Mapping wetland vegetation using Sentinel-1 and SWOT data
Notice bibliographique
Résumé
Flooded vegetation mapping is critical for disaster response, ecological monitoring, wetland mapping and effective water resources management, yet it remains a significant challenge due to the limitations of optical remote sensing in detecting inundation beneath dense canopies and cloud cover. Monitoring of the dynamics of wetlands and flooded areas can help improve hydrological modeling and forecasting by providing accurate and timely information on water storage within a watershed. This study presents a methodology for the automated segmentation and classification of flooded vegetation by combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Surface Water and Ocean Topography (SWOT) mission observations. Previous research has demonstrated the potential of Sentinel-1 SAR for mapping wetlands and delineating flood extents. The proposed method leverages the complementarity between high-resolution spatial information from Sentinel-1 and hydrodynamic information provided by SWOT to map flooded vegetation with an object-based image analysis (OBIA) approach. The method is tested on two study areas, one in the Oromocto River basin in New Brunswick, Canada (45.78 N, 66.55 W) and the other is located around the Mamawi Lake in northern Alberta, Canada (58.66 N, 111.50 W). The first area focuses on wetlands along the Oromocto River which are prone to flooding during the spring freshet and heavy rainfall events. The second area is located within the Peace-Athabasca Delta, which is a complex system of interconnected lakes and wetlands and one of the current test sites for the SWOT and NORthern laKeS (SNORKS) project. Sentinel-1 Interferometric Wide images are selected in alignment with SWOT overpass dates within a 48-hour interval. Observations of high-water levels (including a major flood event for Oromocto) and low-water levels are used to test the algorithm. Preprocessing is applied to Sentinel-1 Ground Range Detected images comprised of radiometric calibration, terrain correction and speckle filtering on both VV and VH polarisations. An image segmentation based on the Mean Shift algorithm is then performed on the dual band images (VV and VH) using the Orfeo Toolbox to create polygons with uniform backscattering behaviour. The SWOT Raster products are then sampled within the segmented polygons to extract statistics of Water Surface Elevation (WSE) and Water Fraction (WF). All polygons with WF greater than 70% and WSE quality rating of 0 or 1 are considered flooded or open water. The segmentation outputs will be validated against flood extent maps provided by Natural Resources Canada where available, as well as maps produced by visual interpretation. Performance is benchmarked using standard metrics such as overall accuracy, class-specific IoU, precision, and recall. Maps produced by visual interpretation are also validated with in situ water gauges to validate water levels measured at the time the images were taken, along with high-resolution Digital Terrain Models (LiDAR), which are used to estimate water extents. The proposed method provides: • High-resolution, temporally consistent maps of flooded vegetation, supporting hydrological and hydraulic modelling and long-term environmental monitoring. • Enhanced understanding of flood propagation dynamics in vegetated landscapes and wetlands to help improve hydrological assessments. • A transferable methodological framework that leverages the complementary strengths of Sentinel-1 SAR and SWOT data, facilitating scalable flood mapping in diverse geographic and climatic contexts. By proposing a simple, automated, and robust method for the detection and classification of inundated vegetation, this project advances the state of flood mapping science and provides critical information for applications in hydrology, hydrodynamics and environmental monitoring, especially in remote areas.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».