Danilo Simões

Wildfires have intensified in several regions worldwide, and the Brazilian Pantanal has become increasingly vulnerable due to the combined effects of human activities and climate change. This study analyzed the spatiotemporal patterns of burned areas and burn severity in the Brazilian Pantanal over 39 years (1985–2023), integrating burned-area dynamics, land use and land cover information, and hydroclimatic variables. Burned areas were quantified using MapBiomas Fire Project data, including annual burned areas, affected land use and land cover classes, seasonal fire distribution, fire-scar size, and fire recurrence. Burn severity was assessed using the Differenced Normalized Burn Ratio (ΔNBR), and hydroclimatic trends were evaluated using the Mann–Kendall test. The largest burned areas occurred in 1999 (27,260.65 km²) and 2020 (25,602.65 km²), with grassland representing the most affected land use and land cover class throughout the historical series. Fires were concentrated during the late dry season, and recurrent burning was more evident in the southwestern Pantanal and in smaller northern areas. The 2020 fire season showed the greatest extent of high-, moderate–high-, and moderate–low-severity classes. Wildfire occurrence, recurrence, extent, and severity were associated with hydroclimatic variability, especially reduced precipitation and relative humidity and increased air and land surface temperatures. These findings provide a long-term basis for understanding changes in fire regimes in the Brazilian Pantanal and can support fire management, ecological restoration, biodiversity conservation, and climate adaptation strategies.

Os processos logísticos florestais representam mais de 50% dos custos de produção, o que reforça a necessidade de otimizar essas etapas. O planejamento da rede viária deve seguir princípios de otimização, especialmente por meio da utilização de modelos de Densidade Ótima de Estradas. No entanto, esses modelos raramente consideram a capacidade de carga dos veículos e o Volume Médio Individual das Árvores. Este estudo teve como objetivo determinar a densidade ótima de estradas florestais em diferentes cenários que combinam o volume médio individual das árvores e as configurações veiculares de carga. A pesquisa foi conduzida no município de Ribas do Rio Pardo, Mato Grosso do Sul, Brasil. A Densidade Ótima de Estradas foi determinada por meio da minimização da soma dos custos operacionais, custos de colheita florestal, custos de construção e manutenção de estradas, e da perda de área produtiva. A análise considerou cenários com volumes médios individuais de 0,15; 0,20 e 0,25 m³.árvore⁻¹, associados às configurações veiculares bitrem, tritrem e hexatrem. Os custos operacionais foram estimados com base na colheita florestal mecanizada, enquanto os custos das estradas foram calculados somando-se os valores de implantação e manutenção do pavimento. Já os custos relacionados à perda de área produtiva foram derivados do valor da terra ocupada pelas estradas e da receita estimada com o uso efetivo da área. A variação da Densidade Ótima de Estradas entre os cenários avaliados foi de 19,57 a 27,61 m·ha⁻¹, correspondendo ao menor volume médio (0,15 m³.árvore⁻¹, com hexatrem) e ao maior volume médio (0,25 m³.árvore⁻¹, com bitrem). No cenário de 0,15 m³.árvore⁻¹, as densidades ótimas foram de 25,57 e 23,68 m·ha⁻¹ para bitrem e tritrem, respectivamente. Para o cenário de 0,20 m³.árvore⁻¹, os valores foram de 26,16; 24,46 e 20,45 m·ha⁻¹ para bitrem, tritrem e hexatrem, respectivamente. Por fim, no cenário de 0,25 m³.árvore⁻¹, as densidades ótimas foram de 26,91 e 22,06 m·ha⁻¹ para tritrem e hexatrem, respectivamente. Concluímos que a densidade ótima de estradas varia conforme o volume médio individual das árvores e a capacidade de carga dos veículos, sendo que menores volume médio individual das árvores combinados com veículos de maior capacidade resultaram em menores valores de densidade ótima de estradas.

The Brazilian Amazon is recognized worldwide for its biodiversity and it plays a key role in maintaining the regional and global climate balance. However, it has recently been greatly impacted by changes in land use, such as replacing native forests with agricultural activities. These changes have resulted in serious environmental consequences, including significant alterations to climate and hydrological cycles. This study aims to analyze changes in land use and land covered in the Brazilian Amazon between 2001 and 2023, as well as the resulting effects on precipitation variability, land surface temperature, and evapotranspiration. Data obtained via remote sensing and processed on the Google Earth Engine platform were used, including MODIS, CHIRPS, Hansen products. The results revealed significant changes: forest formation decreased by 8.55%, while agricultural land increased by 575%. Between 2016 and 2023, accumulated deforestation reached 242,689 km². Precipitation decreased, reaching minimums of 772.7 mm in 2015 and 726.4 mm in 2020. Evapotranspiration was concentrated between 941 and 1360 mm in 2020, and surface temperatures ranged between 30 °C and 34 °C in 2015, 2020, and 2023. We conclude that anthropogenic transformations in the Brazilian Amazon directly impact vegetation cover and the regional climate. Therefore, conservation and monitoring measures are essential for mitigating these effects.

