The frequency and severity of large, destructive fires have increased in the recent past, with extended impacts on the landscape, the human population, and ecosystems. Earth observations provide a means for the frequent, wide coverage and accurate monitoring of fire impacts. This study describes an unsupervised approach for the mapping of burned areas from Sentinel-2 satellite imagery, which is based on multispectral thresholding, and introduces an adaptive thresholding method. It takes into account the localized variability of the spectral responses in a two-phase approach. The first phase detects areas that are burned with a high probability, while the second phase adaptively adjusts this preliminary mapping by expanding and refining its boundaries. The resulting classification contains two main classes of interest: burned and unburned. The latter is further classified into four (4) fire impact severity classes, according to the Copernicus Emergency Management Service (CEMS) and the NASA United States Geological Survey (USGS)’s widely acknowledged nomenclature examples. Three distinct wildfire events are assessed, which occurred during the summers of 2020 and 2021 in Greece and Portugal. The classification accuracy is calculated by juxtaposing the classification outputs to burned area validation maps created through the photointerpretation of very high-resolution (VHR) satellite imagery. The corresponding CEMS On-Demand Mapping products are also juxtaposed against the validation maps for comparison purposes. The accuracy assessment showcases that the unsupervised approach closely follows the capacity provided by the CEMS maps (e.g., the kappa coefficient—k—of the proposed unsupervised approach is 0.91, 0.83 and 0.83 for the events processed, while the CEMS products achieve a k of 0.94, 0.93 and 0.8, respectively). The proposed approach considers the variability of the affected areas’ spectral response; thus, it generalizes well to different areas, e.g., areas characterized by different land cover types. It seems to offer an effective means of mapping the wildfire-induced changes, which can be further incorporated and used by forest fire management services and further decision support systems complementary to the CEMS maps.

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%±4.1% when trained with as few as 40 positive/negative pairs of pixels, compared to 54.0%±9.2% using the random forest model and 42.8%±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.

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