Publication
Title
Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge
Author
Abstract
The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high-resolution image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus peltatus, Callitriche obtusangula, Potamogeton natans L., Sparganium emersum R. and Potamogeton crispus L., were classified from the data using object-based image analysis and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image resulted in 53% overall accuracy. These consistent results not only show promise for species-level mapping in such biodiverse environments but also prompt a discussion on assessment of classification accuracy.
Language
English
Source (journal)
Hydrobiologia. - The Hague
Hydrobiologia. - The Hague
Publication
The Hague : 2018
ISSN
0018-8158
Volume/pages
812 :1 (2018) , p. 157-175
ISI
000426783400012
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
Linking optical imaging techniques and 2D-modelling for stuyding spatial heterogenity in vegetated streams and rivers.
Macrophyte growth in a future world: the effect of Global Change on plant resilience to hydrodynamic forces, on litter quality and on decomposition.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 19.03.2018
Last edited 20.09.2021
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