Bathymetry Acquisition - Technologies and Strategies

Transcription

1 Bathymetry Acquisition - Technologies and Strategies Investigating shallow water bathymetry acquisition technologies, survey considerations and strategies N. D. Quadros Report prepared for the Commonwealth Government of Australia, Department of Climate Change and Energy Efficiency

2 Document Attributes File name File owner File Location CRSI UDEM2 Project4 Report Stage 2 ND Quadros...Coastal and Business Projects\ Projects\04_2011_Bathy_UNA\Mngmt Document Control Version Status & revision notes Author Date Approved by Date 0.1 First Draft ND Quadros 21/12/2012 G Kernich 29/01/ Second Draft ND Quadros 29/01/2012 C Fraser 15/03/ Final Publication ND Quadros 03/04/2013 G Kernich 11/04/

3 Acknowledgements The CRC for Spatial Information (CRCSI) would like to acknowledge the funding and support provided by the Australian Department of Climate Change and Energy Efficiency. A special acknowledgement to Fugro LADS, Pelydryn and EOMAP for their significant inputs into the report. The CRCSI would like to also thank the following individuals for their input into this report: Fugro LADS - Mark Sinclair, Hugh Parker and Nigel Townsend Pelydryn - Andy Waddington EOMAP - Magnus Wettle Members of the Intergovernmental Committee for Surveying and Mapping (ICSM) Bathymetry Working Group Royal Australian Navy (RAN) LADS - Richard Mortimer James Cook University - Rob Beaman Optech Australia - David Collison Airborne Hydrography AB (AHAB) - Swante Welander Riegl - Martin Pfennigbauer Australian Hydrographic Service - Doug White Commonwealth Scientific and Industrial Research Organisation (CSIRO) - Norm Campbell Deakin University - Daniel Ierodiaconou Defence Science and Technology Organisation (DSTO) - Julian Vrbancich Department of Sustainability and Environment Victoria - Christina Ratcliff Department of Science, Information Technology, Innovation and Arts Queensland (DSITIA) - Ramona Dalla Pozza Land Information New Zealand (LINZ) - Stuart Caie Office of Environment and Heritage New South Wales - Bruce Coates Geoimage - David Brady 3

4 Executive Summary Australian Government Departments have recently committed to significant investments in bathymetric LiDAR acquisition for the modelling of coastal processes. This project has been initiated by the Cooperative Research Centre for Spatial Information (CRCSI) and Department of Climate Change and Energy Efficiency (DCCEE) to better understand the requirements for the near-shore bathymetry collection, and to outline the strategies which can be employed to satisfy user needs. The first stage of this research identified user needs and challenges by analysing a questionnaire distributed to bathymetry users in Australia and New Zealand. User concerns on bathymetry extent, quality and accessibility were all discussed within the first research report. This research complements the first stage by outlining alternative technologies, sensors and strategies to acquire bathymetry. To address this research this report is divided into the following five chapters: 1. A description of current bathymetric sensor technologies 2. Project and environmental factors which impact bathymetric survey technologies 3. Recent sample bathymetric surveys conducted with multiple sensors 4. An example strategy for a complex, large area, near-shore bathymetric survey 5. The development of a standard bathymetric LiDAR specification template The most recent development in bathymetric LiDAR is the new range of so-called topo-bathy sensors. These lower power sensors complement the traditional bathymetric LiDAR by acquiring a higher point density onshore and in depths less than 10m. The most suitable technologies to supplement bathymetric and topo-bathy LiDAR are satellite imagery and maritime vessel based multi-beam echo sounders (MBES), of which the latter may be integrated with a terrestrial laser scanner. A number of other technologies exist, however they are still in the research phase or do not have a distinct advantage over LiDAR, satellite imagery nor MBES. When initiating a bathymetric survey a number of project and environmental considerations should be assessed to select the most appropriate technology and sensor. The main project considerations include the required accuracy, point spacing, vertical datum, coverage, budget, timelines, accessibility and supplementary datasets. The main environmental considerations include the depths, coverage, turbidity, temporal variations and seabed bottom type. All these factors affect the choice of technology and sensor in different ways. Knowledge and experience of how each technology and sensor are impacted by these factors should be utilised when planning a bathymetric survey. For large area, near-shore bathymetry acquisition we propose a hierarchy of technologies to produce the most effective survey. The hierarchy is broadly based on a trade-off between acquisition time and cost efficiency, against the measurement density and accuracy. The most cost effective technology over a large area (which is first in the hierarchy) is satellite image derived bathymetry, provided the necessary conditions exist. However, it is less accurate, has a reduced depth penetration, and is not as suitable for as many applications as bathymetric LiDAR and MBES. Bathymetric (and topo-bathy) LiDAR is the second technology in the hierarchy. Airborne LiDAR traverses the littoral zone and produces bathymetry which has both the accuracy and coverage required for coastal modelling. Bathymetric LiDAR is less cost effective than satellite image derived bathymetry, and also produces gaps in areas of high turbidity. The third technology in the hierarchy 4

