Abstract
The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world’s most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R2 = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas.
Highlights
Very high resolution (VHR) images acquired by means of Earth observation satellites or unmanned aerial vehicles (UAV) contain an important amount of Earth surface’s information, which can be used to tackle the world’s most pressing global challenges, such as climate change, population growth, and natural resource depletion
We presented a framework of image analysis for automatic extraction of shoreline both from GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV), as well as shallow water depth extracted on GeoEye-1 satellite image
Automatic shoreline extraction and satellite-derived bathymetry (SDB) methods applied to VHR images, as described in the current study, provided results with reliable intelligence, demonstrating the impact of natural phenomena in the studied area
Summary
Very high resolution (VHR) images acquired by means of Earth observation satellites or unmanned aerial vehicles (UAV) contain an important amount of Earth surface’s information, which can be used to tackle the world’s most pressing global challenges, such as climate change, population growth, and natural resource depletion. Multi-temporal shorelines extracted on VHR satellite images [3] have allowed the reconstruction of shoreline evolution and, eventually, the assessment of coastal erosion process. Advancement both in sensors and algorithms capacity has facilitated onshore [4], as well as offshore [5], studies performed using satellite images. Heavily affected by the impact of climate change [10], VHR images are used for accurate mapping of shoreline evolution [11] and for predicting and determining the coastal storm impacts [12] These VHR images are the main source of information for our past, present, and future Earth system, their acquisition requires sufficient financial means. Whereas the past remote sensing studies conducted using R2V approach were land-based studies [14] or for identification of shoreline changes using moderate spatial resolution images [15], the current study proposed the application of
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
