GIS-based Analysis and Modelling with Empirical and Remotely-Sensed Data on Coastline Advance and Retreat

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941û2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941û2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in GuyanaÆs coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline

An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

Extreme flood-driven fluvial bank erosion and sediment loads: direct process measurements using integrated Mobile Laser Scanning (MLS) and hydro-acoustic techniques: Direct measurement of flood-driven erosion using MLS and MBES

Copyright © 2016 John Wiley & Sons, Ltd. This methods paper details the first attempt at monitoring bank erosion, flow and suspended sediment at a site during flooding on the Mekong River induced by the passage of tropical cyclones. We deployed integrated mobile laser scanning (MLS) and multibeam echo sounding (MBES), alongside acoustic Doppler current profiling (aDcp), to directly measure changes in river bank and bed at high (~0.05 m) spatial resolution, in conjunction with measurements of flow and suspended sediment dynamics. We outline the methodological steps used to collect and process this complex point cloud data, and detail the procedures used to process and calibrate the aDcp flow and sediment flux data. A comparison with conventional remote sensing methods of estimating bank erosion, using aerial images and Landsat imagery, reveals that traditional techniques are error prone at the high temporal resolutions required to quantify the patterns and volumes of bank erosion induced by the passage of individual flood events. Our analysis reveals the importance of cyclone-driven flood events in causing high rates of erosion and suspended sediment transport, with a c. twofold increase in bank erosion volumes and a fourfold increase in suspended sediment volumes in the cyclone-affected wet season. Copyright © 2016 John Wiley & Sons, Ltd

Biomass Density Based Adjustment of LiDAR-derived Digital Elevation Models: A Machine Learning Approach

Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively

Coastal Change in Tropical Overseas and Temperate Metropolitan France Inferred from a National Monitoring Network: A Summary from the Current Special Issue

International audienceThis paper provides an overview of the papers published in this Special Issue « Coastal Evolution under Climate Change along the Tropical Overseas and Temperate Metropolitan France » of Journal of Coastal Research. This special issue reflects the large diversity of mainland and tropical French coast and highlights the complex, site-and timescale-specific, combination of factors driving coastal evolution. Many contributions reveal a strong climate control on storm wave activity and, in turn, coastal response. In some contributions, the inherited geology and anthropogenic factors clearly appear affect coastal change. This special issue emphasises the need to monitor the coast combining different means to improve our understanding and predicting capacities of the natural variability of coastal response in a changing climate

Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007

Tidal Flats are important examples of extensive areas of natural environment that remain relatively unaffected by man. Monitoring of tidal flats is required for a variety of purposes. Remote sensing has become an established technique for the measurement of topography over tidal flats. A further requirement is to measure topographic changes in order to measure sediment budgets. To date there have been few attempts to make quantitative estimates of morphological change over tidal flat areas. This paper illustrates the use of remote sensing to measure quantitative and qualitative changes in the tidal flats of Morecambe Bay during the relatively long period 1991–2007. An understanding of the patterns of sediment transport within the Bay is of considerable interest for coastal management and defence purposes. Tidal asymmetry is considered to be the dominant cause of morphological change in the Bay, with the higher currents associated with the flood tide being the main agency moulding the channel system. Quantitative changes were measured by comparing a Digital Elevation Model (DEM) of the intertidal zone formed using the waterline technique applied to satellite Synthetic Aperture Radar (SAR) images from 1991–1994, to a second DEM constructed from airborne laser altimetry data acquired in 2005. Qualitative changes were studied using additional SAR images acquired since 2003. A significant movement of sediment from below Mean Sea Level (MSL) to above MSL was detected by comparing the two Digital Elevation Models, though the proportion of this change that could be ascribed to seasonal effects was not clear. Between 1991 and 2004 there was a migration of the Ulverston channel of the river Leven north-east by about 5 km, followed by the development of a straighter channel to the west, leaving the previous channel decoupled from the river. This is thought to be due to independent tidal and fluvial forcing mechanisms acting on the channel. The results demonstrate the effectiveness of remote sensing for measurement of long-term morphological change in tidal flat areas. An alternative use of waterlines as partial bathymetry for assimilation into a morphodynamic model of the coastal zone is also discussed

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex