Remote Sensing Of Suspended Sediment In Amazonian Rivers Using Satellite Multispectral Imagery

Patterns of surface sediment concentration distribution in rivers are significant for understanding the broad ranges of fluvial environmental systems. In the case of the Amazon Basin, the complexity in the sediment pattern distribution is affected by the anabranching channel pattern of the Amazon River, the input by tributaries (some of which are among the largest rivers on earth) and the existence of huge and complex floodplains. Until recently, the assessment of sediment fluxes has been concentrated on hydro-sedimentological techniques in the Amazon Basin; however, efforts on characterizing the patterns of sediment transport have been neglected. This study aims to improve the understanding of the pattern of sediment distributions over a large scale in the Amazon River by estimating surface sediment concentration with remote sensing techniques. Field acquired surface sediment concentration values were supplied from three gauging stations representing the upstream, midstream and downstream sections of the Amazon River from 2000 to 2010 and calibrated with MODIS surface reflectance products (N=207, 232, 313, respectively). Empirical models were derived with robust causalities (0.63

Suspended sediment in San Francisco Bay affects the economic and ecological health of the estuary and its surrounding region by limiting light availability for photosynthesis, transporting contaminants, nourishing marsh restoration projects, infilling shipping channels, and providing protection to the shoreline from sea level rise via accretion on mudflats. Traditional efforts to study sediment transport phenomena have relied upon in situ measurements and numerical modeling, but these approaches have limitations. In situ measurement techniques rely on point measurements with high temporal resolution, yet they are difficult to deploy over large spatial areas. Models provide useful insight into the spatial heterogeneity of sediment processes. However, they rely on initial and boundary conditions and parameterizations that are based on observations, therefore the accuracy of models is also constrained in part by the limitations of in situ measurements. This dissertation presents remote sensing measurements from satellites and unmanned aerial vehicles (UAVs) to understand suspended sediment transport processes in estuaries like San Francisco Bay. Twelve methods for inferring suspended sediment concentration (SSC) from Landsat 7 imagery were compared using k-folds validation and assessed based on their abilities to recreate in situ SSC measurements from one meter below the surface. The best performer was the model of Nechad et al. (2010) using the red wavelength band with coefficients determined via Huber regression, with mean absolute error of 5.94 mg L-1 and bias of 0.15 mg L-1. Satellite-derived SSC observations compare well with USGS transects indicating that the method is well-suited to supplement cruise data that is costly to acquire and therefore limited in its frequency. Remote sensing measurements were aggregated by location, season, or tidal phase to understand the variability of SSC and to compare probability densities with in situ measurements. These results show that surface SSC is heightened in the shoals during summer months and has trended downward in Suisun and Grizzly Bays since 1999. Using satellite imagery from 2014-2017, remotely sensed surface SSC derived from the Nechad method was paired with bottom stress estimates based on two-dimensional hydrodynamic and fetch-limited wave models to investigate the relationship between surface SSC and flow. Observations of SSC closely fit a lognormal distribution though the shape, characterized by the modal value, depend on binning criteria including embayment, depth, and wave height. When binned by model-derived bottom shear stress, the modal value of the SSC distribution exhibited an inflection point at the critical shear stress for erosion. This suggests that remote sensing can be used to derive critical stresses that are otherwise difficult to measure. To account for the limitations of satellite imagery such as low spatial resolution and low temporal resolution (Landsat 7 overpasses occurred roughly once every 16 days), a method was developed to infer surface SSC from UAV-based imagery. While traditional remote sensing platforms take imagery at approximately a nadir viewing angle and provide multispectral images that are aligned with one another, an off-the-shelf camera aboard a UAV may not adhere to those qualities. Low cost multi-spectral cameras often include individual sensors for each band. The slight misalignment between images violates assumptions in two-band glint correction algorithms. Additionally, UAVs must tilt to fly and compensate for wind requiring images to occasionally be taken at angles more oblique than most satellite imagery. The method developed in this dissertation adapts previous techniques for sun glint correction for misaligned multispectral images and offers a novel approach to reduce the effects of camera orientation for oblique angles. During a field campaign, the UAV-based method to capture remote sensing reflectance was validated via comparison with in situ measurements made with a hyperspectral radiometer, and its ability to accurately infer SSC was verified based on in situ water samples. It was found that a polarizing filter is necessary to mitigate much of the glare on the water surface. A series of test flights were conducted to measure the surface SSC along a transect parallel to the Dumbarton Bridge during different phases of the tidal cycle. To reduce the impact of variability of incoming light, the flights were conducted over a period of 12 days at the same solar zenith angle during each day. Because the tide arrives later by roughly 50 minutes each day, consecutive daily transects over 12 days provided the variability over a tidal cycle. Cross-sectional sediment flux was computed from the remotely sensed surface SSC measurements and compared well to flux values estimated from in situ USGS observations.

