Optical Earth observation satellites provide scientists and coastal managers with multiple information on the seafloor, water column, environmental parameters, natural or human-induced pressures, ecosystem services or biodiversity. However, the use of remote sensing is strongly constrained by the presence of liquid water which attenuates the light signal depending on the wavelength and depth. All applications that require perceiving the bottom through the water column, such as habitat mapping and bathymetry estimation are thus affected by a limit of about 20 meters beyond which the light signal becomes too weak and noisy to provide accurate information. This study uses hypertemporal (HT) data cubes constituted by long time series of optical images from the Sentinel-2 satellites to improve data quality and significantly extend this commonly accepted limit to depths reaching 70 to 80 m. The generated multi-temporal composite images reveal details of the seabed in remote areas of the world's oceans, never before perceived from space. Satellite-derived bathymetric (SDB) estimates using this new data layers and Random Forest and XGBoost machine learning algorithms show very good correlations with reference data over the extended depth range at 100 m spatial resolution. At the study sites located in the Mediterranean Sea as well as in the Atlantic, Indian, and Pacific Oceans, where the waters are clearest, good estimates were obtained across the entire 0-80 m bathymetric range, with Mean Absolute Errors (MAE) of 3.21 m in Martinique, 2.62 m in Corsica, 1.88 m in La Reunion, and 3.17 m in New Caledonia. The two more turbid sites in the North Atlantic also maintained low MAE levels, even when the bottom signal was no longer visible, thanks to the water column properties information provided by the HT images: 4.74 m in Ouessant and 2.10 m in Glenan. Analyses of cloud cover, time series length, temporal statistics, geographical areas and water types specify the influence of these parameters on data quality as well as the limits of application of the models. The processing chains developed here, using freely accessible Sentinel-2 satellite data available worldwide, enables operational implementation for fast and large-scale data production.

Mangroves are a vital natural heritage. Our ability to ensure their preservation is a key issue in the fight against global warming, the preservation of biodiversity and the livelihoods of a growing human population concentrated along the coasts. Over the last half century, mangroves have suffered an alarming loss of global cover. To improve the understanding of mangrove dynamics, up-to-date mangrove mapping and monitoring tools are crucial assets. MangMap is a new online monitoring platform dedicated to the production and dissemination of specific products and services useful for mangrove monitoring at local scales. MangMap is based on generic processing services using Sentinel-2 data. MangMap is an end user driven, easy-to-use portal designed to support and document scientific studies as well as to help institutional actors and stakeholders involved in mangrove conservation and management.

Accurate global bathymetry mapping underpins natural hazard prediction or marine habitat management. However approximately 80% of the seafloor remains unmapped. Traditional waterborne (multi-/single- beam sonar) and airborne (lidar, spectral inversion) methods provide high precision and complementary coverage but are constrained in cost, accessibility, and temporal resolution. Spaceborne approaches, leveraging high- to very high-resolution multispectral and lidar sensors, increasingly enable shallow-water bathymetry retrieval at regional to global scales. This study investigates the potential of the VEN mu S mission for deriving shallow bathymetry over Glorioso Archipelago (Indian Ocean). VEN mu S superspectral imagery (12 bands, 4 m resolution, daily revisit) was collected in 2022 and attempted to predict similar to 83,000 lidar illuminations acquired in 2009. Three predictor series were investigated: surface reflectance, ln-transformed surface reflectance, and band ratios of ln-transformed surface reflectance. Nine depth ranges, from 0 to -45 m, were modeled using stepwise three-factor linear regressions, with performance assessed across calibration, validation, and test subsets. Results indicate excellent performance with ln-transformed surface reflectance achieving the highest predictive skill. The [0; - 10 m] interval was optimal, with R-test(2) reaching 0.93 using blue, green, and yellow-2 bands, even if the [0; -30 m] range was satisfactorily modelled (R-test(2) = 0.78). VEN mu S-derived bathymetry maps show strong concordance with lidar to similar to 5 m depth, though increasing divergence suggests potential sediment redistribution over the 13-year period. These findings demonstrate that simple, transferable linear models applied to VEN mu S imagery can yield accurate, scalable shallow-water bathymetry, highlighting the mission's value for cost- effective coastal mapping and supporting global seabed initiatives such as Seabed 2030.

