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

Página do item simplificado
dc.contributor.authorMedeiros, Thaís Pereira de [UNESP]
dc.contributor.authorMorellato, Leonor Patrícia Cerdeira [UNESP]
dc.contributor.authorSilva, Thiago Sanna Freire
dc.contributor.institutionEarth Observation and Geoinformatics Division (DIOTG)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversity of Stirling
dc.date.accessioned2023-07-29T16:06:28Z
dc.date.available2023-07-29T16:06:28Z
dc.date.issued2023-02-10
dc.description.abstractModern 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.en
dc.description.affiliationGraduate Program of Remote Sensing National Institute for Space Research (INPE) Earth Observation and Geoinformatics Division (DIOTG)
dc.description.affiliationPhenology Lab Institute of Biosciences Despartment of Biodiversity São Paulo State University (UNESP)
dc.description.affiliationEcosystem Dynamics Observatory (EcoDyn) Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling
dc.description.affiliationUnespPhenology Lab Institute of Biosciences Despartment of Biodiversity São Paulo State University (UNESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: #2010/521113-5 #2009/54208-6 #2019/03269-7
dc.identifierhttp://dx.doi.org/10.3389/fenvs.2023.1083328
dc.identifier.citationFrontiers in Environmental Science, v. 11.
dc.identifier.doi10.3389/fenvs.2023.1083328
dc.identifier.issn2296-665X
dc.identifier.scopus2-s2.0-85148653208
dc.identifier.urihttp://hdl.handle.net/11449/249685
dc.language.isoeng
dc.relation.ispartofFrontiers in Environmental Science
dc.sourceScopus
dc.subjectheterogeneous vegetation
dc.subjectmachine learning
dc.subjectphenology
dc.subjectrandom forest
dc.subjectrupestrian grassland
dc.subjectUAS
dc.subjectunmanned aerial system
dc.titleSpatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by dronesen
dc.typeArtigo

Arquivos

Coleções

Artigos - Botânica - IBRC