Detection and Mapping of Water Hyacinth Growth Cycle in Anzali International Wetland Using Sentinel-2 Time Series

Abstract

Water hyacinth (WH) is a notorious invasive species that significantly threatens ecosystems worldwide. Despite WH's well-documented threats and effects, its spatial distribution is not yet fully understood, especially in complex environments such as wetland systems. This knowledge gap is primarily due to the lack of accurate techniques with high spatial resolution and reliable in situ field data for quantification and monitoring. To address this research gap, we conducted a study to map the spatiotemporal distribution of invasive WH in Anzali International Wetland, Iran, using Sentinel-2 Multispectral Instrument 2022 data. Specifically, our study aimed to identify multispectral remote sensing variables and in situ field data using machine learning (ML) methods to detect and map WH growth cycles. In the first phase of our study, we compared three ML models for detecting WH and discriminating from other classes. Our results demonstrate that ML algorithms can detect WH accurately. In the second phase, we used four images dominated by four growth stages: early, mid, high, and decaying stages to train our ML classifier. We used the random forest algorithm for training our training samples achieving an overall classification accuracy of over 98%. These findings were further supported by statistical analysis, such as F1 (above 96%) and intersection over union (above 92%), indicating the high-performance quality of the used algorithm. Our study provides valuable insights into using ML algorithms for mapping WH growth cycles, which can significantly contribute to effectively managing and monitoring invasive species worldwide.

Keywords

• Anzali wetland
• growth cycle
• machine learning (ML)
• random forest (RF)
• Sentinel-2 (S2) time series
• water hyacinth (WH)