Objetivou-se quantificar os teores de C nos reservatórios da biomassa vegetal de diferentes espécies da Caatinga, floresta tropical seca do Nordeste do Brasil. Foi realizado um estudo florístico e fitossociológico prévio e selecionadas nove espécies de maior densidade absoluta, que representou 87% do fragmento. Foram amostradas biomassa dos compartimentos folhas e lenho de cada uma das espécies para determinação do teor de C. Como resultado, houve diferença nos teores foliares de C entre as espécies. O teor de C foliar das espécies variou entre 448,0 e 454,3 g C por kg de biomassa seca e o teor de C do lenho das espécies variou entre 451,4 e 453,1 g C por kg de biomassa seca. Esses resultados sugerem a utilização de fator de conversão de biomassa para carbono específico por espécie em Caatinga, como alternativa ao fator genérico de 50% recomendado pelo IPCC. Esta pesquisa revela que o uso do fator recomendado pelo IPCC superestima estoques de C em Caatinga. Mais estudos são encorajados para confirmar esse resultado.

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R² = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.

Ilex paraguariensis is an important non-timber forest product in southern Brazil, where it is cultivated in association with native species, given its demand for shading, which contributes to the conservation and carbon fixation in its biomass. However, determining this biomass is difficult, since the estimates do not guarantee additivity between the compartments and the total aboveground biomass. This study aimed to evaluate additive (seemingly unrelated regression - SUR) and non-additive (ordinary least squares - OLS) biomass models in an Ilex paraguariensis stand and comparing its carbon stock with other land use types, consolidating its potential in the face of climate change. A total of 30 trees were cut, compartmentalized and weighed on a digital scale, and four biomass models were adjusted. The carbon stocks were compared with values found in the literature. The bias in the SUR model was less than 2 %, except for the leaves, while the bias in the OLS model varied between 1 and 14 %. The error ranged between 23 and 49 % for SUR, and between 31 and 50 % for OLS. The models adjusted by SUR ensured the accuracy and additivity of the compartments. The Ilex paraguariensis stand stored more carbon than agriculture and pasture areas, removing more CO2, evidencing the sustainability of this system and favoring the climate stability.

ESTIMATIVA DO AFILAMENTO DO FUSTE DE INDIVÍDUOS DE EUCALIPTO POR MEIO DE TÉCNICAS DE INTELIGÊNCIA ARTIFICAL