Equation 9 Pixel Height Mean Difference
2.3 CURRENT LITERATURE ON COASTAL REMOTE SENSING
2.3.4 MULTI-DIMENSIONAL DATA FUSION FOR SHORELINE MEASUREMENT
The preference for multisource data has grown due to the implications of tidal variations on spatial resolution and spectral confusion (Andréfouët et al., 2003). Consolidating data from multiple sources allows for the creation of a unified, centralized perspective of the datasets to assist in formulating well informed decisions. Data must be collected, stored, transformed, and disseminated in a way that preserves the integrity of any study. The repeatability and thus the success of any study lies in its data management plan. It is evident that although each of these remote sensing techniques has their own merits, there are still shortcomings to be addressed. The solution lies in identifying appropriate data fusion or compatibility mechanisms. Several integration software’s, algorithms and processes are designed to accommodate data fusion.
Malthus and Mumby (2010) reaffirmed the necessity of a more synergistic approach to the incorporation of remote sensing tools. Integrating various datasets in coastal erosion studies is inevitable because there’s a need to identify the limits and influence of coastal waters along with human activities (Malthus &Mumby, 2010). For example, LiDAR may supply positional data whilst optical data offers spectral information and radar data focuses on the structural geo-morphodynamical aspects of the coastline. Although studies in the past have focused on individual aspects of coastal environments, there has been a shift towards more all- encompassing studies due to the growing attraction of integrated studies (Boak &Turner, 2005; Toure et al., 2019). Ultimately, shoreline detection is dependent on spatial and spectral resolution. The higher the resolution, the better the accuracy. Being able to control flying heights, acquisition times and sensor positioning means there is more flexibility in scale when using ground and airborne based platforms as well as being able to synchronize data collection according to tides as opposed to satellites which are bound to pre-programmed orbits (Jeong et al., 2018).
Deronde et al., (2008) conducted a study that combined remote sensing and soil science principles in 3 distinct study sites on the Belgian coast for the period of 2000 to 2004 with hopes of insight into the longer-term changes of erosion by using LIDAR and hyperspectral imagery for analysis (Deronde et al., 2008). Morphological filtering and the reduction of point density allowed for the creation of erosion and accretion maps by subtracting subsequent DTM’s from each other. Vertical accuracy was approximated to be 5cm ±7mm.
The volume differences were calculated first by polygon and secondly by coastal zone to integrate larger beach areas between the low water mark and dune foot. The inclusion of hyperspectral imagery further validated the study by using a CASI scanner which operates within the 545nm spectral range (visual and near-infrared range) for the years 2000 to 2003 and an AISA-Eagle (400 and 900 nm range) was used for 2004. Nearest neighbor was used to resample data to 2m x 2m. After geometric and radiometric correction, they were able to classify the beach sediment into 7 sand type classes based on the reflectance values of the sediment top layer using the linear discriminant classifier and sequential floating forward search algorithm (Deronde et al., 2008).
This was confirmed by field work in the form of sampling, which served as a means of training and validation of classifications, ensuring to collect both wet and dry samples to reflect both the intertidal and the supratidal beach because the effect of wetness on the spectral signature needed to be taken into consideration. It was found that these sand classes had a direct correlation to the topography and geomorphology of the shoreline, for example fine sand on the lower shore face features a particular mineralogical, grain size and sorting composition (Bernard O. Bauer &Davidson-Arnott, 2002). If found elsewhere on the beach, then the spatial dynamics of the sand classes can be determined and thus the morphodynamics of the coastal environment. As such sand classification allowed for a tracer analysis to determine sediment movement.
The combination of laser scan and hyperspectral datasets allowed for a comprehensive qualitative analysis of sediment dynamics inclusive of volumetric changes, mean height differences and sediment transport direction further highlighting the necessity to combine multispectral data and the potential of data fusion (Brock &Purkis, 2009; Thomson et al., 2010). Not many studies make use of hyperspectral data due to logistical and financial constraints. The low resolution of the Compact High Resolution Imaging Spectrometer (CHRIS) instrument on the Project for On-Board Autonomy (PROBA) satellite and Hyperion
on the Earth Observing-1 (EO-1) did make the use of space borne hyperspectral data undesirable (Deronde et al., 2008). As such, with adequate financial support airborne platforms are usually preferred especially with concerns of meteorological and tidal constraints. However, the introduction of UAVs with active sensors has led to more targeted data acquisition opportunities in coastal surveying and observing smaller time scales will allow us a better understanding of sediment transport processes. Major limitations were mainly organizational for this study; for example, no data could be sourced for 2003 and the datasets were not always in sync time wise (Deronde et al., 2008).
Amaro et al.(2014) designed a labor-intensive methodology that incorporated both moderate and high-resolution satellite imagery, as well as a Post Processing Kinematic (PPK) GPS survey (Amaro et al., 2014). It allowed for both a short- and long-term analysis of sediment movement on the Ponta Negra beach in Brazil. Their optical imagery proved that tidal height was the most significant factor in image acquisition with an accuracy of 0.5 pixels as such images were acquired during neap tides. 3-Dimensional (3D) modelling was based on geodetic surveys (Amaro et al., 2014). A local positioning reference base was established through the installation of geodetic stations based on the Brazilian Institute of Geography and Statistics NBR 14166 relative to the Brazilian Network of Continuous Monitoring of GPS System (RBMC). 3 other stations were installed with an approximate distance of 3.5km between them. From these stations, coordinates, standard deviation, ellipsoidal elevation and orthometric height were determined (Amaro et al., 2014).
A GPS receiver mounted on a quad-motorcycle acted as a rover receiver linked to the GPS antenna at the main reference base. The shoreline position and the 3D condition of the beach morphology were modelled within a horizontal and vertical accuracy of 5mm. To further validate results, Electronic Distance Measurement (EDM) instruments were used to evaluate the accuracy of the generated DEM through cross-shore topographic profiles. The major advantage of applying this LIDAR technique is the ability to organize a survey with a wide range of points covering a large area and generating sequential DEM’s to estimate volume balance and sediment accretion or erosion rates (Brock &Purkis, 2009). From error analysis it became apparent that the DEM’s generated here were equivalent to those from traditional topographic profiles. The terrestrial survey confirmed the satellite-based shoreline
propagation data and distinguished erosion-accretion segments along the beach (Deronde et al., 2008).
Based on their findings they were able to identify sediment supply zones along the beach and relate their findings to anthropogenic influences such as runoff from drainage systems in the area. This case study illustrated the influence of urbanization on coastal areas, and this presents an avenue to assist with decisions regarding coastal demarcation, defining coastal erosion risk areas and strategic intervention for coastal protection and urban sprawl. Studies such as this showcase the potential of predicting future shoreline positions based on multisource data from various spatio-temporal contributions.
Although there have been attempts to create sensors solely for coastal monitoring, they are only designed to measure a few parameters. The necessity of sensors with specific spectral resolutions based on the spectral signatures of coastal areas has been of particular concern to researchers. For example, mapping sediment transport at the mineral level is next to impossible because sensors cannot distinguish between minerals due to the close variability of reflectance spectra. Future designs need to be based on scientific and practical objectives for an effective data acquisition strategy for efficiency, reliability, flexibility, coverage, frequency, and risk (Malthus &Mumby, 2010).
The studies reviewed here conclude that optimum results are achievable with the combined effort of multiple remote sensing techniques. This chapter showcases how multifaceted coastal erosion studies can be and allows for an appreciation of how varying perspectives can be inspired from underlying theories.