Bindel, Marcus

Hartmann, Knut

Heege, Thomas

Wettle, Magnus

English

Hydraulic Engineering Repository

Article, Published VersionHartmann, Knut; Heege, Thomas; Wettle, Magnus; Bindel, MarcusSatellite-derived bathymetry – An effective surveying toolfor shallow-water bathymetry mappingHydrographische NachrichtenVerfügbar unter/Available at: https://hdl.handle.net/20.500.11970/107881Vorgeschlagene Zitierweise/Suggested citation:Hartmann, Knut; Heege, Thomas; Wettle, Magnus; Bindel, Marcus (2017): Satellite-derivedbathymetry – An effective surveying tool for shallow-water bathymetry mapping. In:Hydrographische Nachrichten 108. Rostock: Deutsche Hydrographische Gesellschaft e.V..S. 30-33. https://doi.org/10.23784/HN108-05.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.30 Hydrographische NachrichtenSpace hydrographychoice of the correct sensor, the bathymetric data quality is the outcome of selecting the ideal data set and extracting bathymetric grids in the correct manner. The next paragraphs highlight these nec-essary steps in the SDB workflow.SDB workflowSimilar to other survey methods SDB requires preparation, data processing, data post-processing and QA/QC routines. For all these steps trained ex-perts are required for these remote sensing, geo-matic and hydrographic exercises.Feasibility assessment and selection of best data setsThe objective of this task is to identify satellite im-age data recorded in near-ideal environmental conditions, in terms of spatial resolution and re-cording dates and other specifications. This proc-ess is similar to the pre-planning phase of acoustic or LiDAR shallow-water surveys where information on local environmental conditions is accessed. But in contrast to survey campaign planning SDB can make use of already archived image data which allows to select the image captures which has most feasible environmental conditions. This is of importance because it allows already at plan-ning phase to quickly provide reliable information on final bathymetric grid coverage and maximum mapping depth.Extra care is compulsory for this selection proc-ess as it can significantly influence the following data processing and finally the data quality and data coverage of the final bathymetric grid.The feasibility assessment includes the check of the archived data from one or typically multiple Satellite data sources: Whole­earth imagery and its archivesDepending on the spatial resolution/pixel size of the satellite images they are typically classified in very high resolution (<2 m spatial resolution), high (<10 m), moderate (better than 100 m) and coarse (several hundred metres) data sets. Only the first three are typically of interest for shallow-water bathymetric mapping. Imaging satellite data re-corded every place on earth in high frequency and archive the image data. We can now look back to an archive of about 15 years for very high-resolu-tion data and almost 40 years of moderate resolu-tion data sets which allows not only mapping of most recent condition but also the change over time. Satellite data coverage is not equally distrib-uted around the globe and ranges from few data sets (e.g. remote places in the higher latitudes) to several hundreds to thousands of data sets (e.g. urban areas).Every satellite sensor comes with unique specifi-cations determining its feasibility for SDB methods. The most important factor is the spatial resolution: The ability to map the seafloor in highest detail, the amount of spectral information and the bit depths in which they are recorded, signal-to-noise ratio and the geolocation accuracies. Modern sat-ellite sensors such as WorldView-2 and 3, Pleiades, Sentinel-2 and Landsat 8 have been used inten-sively and can be seen as feasible sensors to ap-ply SDB methods. Because light penetration depth into the water column decreases with increasing light wavelength (see Fig. 1) those satellite sources which have the ability to record in multiple optical wavelength (bands) are of particular importance for optical SDB methods. However, more than the An article by KNUT HARTMANN, THOMAS HEEGE, MAGNUS WETTLE and MARCUS BINDELAuthorsDr. Knut Hartmann is Director Client Service at EOMAP GmbH & Co KG. in Seefeld, Germany. Dr. Thomas Heege is CEO  and founder of EOMAP.Dr. Magnus Wettle is Managing Director at  EOMAP Australia Pty Ltd.Marcus Bindel is data analyst at EOMAP.hartmann@eomap.deheege@eomap.dewettle@eomap.combindel@eomap.deUnlike other survey methods satellite­derived bathymetry (SDB) offers a remote map­ping of shallow water zones. It is based on the concept of using the reflectance inten­sity of different wavelengths (colours) of the sunlight which is recorded by the satellite sensor. This information in combination with relevant databases and physical models determines the shallow water depth down to the light penetration depth. In the last decade, there has been an increasing interest of SDB methods and derived data. Vari­ous exercises have been done by academia and industry to validate and evaluate SDB data sets (REF). This awareness is based on two major developments: the recent availa­bility of very high­resolution satellite data mapping the seafloor in sufficient detail and the design of robust and standardised algorithms and workflows. The demand of ac­cessing bathymetric data for the shallow water zone, costs and the ability to map with ship and airborne sensors have also led to high interest in this effective surveying tool. The zone is still poorly surveyed for various lo­cations worldwide (IHO 2016a). This article pro­vides an overview of all aspects of SDB surveys from satellite sources, SDB workflows and methods to use cases.Satellite-derived