Quality of eucalyptus wood chips for bioenergy as a function of drying time and horizontal chipper settings.

The increase in the use of energy from forest biomass has increased the demand for knowledge about tree-chipping operations. Therefore, this study aimed to evaluate the effect of different drying times of wood logs and various combinations of knife and anvil configurations in the horizontal chipper on the quality of Eucalyptus dunnii chips produced for energy purposes. The study was conducted in a seven-year-old stand of E. dunnii. A horizontal chipper was used to chip whole trees and obtain nine types of chips, resulting from the interaction between the three drying times of the trees and three configurations of the horizontal chipper. The chips were characterized, followed by an evaluation of energy quality for comparison between the treatments. Among the outcomes discerned, it became evident that the employed wood exhibited a Basic Density (0.506 g.cm-³). The chip dimensions and bulk density presented notable disparities owing to the distinct chipper configurations and tree drying time. The extended drying period (150 days) conferred a lower average moisture content (34.20%) to the study materials. Moreover, the ash content was lower in the treatment with 150 drying days (0.52%). Both the useful calorific value and the Fuel Value Index were also greater in the treatment (150 drying days), measuring 2600.00 kcal kg-1 and 128.06 cal cm-3, respectively. In the analysis of the Fuel Value Index concerning chipper configurations, it was observed that for chips featuring a granulometry of 25 mm, the treatment involving 150 days of drying, four knives, and one shim proved the most efficient (Q = 0.979). Conversely, for chips with a granulometry of 16 mm, the treatment involving 150 days of drying, eight knives, and one shim emerged as the most efficient (Q = 0.970). Consequently, the proposed index is efficacious and underscores the necessity of adapting knife settings in response to moisture content changes to maintain the desired granulometry and apparent density standards.

Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest.

Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model's easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

Gis and fuzzy logic applied to modelling forest fire risk.

Fire risk mapping is a basic planning and protection element. This study presents the application of fuzzy logic in a geographic information system (GIS) as an alternative multi-criteria analysis for determining the areas of highest risk of forest fire in natural forest remnants in the Brazil. In the decision-making process, a set of factors that are relevant to fire safety were identified in the study area. For each input variable chosen for the model, a pertinence function was defined that best described its influence on fire risk. Subsequently, the variables were combined for the presentation of the final fire risk map. Concluded in the study that an increased risk of fire occurs at the wildland - urban interface. A strong relationship was observed between the fire ignition points and proximity to roads and urban areas. The proposed model was efficient to integrate the variables and determine areas of greatest risk.

Allocation of water reservoirs to fight forest fires according to the risk of occurrence.

Although forest fires are indispensable for some ecosystems, they can have profound economic, environmental, and social implications, especially when they reach high intensities. There are two crucial factors in fighting forest fires: the availability of water resources and the service network. The objective of this study was to propose an alternative methodology for allocating water reservoirs to fight forest fires. The research was divided into three stages: zoning of fire risk, delimitation of viable areas for the implementation of water reservoirs, and determining strategic locations for reservoir allocation. The variables analyzed were land use and occupation, provision of watercourses, relief orientation, slope, proximity to roads, temperature, and precipitation. Fuzzy logic, Euclidean distance, and network analysis were used as the modeling techniques. Scenarios with all risk classes and only the high- and very high-risk classes were analyzed. A total of 66% of the area was represented by the low- and moderate-risk fire classes and 53.16% had a low potential for reservoir allocation, influenced by the low availability of water resources in the area. The proposed model efficiently allocated the water collection points in the different scenarios, and allowed the determination of the areas most susceptible to the occurrence of forest fires and the optimal locations for the installation of reservoirs, with the allocation of 21 water reservoirs to attend the areas of high- and very high-risk of occurrence of fires at a safe speed (40 km h-1) and 47 reservoirs to meet all risk classes at the same speed. The proposed methodology is feasible, applicable, and adjustable and can be implemented in other conservation units and areas of economic interest.

Fuzzy logic applied to prospecting for areas for installation of wood panel industries.

Prospecting for suitable areas for forestry operations, where the objective is a reduction in production and transportation costs, as well as the maximization of profits and available resources, constitutes an optimization problem. However, fuzzy logic is an alternative method for solving this problem. In the context of prospecting for suitable areas for the installation of wood panel industries, we propose applying fuzzy logic analysis for simulating the planting of different species and eucalyptus hybrids in Espírito Santo State, Brazil. The necessary methodological steps for this study are as follows: a) agriclimatological zoning of different species and eucalyptus hybrids; b) the selection of the vector variables; c) the application of the Euclidean distance to the vector variables; d) the application of fuzzy logic to matrix variables of the Euclidean distance; and e) the application of overlap fuzzy logic to locate areas for installation of wood panel industries. Among all the species and hybrids, Corymbia citriodora showed the highest percentage values for the combined very good and good classes, with 8.60%, followed by Eucalyptus grandis with 8.52%, Eucalyptus urophylla with 8.35% and Urograndis with 8.34%. The fuzzy logic analysis afforded flexibility in prospecting for suitable areas for the installation of wood panel industries in the Espírito Santo State can bring great economic and social benefits to the local population with the generation of jobs, income, tax revenues and GDP increase for the State and municipalities involved. The proposed methodology can be adapted to other areas and agricultural crops.

GIS applied to location of fires detection towers in domain area of tropical forest.

In most countries, the loss of biodiversity caused by the fires is worrying. In this sense, the fires detection towers are crucial for rapid identification of fire outbreaks and can also be used in environmental inspection, biodiversity monitoring, telecommunications mechanisms, telemetry and others. Currently the methodologies for allocating fire detection towers over large areas are numerous, complex and non-standardized by government supervisory agencies. Therefore, this study proposes and evaluates different methodologies to best location of points to install fire detection towers considering the topography, risk areas, conservation units and heat spots. Were used Geographic Information Systems (GIS) techniques and unaligned stratified systematic sampling for implementing and evaluating 9 methods for allocating fire detection towers. Among the methods evaluated, the C3 method was chosen, represented by 140 fire detection towers, with coverage of: a) 67% of the study area, b) 73.97% of the areas with high risk, c) 70.41% of the areas with very high risk, d) 70.42% of the conservation units and e) 84.95% of the heat spots in 2014. The proposed methodology can be adapted to areas of other countries.

Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil.

A forest fire risk map is a basic element for planning and protecting forested areas. The main goal of this study was to develop a statistical model for preparing a forest fire risk map using GIS. Such model is based on assigning weights to nine variables divided into two classes: physical factors of the site (terrain slope, land-use/occupation, proximity to roads, terrain orientation, and altitude) and climatic factors (precipitation, temperature, water deficit, and evapotranspiration). In regions where the climate is different from the conditions of this study, the model will require an adjustment of the variables weights according to the local climate. The study area, Espírito Santo State, exhibited approximately 3.81% low risk, 21.18% moderate risk, 30.10% high risk, 41.50% very high risk, and 3.40% extreme risk of forest fire. The areas classified as high risk, very high and extreme, contemplated a total of 78.92% of heat spots.