No abstract

No abstract

No abstract

In Amazonian native forest management, forest road infrastructures, such as log storage yards and skid trails, are the most expensive attributes and are responsible for the significant environmental impacts with selective tree felling. Road optimization is crucial for reducing environmental impacts and production costs and is strongly linked to the optimal location of storage yards, which are essential to forest road planning. Considering the present problem and the current solutions available, this case study aimed to evaluate the efficiency and eventual gains of optimized forest planning (OFP), as compared to traditional forest planning (TFP). The TFP method is currently used most frequently by forest companies in the Amazon region. The study area of 126.41 ha belongs to the National Forest (NAFO) Saracá-Taquera, Forest Management Unit II (FMU-II), Annual Production Unit (APU) 04/2018, and Work Unit (WU) 2. For the analysis, two areas were defined for exploration: the first was explored using the OFP mathematical model (57.75 ha) and the second followed the TFP plan of the company (68.66 ha). Plans and executions for both areas were compared. The OFP model significantly reduced the Euclidean distances between tree and yards, with only 0.23 km difference in the amount of planned forest roads, when compared to TFP. Additionally, OFP demonstrated a higher productivity (trees.h -1 and m 3 .h -1 ), a reduction of skid distance (by an average of 17.16%), and reduced the cost of log skidding (m 3 by 25.76%). Thus, this study proved that OFP is a viable solution that can be adopted by companies to increase productivity. Selective logging is intended to only remove trees of commercial value. Its use is associated with forest management techniques and is referenced in the current legislation (Implementing rule no.

This paper aimed to estimate the basic density (DB) of the wood of Cerrado species using mixed linear models. For performing the DBH measurement, the sampling of 334 individuals was carried out. By keeping the Pilodyn apparatus in the DBH position, two measurements were made on opposite sides. Further, for determining DB, the trees were knocked down, followed by removal of five wood discs at different height of stem positions. For this purpose, two sets of modeling alternatives were proposed, which take into account with and without random effects, employing species as a random effect grouping variable. Thus, it was elucidated that, for the estimation of DB, the mixed model that considered the random effects performed better as compared to the alternative model without random effects. The inclusion of random effects leads to the estimation of DB with high accuracy.

A densidade básica da madeira é uma propriedade importante, pois está relacionada ao produto final nos diversos usos que a madeira possui. Porém, sua determinação demanda tempo e custos, o que justifica o emprego de técnicas mais refinadas para a sua estimação, como as redes neurais artificias (RNA). Objetivou-se avaliar a utilização das RNA para estimar a densidade básica de espécies do cerrado sensu stricto com o uso do aparelho Pilodyn e variáveis dendrométricas. Para comparação dos resultados obtidos pelas RNA, foram ajustados modelos de regressão. A rede neural com melhor desempenho foi a que utilizou como variáveis de entrada a profundidade de penetração (Pilodyn), espécie e o DAP, apresentando valores de R² de 0,72 e com raiz do erro quadrado médio em porcentagem (RMSE%) de 5,69. Já o modelo de regressão apresentou valor de R² de 0,72 e RMSE% de 9,19. As redes neurais artificiais conseguem estimar a densidade básica da madeira de espécies do cerrado stricto sensu com resultados satisfatórios.

The maintenance of biodiversity is a global concern in economic, social and environmental terms. Thus, conservation units for the protection of natural environments were created. Despite the importance of these areas, forest fires have caused immeasurable and constant damage to Conservation Units. In view of this, the objective of this study was to determine the risk areas of forest fire occurrence through Fuzzy logic modeling in the Córrego Grande Biological Reserve, located in the Mata Atlântica Brazilian biome. In order to prospect for areas at risk of forest fires, the following variables were inserted in the model: land use, road network, slope and relief orientation. Finally, the model was validated by comparing the location of fire occurrences between the years 2008 and 2018, and a layout of the risk classes in the study area. In doing so, it was found that 65.87% of the area is between the 'moderate' and 'very high' range of fire risk classes, and that 70.22% of the fires which occurred in the studied period also occurred in that class range. The study concluded that the most influential variable on the risk level of fire occurrence is the forest road network. In this way, the proposed methodology can be applied to any other areas and types of land cover.

The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.