Remote sensing application in forest monitoring and climate changes

• Ano de defesa: 2023
• Autor(a) principal: Haneda, Léo Eiti
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Degradação florestal; Forest degradation; Forest types; Imagem orbital multiespectral; LiDAR; Multispectral orbital images; Tipologias florestais
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11150/tde-03102023-153512/

Remote sensing technologies have made significant advancements in recent decades, with the introduction of new sensors and data manipulation techniques that allow us to observe forests in previously inaccessible ways. With these advancements, there are high expectations for these technologies to address the challenges posed by climate change. This master\'s thesis consists of two chapters, one using a passive sensor and the other using an active sensor. The first chapter investigates the potential of high-resolution multispectral satellite imagery and different data manipulation techniques for monitoring forest landscapes and classifying different forest types, with the aim of supporting landscape forest restoration programs. The second chapter focuses on the use of LiDAR data for monitoring degradation in REDD+ projects at a local level, aiming to explore the applications of this technology in forest monitoring and conservation. Our results have shown the great potential of remote sensing technologies in addressing various issues related to climate change mitigation, both for forest restoration and conservation. However, further work needs to be done to develop robust and replicable methodologies that allow remote sensing technologies to play a key role in overcoming the significant challenges posed by climate change.