Identification of riparian vegetation types with machine learning based on LiDAR point-cloud made along the lower Tisza’s floodplain

Fehérváry István and Kiss Tímea: Identification of riparian vegetation types with machine learning based on LiDAR point-cloud made along the lower Tisza’s floodplain. In: Journal of environmental geography, (13) 1-2. pp. 53-61. (2020)

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Abstract

The very dense floodplain vegetation on the artificially confined floodplains results in decreased flood conveyance, thus increase in flood levels and flood hazard. Therefore, proper floodplain management is needed, which must be supported by vegetation assessment studies. The aims of the paper are to introduce the method and the results of riparian vegetation classification of a floodplain area along the Lower Tisza (Hungary) based on automatized acquisition of airborne LiDAR survey. In the study area 15x15 m large training plots (voxels) were selected, and the statistical parameters of their LiDAR point clouds were determined. Applying an automatized parameter selection and 10-fold cross-validation he most suitable decision tree was selected, and following a series of classification steps the training plots were classified. Based on the decision tree all the pixels of the entire study area were analysed and their vegetation types were determined. The classification was validated by field survey. On the studied floodplain area the accuracy of the classification was 83%.

Item Type: Article
Journal or Publication Title: Journal of environmental geography
Date: 2020
Volume: 13
Number: 1-2
ISSN: 2060-467X
Page Range: pp. 53-61
Language: English
Place of Publication: Szeged
Related URLs: http://acta.bibl.u-szeged.hu/70901/
DOI: 10.2478/jengeo-2020-0006
Uncontrolled Keywords: Térinformatika
Additional Information: Bibliogr.: 61. p. ; ill. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.05. Earth and related environmental sciences
Date Deposited: 2022. Jun. 24. 13:15
Last Modified: 2022. Jun. 24. 13:15
URI: http://acta.bibl.u-szeged.hu/id/eprint/76073

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