5 is maritime vessel-based MBES. MBES has the slowest rate of coverage and is the least cost effective over large, near-shore areas. However, unlike the previous two technologies it is not as affected by turbidity and adverse seafloor conditions. This enables MBES to be acquired strategically within data gaps following a bathymetric LiDAR and satellite imagery acquisition. For large area, near-shore bathymetry surveys, there are numerous options once multi-sensor bathymetry acquisitions are considered. Single sensor surveys can be used, however they do not necessarily present the best value to end users. A multi-sensor approach is able to take advantage of the suitability of each sensor for particular near-shore environments, and when used together are able to complement each other to provide an optimal, value solution for a survey area. A multi-sensor bathymetry acquisition strategy is presented in the report for the Queensland coast south of Cooktown. The strategy is based on minimising the acquisition cost, whilst providing data that would be suitable for a number of applications, thereby providing value for money. The proposed strategy recommends deriving bathymetry from satellite imagery, followed by a bathy LiDAR acquisition, and then an MBES survey in critical areas and LiDAR data gaps. To assist with commissioning a bathymetric LiDAR project for coastal and environmental applications specifications are attached in Appendix B. These specifications are similar to those developed for topographic LiDAR in Australia and New Zealand. The specifications have been reviewed and approved by bathymetric LiDAR operators in Australia and New Zealand. The specifications uphold the International Hydrographic Organisations (IHO) standards and provide a mechanism to commission a bathymetric LiDAR survey. 5

6 Table of Contents 1 Introduction Background Aim and Outcomes Bathymetric Acquisition Technologies LiDAR Bathymetry Satellite and Airborne Derived Bathymetry Satellite and Aerial Imagery Hyperspectral Imagery Algorithms to Derive Bathymetry from Imagery Satellite Radar Maritime Vessel Bathymetry Multi-Beam and Single-Beam Echo Sounders Side-Scan Sonar and Sub-bottom Profilers Specialised Technologies Airborne Electro-Magnetic Bathymetry (AEMB) Autonomous Underwater Vehicles (AUV) Satellite LiDAR Bathymetry Profiling Shallow Water Bathymetric Survey Considerations Project Considerations Extent and Internal Coverage Accuracy, Object Detection and Point Spacing Vertical Datums Budget and Timelines Supplementary Datasets Environmental Considerations Minimum, Maximum and Average Depths Turbidity Impacts and Temporal Variations Sea State and Seabed Bottom Type Environmental Changes - Tide, Water Flow, Seasons, Wind and Daylight Project and Environment Considerations Conclusion LiDAR Bathymetry Survey Strategies Elevation Acquisition of a Turbid Bay Using Multiple Technologies A Shallow Water Dataset Surveyed by Multiple MBES Sensors Progressive Statewide Bathymetry in Critical Areas One Capture Using Both Topographic and Bathymetric LiDAR Integrated LiDAR Acquisition Using Two Concurrent Bathy LiDAR Sensors Future Bathymetry Survey Strategy Recommendations For a Queensland Large Area, Near-Shore Bathymetry Survey

7 6 Bathymetric LiDAR Acquisition Specifications Specification Development References Appendix A - Summary of Current LiDAR Sensors Fugro LADS Mk 3 Bathymetric LiDAR Optech SHOALS 3000, CZMIL and ALTM Aquarius Bathymetric LiDAR AHAB Hawk Eye II and Chiroptera Bathymetric LiDAR Riegl VQ-820-G Bathymetric LiDAR Appendix B - Bathymetric LiDAR Specifications

8 1 Introduction 1.1 Background Bathymetric data has traditionally been acquired by hydrographers for nautical charting purposes, and it continues to be acquired predominantly by maritime vessels. In recent years, near-shore bathymetry has been used within an array of new applications. Near-shore bathymetry, along with river, lake and estuarine bathymetry, has rapidly been gaining importance, particularly in regards to coastal hazard and habitat conservation. Significant progress was made during the early 1990s in near-shore bathymetric surveying with the development of airborne bathymetric Light Detection and Ranging (LiDAR) systems. These systems allow for the rapid acquisition of shallow water (<30-50m depth) bathymetry. Airborne surveys provide a natural complement to maritime vessel surveys, with the former being suited to clear, shallow water and the latter being largely restricted to deeper waters, due to the physical limitations of safely and efficiently operating maritime vessels in shallow water. Bathymetric LiDAR is increasingly being collected to derive elevation for applications such as storm surge modelling, coastal inundation and vulnerability assessments. Bathymetry also supports less obvious tasks such as marine habitat classification, which takes advantage of the LiDAR pulse intensity. The addition of the water column makes bathymetric LiDAR systems more vulnerable than their topographic counterparts to the adverse impacts of environmental effects, which can lead to data gaps, reduced data coverage and quality. For example, measurements tend to fail when water clarity is poor (e.g. high turbidity), which is often the case in the critical inter-tidal zone due to suspended sediments and breaking waves. The Cooperative Research Centre for Spatial Information (CRCSI) and Department of Climate Change and Energy Efficiency (DCCEE) commissioned this report to inform the growing interest and investment in bathymetric LiDAR surveys around Australia. Recently, the Victorian and Western Australian Governments have undertaken significant near-shore bathymetric surveys. New South Wales and Queensland have started with smaller operations, with the desire to expand into large surveys in upcoming years. This report highlights the importance of choosing the most suitable bathymetric LiDAR sensor for a survey and its associated environment. It also recognises the limitations of existing bathymetric LiDAR systems and therefore provides a review of alternative and supporting acquisition technologies. The report takes into consideration different project requirements, coastal environments, and suggests possible multi-sensor survey strategies. This report is the second stage to the bathymetry research within the Urban Digital Elevation Modelling (UDEM) project. The first stage of the research identified a list of bathymetry users in Australia and New Zealand, and their respective bathymetric needs and challenges. This research provides an overview of bathymetry acquisition alternatives to ensure that the most suitable technology is employed. Example projects and bathymetric LiDAR specifications are provided to assist in the planning of future bathymetric LiDAR surveys. This report is intended to be used to guide future shallow water bathymetry collection in Australia. 8