The objective of this study was to evaluate the influence of the mean individual volume per tree (MIV) on the productivity of forwarder machines and the production cost in eucalyptus plantations located in southern Bahia, Brazil. MIV positively influenced the productivity and production costs, promoting a more attractive cost in the latter when the individual volume per tree increased. The machine’s productivity for MIV of 0.13 m³ was 42.06 cubic meters per effective working hour (m³Ewh⁻¹), while the productivity for the MIV of 0.58 m³ reached 60.97 m³Ewh⁻¹, corresponding to an increase of 42.59% between the minimum and maximum MIV classes. The extracted cost (m³) decreased by 30.12% from USD 2.49 to 1.74, respectively, when comparing the minimum and maximum MIV classes. The coefficient of determination obtained in the forwarder productivity modeling was significant (R² = 92%), indicating the machine’s productivity can be explained by the mean individual volume per tree. The highest forwarder yields in the highest average volume per tree (MIV) classes provided better energy efficiency indices for the machine; that is to say, when the forwarder became more productive, the ratio between fuel consumption per cubic meter of timber harvested decreased, providing better performance for the respective index. There was a difference in extraction costs of USD 147.83 per hectare between the lowest and highest productivity forests (MIV varying from 0.15 to 0.58). The mechanical availability and mean operational efficiency of all forwarders evaluated were above 80%, which contributed to effective machine productivity performance. Maintenance and repairs represented the largest portion of operational costs (33.59%), followed by labor (22.49%), depreciation (14.33%), and fuel (10.11%).

One important element influencing the efficiency of automated timber harvesting is harvester maintenance. However, the understanding of this effect is limited, which can lead to more frequent harvest interruptions and consequently higher production costs. Data modeling can be used to evaluate how mechanical aspects affect harvester maintenance in plantation forests, which can help with forest planning. This study aimed to ascertain if mechanical harvester characteristics may be utilized to develop a high-performance model capable of properly forecasting harvester maintenance using machine learning. A free web application to help forest managers implement the approach was also developed as part of the study. For the modeling, we considered eight mechanical features and the mechanical status as the target feature. In default mode, we ran 25 popular algorithms through the database and compared them based on accuracy and error metrics. Although the combination models performed well, the Random Forest model performed better in the default mode with an accuracy of 0.933. In addition, the generated model makes it possible to create a harvester maintenance prediction tool that provides a quick visualization of the mechanical status feature and can help forest managers make informed decisions. Along with the data from the experimental research, we will make available the complete file containing the predictive model, as well as the software, both developed in the Python language.

Recent innovations in wood harvesting, with a view to improving forest operations techniques, have allowed operators to be pioneers in the use of new technologies. On the other hand, there is the emergence of a new class of occupational diseases related to musculoskeletal disorders that may be associated with this advance. In this way, it was evaluated whether operators of self-propelled forest machines of the full tree system are susceptible to musculoskeletal discomfort at levels of intensity that are harmful to occupational health. The Nordic Musculoskeletal Questionnaire was applied to identify the body parts with the highest reports of discomfort and the degree of intensity of these discomforts indicated by feller buncher, grapple skidder and grapple saw operators in Eucalyptus planted forests. The body regions with reports of discomfort were the lower back and shoulder regions, with a moderate degree of intensity. The grapple skidder is the self-propelled forestry machine with the highest number of operators experiencing discomfort during work.

Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−0.69 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development.

Reduced-impact logging is a key aspect of sustainable forest management in the Brazilian Amazon. Real Options Analysis (ROA) improves the evaluation of certified projects, increases their value, and supports investor confidence. This study evaluates the economic viability of a forest management investment project in certified areas of the Brazilian Amazon, using ROA to incorporate uncertainty and managerial flexibility into the decision-making process. The analysis focused on an Ombrophilous Dense Forest in its first cutting cycle, with 30 species of diameter at breast height ≥ 50 cm and a projected activity period of 30 years. Timber value was modeled using the mean reversion motion, and a binomial decision model was applied, incorporating options such as deferral, abandonment, and interruption. The deferral option should be exercised with a 100% probability, both in the joint calculation scenario and in separate analyses, highlighting the significant value of this managerial flexibility. In addition, when analyzed using ROA, the value of the project increased by 105% (USD 91,784,176) compared to the traditional net present value approach. The forest management investment project in the Brazilian Amazon is economically viable and the application of ROA reveals significant value added, demonstrating the importance of incorporating flexibility into investment decisions.

Background: In the context of mechanized timber harvesting, alterations in technical parameters, such as skidding distance and terrain slope, have the potential to influence the productivity and production costs associated with the self-propelled grapple skidder. Furthermore, these variables are inherently uncertain, which could potentially cause forest managers to make inaccurate decisions. The objective was to analyze whether four skidding distances and two slope classes influence the productivity and production costs of the grapple skidder in Eucalyptus planted forests from a stochastic perspective using the Monte Carlo method. Methods: Productivity was estimated using the time study protocol. To calculate the cost per scheduled hour of the grapple skidder, both fixed and variable costs were considered, and subsequently, the production cost was determined. Results: The mean productivity of the grapple skidder on flat slopes was 114.35 m³ h⁻¹, while on wavy to strong wavy slopes it was 80.43 m³ h⁻¹. In flat slopes, considering all skid distance ranges, the mean production cost was 0.82 USD m⁻³, while in wavy to strong wavy slopes it was 1.48 USD m⁻³. The mean values for operator labor costs and fuel account for 58.1% of the cost per scheduled hour of the grapple skidder. Conclusions: The mean productivity of the grapple skidder in Eucalyptus planted forests decreased with increasing skidding distance in both slope classes but was 29.7% lower on wavy to strong wavy slopes compared to flat slopes. The mean production cost of the grapple skidder during timber skidding on flat slopes is 80.0% lower than on wavy to strong wavy slopes. For future investigations, the impact of other slope classes, skid distances, and silvicultural aspects on productivity and production costs can be considered from a stochastic perspective using the Monte Carlo method.