Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery ($GSD$ = 3–6 cm) with an $F1$ score of $86.5\%\pm 4.1\%$ when trained with as few as 40 positive/negative pairs of pixels, compared to $54.0\%\pm 9.2\%$ using the random forest model and $42.8\%\pm 6.2\%$ using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications.

Wildfire detection and mapping is crucial for managing natural resources and preventing further environmental damage. In this study, we compared two methods of mapping burn scars using Sentinel-2 satellite imagery, a pixel-based approach and an object-based approach, at test sites located in various climatic zones with diverse land cover synthesis. The study aimed to determine the advantages and limitations of each method in terms of accuracy and precision in detecting burn scars. The results showed that both methods could detect burn scars with high accuracy, but with some limitations. The F1 score was in the range of 0.64–0.89 for the object-based approach, and 0.58–0.90 for the pixel-based approach. The pixel-based method produced a more precise delineation of the burnt area, but it was only suitable for detecting burn scars in a limited area of interest. The object-based method, on the other hand, was able to detect burn scars over a larger area accurately but with some commission errors. The results of both methods were also compared to the Copernicus Emergency Management Service (CEMS) rapid mapping product.

Water utilities often use extended open surface water reservoirs to produce drinking water. Biotic and abiotic factors influence the water level, leading to alterations in the concentration of the dissolved substances (in cases of flood or drought), entry of new pollutants (in case of flooding) or reduction in the availability and inflow speed of water to the treatment plant (in case of drought). Spaceborne image analysis is considered a significant surrogate for establishing a dense network of sensors to monitor changes. In this study, renowned inundation mapping techniques are examined for their adaptability to the inland water reservoirs’ conditions. The results, from the Polyphytos open surface water reservoir in northern Greece, showcase the transferability of the workflows with overall accuracies exceeding—in cases—98%. Hydroperiod maps generated for the area of interest, along with variations in the water surface extent over a four-year period, provide valuable insights into the reservoir’s hydrological patterns. Comparison among different inundation mapping techniques for the surface water extent and water level reveal challenges and limitations, which are related to the spatial resolution, the data take frequency and the influence of the landscape synthesis beyond the water reservoir boundaries.