9 1.2 Aim and Outcomes The aim of the reported investigation has been to evaluate the applicability of alternative bathymetric mapping technologies to supplement, or in some cases replace, bathymetric LiDAR surveys. To address the above aim, this report includes: - A review of the currently available bathymetric LiDAR sensors. Each bathymetric LiDAR sensor has unique characteristics. The review focuses on the advantages and disadvantages of each sensor. (Chapter 2 and Appendix A) - A detailed listing of alternative bathymetric acquisition techniques. In light of the limitations of bathymetric LiDAR, the advantages of each technique as an alternative or supplement to bathymetric LiDAR are presented. The details listed for each technique include key features, availability, cost, environmental suitability, accuracy, efficiency and effectiveness. (Chapter 2) - A list of environmental and project features which impact upon the technology/sensor selection, as well as upon design and strategy for bathymetry acquisition. (Chapter 3) - An outline of multi-sensor bathymetry acquisition projects from Australia, New Zealand and overseas. The positive lessons learnt from each project are provided to guide similar projects in the Australian region. (Chapter 4) - A strategy and recommendations for a large area, near-shore bathymetric survey. The recommended survey strategy is exemplified via a project within Queensland, Australia. (Chapter 5) - Specification standards for the collection of bathymetric LiDAR. (Chapter 6 and Appendix B) 9

10 2 Bathymetric Acquisition Technologies The near-shore environment impacts each bathymetric survey technology in different ways. It is important to understand each technology and its nuances so the most suitable technology is engaged to optimise a survey. Each technology has different sensors available, with unique characteristics that can impact upon a survey. This chapter provides a brief description of each bathymetry acquisition technology. The review analyses each sensor's suitability to near-shore surveys, and as a supplement to bathymetric LiDAR. 2.1 LiDAR Bathymetry Bathymetric LiDAR (or bathy LiDAR) has been gathered since the development of combined Global Positioning System (GPS) and Inertial Measurement Unit (IMU) systems in the early 1990s. For a number of years there were two main sensors, Fugro LADS and Optech SHOALS, used for bathy LiDAR surveys. Since then, Airborne Hydrography AB (AHAB) and the National Aeronautics and Space Administration (NASA) have established bathy LiDAR sensors, and more recently Riegl have entered the market. All companies have released new sensors in the past year, with a number of new developments attached to each sensor. This chapter provides an overview of each bathy LiDAR sensor, and highlights some of the technical and operational differences between the various sensors. All LiDAR systems operate on the principle of measuring the time a laser pulse takes to travel from a transmitter to a surface and back to the receiver. Airborne LiDAR systems require a differential GPS (DGPS) or Precise Point Positioning (PPP) solution to position the aircraft before computing the location of the measured surface. In addition an IMU is used to orient the aircraft and sensor. Airborne LiDAR systems have been typically divided into topographic LiDAR (topo LiDAR) and bathy LiDAR systems. However, in the past year new LiDAR systems have been developed which bridge the divide between topo and bathy LiDAR systems. These new systems, which efficiently measure both topographic and bathymetric elevations, are termed topo-bathy LiDAR systems. This report focuses on bathymetry acquisition, and will therefore include both bathy LiDAR and topo-bathy LiDAR systems in its review. Bathy LiDAR systems acquire elevations using a high-powered green (532nm) laser. The 532nm wavelength is optimal for penetrating the water column and it provides the best chance of measuring the seafloor. The wavelength is the crucial difference between bathy LiDAR and topo LiDAR systems, as the latter use an infra-red wavelength of 1064nm, which is unable to penetrate water bodies. Some bathy LiDAR systems use an infra-red wavelength to determine the height of the water surface. However, this is not the case in all systems. All current bathy LiDAR systems can measure both topographic data and bathymetry. The topographic data gathered from a bathy LiDAR system tends to be of a lower quality and density compared to that provided by topo LiDAR. The depth range of bathy LiDAR systems is typically 2-3 times the Secchi Depth, which is a measure of the transparency of the water and is related to turbidity. To obtain the Secchi Depth a small black and white patterned disk is lowered into the water and when it can no longer be seen, the depth is recorded. The bathy LiDAR depth limit is typically 25-40m in Australian coastal waters, however in clear waters deeper observations have been acquired. 10

11 Figure 1 - Airborne bathy LiDAR diagram depicting a measurement of the seafloor using a 532nm green laser. Traditionally, advances in bathy LiDAR involved increases in laser power to obtain greater depth penetration. This has limitations due to the maintenance of eye-safe operation. As the power of the laser pulse is increased, likewise the laser footprint is increased to maintain eye safety. This requirement limits the advantages of endlessly increasing the laser power for bathymetric surveys. In the past couple of years there has been a shift away from high laser power, towards lower laser power, narrower transmitted beams, more frequent measurements and a smaller receiver field-ofview (FOV) (Dewberry 2012). These changes have resulted in topo-bathy LiDAR systems that are similar to topo LiDAR due to their power requirements and footprint diameter, except that they use a green laser. These topo-bathy LiDAR systems are primarily focussed on acquiring topographic data and bathymetry in and surrounding very shallow coastal waters, rivers and lakes. The smaller footprint topo-bathy LiDAR sensors have a shorter laser pulse and a narrow FOV, which are beneficial in coastal submerged environments in the determination of topographic data under short vegetation (USGS 2012). The small receiver FOV rejects ambient light and scattered photons from the water column and bottom-reflected backscatter (Feygels et al. 2003), thereby ensuring a higher contrast for the detection of the bottom return signal. Table 1 provides a summary of the currently available bathy and topo-bathy LiDAR sensors. Older systems still exist, however these have been superseded by at least one of the sensors listed in the table. 11

12 Fugro LADS Mk3 Optech SHOALS 3000 Optech SHOALS 1000T Optech CZMIL Optech ALTM Aquarius AHAB HawkEye IIB AHAB Chiroptera Riegl VQ-820-G Typical Sensor Bathy Bathy Bathy Topo-Bathy Topo-Bathy Bathy Topo-Bathy Topo-Bathy Environment Origin Australia Canada Canada Canada Canada Sweden Sweden Austria Year Released Laser Wavelength/s Green 532nm Green 532nm Green 532nm Laser Energy Per Pulse Measurement Frequency Nominal Laser Water Surface Nominal Flying Height Swath Width (as a function of point spacing or altitude) Typical Point Spacings Minimum Water Depth Typical Maximum Water Depth Commercial Opportunities in Australia and Pacific Green 532nm Infra-Red 1064nm Green 532nm Infra-Red 1064nm Green 532nm Infra-Red 1064nm Green 532nm Infra-Red 1064nm Green 532nm Infra-Red 1064nm 7mJ@532 4mJ@532 4mJ@532 3mJ@ mJ@532 3mJ@ mj@ kHz@532 3kHz@532 1kHz@532 10kHz@ kHz 4kHz@532 36kHz@ kHz 3m@532 2m@532 2m@532 2m@ m AGL m AGL m AGL Nominal 400m AGL 291m m AGL up to 0.93 x AGL 4.m@ m@ m AGL AGL AGL 1.5m@ m@ m AGL AGL 0.6m@532 Nominal 600m AGL 2x2m -8x5m 2x2m - 5x5m 2x2m - 5x5m 2x2m 0.4x0.4m - 0.5x0.5m - 0.4x0.4m - 0.2x0.2m - 1x1m 3.5x3.5m 1 x 1m 0.8x0.8m ~0.5m ~0.3 m ~0.3 m ~0.1 m ~0.1 m ~0.5 m ~0.1 m ~0.1 m ~60m x Secchi depth Fugro LADS operated ~50m x Secchi depth No Commercial Operations ~50m x Secchi depth SHOALS 1000T was used in Northern Australia by Fugro Pelagos ~50m x Secchi depth None Currently ~20m 1 x Secchi depth ALTM Gemini operators - AAM Pty Ltd and Photo- Mapping Services ~40m 2-3 x Secchi depth AAM Pty Ltd - Pelydryn operated. BLOM has operated in Victoria. ~20m 1 x Secchi depth None Currently 400m ~15m 1 x Secchi depth Fugro LADS operated Table 1 - Summary of the bathy and topo-bathy LiDAR systems. The laser wavelengths 532nm and 1064nm are the green bathymetry laser and infra-red topographic/sea surface laser respectively. Specifications supplied by Fugro LADS, Optech, AHAB and Riegl. Note: The NASA EAARL-B topo-bathy LiDAR sensor specifications were not available at the time of publication. Each bathy and topo-bathy LiDAR sensor exhibit characteristics which impact upon bathymetry capture. All of the sensors have unique capabilities which provide advantages and disadvantages depending upon the survey requirements. Appendix A - Summary of Current LiDAR Sensors presents each of the LiDAR sensors shown in Table 1 in more detail. 12

13 2.2 Satellite and Airborne Derived Bathymetry Satellite and Aerial Imagery Satellite imagery provides bathymetry estimates which are not reliable enough to be used for navigation purposes. However, they do provide a cost effective option for bathymetry over large areas. These products are suitable for a range of environmental and scientific applications. The same techniques are used to extract bathymetry from aerial imagery as from high resolution satellite imagery. The most significant difference between these two sources of data is the spatial resolution of the imagery. Typically aerial imagery is a higher resolution than satellite derived imagery. This higher resolution also equates to higher acquisition costs and restricted coverage. As such, more research has been conducted into satellite image derived bathymetry which does not have any mobilisation requirements. Imagery derived bathymetry is not directly measured, it is inferred, and as such the bathymetry is estimated, with a lower accuracy than LiDAR or multibeam echo sounders. Satellite derived bathymetry is used by a number of research organisations and private companies. The depths to which the imagery is used is limited by light attenuation. Depending on water clarity, depths derived from aerial or satellite imagery are limited to 25-30m because of light penetration issues (Collet et al., 2000). Deriving near-shore bathymetry from imagery is based on the principle that different wavelengths of light are attenuated by water to differing degrees depending upon the water depth. This principle is demonstrated practically by inferring lighter areas in the image to shallower water. The bathymetry is derived by establishing the relationship between water depth and pixel values. This relationship can be established by two main approaches (Collet et al., 2000). The first is a physical approach which attempts to take into account the parameters of the physical process affecting pixel values. The second is an empirical approach which requires points of known bathymetry for the purposes of image calibration. The physical approach requires data related to water composition, nature of the sea bottom, and atmospheric conditions amongst others, whereas the empirical approach requires seafloor classification and measured water depths for control (Collet et al., 2000). For the empirical approach, the accuracy of the depth control data will be reflected in the heights derived from the imagery. As with bathy LiDAR, there are various camera sensors available to derive bathymetry from satellite imagery. Table 2 lists some of the satellites from which bathymetry can be derived. Each of these satellites has characteristics which impact upon the imagery's suitability for deriving bathymetry. The number of wavelengths (or bands), especially in the blue-green spectrum enables better penetration of the water column and typically more accurate bathymetry. The satellite processing company GRAS has used WorldView2 imagery to compute bathymetry. The additional wavelengths available in the blue-green spectrum with WorldView2 have enabled a depth penetration of 10-15m deeper than from other satellites (GRAS 2011). Table 2 provides a list of satellites and parameters which affect the suitability of a sensor to derive bathymetry. In the table, the spectral resolution is the number of wavelengths measured by the sensor, which in turn effects its ability to distinguish between different surface types. The radiometric resolution is the sensor's ability to discriminate small differences in the area that corresponds to a single raster pixel. The spatial resolution of the sensor effects the amount of bathymetric detail that can be derived from within the image. The swath width relates to the 13

