Machine Learning Approach for Detection of Water Overgrowth in Azov Sea with Sentinel-2 Data

Abstract

The Azov Sea estuaries play an important role in the reproduction of semi-anadromous fish species. Spawning efficiency is closely connected with overgrowing of those species spawning grounds; thus, the objective of the water vegetation research has vital fisheries importance. Thus, the main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS. For the classification of Azov Sea estuaries, a random forest algorithm was used. The obtained results showed that in 2020 the areas occupied by reeds reached 0.37 km2, while in 2021, they increased to 0.51 km2. There was a high level of Phragmites australis growth in the Soleniy and Chumyanniy firths. The rapid growth of Phragmites australis in the period of 2020–2021, where the area covered by the reed doubled, is primarily attributed to eutrophication. This is due to the nutrient enrichment from agricultural lands located in the northern part of the research area near Novonekrasovskiy village. Additionally, changes in water flows and hydrological conditions can also contribute to the favorable growth of the reed. This can result in a high growth rate of Phragmites australis, which can reach up to 2 m per year and can propagate both through vegetative and sexual means, leading to the formation of large and dense clusters.