@misc{jesus franca_acerbi junior_silva_ussi monti_ferreira_oliveira santana_gomide_2022, title={Forest landscape planning and management: A state-of-the-art review}, volume={8}, ISSN={["2666-7193"]}, DOI={10.1016/j.tfp.2022.100275}, abstractNote={Visual environmental aesthetics as a combinatorial output of a mathematical model can enhance public acceptance of forest activities and increase the perception of sustainability of forest enterprises. This article provides a comprehensive review of the state of the art in landscape management in forest areas worldwide. In forest planning, little research has examined how the visual impact management on wood production can be compatible with the economic viability of forest enterprises. With this review, we seek to contextualize the problem, listing the challenges, trends, and advances achieved recently. The first part of the review is devoted to considerations about the following: (i) landscape management in forested areas, with a history of the landscape planning in major global regions; and (ii) spatial forest planning, including operational research, forest optimization, and GIS to solve problems at the landscape scale. In the second part, we present a bibliometric survey to statistically examine the growth of the landscape planning between 1980 and 2021. The number of studies related to the topic has increased, especially in the last decade. North America and Europe are the regions with the highest scientific production in forest landscape planning and management. There is still little research dedicated to landscape management in commercially planted forests. The approach in the form of spatial structure, considering the inclusion of multi-objective restrictions and functions, is a desirable evolution in the planning and management of sustainable forest plantations.}, journal={TREES FORESTS AND PEOPLE}, author={Jesus Franca, Luciano Cavalcante and Acerbi Junior, Fausto Weimar and Silva, Carolina Souza and Ussi Monti, Cassio Augusto and Ferreira, Thais Cunha and Oliveira Santana, Cesar Junio and Gomide, Lucas Rezende}, year={2022}, month={Jun} }
 @article{monti_oliveira_roise_scolforo_gomide_2022, title={Hybrid Method for Fitting Nonlinear Height-Diameter Functions}, volume={13}, ISSN={["1999-4907"]}, url={https://www.mdpi.com/1999-4907/13/11/1783}, DOI={10.3390/f13111783}, abstractNote={Regression analysis is widely applied in many fields of science to estimate important variables. In general, nonlinear regression is a complex optimization problem and presents intrinsic difficulties in estimating reliable parameters. Nonlinear optimization algorithms commonly require a precise initial estimate to return reasonable estimates. In this work, we introduce a new hybrid algorithm based on the association of a genetic algorithm with the Levenberg–Marquardt method (GALM) to adjust biological nonlinear models without knowledge of initial parameter estimates. The proposed hybrid algorithm was applied to 12 nonlinear models widely used in forest sciences and 12 databases under varying conditions considering classic hypsometric relationships to evaluate the robustness of this new approach. The hybrid method involves two stages; the curve approximation process begins with a genetic algorithm with a modified local search approach. The second stage involves the application of the Levenberg–Marquardt algorithm. The final performance of the hybrid method was evaluated using total fitting for all tested models and databases, confirming the reliability of the proposed algorithm in providing stable parameter estimates. The GA was able to predict the initial parameters, which assisted the LM in converging efficiently. The developed GALM method is effective, and its application is recommended for biological nonlinear analyses.}, number={11}, journal={FORESTS}, author={Monti, Cassio Augusto Ussi and Oliveira, Rafael Menali and Roise, Joseph Peter and Scolforo, Henrique Ferraco and Gomide, Lucas Rezende}, year={2022}, month={Nov} }
 @article{miranda_groenner barbosa_godinho silva_ussi monti_tng_gomide_2022, title={Variable selection for estimating individual tree height using genetic algorithm and random forest}, volume={504}, ISSN={["1872-7042"]}, DOI={10.1016/j.foreco.2021.119828}, abstractNote={Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables.}, journal={FOREST ECOLOGY AND MANAGEMENT}, author={Miranda, Evandro Nunes and Groenner Barbosa, Bruno Henrique and Godinho Silva, Sergio Henrique and Ussi Monti, Cassio Augusto and Tng, David Yue Phin and Gomide, Lucas Rezende}, year={2022}, month={Jan} }
