[Fonte: ISA] O próximo seminário CEF/CEABN: Ecologia, Florestas e Conservação ocorrerá no dia 18 de Outubro, entre as 12h30 e as 13h30, no Auditório Florestal do Instituto Superior de Agronomia, com o título “Remote sensing of forest systems using new and emerging technology” por Kyle Kovack, University of Freiburg, Alemanha.
Abstract: Modern forestry as we know it utilizes many of the same techniques commonly practiced throughout the past century. Timber cruising, biomass estimation, plantation management, assessing tree physiology, insect, fire, and drought impact; the means of quantifying these metrics requires a huge investment of time, and large groups of highly trained individuals to be done accurately. Remote sensing has the potential to change this. Next generation technology implemented across spatial scales allows for high accuracy estimation of forest metrics, at temporal frequencies logistically impossible using previous methods. In this talk, Kyle Kovach (PhD Candidate – University of Freiburg) will discuss emerging methods for analyzing forest systems remotely, and cover the field of tools, from satellite, airborne, and drone solutions, to ground-based laser and spectral sensors, and in-situ monitoring networks. General application and viability of these tools for broad forestry applications will be covered. This will be a comprehensive overview, accessible to all levels of forestry focused individuals.
Biography:
Kyle Kovach is a PhD candidate in Forest Ecology working under Michael Scherer-Lorenzen and Charles Nock at the University of Freiburg, Germany. An American native, he holds a B.S. in Environmental Resource Management, a B.A. in Political Science, and an M.S. in Forest Resources from the Pennsylvania State University, USA. His thesis focuses on utilizing drone hyperspectral remote sensing for functional trait mapping in experimental forest systems. His interests include the use of high resolution hyperspectral and lidar data for forest biomass estimation, trait analysis, tree structure, and species classification, drone sensor and platform design and engineering, and the influence of tree species diversity on forest productivity.