14 coverage efficiency of each sensor. The higher resolution satellites tend to have a smaller swath. The cost of each satellite's imagery varies, and depending upon the application and budget, the various factors in Table 2 will determine which satellite's imagery is most suitable for a project. A guide for the imagery costs are provided by Geoimage per scene or km 2, and are dependent upon whether the imagery is archived or a new capture. The provider supplied metric accuracy of the unrectified orthoimagery is also listed. The only pertinent factor not provided which may influence the sensor choice is the signal-to-noise ratio. Each sensor should have the signal-to-noise ratio calculated on a scene-by-scene basis, therefore it is not relevant to list as a satellite parameter as it is variable. Spectral Resolution Radiometric Resolution Spatial Resolution Swath Width Cost Per... Cost (Archive) Cost (New) Metric Accuracy ENVISAT MERIS bits 300m 1100km Scene Mid N/A m MODIS bits 250m 2330km Scene Low m LANDSAT7 ETM+ 7 8 bits 30m 180km Scene Low N/A m EO1 HYPERION bits 30m 7.75km Scene Mid Mid 15-25m ALOS AVNIR bits 10m 70km Scene Low N/A 20m SPOT bits 10m 60km Scene High High 15-25m IKONOS 4 11 bits 4m 11.3km Km 2 Low Low 15m QUICKBIRD 4 11 bits 2.6m 18km Km 2 Mid Low 17-23m PLEIADES 4 11 bits 2m 20km Km 2 Mid Low m GEOEYE bits 2m 15.5km Km 2 Mid Mid m WORLDVIEW bits 2m 17.7km Km 2 High High m Table 2 - Satellite imagery parameter comparison. Costs per scene per scene - Low $0-$1000, Mid $1000-$5000, High Above $5000. Costs per km 2 (archive) - Low $10-$12, Mid $12-20, High Above $20. Costs per km 2 (new capture) - Low $20-$25, Mid 25-$30, High Above $30. Costs are based on imagery costs, no processing applied. Accuracies are based on vendor supplied information. Data provided by Geoimage. MERIS, MODIS, Landsat and Hyperion have a low spatial resolution and should only be used for regional or national bathymetry derivation. The detail provided by these products is coarse. WorldView2 is the most suitable for deriving detailed near-shore bathymetry as it contains more bands and a high resolution, however it is one of the most expensive sources of imagery, limiting its usability. WorldView2 alleviates some of the concerns over spatial versus spectral resolution identified by researchers (Mumby and Edwards 2002, Capolsini et al and Hochberg et al. 2003). As seen in Table 2, till WorldView2 was launched the higher spatial resolution satellites contained less spectral bands than the lower resolution satellites. This resulted in a trade-off when choosing a satellite other than WorldView2. If cost and coverage are factors WorldView2 may not be an option for a project. In these cases a sensor such as Landsat or SPOT may provide the optimal balance for spatial and spectral resolution depending upon the budget, and mission requirements. Satellite derived bathymetry has a significant cost and acquisition advantage over maritime vessel or airborne platforms. Much less time and costs are required to derive bathymetry for substantially larger areas with accompanying resolution and accuracy tradeoffs. The efficiency, cost and area covered by each satellite differs depending upon the satellite's parameters, particularly its swath. As an example Sagar and Wettle have derived bathymetry from the ALOS Advanced Visible and Near Infrared Radiometer (AVNIR-2) sensor. With a swath width of 70km, each scene covers 4,900 km 2 (Sagar and Wettle 2010). To survey the same area with bathy LiDAR would take at least five months of airborne survey and ten months of data processing. For an area such as the Great Barrier Reef which covers more than 340,000km 2 satellite derived bathymetry provides a natural complement to maritime and airborne bathymetry platforms in less critical areas. 14

