Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

Mapping Nearshore Bathymetry with Spaceborne Data Fusion and State Space Modeling

Despite numerous techniques for measuring and estimating water depth, bathymetry in the nearshore zone is notoriously difficult to map. Dangerous sea states, noisy environmental conditions, and expensive survey operations, particularly in remote areas, contribute to the difficulties of obtaining data along the coast. Global datasets, derived mainly from satellite altimetry methods, do exist, but they have significant limitations nearshore. Numerous high-resolution datasets, conventionally acquired with acoustic and lidar techniques, also exist, but they cover only a small percentage of the world's coasts. Spaceborne data fusion employing multispectral satellite derived bathymetry (SDB) offers the potential to significantly reduce the global lack of nearshore bathymetry, coined the "white ribbon" by the hydrographic community, referring to the alongshore data gap on many nautical charts. A broad term, multispectral SDB spans a diverse spectrum of methods that have been used extensively in specific case studies, but the application of multispectral SDB on a global or regional scale is significantly limited by the availability of in situ reference depths needed to tune derived values. Additionally, many existing approaches only use a single multispectral image, which can result in significant errors or missing data if the image contains environmental or sensor noise, such as clouds, sediment plumes, or detector-edge artifacts. This dissertation presents two spaceborne empirical multispectral SDB methods to address shortcomings of existing SDB approaches and reduce the global shortage of nearshore bathymetry – (1) active/passive spaceborne data fusion combining MABEL/ICESat-2 and multispectral data and (2) state space modeling of Sentinel-2 and Landsat 8 multispectral data to generate gap-free models of relative SDB (rSDB) with corresponding uncertainty estimates. The recently launched ICESat-2 mission offers an opportunity for a completely spaceborne active-passive data fusion approach to nearshore bathymetry by potentially providing a global source of nearshore reference depths to tune empirical multispectral SDB algorithms. The main objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass, but ICESat-2’s 532-nm wavelength photon-counting Advanced Topographic Laser Altimeter System (ATLAS) was first posited, then demonstrated capable of detecting bathymetry in certain nearshore environments. Presented in two studies conducted prior to ICESat-2’s launch, the active-passive approach is demonstrated with data from MABEL, NASA’s high-altitude ATLAS simulator system. The first study assessed the ability to derive bathymetry from MABEL and then evaluated the accuracy and reliability of MABEL bathymetry using data acquired in Keweenaw Bay, Lake Superior. The study also developed and verified a baseline model to predict numbers of bottom returns as a function of water depth. The second study completed the demonstration of the spaceborne active/passive data fusion method by synergistically fusing MABEL-derived bathymetry and Landsat 8 multispectral Operational Land Imager (OLI) imagery over the entire Keweenaw Bay study site using the Stumpf band-ratio algorithm. The study also assessed the spatiotemporal viability of the data fusion approach by characterizing the variability of global coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data. The calculated SDB agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.7 m, and the spatiotemporal viability analysis indicated that the spaceborne active-passive data fusion approach may be viable over many regions of the globe throughout the course of a year. State space modeling of empirical multitemporal SDB overcomes limitations of single-image SDB by leveraging the bathymetric signal in multispectral time series to create gap-free models of relative SDB (rSDB) for an arbitrary date, enabling SDB for dates with noisy or no data. State space models (SSMs) are well established in many applications but are absent in empirical SDB literature. Consisting of a state equation, which relates consecutive state vectors, and an observation equation, which relates observations to the state vector, SSMs are typically solved using Kalman filtering techniques, which provide estimates of uncertainties along with state estimates. SSMs also provide a mechanism for data fusion by allowing an observation equation for multiple observed time series. The third study demonstrates a state space approach to empirical multispectral SDB by applying local level SSMs to Landsat 8 OLI and Sentinel-2 MSI rSDB time series, both separately and fused. A representative single-sensor SSM (Landsat 8) was transformed to SDB that agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.29 m, which indicates the promising performance of the state space framework. Internally consistent fused-sensor SSMs verified that state space modeling also offers a data-fusion method capable of incorporating time series from a diverse suite of multispectral sensors

Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: a review

Integrated Hydrological Models (IHMs) dynamically couple surface and groundwater processes across the unsaturated zone domain. IHMs are data intensive and computationally demanding but can provide physically realistic output, particularly if sufficient input data of high quality is available. In-situ observations often have a small footprint and are time and cost-demanding. Satellite remote sensing observations, with their long time series archives and spatially semi-continuous gridded format, as well as hydrogeophysical observations with their flexible, ‘on-demand’ high-resolution data coverage, perfectly complement in-situ observations. We review the contribution of various satellite remote sensing products for IHM: (1) climate forcings, (2) parameters, (3) boundary conditions and (4) observations for constraining model calibration and data assimilation. Our review of hydrogeophysics focuses on the four mentioned IHM contributions, but we analyze them per data acquisition platform, i.e., surface, drone-borne and airborne hydrogeophysics. Finally, the review includes a discussion on the optimal use of satellite remote sensing and hydrogeophysical data in IHMs, as well as a vision for further improvements of data-driven, integrated hydrological modelling

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

Between the tides: modelling the elevation of Australia’s exposed intertidal zone at continental scale

The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km2 of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach (±0.41 m RMSE) and tidal flat environments (±0.39 m RMSE). Model performance was least accurate (±2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data

Physics-based satellite-derived bathymetry for nearshore coastal waters in North America

Accurate bathymetric information is fundamental to safe maritime navigation and infrastructure development in the coastal zone, but is expensive to acquire with traditional methods. Satellite-derived bathymetry (SDB) has the potential to produce bathymetric maps at dramatically reduced cost per unit area and physics-based radiative transfer model inversion methods have been developed for this purpose. This thesis demonstrates the potential of physics-based SDB in North American coastal waters. First the utility of Landsat-8 data for SDB in Canadian waters was demonstrated. Given the need for precise atmospheric correction (AC) for deriving robust ocean color products such as bathymetry, the performances of different AC algorithms were then evaluated to determine the most appropriate AC algorithm for deriving ocean colour products such as bathymetry. Subsequently, an approach to minimize AC error was demonstrated for SDB in a coastal environment in Florida Keys, USA. Finally, an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, was demonstrated. Based on the findings of this thesis, it was concluded that: (1) Landsat-8 data hold great promise for physics-based SDB in coastal environments, (2) the problem posed by imprecise AC can be minimized by assessing and quantifying bias as a function of environmental factors, and then removing that bias in the atmospherically corrected images, from which bathymetry is estimated, and (3) an ensemble approach to SDB can produce results that are very similar to those obtained with the best individual image, but can be used to reduce time spent on pre-screening and filtering of scenes

Dynamique des inondations dans le continuum rivière-estuaire-océan littoral du delta du Bengale : synergie de la modélisation hydrodynamique et de la télédétection spatiale