bathymetrySatellite-derived bathymetry – SDB | Remote sensing | Earth observation | Mapping depthAn effective surveying tool for  shallow-water bathymetry mappingHN 108 — 10/2017 31Space hydrographyimage data sources, which can contain commercial and none commercial image data. In total, about a dozen satellite data archives are being accessed for each site. For each stored data set – alongside accessing tidal information, multi-temporal image data comparisons and charting data checks – an assessment of, e.g., cloud coverage, turbidity con-ditions, sunglint risk is generated. With the feasibility assessment one or more op-tions for creating bathymetric grids including esti-mated data coverage area and approximate depth range are available.SDB processingSDB data processing or the extraction of bathymetric grids based on the selected image data sets is the centre of the workflow. Two meth-ods can be classified: empirical and physics-based methods.Initially, empirical methods were used to require known depth information over the study area. By comparing these known depths with the satel-lite signal, a statistical relationship can be derived that adequately describes depth as a function of the signal. Aside from requiring known data, these methods will only work for a given satellite image. A subsequent satellite scene, even of the same location, may contain different atmospheric and in-water parameters the statistical relation-ship needs to be recalculated. Another aspect of these methods is that the statistical relationship is valid for one water type and one seafloor type. And one also need to apply the correct formula to each pixel in the image, i.e., you need to inform the algorithm a priori which substrate type is en-countering in that image pixel. This brings the problem full circle back to one of the fundamental challenges of satellite-derived bathymetry: How do you know that a darker signal is due to deeper water, a darker substratum, or a bit of both? Still, these methods find their use as they are relatively straightforward to apply (»Cookbook approach«, IHO 2016b) and, as well as being computationally fast and could serve as an initial step to identify seafloor topography.Physics-based methods on the other hand do not require known depth information for the study area, and can therefore be applied independent of satellite data type and study area. These methods rely on fully describing the physical relationship between the measured light signal and the wa-ter column depth (Fig. 2). Variability in the atmos-phere and water column is accounted for within the algorithm inversion, and no ›tuning‹ to known depths is required. In principle, therefore, an area can be targeted that is physically inaccessible and for which there is no previous known informa-tion. Not surprisingly, these physics-based meth-ods do require more sophisticated algorithms and powerful processing capacity. But they also proof more accurate, especially in areas with vari-ous bottom type, turbidity or atmospheric condi-tions. Amongst these methods CSIRO’s SAMBUCA (Wettle et al. 2006) and EOMAP’s MIP are most used (Siermann et al. 2014). Although not man-datorily necessary these algorithms can include vertical sounding information in calibration of the model to optimise the vertical quality.Because of the advantages of physics-based methods they must be seen as the modern state-Fig. 2: Where does all the light come from? The diagram shows the relative amount of measured light energy which contains water depth information. It is critical to accurately account for the other sources of light energy in order to separate out the relevant water column depth contribution to the measured signalFig. 1: Maximum light pen-etration depth of sunlight highlighted for the Baltic Sea (Denmark) using very high-resolution WorldView-2 satel-lite image data. The colours represent the different wave-lengths of the sensor (colours) which are overlaid by 5 m and 10 m depth contours of the nautical chart. With increas-ing depth seafloor reflect-ance is dominated by shorter wavelengths. In organic rich waters such as the Baltic Sea the green wavelength(s)  penetrate deepest32 Hydrographische NachrichtenSpace hydrographycording (known as optically shallow-water zone). It is strongly depending on water clarity and as a rule of thumb is found to be in the range of 1 to 1.2 times Secchi disc depth. As already highlighted, a great advantage of SDB methods is the use of already archived data and the selection of an ideal image. This is of particular importance for areas with a high seasonal dynamic of turbidity and ice coverage. For example, for the Wadden Sea area classified as a high-turbid coastal zone it is possible to map down to 7 m to 9 m (below LAT) in good environmental conditions and selecting feasible data sets (see Fig. 4), and in the southern Baltic Sea region mapping can be done down to approxi-mately 10 m (see table and Fig. 1).Location MaximumSouthern Baltic Sea 8 m to 10 mNorthern Baltic Sea 1 m to 3 mNorth Sea, Wadden Sea area 7 m to 9 mCaspian Sea 5 m to 10 mBlack Sea 3 m to 5 mRed Sea 25 m to 30 mPersian Gulf,  southern Arabian countries10 m to 15 m Persian Gulf,  northern Arabian countries2 m to 10 m Mediterranean Sea 20 m to 30 mSince the last decade, SDB has been seen as an ef-fective surveying tool. Motivations to consider SDB data sets differ depending on the final application as described below.Use case: Access to historical data Using archived image data allows creating historic topographic conditions. This information is of vital interest to understand the impact of human activi-ties and/or natural disasters such as hurricanes