Mind the gap: reconciling tropical forest carbon flux estimates from earth observation and national reporting requires transparency.

BACKGROUND: The application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be "managed" by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs. RESULTS: Between 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (- 0.2 GtCO2yr-1), while Brazil's NGHGI reported a net carbon source (+ 0.8 GtCO2yr-1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory's methodology, the EO net flux became a source of + 0.6 GtCO2yr-1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr-1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr-1; Global EO: + 0.2 GtCO2 yr-1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments. CONCLUSIONS: By adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification.

Cerrado deforestation threatens regional climate and water availability for agriculture and ecosystems.

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.

Optimizing biomass estimates of savanna woodland at different spatial scales in the Brazilian Cerrado: Re-evaluating allometric equations and environmental influences.

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.