Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms

Lake Tana is Ethiopia’s largest lake and infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity habitat. Using appropriate remote sensing detection methods determining seasonal distribution of weed important for decision-making, resource management, environmental protection. As demand reliable estimation E. crassipes mapping from satellite data grows, comparing performance different machine learning algorithms could help in identifying most effective method lake. Therefore, this study aimed to examine ability random forest (RF), support vector (SVM), classification regression tree (CART) detect estimating spatial coverage on Google Earth Engine (GEE) platform using Landsat 8 Sentinel 2 images. Cloud-masked monthly median composite October 2021 2022, January 2022 2023, March June were used represent autumn, winter, spring, summer, respectively. Four spectral indices derived combination bands improve accuracy. All achieved greater than 95% 90% overall accuracy when images, both sets, all a 93% F1 score detection. Though difference between was small, RF accurate, while SVM CART had same The maximum area observed autumn (22.4 km2), minimum (2.2 km2) summer. Based data, decreased significantly by 62.5% winter spring increased 81.7% summer autumn. findings suggested that classifier accurate algorithm, an season Tana.