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UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest
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Orientador(es)
Resumo(s)
Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need
for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and
mapping of vegetation by image classification remains limited in the Antarctic environment due to the
complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to
identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on
the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA)
approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation
classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR
bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are
identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result
for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa
coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and
lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can
provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images
obtained by sensors with low spectral resolution
Descrição
Palavras-chave
Vegetation mapping Antarctica UAV GEOBIA Image classification Remote sensing
Contexto Educativo
Citação
Sotille, M. E., Bremer, U. F., Vieira, G., Velho, Luiz F., Petsch, C., Auger, J. D. Simões, J. C. (2022). UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest. Ecological Informatics, 71, 101768, https://doi.org/10.1016/j.ecoinf.2022.101768
Editora
Elsevier