15 A comparison of satellite derived bathymetry with bathy LiDAR was conducted by CSIRO on multispectral WorldView2 data over the Marmion Marine Park (just north of Perth, Australia). This is but one example of analysing WorldView2 for bathymetry derivation. Table 3 summarises the results from the report published by Campbell et al. (2012) which analysed the relationship between the LiDAR depth values and the WorldView2 bathymetry derived using the Lyzenga algorithm (as described in Table 4). The R 2 value is the coefficient of determination which represents how well the derived bathymetry matches the LiDAR data. The RMSE represents the accuracy of the derived bathymetry. Coastal Blue Green Yellow R 2 RMSE Blue Table 3 - Four WorldView2 wavelengths and their significance to deriving bathymetry in the Campbell et al 2012 study. The correlation is shown as the R 2 value and the root mean square error (RMSE) is provided. The combination of the blue, green and yellow bands provided the optimal solution for deriving the bathymetry using the Lyzenga algorithm in Marmion Marin Park. In this study the coastal blue band added very little to the regression analysis. The blue and green bands accounted for most of the variation, and with the yellow band adding very little to the correlation. In the Campbell et al. (2012) study three seafloor bottom types were identified from the bathy LiDAR reflectance data: sand, mixed bottom and reef. The satellite derived bathymetry in sand showed the strongest R 2 correlation (0.53) with the bathy LiDAR, and an RMSE 2.31m. The mixed bottom showed a lower R 2 correlation (0.35), with an RMSE of 2.99m. The reef showed poorer results with a R 2 correlation of 0.11 and RMSE 3.50m. The relationship for the sand areas is better than the mixed areas, while the predicted depths appear to be poorly correlated for the reef areas. This study demonstrates that the accuracy and quality of satellite derived bathymetry is not only dependent on depth, but also on the seafloor composition. The study also provides an example of the contribution of each wavelength to the WorldView2 imagery derived bathymetry. The optimal band combination is important for the efficiency of deriving bathymetry from imagery. The contribution of each band to the bathymetry will vary depending upon the water conditions. To summarise, satellite imagery provides bathymetry estimates which are not intended for navigation purposes. In areas where sediment concentration is too high for optical data to provide results, alternative technologies, such as Synthetic Aperture Radar (SAR) data can be used to indicate shallow water shoal locations. This is addressed further in section Satellite Radar. 15

16 2.2.2 Hyperspectral Imagery Hyperspectral imagery is more complex than multispectral imagery. The derivation of bathymetry from hyperspectral imagery is still in its infancy. The increased number of spectral bands used by hyperspectral sensors enables the discrimination between different components of the water column and seabed, however this extra complexity has generally restricted its usage to the research sector. The additional spectral bands enable a more accurate estimation of water depth and bottom type than is possible from the multispectral sensors discussed in Satellite and Aerial Imagery. Although, hyperspectral imagery is still primarily acquired using airborne acquisition and so does not have the advantages associated with satellite imagery. The first hyperspectral sensor in space, Hyperion, has been used for mapping shallow water benthic habitat (Kutser and Jupp, 2002). Hyperion on Earth Observing One (EO-1) was designed as a technical demonstration instrument with a short lifetime. It has already collected data for a longer period than was originally planned with the mission being close to an end. Airborne hyperspectral instruments can also provide the spectral and spatial resolutions needed for deriving bathymetry. However, the cost of acquisition is higher, to the point of limiting the usage of hyperspectral airborne imagery in mapping large coastal areas. In 2008 a hyperspectral sensing survey using the HyMap system was conducted in Jervis Bay, located about 180km south of Sydney, Australia. HyMap is an airborne hyperspectral remote sensing instrument that collects 126 spectral bands from the visible to the shortwave infrared wavelength regions (0.45 to 2.5um). Like multispectral imagery the HyMap radiance data needs to be atmospherically corrected to surface reflectance. Following surface sun glint corrections, the remote sensing reflectance is transformed to subsurface reflectance (Jing 2010). The HyMap results demonstrated that water depths up to 20m can be extracted from hyperspectral imagery and showed a good correlation with bathymetry from conventional hydrographical surveys (Jing 2010). Increased developments and launches of satellite-based hyperspectral sensors will make this acquisition technique more feasible for large area bathymetry processing. As an airborne technique, the advantages for deriving bathymetry from hyperspectral sensors are limited compared to other available airborne technologies. There are no easily identifiable depth, coverage or environmental advantages to airborne hyperspectral imagery derived bathymetry over bathy LiDAR Algorithms to Derive Bathymetry from Imagery As bathymetry derived from imagery is estimated rather than directly measured, it is worth discussing the influence and impact of the algorithms used to model depths. A number of different algorithms have been derived to determine bathymetry from imagery. For hyperspectral and multispectral imagery-erived bathymetry, it is just as important to select the most appropriate algorithm, as it is to select the most suitable image sensor. Mobley (2009) compared six common algorithms used to derive bathymetry, following a workshop held in Brisbane, Australia in February The tests were sponsored by the U.S. Office of Naval Research (ONR-Global) and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO). Participants compared the results obtained by applying algorithms to a common set of images. 16

