Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorEarth Observation and Geoinformatics Division (DIOTG)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Stirling-
Autor(es): dc.creatorMedeiros, Thaís Pereira de-
Autor(es): dc.creatorMorellato, Leonor Patrícia Cerdeira-
Autor(es): dc.creatorSilva, Thiago Sanna Freire-
Data de aceite: dc.date.accessioned2025-08-21T17:37:17Z-
Data de disponibilização: dc.date.available2025-08-21T17:37:17Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-02-09-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fenvs.2023.1083328-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249685-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249685-
Descrição: dc.descriptionModern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionGraduate Program of Remote Sensing National Institute for Space Research (INPE) Earth Observation and Geoinformatics Division (DIOTG)-
Descrição: dc.descriptionPhenology Lab Institute of Biosciences Despartment of Biodiversity São Paulo State University (UNESP)-
Descrição: dc.descriptionEcosystem Dynamics Observatory (EcoDyn) Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling-
Descrição: dc.descriptionPhenology Lab Institute of Biosciences Despartment of Biodiversity São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: #2010/521113-5 #2009/54208-6 #2019/03269-7-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Environmental Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectheterogeneous vegetation-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectphenology-
Palavras-chave: dc.subjectrandom forest-
Palavras-chave: dc.subjectrupestrian grassland-
Palavras-chave: dc.subjectUAS-
Palavras-chave: dc.subjectunmanned aerial system-
Título: dc.titleSpatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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