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Evaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetation

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dc.contributor.authorSotille, Maria E.
dc.contributor.authorBremer, Ulisses F.
dc.contributor.authorVieira, Gonçalo
dc.contributor.authorVelho, Luiz F.
dc.contributor.authorPetsch, Carina
dc.contributor.authorSimões, Jefferson C.
dc.date.accessioned2020-10-12T11:54:45Z
dc.date.available2020-10-12T11:54:45Z
dc.date.issued2020
dc.description.abstractExpansion of Antarctic vegetation in ice-free areas underlines the need for effective remote sensing techniques to properly monitor the changes. Detection and mapping of vegetation remains limited in the Antarctic environment given the complexity of its surface coverage. Some cryptogamic species exhibit low reflectance in the nearinfrared region and are not easily detected by vegetation indices, such as the normalized difference vegetation index (NDVI). In addition, spectral reflectance of Antarctic vegetation is highly variable according to seasonal conditions, which may influence NDVI results. As ultra-high resolution aerial imagery allows for a detailed analysis of vegetation and enables the validation of satellite imagery, in this study we assess the ability of the NDVI from unmanned aerial vehicle (UAV), Sentinel-2, and Landsat 8 to identify vegetated areas in the ice-free environment of Hope Bay, Antarctic Peninsula. NDVI classification with class ranges set by statistical parameters (i.e., mean and standard deviation) is performed. The results show that different sensors provide different NDVI values for the same vegetation class. NDVI classification enabled the identification of areas showing vegetation cover, which are in accordance with the manually mapped areas in the UAV image. Correspondence in vegetation distribution and classes can be observed across all classifications, demonstrating that aerial and satellite imagery may be used for Antarctic vegetation monitoring. A close association between NDVI classes and Antarctic vegetation type is identified, where lichens are generally classified in lower probability classes, and algae and moss in higher probability classes. This article shows the potential of NDVI applied to Antarctic vegetation and the significance of data statistical parameters in the selection of thresholds, reducing the need for groundtruth information in remote areas.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSotille, M. E., Bremer, U. F., Vieira, G., Velho, L. F., Petsch, C., & Simões, J. C. (2020). Evaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetation. Applied Geography, 125, 102322. https://doi.org/10.1016/j.apgeog.2020.102322pt_PT
dc.identifier.doi10.1016/j.apgeog.2020.102322pt_PT
dc.identifier.issn0143-6228
dc.identifier.urihttp://hdl.handle.net/10451/44561
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0143622819313554?via%3Dihubpt_PT
dc.subjectVegetation mappingpt_PT
dc.subjectNDVIpt_PT
dc.subjectAntarcticapt_PT
dc.subjectUAVpt_PT
dc.subjectLandsat Sentinel-2pt_PT
dc.subjectRemote sensingpt_PT
dc.titleEvaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage102322pt_PT
oaire.citation.titleApplied Geographypt_PT
oaire.citation.volume125pt_PT
person.familyNameBrito Guapo Teles Vieira
person.givenNameGonçalo
person.identifierG-5958-2010
person.identifier.ciencia-id2519-6583-CAEA
person.identifier.orcid0000-0001-7611-3464
person.identifier.scopus-author-id7005863976
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7039fbb2-e1f8-4c3e-80f1-603b12d33c1c
relation.isAuthorOfPublication.latestForDiscovery7039fbb2-e1f8-4c3e-80f1-603b12d33c1c

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