This book describes the water security challenges with focus on water scarcity and quality in our rapidly changing world. Achieving water security is essential to promoting economic and social development, as well as resource sustainability and ecosystem integrity. Questions of water security are central to recent global agreements such as the Sustainable Development Goals (SDGs), the Paris Agreement on Climate Change, and the Sendai Framework for Disaster Risk Reduction. The thematic areas discussed here support the SDGs, with special attention to Goal 6 (“Ensure availability and sustainable management of water and sanitation”). The book is a collection of studies from engineering, social and environmental disciplines and aims at giving a balanced overview of the current , complex discourse on water scarcity and quality. It offers a source of inspiration and information for researchers, policymakers, planners, and practitioners concerning the further development of concepts, approaches, and methodologies for promoting water secure societies.

This book is a printed edition of the Special Issue "The Use of Remote Sensing in Hydrology" that was published in Water

Large Rivers: Geomorphology and Management explores an important topic in geomorphology and sedimentology: the form and function of major rivers. Our knowledge of the big rivers of the world is limited. It is currently difficult to recognise large rivers of the past from relict sedimentary deposits or to structure management policies for long international rivers. This exciting book brings together a set of papers on large rivers of the world, as a unique introduction to a demanding subject. The book includes thirty chapters and is organised into three sections. The first part is on the environmental requirements for creating and maintaining a major river system. The second is a collection of case studies on 14 large rivers from different continents, covering a range of physical environments. The third section includes chapters on the measurement and management of large rivers. First book to offer in a single volume state-of-the-art knowledge on management and geomorphology of large rivers of the world A pioneering study, pushing the boundaries of our knowledge related to big rivers Includes comprehensive case studies covering the major large rivers of the world including Amazon, Mississippi, Nile, Congo, Indus, and Mekong Written by a leading team of distinguished, international contributors Large Rivers: Geomorphology and Management is essential reading for postgraduate students and researchers in fluvial geomorphology, hydrology, sedimentary geology, and river management. It is also of relevance to engineers and environmental consultants in the private and public sectors working on major rivers of the world.

The Mekong Basin in Southeast Asia is one of the largest international river basins in the world. Its abundant natural resources are shared by six riparian countries and provide the basis for the livelihoods of more than 75 million people. However, ongoing socio-economic growth and related anthropogenic interventions impact the region’s ecosystems, and there is an urgent need for the monitoring of the basin's land surface dynamics. Remote sensing has evolved as a key tool for this task, allowing for up-to-date analyses and regular monitoring of environmental dynamics beyond physical or political boundaries and at various temporal and spatial scales. This book serves as a forum for remote-sensing scientists with an interest in the Mekong River Basin to present their recent basin-related works as well as applied case studies of the region. A broad range of sensors from high to medium resolution, and from multispectral to SAR systems, are applied, covering topics such as land cover/land use classification and comparison, time series analyses of climate variables, vegetation structure and vegetation productivity, as well as studies on flood mapping or water turbidity monitoring. This book was originally published as a special issue of the International Journal of Remote Sensing.