring these stocks is not trivial. To address this challenge, we built an aboveground woody biomass (AGWB) model for the Cerrado biome using 30-m resolution optical satellite imagery (Landsat-5 and Landsat-8), 25-m resolution SAR imagery (ALOS and ALOS-2), and a set of plot-based and LiDAR-derived AGWB estimates. We implemented both a Classification and Regression Tree (CART) and a Random Forest (RF) algorithm to model AGWB over the native vegetation for the year 2019 in the Cerrado. The RF algorithm resulted in a slightly better result $(\mathrm{R}^{2}=53\%;\text{ rel. RMSE} =57\%)$ than, the CART model $(\mathbf{R}^{2}=45\%\ \text{ rel. RMSE} =63\% 1$ , but our map shows an underestimation of very high AGWB (negative bias over 200 t.ha-1) and a slight overestimation of low AGWB (positive bias).

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text Made available in DSpace on 2020-12-15T09:04:29Z (GMT). No. of bitstreams: 1 SANO-WOODY-ABOVEGROUND-BIOMASS-MAPPING-OF-THE-BRAZILIAN-SAVANNA.pdf: 7895412 bytes, checksum: d5c0e21e63f6269019a19c5dfd64005a (MD5) Previous issue date: 2020

The Brazilian Savanna, known as Cerrado (Cerrado sensu lato (s.l.)), is the second largest biome in South America. It comprises different physiognomies due to variations of soil, topography and human impacts. The gradients of tree density, tree height, above ground biomass (AGB) and wood species cover vary according to the Cerrado formation, ranging from different grassland formations (Campo limpo, campo sujo), savanna intermediary formations (Campo cerrado and Cerrado sensu stricto - s.s) and forest formations (Cerradão, Mata ciliar, Mata de galeria and Mata Seca).Although the carbon stock in Cerrado is lower than in the Brazilian Amazon, the conversion of this biome to other types of land use is occurring much faster. In the last ten years, the degradation of Cerrado forest was the second largest source of carbon emissions in Brazil. Therefore, effective methods for assessing and monitoring aboveground woody biomass and carbon stocks are needed. A multi-sensor Earth observation approach and machine learning techniques have shown potential for the large-scale characterization of Cerrado forest structure.The aim of this study is to present a method to estimate the AGB of an area of the Brazilian Cerrado using ALOS-PALSAR (L-band SAR), Landsat, LIDAR (LIght Detection And Ranging) and field datasets. Field data consisted of 15 plots of 1 ha area located in Rio Vermelho watershed in Goiás-State (Brazil). We used a 2-step AGB estimation (i) from the field AGB using LIDAR metrics and (ii) from LIDAR-AGB to satellite Earth Observation scales following a Random Forest regression algorithm. The methodology to estimate ABG of Cerrado Stricto Sensu vegetation is part of the Forests 2020 project which is the largest investment by the UK Space Agency, as part of the International Partnerships Programme (IPP), to support in the improvement of the forest monitoring in six partner countries through advanced uses of satellite data.

The Brazilian Cerrado biome consists of a highly heterogeneous tropical savanna, and is one of the world’s biodiversity hotspots. High rates of deforestation, however, place it as the second-largest source of carbon emissions in Brazil. Due to its heterogeneity, biomass and carbon stocks in the Cerrado vegetation are highly variable, and mapping and monitoring these stocks are not a trivial effort. To address this challenge, we built an aboveground woody biomass (AGWB) model for the Cerrado biome using 30-m resolution optical satellite imagery (Landsat-5 and Landsat-8), 25-m resolution SAR imagery (ALOS and ALOS-2), and a set of plot-based and LiDAR-derived AGWB estimates (n = 1858) from a wide network of researchers in Brazil. We implemented both a Classification and Regression Tree (CART) and a Random Forest (RF) algorithm to model AGWB over the native vegetation in the year 2019 (as classified by MapBiomas) in the Cerrado. The RF algorithms resulted in a slightly better result (R2 = 53%; rel. RMSE = 57%) than the CART model (R2 = 45%; rel. RMSE = 63%), but our map shows an underestimation of very high AGWB (negative bias over 200 t ha−1) and a slight overestimation of low AGWB (positive bias), especially in the RF model (bias of 1.19 t ha−1 against 0.86 t ha−1 for the CART model). We believe we have contributed to knowledge on the woody biomass stocks in the biome, especially in the predominant savanna woodlands, which is where the highest current rates of conversion take place in the Cerrado.

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass ( p -value = 0.31, r 2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter ( p -value = 0.004, r 2 = 0.51) and tree density ( p -value = 0.002, r 2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area ( p -value = 0.003, r 2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties.