 @article{santos_monti_carvalho_lacerda_schwerz_2021, title={Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Koppen-Geiger climate classification system}, volume={45}, ISSN={["1981-1829"]}, DOI={10.1590/1413-7054202145023920}, abstractNote={ABSTRACT Air temperature significantly affects the processes involving agricultural and human activities. The knowledge of the temperature of a given location is essential for agricultural planning. It also helps to make decisions regarding human activities. However, it is not always possible to determine this variable. It is necessary to make a precise estimate, using methods that are capable of detecting the existing variations. The aim of this study was to develop models of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) to estimate the mean (Tmean), maximum (Tmax), and minimum (Tmin) monthly air temperatures as a function of geographic coordinates and altitude for different localities in Minas Gerais state, Brazil, with climatic classification Cwa or Cwb. The average monthly data (Tmean, Tmax, and Tmin), over a period of 30 years, were collected from 20 climatological stations. The MLR was able to estimate the Tmax with accuracy. However, the predictive capacity of estimating Tmean and Tmin was low. The algorithms RF and ANN were used to estimate Tmean, Tmax, and Tmin with high accuracy. The best results were obtained using the RF model.}, journal={CIENCIA E AGROTECNOLOGIA}, author={Santos, Pietros Andre and Monti, Cassio Augusto Ussi and Carvalho, Luiz Gonsaga and Lacerda, Wilian Soares and Schwerz, Felipe}, year={2021} }
 @article{gomes_monti_silva_gomide_2021, title={Operational harvest planning under forest road maintenance uncertainty}, volume={131}, ISSN={["1872-7050"]}, DOI={10.1016/j.forpol.2021.102562}, abstractNote={We solve two related problems: i) the harvest scheduling problem and ii) the forest road maintenance scheduling problem. The main objective is to evaluate the effect of stochastic delays of forest road maintenance on forest harvesting. We built a control scenario to evaluate a deterministic model without road maintenance delay and measured the impacts of the delay through a stochastic programming model and simulations. The example used has 400 stands of planted Pinus sp. managed for pulp production. The considered road network is approximately 570 km. A deterministic programming model was formulated for the forest regulation problem, maximizing the number of harvested stands. A Monte Carlo simulation was applied to generate a random seed disturbance. In the tested instances, the number of stands that would have been harvested according to the deterministic schedule but were not harvested, due to delays in the maintenance of segments of the roads, varied from 1 to 400. The timber volume harvested over the planning horizon varied considerably, with periods in which the value was even zero. The stochastic model proposed can be useful to assist managers in decision making. In addition, the approach may help with road classification and reducing risks for better management practices.}, journal={FOREST POLICY AND ECONOMICS}, author={Gomes, Vanessa de Souza and Monti, Cassio Augusto Ussi and Silva, Carolina Souza Jarochinski e and Gomide, Lucas Rezende}, year={2021}, month={Oct} }
 @article{monti_gomide_oliveira_franca_2020, title={Optimization of Wood Supply: The Forestry Routing Optimization Model}, volume={92}, ISSN={["1678-2690"]}, DOI={10.1590/0001-3765202020200263}, abstractNote={The main purpose of this paper was to present the Forestry Routing Optimization Model (FRoM) as a version of the classical Vehicle Routing Problem (VRP). This work approaches for wood logistic problems consisting of simple displacement and multiple displacements of trucks toward the stands. The FRoM encompasses both steps into one single integer mixed linear programming model, considering cranes and trucks schedule, fleet reduction, reduction of overtime, reduction of half-load transportation, and approaching the minimum distance traveled along a fixed planning horizon. Some technique constraints were implemented to provide accurate model function. An executed real problem data was used to compare the outcomes. The objective was to carry and transport 21,881.82 tons of lumber from 10 stands using a total of 48 trucks and 5 cranes in a planning horizon of 6 days, which each day has 20 hours of effective work. The FRoM has performed a fleet reduction of 72.92%, eliminating overtime. It has reduced the half-load trips to the order of 3.17% of all routes. The crane's analysis allowed catching points of inefficiency due to operational idleness. The FRoM provided savings of 49.12% at all logistic costs. FRoM has shown to be a good option as a route optimizer for forestry logistics.}, number={3}, journal={ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS}, author={Monti, Cassio A. U. and Gomide, Lucas R. and Oliveira, Rafael M. and Franca, Luciano C. J.}, year={2020} }