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An increase in atmospheric CO2 levels and global climate changes have led to an increased focus on CO2 capture mechanisms. The in situ quantification and spatial patterns of forest carbon stocks can provide a better picture of the carbon cycle and a deeper understanding of the functions and services of forest ecosystems. This study aimed to determine the aboveground (tree trunks) and belowground (soil and fine roots, at four depths) carbon stocks in a tropical forest in Brazil and to evaluate the spatial patterns of carbon in the three different compartments and in the total stock. Census data from a semideciduous seasonal forest were used to estimate the aboveground carbon stock. The carbon stocks of soil and fine roots were sampled in 52 plots at depths of 0-20, 20-40, 40-60, and 60-80 cm, combined with the measured bulk density. The total estimated carbon stock was 267.52 Mg ha-1, of which 35.23% was in aboveground biomass, 63.22% in soil, and 1.54% in roots. In the soil, a spatial pattern of the carbon stock was repeated at all depths analyzed, with a reduction in the amount of carbon as the depth increased. The carbon stock of the trees followed the same spatial pattern as the soil, indicating a relationship between these variables. In the fine roots, the carbon stock decreased with increasing depth, but the spatial gradient did not follow the same pattern as the soil and trees, which indicated that the root carbon stock was most likely influenced by other factors.

This study aimed to analyze relations between the distribution of tree species and variations in soil texture, fertility and organic matter levels in a rupestrian cerrado area located in Diamantina-MG. Individual trees with circumference at soil level (PC) ≥ 3 cm were sampled in 10 plots of 20x50m. Samples of surface soil (0.00-0.20 m of depth) were collected from each plot and analyzed for their chemical and physical properties. Through canonical correspondence analysis, Eremanthus incanus, Kielmeyera lathrophyton, Terminalia sp and Vochysia discolor show a stronger relation to higher potassium and remaining phosphorus levels; the species Xylopia sericea, Protium spruceanum and Protium heptaphyllum show a stronger relation to higher levels of organic matter and total cation exchange capacity; the species Roupala montana, Qualea grandiflora, and Erythroxylum suberosum grew uniformly in all plots. There is relation between species distribution and chemical and physical soil variables in the area.

Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus treesReducción de la intensidad de muestreo en inventarios forestales para estimar la altura total de eucaliptos