Monitoring the demography and diversity of forests undergoing restoration using a drone-lidar-hyperspectral system

• Grant number: 19/14697-0
• Support type: Scholarships abroad - Research Internship - Post-doctor
• Effective date (Start): October 01, 2019
• Effective date (End): September 30, 2020
• Field of knowledge: Agronomical Sciences - Forestry Resources and Forestry Engineering
• Principal Investigator: Pedro Henrique Santin Brancalion
• Grantee: Danilo Roberti Alves de Almeida
• Supervisor abroad: Eben North Broadbent
• Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Local de pesquisa : University of Florida, Gainesville (UF), United States
• Associated to the scholarship: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using LIDAR and hyperspectral remote sensing, BP.PD

Abstract

The accountability of ambitious forest and landscape restoration pledges rely on the development of cost-effective methods for characterizing and monitoring undergoing restoration. New remote sensing tools, such as lidar and hyperspectral sensors, have demonstrated great potential for achieving these goals, yet the estimation of tree demography and diversity by these technologies is still limited. Drone-based remote sensing is also a promising innovative technology that may combine the benefits of ground-based and satellite-derived monitoring by providing fine-scale data over large areas at a relatively low cost. However, data processing and analysis methodologies still need to be refined and calibrated for the success of using these technologies to monitor forest and landscape restoration. The objective of this study is to estimate tree demography and diversity of forests undergoing restoration using a drone-lidar-hyperspectral system. We will use a database collected in several mixed forest plantations, monoculture plantations (at least ten different species), agroforests, and second-growth forests established within an experimental station in São Paulo state. We expected to produce two scientific papers and contribute significantly to the development of forest restoration monitoring by advancing the capacity of this new drone-lidar-hyperspectral technology for detecting detailed ecological information.