scholarly journals Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3027 ◽  
Author(s):  
Xuechun Zhang ◽  
Yi Ma ◽  
Jingyu Zhang
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Linear Model ◽  
Spatial Resolution ◽  
Water Depth ◽  
High Spatial Resolution ◽  
Optical Parameters ◽  
Optical Remote Sensing ◽  
Depth Measurement ◽  
Dongdao Island

Bathymetric surveys are of great importance for submarine topography mapping and coastal construction projects. They are also of great significance for terrain surveys of islands and coastal zones, maritime navigation and marine management planning. Traditional ship-borne water depth measurement methods are costly and time-consuming, therefore, in recent years, passive optical remote sensing technology has become an important means for shallow water depth measurements. In addition, multispectral water depth optical remote sensing has wide application values. Considering the relationship between water depth and the inherent optical characteristics of water column, an inherent optical parameters linear model (IOPLM) is developed to estimate shallow water bathymetry from high spatial resolution multispectral images. Experiments were carried out in the shallow waters (≤20 m) around Dongdao Island in China’s Paracel Islands and Saipan Island in the Northern Mariana Islands. Different accuracy evaluation indexes were used to verify the model. The comparisons with the traditional log-linear model and the Stumpf model show that in terms of overall accuracy and accuracy in different water depths, the IOPLM has slightly better results and stronger retrieval capabilities than the other models. The mean absolute error (MAE) of Dongdao Island and Saipan Island reached 1.17 m and 1.92 m, and the root mean square error (RMSE) was 1.49 m and 2.4 m, respectively.

Towards a high spatial resolution limit for pixel-based interpretations of optical remote sensing data

2008 ◽  
Vol 41 (11) ◽  
pp. 1724-1732 ◽  
Author(s):  
Jean-Luc Widlowski ◽  
Thomas Lavergne ◽  
Bernard Pinty ◽  
Nadine Gobron ◽  
Michel M. Verstraete
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Resolution Limit ◽  
Sensing Data

scholarly journals Analysis of Very High Spatial Resolution Images for Automatic Shoreline Extraction and Satellite-Derived Bathymetry Mapping

Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 172
Author(s):  
Giovanni Randazzo ◽  
Giovanni Barreca ◽  
Maria Cascio ◽  
Antonio Crupi ◽  
Marco Fontana ◽  
...  
Keyword(s):  
Shallow Water ◽  
Spatial Resolution ◽  
Water Depth ◽  
High Spatial Resolution ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Coastal Areas ◽  
Ratio Method ◽  
Very High Spatial Resolution ◽  
Very High

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.

scholarly journals A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

2018 ◽  
Author(s):  
Chunming Wu ◽  
Xiao Li ◽  
Weitao Chen ◽  
Xianju Li
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Fault Analysis ◽  
Optical Remote Sensing ◽  
Learning Technology ◽  
Sar Images ◽  
Sensing Data ◽  
Geological Information

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

2019 ◽  
Vol 29 (06) ◽  
pp. 2030006
Author(s):  
Chunming Wu ◽  
Xiao Li ◽  
Weitao Chen ◽  
Xianju Li
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Fault Analysis ◽  
Optical Remote Sensing ◽  
Learning Technology ◽  
Sar Images ◽  
Sensing Data ◽  
Geological Information

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. By reviewing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, it is necessary for geological remote sensing research to be truly multi-disciplinary or inter-disciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved the capabilities of automatic processing and data analysis with HR images.

Sign in / Sign up

Export Citation Format

Share Document