and tsunamis. The arbitration of the United Nations Convention on the Law of the Sea between the Republic of the Philippines and the People’s Re-public of China is one of special use cases where historic information was necessary. Access rights and responsibilities to certain areas in the South China Sea were disputed, especially on reefs which have significantly been reshaped by engineering of-the-art for processing optical satellite image data (see Fig. 3). Post-processingData cleaning, peak detection and QA/QC pro-cedures are as important to shallow-water bathymetric grids as they are to LiDAR and acoustic point clouds. The post-processing cleans data sets for biased data, such as ship wakes, cloud shadows or wave breaking areas and distinguishes between shallow-water bathymetric information and infor-mation that cannot be interpreted in this way. Fur-thermore, information on vertical and horizontal displacement introduced by the water refraction are minimised and the bathymetric data are cor-rected to a relevant vertical datum (LAT). Parts of these steps can be automised but a QA/QC proce-dure is an expert’s tasks which requires knowledge and relevant hydrographic software tools.Results and use casesThe result of the SDB workflow is a bathymetric grid whose file formats are fully in line with other surveys and includes the bathymetric grid and metadata (ISO 19115).The data coverage ranges from coastline to light extinction depth at time of the satellite image re-Fig. 4: Comparison of acoustic and LiDAR bathymetric data sets and a satellite-derived bathymetric grid for the German Wadden Sea area. It shows the validity and relatively deep mapping potential of the Wadden Sea area as well as the ability to identify movements of seabed topographyFig. 3: Satellite-derived bathymetry grid for the shallow waters of Poel down to 10 m depth using satellite image data recorded in 2016HN 108 — 10/2017 33Space hydrographyactivities in the past years. The court required an objective view to shallow-water bathymetric data in order to understand the presence of historical low-tide features. These features determine the responsibilities and boundaries and are of high importance. EOMAP provided this data using SDB methods obtained in archived imagery and sup-plied strong arguments for this international court case (Permanent Court of Arbitration, 2016).Use case: Integrated survey concept of SDB and acoustic methodsAlthough SDB can be seen as a stand-alone meth-od the combination of SDB and acoustic survey methods form a powerful combination. The shal-low-water areas of down to 5 m or 10 m are often not accessible for acoustic methods or require sig-nificantly higher survey efforts and costs. This is the depth range in which SDB methods work best and by combining acoustic and SDB survey methods a perfect data coverage from shore to deep can be obtained. The overlapping of SDB and acoustic methods can also serve to calibrate and validate SDB methods and thus provide detailed informa-tion on data quality of the derived SDB methods (Fig. 4). This concept has been successfully applied by the UK Hydrographic Office in the recent Brit-ish Admiralty Chart BA 2066 of Southern Antigua. EOMAP’s SDB grid has been selected by the UKHO following a competitive exercise and is now an in-tegrated part of the BA 2066 chart.Future perspectives –  Where are we heading?Over the intermediate term it is expected that satellite-derived mapping of the seafloor will con-tinue to be increasingly accepted and integrated as a survey tool – as is now already the case for a number of innovative user groups. Certain de-velopments still need to be done on how to best quantify uncertainties, standardised error budget, operationalise workflows and last but not least an international agreement on standards. One likely technical development will be the multi-temporal and sensor-agnostic mapping ap-proach which oversimplified means: Use all the image data which is available and do the best out of it. With the advances of cloud computing, phys-ics based algorithms and an increasing selection of image data, this would be a natural evolution phase for satellite-derived bathymetry.Parts of these future developments are cur-rently co-funded by the European Commission in the frame of the EU Horizon 2020 BASE-platform project, which furthermore introduces other re-mote mapping solutions for the worlds ocean. “ReferencesIHO (2016a): Status of Hydrographic Sur-veying and Charting Worldwide; IHO Publication c-55, Monaco, December 2016, 528 pp.IHO (2016b): The IHO-IOC GEBCO Cook Book; IHO Publication B-11, Monaco, December 2016, 429 pp.Permanent Court of Arbitration (2016) Case Nº 2013-19. In the matter of the south China sea arbitration. An arbitral tribunal constituted under annex VII to the 1982 United Nations Convention on the Law of the Sea – between – the Republic of the Philippines – and – the People’s Republic of China; URL: https://pca-cpa.org/wp-content/uploads/sites/175/2016/07/PH-CN-20160712-Award.pdfSiermann, Joris; Craig Harvey; Gareth Morgan; Thomas Heege (2014): Satel-lite derived Bathymetry and Digital Elevation Models (DEM); International Petroleum Technology Conference, Doha, QatarWettle, Magnus; Vittorio Ernesto Brando (2006): SAMBUCA – Semi-Analytical Model for Bathymetry, Un-mixing and Concetration Assessment; CSIRO, Clayton South Victoria, Australia

Satellite-derived bathymetry – An effective surveying tool for shallow-water bathymetry mapping

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Satellite-derived bathymetry – An effective surveying tool for shallow-water bathymetry mapping

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