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• The project 'Tropical Biomass & Carbon' and its main product TB&C App is introduced. • TB&C App is an user-friendly tool that estimates C stored in forests of Brazil. • Model performance analysis proved reliability >95% of the TB&C App's models. • Carbon in Amazonian forests can be accurately and precisely estimated. • A dataset containing 64,000 plots with forest variables is delivered as supplement. This article introduces the Tropical Biomass & Carbon Application – the 'TB&C App', a web application available on the permanent link www.tropicalbiomass.com. The TB&C App requires as input attributes 'the smallest and largest diameters', 'number of trees ha−1', basal area ha−1, and 'parameters of the diameter (beta) distribution' describing stand structure. The App delivers outputs at two levels: (1) Stand level, including mean aboveground biomass (AGB) and carbon (AGC), in Mg ha−1, along with confidence intervals (CIs) as measures of uncertainty, and; (2) Tree level estimates, with AGB and diameter for every simulated tree. Phase 1 of the project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila , and Savana. This article aims to (i) describe the algorithm written for the TB&C App, and (ii) present results of Phase 1. This first phase counts on a standardized database of 1,428 trees with field-measured dry AGB, from plots across the different formations, which is the largest tree-biomass database compiled so far in Brazil. Model uncertainties were incorporated into the modeling process, allowing computation of CIs through an uncertainty approach. The total variance of residuals of AGB was also modeled, aiming at predicting CIs as a function of the quantity of AGB. An analysis of reliability of the equations implemented in the TB&C App indicates that more than 95% (n = 64,000) of the true AGB's fit into the CI outputted by the TB&C App. A comparison with other approaches in the literature shows significant agreement with previous estimates and more conservative estimates where previously-published estimates disagreed with the TB&C App. We cite as advantages of the TB&C App; (i) reliability of the outputs, (ii) a user-friendly layout, (iii) AGB and AGC estimates provided along with robust CIs, and (iv) estimates at the stand and tree levels with consistent totals. A biomass dataset containing information on 64,000 plots is also delivered as supplement of this paper. [ABSTRACT FROM AUTHOR]

Mapping aboveground forest biomass is of fundamental importance for estimating CO2 emissions due to land use and land cover changes in the Brazilian Amazon. However, existing biomass maps for this region diverge in terms of the total biomass estimates derived, as well as in the spatial patterns of mapped biomass. In addition, no regional or location-specific measure of reliability accompanies most of these maps. In this study, 330 one-hectare plots from the RADAMBRASIL survey, acquired over and along areas adjacent to the state of Rondônia, were used to generate a biomass map over the entire region using geostatistics. The RADAMBRASIL samples were used to generate a biomass map, along with a measure of reliability for each biomass estimate at each location, using kriging with external drift with elevation, vegetation type and soil texture considered as biomass predictor variables. Cross-validation was performed using the sample plots to compare the performance of kriging against a simple biomass estimation using the sample mean. Overall, biomass varied from 225 to 486Mgha−1, with a local standard deviation ranging from 62 to 202Mgha−1. Large uncertainty values were obtained for regions with low sampling density, in particular in savanna areas. The geostatistical method adopted in this paper has the potential to be applied over the entire Brazilian Amazon region to provide more accurate local estimates of biomass, which would aid carbon flux estimation, along with measures of their reliability, and to identify areas where more sampling efforts should be concentrated. [Copyright &y& Elsevier]

In the context of the REVIZEE programme, an extensive sampling cruise was carried out in the northern section of the Brazilian Exclusive Economic Zone on board O.V. Antares (Operation North III). Several parameters were surveyed along the Amazon continental shelf during this cruise, such as horizontal and vertical distributions of temperature, salinity, dissolved oxygen, pH, dissolved inorganic nutrients, and phytoplankton biomass. Water with salinity below 33 psu and high silicate content was observed throughout the shelf, indicating the influence of the Amazon River plume up to a distance of 147 km from the coast; pH always remained alkaline. Water temperature was relatively homogeneous at the surface, with an average of 28°C. A permanent thermocline was detected in oceanic regions. Coastal waters were eutrophic, well oxygenated (maximum of 6.54 mL O2 L-1), and presented high chlorophyll a (maximum of 9.8 mg chlorophyll a m-3) and nutrient concentrations (up to 9.54 μmol L-1 NO3-, 0.89 μmol L-11 PO42-, and 108.59 μmol L-11 Si044-). Oceanic waters adjacent to the Amazon shelf presented high transparency (Secchi depth up to 35 m) and chlorophyll a (mean: 0.1 mg m-3) and low nutrient concentrations (maximum of 1.58 μmol L-1 NO3-1, 0.18 μmol L-1 PO42-, and 5.92 μmol L-1 Si04-4). Cluster analysis performed with all data showed a clear separation between coastal and oceanic waters at the surface layer and at 1% light penetration layers. This separation into two groups of stations clearly represents areas with different degrees of influence from the discharges of the Amazon Basin. The Amazon River is very important to the nutrient budgets of the shelf. [ABSTRACT FROM AUTHOR]