17 The six algorithms were applied to two different airborne hyperspectral images, one from a PHILLS sensor flown near Lee Stocking Island (LSI) in the Bahamas and one from a CASI sensor flown over Moreton Bay (MB) in eastern Australia. Each investigator applied their algorithm to both images and sent the results to an independent third party (S. Phinn at the University of Queensland) for comparison with the ground-truth measurements at each site (Mobley 2009). A summary of the algorithms and test results is provided in Table 4. Even though the tests were performed on hyperspectral imagery the results are still relevant to multispectral imagery-derived bathymetry. Algorithm Algorithm Description Area Accuracy to LiDAR Bathymetry Processor Speed Pixels Processed Per Second HOPE (Hyper-spectral Optimization Process Exemplar) BRUCE (Bottom Reflectance Unmixing Computation of the Environment) SAMBUCA (Semi-Analytical Model for Bathymetry, Unmixing, and Concentration Assessment) SMLUT (Spectrum- Matching and Look- Up-Table) ALLUT (Adaptive Linearized Look-Up Trees) LYZENGA - Common empirical, multispectral technique This method is an implementation of the semianalytical, non-linear search algorithm developed by Lee et al. (1998, 1999). The model retrieves five parameters: phytoplankton absorption at 440 nm, CDOM absorption at 440 nm, particulate backscatter at 550 nm, bottom reflectance at 550 nm, and bottom depth. This inversion technique incorporates the HOPE model with a modification to the bottom reflectance parameterization (Klonowski et al. 2007). SAMBUCA is an implementation of the HOPE algorithm, modified to (1) retrieve water-column concentrations of chlorophyll-a, CDOM, and nonalgal-particles, (2) account for more than one substratum cover type, and (3) estimate the contribution of the substratum to the remote sensing signal (Brando et al. 2009). This method is based on spectrum matching by searching through a pre-computed database of remote-sensing reflectance spectra, as described in Mobley et al. (2005). This algorithm facilitates spectrum-matching inversion of any radiative transfer model parameterized by a set of real-valued and integer parameters. The method used here is identical to that described in Hedley et al. (2009), but in addition includes a local linear gradient calculation. This empirical, multi-spectral technique developed by Lyzenga (1978) can retrieve bathymetry in areas of constant water clarity and homogenous benthos/substrate composition. Although limited to retrieval of bathymetry under restrictive environmental conditions and therefore much less general than the above techniques, bathymetry retrieved by the Lyzenga algorithm was included in this comparison study because of its historical importance and continued widespread use under certain conditions. LSI RMSE = 1.12 R 2 =0.85 MB RMSE = 3.17 R 2 =0.78 LSI RMSE = 0.86 R 2 =0.91 MB RMSE = 2.11 R 2 =0.80 LSI RMSE = 1.30 R 2 =0.85 MB RMSE = 0.96 R 2 =0.91 LSI RMSE = 1.14 R 2 =0.88 MB RMSE = 4.71 R 2 =0.38 LSI RMSE = 2.36 R 2 =0.81 MB RMSE = 2.24 R 2 =0.78 LSI RMSE = 1.68 R 2 =0.72 MB RMSE = 3.12 R 2 = GHz GHz GHz GHz processors processors 2.00GHz GHz GHz GHz Table 4 - Results from a test which compared six different algorithms used to derived bathymetry from imagery. The tests were performed on two datasets near Lee Stocking Island (LSI) in the Bahamas and Moreton Bay (MB) in Australia (Mobley 2009). 17

18 Lyzenga is a generic empirical algorithm as opposed to the other five algorithms which are nonempirical. HOPE, BRUCE and SAMBUCA are semi-analytical approaches which use a specific code to derive the bathymetry. SMLUT and ALLUT are non-empirical, look-up table approaches to deriving bathymetry. Of the six algorithms in Table 4 SAMBUCA had the highest correlation with the bathy LiDAR and lowest RMSE. However, it was also the slowest to derive the bathymetry. It is important to note that the algorithms and processes to derive bathymetry from imagery are not limited to the six provided in the study. The study is provided to demonstrate the variability in algorithms to derive bathymetry from imagery, and the importance of using an algorithm which is the most suitable to the imagery, project area, timeframes and budget. Technical expertise and experience is required to select the most appropriate processing technique to derive bathymetry from imagery Satellite Radar Similar to multispectral and hyperspectral imagery which infer depth, radar does not directly measure bathymetry; it infers depths from changes in the sea surface. This enables radar to provide a potential solution in turbid environments where other remote sensing techniques are unsuccessful. It also has the benefit of being unaffected by cloud cover. Radar produces relative bathymetry, rather than absolute depths. The technique is particularly suited to areas of sandbanks and shoals where there are often frequent or continuous changes in bathymetry. Radar bathymetry determination is based on its ability to measure the change in height and roughness of the sea surface. The roughness changes as tidal currents approach a shoal, as they have to accelerate to flow over the shoal. As shown in Figure 2, this effect results in a zone of divergence of the surface currents on the side of the shoal facing the current, which in turn leads to a reduction of the surface roughness and a darker tone on the radar image. The rougher water enhances the radar backscatter giving a brighter zone on the radar image (Huang et al., 2001; Robinson, 2004). Figure 2 - Technique to derive satellite radar bathymetry (Quadros 2009) Practical implementation of this form of bathymetric measurement requires knowledge of the tidal currents and the wind, as the wind speed and direction affects the roughness modulation. There are several uncertainties inherent in the measurement and manipulation of satellite radar altimetry observations used to estimate ocean depth. These make radar derived bathymetry notoriously difficult to determine and inherently unreliable compared to other technologies. Radar is one of the least frequently used technologies employed to determine near-shore bathymetry and the technique is not currently reliable enough to be used as a supplementary technology in bathymetry gaps caused by turbidity. 18

19 2.3 Maritime Vessel Bathymetry Multi-Beam and Single-Beam Echo Sounders Multi-beam echo sounders (MBES) are the most efficient and reliable sensor to gather bathymetry from a maritime vessel platform. Single-beam echo sounders (SBES) only generate a single pulse at nadir and do not have the swath coverage of MBES. Notably, SBES can provide spot heights to calibrate other bathymetry acquisition techniques such as satellite derived bathymetry. However, the focus of this section is on MBES due to its efficiency and reliability. MBES systems are operated by a number of commercial, government and research institutions. As shown in Figure 3, MBES sensors produce measurements either side of a vessel. The highperformance systems have wide-angle swaths that cover an area up to seven times the water depth; although it is more typically twice the water depth (Ozcoasts 2012). As the water depth decreases the swath width also decreases, and the coverage efficiency of the survey is reduced. In order to generate a complete coverage in shallower water, the survey lines need to be closer together. MBES have the advantage of producing a higher measurement density than airborne sensors. This results in detailed measurements of features which span more than a decimetre. The increased point density makes MBES more suitable to small object detection than airborne sensing. It is standard to record backscatter (reflected energy) from MBES surveys, which is similar to the LiDAR reflectivity/intensity, as a measurement of the strength of the return signal. The accuracy of MBES systems is dependent on the correction applied for the vessel and sensor position and motion, especially in higher sea states. The success of the MBES solution is dependent not only on the MBES sensor, but also on the inertial sensor (vessel motion), DGPS sensor (vessel position), acoustic refraction correction (speed of sound in the water column) and operator (management and processing). Errors in any of these factors will be reflected in the bathymetry. Figure 3 - Diagram with airborne bathy LiDAR and maritime vessel MBES Comparisons between currently available systems have not been widely published. MBES systems comprise the MBES sensor, an inertial sensor for vessel orientation and a GNSS receiver for positioning the vessel. Hydro International has published a comparison of technical specifications for 23 MBES sensors 1, 21 inertial motion sensors 2 and 61 GNSS receivers 3. The MBES sensors listed by

