Detection and Mapping of Water Hyacinth Growth Cycle in Anzali International Wetland Using Sentinel-2 Time Series
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".