Par leur situation géographique le long des côtes, les récifs coralliens forment une barrière naturelle vivante atténuant l’énergie des vagues entrantes, protégeant les écosystèmes côtiers et les populations humaines des risques de submersion, d'inondation et d’érosion. Ces récifs font face à des pressions et perturbations croissantes, conduisant à des épisodes de mortalités des coraux qui les constituent et à une dégradation de leur état à l’échelle mondiale. Le pourcentage de couverture corallienne vivante est l'indicateur le plus communément utilisé pour évaluer l'état de santé des récifs coralliens. Les données de télédétection, notamment aériennes hyperspectrales ont depuis longtemps été mobilisées à La Réunion et ont montré leur efficacité pour ces suivis. Il s’agit de données coûteuses donc peu exploitables comme outils de gestion. Cet article montre l’apport de la donnée multispectrale satellite pour établir le suivi des récifs, notamment avec l’essor de la Très Haute Résolution Spatiale. Nous testons ici la capacité de différents indices spectraux calculés sur quatre types d’images satellites multispectrales (Sentinel-2, SPOT6/7, Pléiades et Pléiades Neo) à détecter les couvertures coralliennes vivantes des récifs coralliens. Nous utilisons La Réunion comme cas d’étude. Grâce à des données de références historiques et in-situ nous mettons en évidence que : (1) Les images satellites peuvent détecter les coraux vivant grâce l’indice de brillance Bleu Vert BIBG (R2 > 0,60 sur données historiques) (2) La nouvelle image satellite Pléiades Neo à très haute résolution, comprenant une bande Deep Blue, permet une meilleure détection avec l’indice spectral de brillance Deep Blue Bleu BIDBB, (R2 = 0,63 sur données in-situ). La méthode développée appliquée au couple indice-image a permis de générer une carte actualisée des couvertures coralliennes à haute résolution de l’ensemble de la plateforme récifale de l’Hermitage à La Réunion.

Understanding factors influencing seawater chemistry variability in coral reef environments is a major challenge to improve predictions of their evolution in the context of ocean acidification. In this study, autonomous sensors for current speed and direction, photosynthetically active radiation (PAR), temperature, salinity, dissolved oxygen (DO) and pHT were deployed three times between 2021 and 2022 offshore and on three reef flat sites of the main fringing reef of La Reunion Island. Discrete sampling of seawater for DO, pHT and total alkalinity (TA) at different times of the day complemented the monitoring. Diurnal variation of those variables on the reef flat was mainly driven by benthic community metabolism but hydrodynamics and reef geomorphology played also a key role. DO and pHT variations were decoupled in time, especially at night when we observed DO rebounds while pHT values were stationary. The reef flat was also largely TA depleted compared to offshore waters. We hypothesize that the strong offshore-alongshore current redirected TA depleted water exiting from reef channels into the reef flat. This seawater re-entrainment could also explain specific variations of DO and pHT values. This result highlights the important role of reef geomorphology in modulating changes in seawater chemistry. Neglecting this phenomenon could lead to substantial errors in the estimation of carbonate budgets when using the Eulerian approach.