20 Hydro International are those most widely used for depths of less than 500m. The inertial motion sensors and GNSS receivers are manufactured by a large number of companies, with Hydro International only providing a selection of those on the market. Each manufacturer offers a range of MBES models at different costs and specifications. For a bathymetric survey the MBES beam spacing and frequency need to be considered. The depth of operations also needs to be considered, with some newer units having multi-frequency options which are useful across a range of depths. When commissioning a MBES survey the data acquisition quality should be specified in line with IHO (International Hydrographic Organisation) standards. For multi-use purposes, backscatter data should always be logged, processed and delivered. Different MBES sensors produce different quality backscatter results, which may need to be corrected for several parameters. Some considerations for commissioning a MBES survey include: The vessel plan with sensor offsets to indicate the quality of measurements. The processing of backscatter data with corrections. The expected percentage overlap between run lines. The proposed survey speed to indicate the density of data along track. Accessibility and vessel safety The required IHO standard for data quality. The vertical positioning corrections applied to the GNSS positions, and whether corrections are made using tide gauge measurements or modelled tides. A description of the frequency at which sound velocity corrections are measured or cast. The vessel capacity in regards to the daily/weekly schedule for acquisition. Larger vessels will be able to operate for longer periods, however the draught will generally be greater resulting in less accessibility to the very near-shore. MBES surveys can generate bathymetry to the same or better resolution and accuracy as bathy LiDAR. These factors ease the MBES data integration process to produce a seamless, multi-source elevation model. Within a large area, near-shore survey a MBES is ideally used following the bathy LiDAR acquisition to provide measurements within the LiDAR data gaps. The MBES is able to penetrate gaps within the bathy LiDAR data caused by turbidity, seafloor absorption and maximum depth limitations. The most significant limitation of a MBES survey to filling LiDAR gaps is vessel access. Therefore a MBES would be unable to fill-in very shallow (<2m) depths and gaps in hazardous areas

21 2.3.2 Side-Scan Sonar and Sub-bottom Profilers Traditionally, side-scan sonar and sub-bottom profilers have not been used for gathering bathymetry. Side-scan sonar has been primarily used to detect small, off-nadir bottom objects, and sub-bottom profilers have been used to measure the depths of geomorphic layers beneath the seafloor. Side-scan sonar uses oblique acoustic images to provide a wide coverage of the seafloor. By "looking across" the seafloor, protrusions are easily identified. A recent development has provided an exception for using side-scan sonar to generate bathymetry. Interferometric side-scan sonar (ISSS) uses the phase measurement at multiple receivers to determine the angle from which the acoustic return originates. This angle of origin, in combination with the range, provides bathymetry (NOAA 2012). ISSS is still not widely used, however it can be compared MBES due to its swath coverage. ISSS has the advantage of a greater swath width than MBES in shallow waters less than 10m. The increased swath enables higher acquisition rates in shallow waters and allows vessels to survey hazardous features from a greater distance. However the two downsides of ISSS include data noise and a lower data density ("gap") at nadir. A 60%-75% overlap is required between adjacent lines for complete coverage of the nadir gaps. If the nadir gap could be eliminated then the efficiency of an entire ISSS operation would increase dramatically (Brisette 2006). Data noise and artefacts have been an issue with ISSS sensors. However, in comparisons with two MBES datasets, a GeoAcoustics GeoSwath ISSS sensor still met the IHO Order 1 accuracy requirements (see Table 5) (Gostnell 2005). The standard deviations over 5m 2 regions of point data in relatively flat sections of seafloor tended to be three to five times higher for the GeoSwath than for the MBES systems. The ISSS datasets are inherently noisy requiring more post-processing and making them difficult to clean (Gostnell 2005). In a separate assessment of the Geoswath ISSS sensor artefacts in the dataset were in the order of 10cm in amplitude (NOAA 2012). Sub-bottom profilers use a single pulse to measure the seafloor and bathymetry sub-surface. A portion of the incident energy is reflected from the sediment-water interface, whereas the remainder is transmitted deeper into the substrate (McQuillan et al., 1984, Stoker et al., 1997). The profiler records changes in the acoustic impedance between different substrates. Sub-bottom profilers are not efficient at recording bathymetry, as the sensor is primarily designed for subsurface measurements. The bathymetry retrieved is similar to SBES as it does not have the swath coverage that is produced by MBES and side-scan sonar. Side-scan sonar and sub-bottom profilers are generally not recommended for large area bathymetry surveys unless there are additional requirements to collect bottom objects or sub-surface data. ISSS could be used in combination with MBES to infill bathy LiDAR gaps. The combination of the two sensors will maximise possible coverage, utilising the coverage advantage of ISSS, with the accuracy and consistent point spacing of the MBES system, although the use of the two sensors has to be weighed against the higher cost. 21