Cerrado is the second largest biome in South America and accounted for the second largest contribution to carbon emissions in Brazil for the last 10 years, mainly due to land-use changes. It comprises approximately 2 million km2 and is divided into 22 ecoregions, based on environmental conditions and vegetation. The most dominant vegetation type is cerrado sensu stricto (cerrado ss), a savanna woodland. Quantifying variation of biomass density of this vegetation is crucial for climate change mitigation policies. Integrating remote sensing data with adequate allometric equations and field-based data sets can provide large-scale estimates of biomass. We developed individual-tree aboveground biomass (AGB) allometric models to compare different regression techniques and explanatory variables. We applied the model with the strongest fit to a comprehensive ground-based data set (77 sites, 893 plots, and 95,484 trees) to describe AGB density variation of cerrado ss. We also investigated the influence of physiographic and climatological variables on AGB density; this analysis was restricted to 68 sites because eight sites could not be classified into a specific ecoregion, and one site had no soil texture data. In addition, we developed two models to estimate plot AGB density based on plot basal area. Our data show that for individual-tree AGB models a) log-log linear models provided better estimates than nonlinear power models; b) including species as a random effect improved model fit; c) diameter at 30 cm above ground was a reliable predictor for individual-tree AGB, and although height significantly improved model fit, species wood density did not. Mean tree AGB density in cerrado ss was 22.9 tons ha-1 (95% confidence interval = ± 2.2) and varied widely between ecoregions (8.8 to 42.2 tons ha-1), within ecoregions (e.g. 4.8 to 39.5 tons ha-1), and even within sites (24.3 to 69.9 tons ha-1). Biomass density tended to be higher in sites close to the Amazon. Ecoregion explained 42% of biomass variation between the 68 sites (P < 0.01) and shows strong potential as a parameter for classifying regional biomass variation in the Cerrado.

Understanding structural variations in natural systems can help us understand their responses to disturbance and environmental changes and plan for the mitigation of human-induced impacts. Terrestrial laser scanning (TLS) is a technological solution to quickly and accurately capture and model vegetation structure. In the Brazilian Cerrado biome, characterized by highly heterogeneous plant formations and marked seasonality, TLS may help improve aboveground biomass (AGB) estimates. This study aimed to use single-scan TLS-derived metrics to predict plot-scale aboveground biomass for three vegetation types with high structural diversity and biomass content (woodland savanna, forested savanna, and gallery forest). Ten plots were scanned in each vegetation type, and variables related to point density at different strata and height distribution were extracted from the point cloud to predict AGB measured in local field inventories. The woodland savanna provided good fit models with only two metrics (rainy season: proportion of points below 1.37 m and height of the 99th percentile; adj-R2 = 0.92, RMSE (root mean square error) = 2.67 Mg/ha or 12%; and dry season: height of the 20th and the 99th percentiles; adj-R2 = 0.88, RMSE = 3.32 Mg/ha or 15%). The model for the forested savanna had relatively less explanatory power with one influential predictor (forested savanna: height of the 20th percentile; adj-R2 = 0.58, RMSE = 6.85 Mg/ha or 21%). For the gallery forest, however, the canopy structure could not be adequately characterized due to occlusion of laser returns by dense sub-canopy strata. Our study shows the strong potential of the terrestrial LiDAR technology for estimation of plot-based biomass across diverse savanna vegetation types, where the sparse tree structure allows for better laser penetration the accurate generation of height profiles.

We analyzed the 10-year record (1999-2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1-2 dB in areas of known large-scale deforestation, and increased by up to 1-2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = -0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = -0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds ( ~ 10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing.

The paper “Are Brazil deforesters avoiding detection?” recently published in Conservation Letters by Richards et al. 2016 has critical shortcomings and conclusions based on biased and not very robust analyses. Here we provide clarifications to some of the most critical points regarding the monitoring of land use changes in the Brazilian Amazon and related greenhouse emissions. Such clarifications are relevant to the readers of Conservation Letters and to a broader audience that rely on sound and robust science for a better management of environmental issues. This article is protected by copyright. All rights reserved

To effectively implement the Paris Agreement, capacity in carbon accounting must be strengthened in the developing world, and partnerships with local academic institutions can do the accounting for governments and fill the capacity gap. This paper highlights the Brazilian case, focusing on ways in which climate change science information and transparency are being incorporated in national C accounting initiatives, particularly the national inventory of greenhouse gas (GHG) emissions and removals. We report how the third inventory for the sector of land use, land-use change and forestry (LULUCF) was implemented to address scientific challenges involved in the monitoring of carbon stocks and land-use changes of diverse and complex biomes while addressing international and national policy demands (report and decision support) and transparency to various stakeholders. GHG emissions and removals associated with 2002–2010 carbon changes in aboveground, belowground biomass, necromass and soil carbon by land use and land cover changes were estimated for all Brazilian biomes, and for the Amazon estimates were also presented for the periods of 2002–2005 and 2005–2010. The inventory improved regional estimates for carbon stock and national emission factors with the support and engagement of the scientific community. Incorporation of local context is essential to reduce uncertainties and properly monitor efforts to contribute to GHG emission/reduction targets. To promote transparency and make information more accessible, the national inventory results were made available by the National Emissions Registry System (SIRENE). This system was built to support climate change policies as an important legal apparatus and by increasing access to emissions and land-use change data.