Machine learning for identifying emergent and floating aquatic vegetation from space: a case study in the Dniester Delta, Ukraine

Alagialoglou, Leonidas; Manakos, Ioannis; Katsikis, Eleftherios; Medinets, Sergiy ORCID: https://orcid.org/0000-0001-5980-1054; Gazyetov, Yevgen; Medinets, Volodymyr; Delopoulos, Anastasios. 2024 Machine learning for identifying emergent and floating aquatic vegetation from space: a case study in the Dniester Delta, Ukraine [in special issue: Advances on geographical information systems theory, applications and management] SN Computer Science, 5, 597. 13, pp. https://doi.org/10.1007/s42979-024-02873-7

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Abstract/Summary

Monitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88 ± 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s42979-024-02873-7
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	2661-8907
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	wetland, Sentinel-2, floating vegetation, emergent vegetation, machine learning, thresholding, multi-class segmentation, feature importance
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	05 Jun 2024 12:17 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537520

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