The increasing availability of free high-resolution earth observation data covering any point on the globe every few days led to the emergence of new remote sensing tools that can manipulate the very large volumes of data generated by those satellites. We present Sen2Chain, an open-source Python tool that can automate the processing of large time series of Sentinel-2 images for their use in various fields (e.g., environmental health, natural hazards, ecology). Sen2Chain allows downloading images from various earth observation data suppliers, applying geometric and atmospheric corrections using ESA's Sen2Cor tool, and generating and applying cloud masks. Sen2Chain's ability to extract time series of spectral indices (e.g NDVI, NDWI) provides simplified access to value-added environmental information for a wide range of end-users and applications. Sen2Chain enables all data processing stages to be customized and chained together, with the possibility to automate and parallelize the processing, and optimize data management. Sen2Chain is paving the way for the creation and processing of a large earth observation image database dedicated to users who require time-series and/or perform regular environmental observations. The Web tool Sen2Extract is also presented, which enables end-users with no expertise in remote sensing to easily extract time-series for 11 spectral indices values for specific regions of interest.

Among induced mass-mortality events on coral reef, extreme low tides may ultimately lead to considerable reef community deaths on intertidal reef flats due to unusually long and significant aerial exposure. Here, we report an extensive coral mortality event induced by a negative sea level anomaly (nSLA) that occurred across Reunion Island during the austral winter season between June and October 2015 preceding the 2015-2016 El Nin & SIM;o Southern Oscillation (ENSO) event. The nSLA was strong and long in duration with a rapid drop of 35 cm in the mean sea level over a one-month period. Surveys conducted over seven reef flat sites before and after the nSLA revealed that mean coral cover drastically decreased from 54.5 & PLUSMN; 12.7% in early 2015, to 27.4 & PLUSMN; 6.9% in November 2015, which is an equivalent cover loss of 50% following the 2015 nSLA event. The shallowest sites showed a greater decrease in coral cover while the deepest parts of the reef flat remained unaffected. We found a significant correlation between the bathymetry and the relative coral cover variation. Using this relationship between depth and coral cover changes, high-resolution hyperspectral imagery and Lidar bathymetric airborne data, we mapped the impacts of this event at the scale of the whole reef. Overall the modeled loss reached 13.0 ha, which represents a decrease of 45.5% of all live coral cover in this area during the 2015 nSLA event. The impact of a nSLA on emersion times is much greater than the regular variation in tide amplitude between neap and spring tides, reaching new bathymetric ranges that are usually stable in terms of water submersion. Temporal variation of coral cover on Reunion Island reef flat revealed regular decreases to be compared with mean low-water-level events among other sea and climatic related disturbances and stressors.

This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. The methodology relies on change detection, comparing pre-event and post-event images. The change-detection method is based on the normalised difference ratio. Additionally, the HAND model is employed to delineate zones for processing only in flood-prone areas. The approach was tested and calibrated at a small scale to optimise parameters. In these calibration tests, an accuracy of 85% is achieved. The approach was then applied to the whole of the island of Madagascar after Cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google Tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario.

Despite the large number of species distribution modelling (SDM) applications driven by tracking data, individual information is most of the time neglected and traditional SDM approaches commonly focus on predicting the potential distribution at the species or population-level. By running classical SDMs (population approach) with mixed models including a random factor to account for the variability attributable to individual (individual approach), we propose an innovative five-steps framework to predict the potential and individual-level distributions of mobile species using GPS data collected from green turtles. Pseudo-absences were randomly generated following an environmentally-stratified procedure. A negative exponential dispersal kernel was incorporated into the individual model to account for spatial fidelity, while five environmental variables derived from high-resolution Lidar and hyperspectral data were used as predictors of the species distribution in generalized linear models. Both approaches showed a strong predictive power (mean: AUC > 0.93, CBI > 0.88) and goodness-of-fit (0.6 < adjusted R-2 < 0.9), but differed geographically with favorable habitats restricted around the tagging locations for the individual approach whereas favorable habitats from the population approach were more widespread. Our innovative way to combine predictions from both approaches into a single map provides a unique scientific baseline to support conservation planning and management of many taxa. Our framework is easy to implement and brings new opportunities to exploit existing tracking dataset, while addressing key ecological questions such as inter-individual plasticity and social interactions.

