Uma abordagem de aprendizagem de máquina para o mapeamento da vegetação florestal em zonas ripárias em um ambiente de bioma atlântico usando imagens sentinel-2

• Ano de defesa: 2021
• Autor(a) principal: FURUYA, Danielle Elis Garcia
• Orientador(a): Ramos, Ana Paula Marques
• Banca de defesa: Ramos, Ana Paula Marques , Silva, Paulo Antônio da , Oliveira, Renan Furlan de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Oeste Paulista
• Programa de Pós-Graduação: Mestrado em Meio Ambiente e Desenvolvimento Regional
• Departamento: Mestrado em Meio Ambiente e Desenvolvimento Regional
• País: Brasil
• Palavras-chave em Português: Aprendizagem de máquina; Decision tree; Imagens sentinela; Classificação de imagens; Mapeamento da vegetação florestal.
• Palavras-chave em Inglês: Machine learning; Decision tree; Sentinel images; Image classification; Forest vegetation mapping
• Área do conhecimento CNPq: OUTROS::CIENCIAS
• Link de acesso: http://bdtd.unoeste.br:8080/jspui/handle/jspui/1372

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context.