UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
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| Institution: | 1. Programa de Pós-Graduação em Sensoriamento Remoto, Universidade Federal do Rio Grande do Sul, 91501-970, Porto Alegre, Brazil;2. Centro Polar e Climático, Instituto de Geociências, Universidade Federal do Rio Grande do Sul, 91501-970, Porto Alegre, Brazil;3. Departamento de Geografia, Instituto de Geociências, Universidade Federal do Rio Grande do Sul, 91501-970, Porto Alegre, Brazil;4. Centro de Estudos Geográficos, Instituto de Geografia e Ordenamento do Território, Universidade de Lisboa, 1600-276, Lisboa, Portugal;5. Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul – Campus Porto Alegre, 90030-040, Porto Alegre, Brazil;6. Departamento de Geociências, Universidade Federal de Santa Maria, 97105-900, Santa Maria, Brazil;1. CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China;2. Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan CN-430074, China;3. University of Chinese Academy of Sciences, Beijing CN-100049, China;4. Center for Development Research - ZEF, University of Bonn, Genscherallee 3, 53119 Bonn, Germany;5. East African Herbarium, National Museums of Kenya, P. O. Box 451660-0100, Nairobi, Kenya;1. Jilin Provincial Laboratory of Grassland Farming/Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;1. VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam;2. Hanoi National University of Education, 136 Xuan Thuy, Cau Giay, Hanoi, Viet Nam;3. School of Biological and Marine Science, University of Plymouth, Plymouth, Devon PL4 8AA, United Kingdom;1. Faculty of Pure and Applied Sciences, Open University of Cyprus, Giannou Kranidioti 33, Latsia 2220, Cyprus;2. Terra Cypria, The Cyprus Conservation Foundation, Agiou Andreou 341, Limassol 3035, Cyprus |
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| Abstract: | 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. |
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| Keywords: | Remote sensing |
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