Le delta du Bengale est le plus vaste au monde. Il est formé par la confluence des trois rivières transfrontalières que sont le Gange, le Brahmapoutre et la Meghna. Des inondations massives frappent régulièrement cette région côtière très densément peuplée, située à seulement quelques mètres au-dessus du niveau moyen de la mer. Elles résultent du puissant cycle saisonnier des débits fluviaux, de la marée océanique très ample, et des cyclones tropicaux fréquents. Au cours des cinquante dernières années, les inondations de la partie littorale du delta ont fait plus de 500'000 victimes. La montée du niveau moyen de la mer ne va faire qu'aggraver la vulnérabilité de cette région où le taux de pauvreté est très élevé. Le long du littoral, les estrans sont les zones alternativement inondées à marée haute et découvertes à marée basse. Leur topographie joue un rôle important dans l'hydrodynamique littorale et dans les submersions qui surviennent lors des évènements extrêmes. En mettant en œuvre une synergie entre l'imagerie par télédétection spatiale de la constellation Sentinel-2 et la modélisation numérique de la marée, nous avons cartographié la topographie de l'estran du delta du Bengale sur une superficie de 1134 km2, avec une résolution de 10 m. Les marées, qui sont le facteur dominant de la variabilité du niveau de la mer côtier, sont apparues comme sensibles à la montée du niveau de la mer. Dans une hiérarchie de scénarios de montée du niveau de la mer représentatifs de l'évolution attendue au 21ème siècle, nous avons conclu que l'amplitude de marée devrait augmenter significativement avec la montée du niveau de la mer, à la fois dans le Sud-Ouest et dans le Sud-Est du delta. Au contraire, l'extension graduelle et massive de la superficie des estrans dans la partie centrale du delta devrait induire une nette atténuation de la marée, dans ces scénarios futurs. La marée joue par ailleurs un rôle central dans l'évolution des surcotes cyloniques. Un exercice de prévision du dernier super-cyclone ayant frappé le delta du Bengale avec notre plate-forme de modélisation hydrodynamique couplée marée-surcote-vagues a révélé la nécessité du couplage dynamique entre ces trois composantes de la submersion, et nous avons pu confirmer le rôle-clé de la topographie côtière dans le succès des prévisions numériques. Grâce à une approche ensembliste basée sur la simulation numérique hydrodynamique de plusieurs milliers de cyclones synthétiques, cohérents tant du point de vue de la physique que de la statistique, nous avons pu conclure qu'il y a à l'heure actuelle de l'ordre de 10% de la population côtière du delta, soit trois millions de personnes, résidant dans la zone exposée à la submersion cinquentennale. La compréhension et la quantification des mécanismes de l'inondation exposés dans cette thèse constituent une information pertinente pour contribuer à l'ingénierie des infrastructures côtières, à la gestion du risque, ainsi qu'à l'élaboration de l'agenda de la recherche en hydrodynamique côtière sur le delta du Bengale.The Bengal delta is the largest in the world. It is formed by the confluence of three transboundary rivers - Ganges, Brahmaputra, and Meghna. Flooding induced by large seasonal continental discharge, strong tide, and frequent deadly storm surges, regularly strikes this densely populated (density > 1000 person/km2), low-lying coastal region (<5 m above mean sea level). In the last five decades, coastal flooding took more than half a million lives. Ongoing global sea level rise (SLR) will only further aggravate the vulnerability of this impoverished region. Along the shoreline, intertidal zones are the first landmass that gets flooded, periodically between each high- and low-tide. Their topography plays an important role in the coastal hydrodynamics and associated flooding during extremes. A synergy between remote sensing from Sentinel-2 constellation and tidal numerical modelling allowed us to map an intertidal area of 1134 km2 and its topography at 10 m resolution. Tides, that prominently drive the variability of coastal sea level, are shown to be sensitive to SLR. In future SLR scenarios in line with the 21st century forecasts, we found that the tidal amplitude will significantly increase with SLR over both the south-western and south-eastern parts of the delta. In contrast, the central part of the delta will potentially experience massive free-flooding of river banks, hereby inducing a decay of the tidal amplitude. Tide plays a vital role in the evolution of storm surges also. Hindcast simulation of a recent super cyclone with our coupled tide-surge-wave model reveals the necessity of the coupling between tide, surge and wave modelling, and confirmed the crucial role played by the coastal topography for effective inundation modelling and forecast. With an ensemble forecast of thousands of physically and statistically consistent synthetic cyclones, we could conclude that about 10% of the coastal population of the Bengal delta, amounting to 3 million people, currently lives exposed to the 50-year return period flooding. The understanding and quantification of the inundation mechanisms extended in this study is expected to help with coastal infrastructure engineering, risk zoning, resource allocation and future adaptation to coastal flood across the Bengal delta

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet