Automated operational logging plan considering multi-criteria optimization

• Published in: Computers and electronics in agriculture Vol. 170; p. 105253
• Main Authors: Görgens, Eric Bastos, Mund, Jan-Peter, Cremer, Tobias, de Conto, Tiago, Krause, Stuart, Valbuena, Ruben, Rodriguez, Luiz Carlos Estraviz
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-03-2020; Elsevier BV
• Subjects: Airborne lasers; Constrictions; Drainage; Elevation; Forest management; Forestry; Multiple criterion; Optimization; Soil conservation; Sustainable development
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2020.105253

•Field information is limited by the need to cover large and remote areas.•Multiple objectives should be considered while planning and executing forest management.•Forest management should be multi-functional, multipurpose and sustainable.•Novelty automated and multiobjective approach to forest management planning.
Multiple goals should be considered while planning and executing forest management. This study proposes a new multidimensional framework for a precision forestry approach assisted by airborne laser scanning data (ALS). Therefore, successful management operations become a key element in the process of promoting protection through sustainable development. Thirteen relevant variables were derived from ALS data, such as: canopy height, terrain topography, relative vegetation density, forest gaps, slope restrictions, skidder restrictions, load truck restrictions, topographic wetness, flow accumulation, horizontal distance to drainage, vertical elevation from drainage, stream and headspring restrictions. Four different scenarios for the management plan optimization were studied: shortest distance, forest conservation, soil conservation and all combined. Results showed that the detailed forest information from ALS point clouds is useful to indicate regions not suitable for forest operations. Failure to properly consider the different factors involved may result in inadequate infrastructures, lower operational performance and constant re-planning requirements.