Multi-criteria analysis on mapping of areas for mechanized forest harvesting

• Autor(a) principal: Stanley Schettino
• Data de Publicação: 2019
• Outros Autores: Luciano José Minette, Fabricio Silva, Isabela Dias Reboleto, Ítalo Lima Nunes, Carolina Freitas Schettino
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: http://hdl.handle.net/1843/47504
• topic: Forest harvesting planning; Fuzzy logic; Decision support system; Colheita florestal; Mecanização florestal; Eucalipto; Lógica difusa; Sistemas de suporte de decisão; Análise hierárquica (Psicologia)

This study aimed to test the method of multi-criteria analysis, in the platform of the Geographic Information System (GIS), to perform mapping in levels of suitability for the mechanized harvesting of eucalyptus forests. The study was carried out using eucalyptus stands for cellulose production in the State of Minas Gerais, Brazil. The main factors that influenced the mechanized forest harvesting were determined, as well as the technical and environmental constraints. The quantitative factors (declivity, productivity of the plots, volume per tree, age of planting and number of trees per hectare) were standardized using fuzzy logic. To combine the factors, weights for each of them were established through the AHP (Analytic Hierarchy Process) technique. From these weights, Weighted Linear Combination (WLC) was performed and a map of suitability for mechanized forest harvesting was generated. The mapping process allowed to classify the forest area into five suitability classes: very low (0.2%); low (3.3%); average (15.2%); high (53.0%) and very high (28.3%). The declivity factor was the criterion that most influenced the spatialization in the areas of suitability for forest harvesting with the harvester. The method of multi-criteria analysis has been shown to be an efficient tool to create maps of suitability for the operation with the harvester and to support wood harvest planning by identifying areas that tend to have lower or higher productivity.