Today, resilience in the face of cyclone risks has become a crucial issue for our societies. With climate change, the risk of strong cyclones occurring is expected to intensify significantly and to impact the way of life in many countries. To meet some of the associated challenges, the interdisciplinary ReNovRisk programme aims to study tropical cyclones and their impacts on the South-West Indian Ocean basin. This article is a presentation of the ReNovRisk programme, which is divided into four areas: study of cyclonic hazards, study of erosion and solid transport processes, study of water transfer and swell impacts on the coast, and studies of socio-economic impacts. The first transdisciplinary results of the programme are presented together with the database, which will be open access from mid-2021.

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

This study focuses on the use of earth observation data for the monitoring of tropical cyclones and their impact in the South-western Indian Ocean. Two recent events that occurred in Madagascar are investigated: (i) Haruna, that hit the south-western coast in February 2013 and (ii) Enawo, that hit the north-eastern coast in March 2017. The results showed that satellite imagery is suitable for the quantification of hurricane-related floods over Madagascar territory. The synergetic use of optical and SAR (Synthetic Aperture Radar) sensors, with high temporal and spatial resolution allowed to : (i) carry out a near-real time monitoring of floods caused by Haruna during the crisis and a characterization of water level subsidence in post-crisis (combining SPOT 5, RADARSAT-2 and Pléiades images), (ii) a mapping of floods related to Enawo in urban areas and in rice fields in the regions of Sambava and Maroantsetra (combining Sentinel-1, Sentinel-2, and participatory mapping).

Monitoring the spatial footprint of cyclone impacts by remote sensing offers great potential for assessing the extent of damage and monitoring the resilience of the affected territories. For this purpose, as part of the Renovrisk-Impact project, we have developed two change detection processing chains based on optical (Sentinel-2) and SAR (Sentinel-1) data. These chains have been used to track different events in different regions of the world. In this article we focus on two study sites in Madagascar: the city of Miandrivazo, which was heavily affected by severe rainfall from Cyclone AVA in January 2018, and more recently the town of Marovoay which suffered a major disaster following the passage of tropical storm DIANE in January 2020. The obtained results were evaluated and compared with the Copernicus Emergency Mapping Service product, showing good consistency with this product and between them. These results confirm the potential of these Sentinel data and the developed processing chains for monitoring the impacts of cyclones, but also open up prospects for longer-term monitoring.

This chapter gives an overview on the use of hyperspectral imagery in remote sensing. Specifically, four thematic applications dealing with the characterization of natural landscapes are presented, giving the reader a glimpse of the role that hyperspectral imaging technologies play in environmental monitoring. Namely, applications related to planetary sciences, coastal areas, cryosphere, and vegetation are reported. For each remote sensing application considered in this chapter, some context is recalled allowing then to introduce a real case study and finally to present some open challenges.

BACKGROUND:A strong behavioural plasticity is commonly evidenced in the movements of marine megafauna species, and it might be related to an adaptation to local conditions of the habitat. One way to investigate such behavioural plasticity is to satellite track a large number of individuals from contrasting foraging grounds, but despite recent advances in satellite telemetry techniques, such studies are still very limited in sea turtles.METHODS:From 2010 to 2018, 49 juvenile green turtles were satellite tracked from five contrasting feeding grounds located in the South-West Indian Ocean in order to (1) assess the diel patterns in their movements, (2) investigate the inter-individual and inter-site variability, and (3) explore the drivers of their daily movements using both static (habitat type and bathymetry) and dynamic variables (daily and tidal cycles).RESULTS:Despite similarities observed in four feeding grounds (a diel pattern with a decreased distance to shore and smaller home ranges at night), contrasted habitats (e.g. mangrove, reef flat, fore-reef, terrace) associated with different resources (coral, seagrass, algae) were used in each island.CONCLUSIONS:Juvenile green turtles in the South-West Indian Ocean show different responses to contrasting environmental conditions - both natural (habitat type and tidal cycle) and anthropogenic (urbanised vs. uninhabited island) demonstrating the ability to adapt to modification of habitat.