Mapping shoreline indicators on a sandy beach with supervised edge detection of soil moisture differences

This study describes a method to map shoreline indicators on a sandy beach. The hypothesis is that, on this beach, spectral albedo is predominantly determined by moisture content and water lines can, therefore, be detected as albedo contrasts. A laboratory experiment is performed to relate moisture content to image albedo, and supervised edge detection is subsequently used to map the shoreline indicators with remote sensing imagery. The algorithm is tested with data from visible, near-infrared and shortwave-infrared wavelength regions. These results are compared to shoreline indicators obtained by a field survey and a shoreline indicator derived from a digital elevation model. Both the water line present when the imagery was acquired, as well as the maximum extent of the last flood, can be detected as a single edge. Older high water lines are confused with the last high water line and appear dispersed, as there are multiple debris lines present on the beach. The low water line, usually in saturated sand, also appears dispersed due to the presence of channels and troughs. Shorelines are constant moving boundaries, which is why shoreline indicators are used as a proxy. Unlike a mathematical indicator that is based on an elevation model, our method is more sensitive to the dynamic nature of shorelines. Supervised edge-detection is a technique for generating reproducible measurements of shoreline indicator positions over time, and aids in the monitoring of coastline migration

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

Fusion of airborne LiDAR, multispectral imagery and spatial modelling for understanding saltmarsh response to sea-level rise

Coastal ecosystems are considered to be sensitive to changes in environmental forcing, particularly sea-level rise. Saltmarshes occupy a discrete lateral and vertical position that is fundamentally controlled by the position of sea level, but the nature of other factors such as broader scale shoreline dynamics and anthropogenic ensure that the nature and extent of sea-level rise impacts on saltmarshes are globally variable, and locally complex. Thus, there is a need to understand these controls and to predict the potential response of saltmarsh systems to sea-level change at the local scale. The present research presents a multifaceted methodology for investigating the response of saltmarshes due to sea-level rise at local scales with application to the Odiel saltmarshes (SW-Spain), using elevation data derived from Light detection and ranging (LiDAR), high spatial resolution multispectral imagery and spatial modelling, that in combination with historical estuary evolution and field observation can be applied for effective management and conservation of saltmarshes in the context of sea-level change. SLAMM (Sea Level Affecting Marshes Model) has been used to evaluate coastal wetland habitat response to sea-level rise Accurate model spatial model inputs such as digital elevation models (DEMs) and saltmarsh habitat map are essential to reduce uncertainties in the model outputs, and part of this thesis has been focused on improving accuracy in saltmarsh elevation and habitat maps. Additionally, a sensitivity and uncertainty analysis was undertaken to explore first the relative importance of data quality and resolution (spatial and vertical) in the elevation data and saltmarsh habitat classification layers, and then the global uncertainty of the model outputs using a Monte Carlo approach. Our findings suggested that model is sensitive to DEM and habitat map resolution, and that historical sea-level trend and saltmarsh accretion rates are the predominant factors that influence uncertainty in predictions of change in saltmarsh habitats

NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation

EXECUTIVE SUMMARY: The Coastal Change Analysis Programl (C-CAP) is developing a nationally standardized database on landcover and habitat change in the coastal regions of the United States. C-CAP is part of the Estuarine Habitat Program (EHP) of NOAA's Coastal Ocean Program (COP). C-CAP inventories coastal submersed habitats, wetland habitats, and adjacent uplands and monitors changes in these habitats on a one- to five-year cycle. This type of information and frequency of detection are required to improve scientific understanding of the linkages of coastal and submersed wetland habitats with adjacent uplands and with the distribution, abundance, and health of living marine resources. The monitoring cycle will vary according to the rate and magnitude of change in each geographic region. Satellite imagery (primarily Landsat Thematic Mapper), aerial photography, and field data are interpreted, classified, analyzed, and integrated with other digital data in a geographic information system (GIS). The resulting landcover change databases are disseminated in digital form for use by anyone wishing to conduct geographic analysis in the completed regions. C-CAP spatial information on coastal change will be input to EHP conceptual and predictive models to support coastal resource policy planning and analysis. CCAP products will include 1) spatially registered digital databases and images, 2) tabular summaries by state, county, and hydrologic unit, and 3) documentation. Aggregations to larger areas (representing habitats, wildlife refuges, or management districts) will be provided on a case-by-case basis. Ongoing C-CAP research will continue to explore techniques for remote determination of biomass, productivity, and functional status of wetlands and will evaluate new technologies (e.g. remote sensor systems, global positioning systems, image processing algorithms) as they become available. Selected hardcopy land-cover change maps will be produced at local (1:24,000) to regional scales (1:500,000) for distribution. Digital land-cover change data will be provided to users for the cost of reproduction. Much of the guidance contained in this document was developed through a series of professional workshops and interagency meetings that focused on a) coastal wetlands and uplands; b) coastal submersed habitat including aquatic beds; c) user needs; d) regional issues; e) classification schemes; f) change detection techniques; and g) data quality. Invited participants included technical and regional experts and representatives of key State and Federal organizations. Coastal habitat managers and researchers were given an opportunity for review and comment. This document summarizes C-CAP protocols and procedures that are to be used by scientists throughout the United States to develop consistent and reliable coastal change information for input to the C-CAP nationwide database. It also provides useful guidelines for contributors working on related projects. It is considered a working document subject to periodic review and revision.(PDF file contains 104 pages.

Evaluating Vascular Plant Composition and Species Richness on Horn Island, Mississippi, Using Passive and Active Remote Sensing in Conjunction with Ground Based Measurements

Barrier island vegetation is subjected to chronic abiotic stressors combined with periodic storm events that favor species adapted to harsh environments. These islands are the first landforms to be affected by changes in coastal subsidence and sea-level rise. Evaluating changes in vegetation is important for understanding the impact of global climate change on coastal environments. This study assesses vegetation composition and plant species richness on Horn Island, Mississippi using ground data in conjunction with remotely sensed spectral and LIDAR data. The goals of this research are to: 1) classify and map vegetation composition on Horn Island using hyperspectral and LIDAR data, 2) evaluate changes in vegetation composition through comparison with a vegetation study and classification map from 1979, 3) determine the extent to which vascular plant species richness might be estimated using remotely sensed spectral reflectance indices and spatial variability within these indices, and 4) utilize the vertical distribution of airborne multiple-return LIDAR data to evaluate vascular plant species richness. The vegetation composition of habitat-types on Horn Island can be identified by indicator species that are consistent both over time and among other barrier islands. Additionally, combining airborne hyperspectral and LIDAR data improved the overall classification accuracy of habitats. Although only broad comparisons in vegetation changes could be made between this study and previous maps, these changes were linked with geomorphological changes. In simple linear regressions, various reflectance- and LIDAR-indices correlated significantly (p \u3c 0.05) with richness when habitat-types were considered individually. Regressions of richness with indices derived from within-transect means or spatial variability in reflectance, reflectance band ratios, as well as vertical distribution descriptors and height percentiles from LIDAR data produced estimation errors of 0.4-2.5 species per transect. Best-fit indices from hyperspectral data indicate spectral bands in the near- and mid-infrared spectra are most important in the estimation of plant species richness while LIDAR indices indicate the importance of vegetation height and structural complexity in estimating plant species richness. The capability of utilizing remotely sensed data to classify vegetation composition and estimate species richness provides a promising means of assessing and monitoring vegetation on barrier islands

Remote sensing technologies for the assessment of marine and coastal ecosystems

Abstract This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversityinfo:eu-repo/semantics/submittedVersio