OBIA Classification of Riverine Vegetation in a Small Open Channel Using RGB Drone Imagery

In temperate areas, small watercourses, especially agricultural ditches, are typically surrounded by seasonally changing vegetation, which significantly influences hydrodynamic and ecological processes within and around the channel.However, research on how different vegetation maintenance practices affect flow and mixing processes at the reach scale remains limited.Addressing this knowledge gap requires a series of field experiments conducted under varying flow and vegetation conditions, along with a simple and accessible method for vegetation characterisation.This study evaluates the efficiency of RGB drone imagery in mapping riverine vegetation using OBIA classifiers.For low vegetation coverage, SVM combined with Haralick textural features provided the best results, while for high vegetation, SVM combined with DEM delivered the best classification outcomes.

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Publication Details

Journal
Publications of the Institute of Geophysics Polish Academy of Sciences. C/Publications of the Institute of Geophysics. C
Published
2025-05-15
DOI
https://doi.org/10.25171/instgeoph_pas_publs-2025-056
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
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article

OBIA Classification of Riverine Vegetation in a Small Open Channel Using RGB Drone Imagery

Publications of the Institute of Geophysics Polish Academy of Sciences. C/Publications of the Institute of Geophysics. C
Remote Sensing and LiDAR Applications
article

OBIA Classification of Riverine Vegetation in a Small Open Channel Using RGB Drone Imagery

article en

Abstract

In temperate areas, small watercourses, especially agricultural ditches, are typically surrounded by seasonally changing vegetation, which significantly influences hydrodynamic and ecological processes within and around the channel.However, research on how different vegetation maintenance practices affect flow and mixing processes at the reach scale remains limited.Addressing this knowledge gap requires a series of field experiments conducted under varying flow and vegetation conditions, along with a simple and accessible method for vegetation characterisation.This study evaluates the efficiency of RGB drone imagery in mapping riverine vegetation using OBIA classifiers.For low vegetation coverage, SVM combined with Haralick textural features provided the best results, while for high vegetation, SVM combined with DEM delivered the best classification outcomes.

Publications of the Institute of Geophysics Polish Academy of Sciences. C/Publications of the Institute of Geophysics. CVol. 453(E-14)
Institute of Geophysics Polish Academy of Sciences (PL), Polish Academy of Sciences (PL)
Openalex Percentile: Top 91%
Remote Sensing and LiDAR Applications
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