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Limitations and assumptionsA number of limitations and assumptions were identified throughout the classification process asoutlined below.Key Issues associated with substrate mappingGeoimage appreciates that substrate mapping is dependent on the following conditions:Range and type of substrates throughout the area of interest;Preference for a light coloured, uniform substrate;Appropriate wind conditions to limit water surface glint;Appropriate swell conditions to limit wave refraction artefacts;Appropriate capture angle for the imagery i.e. 0 – 20 degrees off nadir likely to achieve goodbathymetric outcomes, with angles closer to nadir offering the best outcomes;Appropriate tidal conditions to limit turbidity due to tidal changes; and,Appropriate current movement to limit turbidity due to significant water flows.There are specific conditions, including capture angle and date of capture between the two scenes,which have caused some challenges for the substrate mapping between scenes.Imagery Acquisition ConditionsWater conditions at the time of acquisition are a critical factor in determining the level of detail whichcan be detected and classified. There were two scenes acquired over this site from two differentsensors across multiple dates. The acquisition conditions of the two scenes were variable. Theresultant colouring and contrast levels of submerged or emergent features at varying depths underdifferent water conditions at the time of the image acquisitions meant the two scenes were treatedseparately and could not be mosaicked.Where the GE-1 data was available over the site, the output classification was based on this data.The WV-2 data was only used in locations where it was the only available data source. While 8-bandinformation can detect substrates at greater depths than other sensors, this only applies in good waterquality and environmental conditions. For this site, the conditions at the time of acquisition were notfavourable for the discrimination of substrates at depths greater than 15m. The GE-1 scene was morefavourable to identify sea-floor features compared to the WV-2 scene and therefore became theprimary scene for analysis.Spatial ScaleTo reduce any influence of misregistration, a minimum mapping area of 3 x 3 pixels or 36 squaremetres was used, corresponding to nine pixels. This was carried out to improve the accuracy ofdetecting real substrate features and boundaries. The noise in the data generated by waves andremaining glint impacted the spatial scale of the classification.Classification scale and automationThe two tiers mapped in this hierarchical classification form a broad then detailed classification of thesubstrates in this region. Further refinement could be made should suitable archive satellite imagerybecome available over the site for future mapping at a more detailed classification scale. In somelocations, water depth prevented the classification from being assigned to the Tier 2 class and theTier 1 class was deemed the limit of the classification scale in these locations (usually at depthsgreater than 20m experiencing compounding artefacts such as sun glint, and/or wave refraction).While a range of substrates was observed in the field data, only Rock, Sand and Seagrass were ableto be detected and mapped with confidence given the depth of the overall site and the artefactsacross the imagery. This project required the manual assignment of Tier 1 and Tier 2 classes.Quality AssuranceAn independent review within the vector GIS environment was undertaken to visually assess theassignment of classes. Field data was not able to be used as an independent, quantitative accuracyassessment tool in this project as the field data became vital as input to the classification process dueto the artefacts existing in the imagery for this project. Metadata was also supplied which complieswith AZ/NZS ISO 19115:2005 standards.Specialists in Satellite Imagery and Geospatial Solutions | Commercial in confidence Page | 10
Conclusions and RecommendationsThe substrates were classified into two tiers of information across the study area based on integratinginformation from both GE-1 and WV-2 imagery, GEOBIA analysis and field data integration. Waterconditions at the time of image acquisition impacted the overall classification in combination with thewater depth associated with the site. Derived classifications from optical sensors perform best whenanalysing substrates in shallow water environments where water depths do not exceed 15m. Siteswhere median depths are considered beyond the shallow water environment are more susceptible tosignificant impacts from imagery artefacts and there is an increased risk of not detecting true sea floorconditions. Turbidity, water quality, wave and current conditions, sun angle at the time of imagecapture and capture angle of the sensor all contribute to providing suitable conditions for opticalimagery analysis in marine environments.Uncertainty in the results increased with depth, particularly beyond the 20m depth contour and wherecompounding artefacts were present in the imagery. The GE-1 data in combination with the WV-2data was able to provide information on which to base the indicative extent of substrates.The Tier 1 substrate classification discriminated between light and dark substrates and the Tier 2classification was able to attribute the dark substrate into substrates either dominated by rock orseagrass communities. There may be other less dominant substrates present on the site such aspresence of algae on rock or finer pebble substrates, however, these were deemed beyond theclassification scale for this project given the overall limitations as discussed above.The integration of the field data with the satellite imagery enabled the derivation of the Tier 2classification from the Tier 1 classification. Segmenting the imagery based on spectral, shape andcontextual features significantly contributed to the capacity to derive the Tier 1 classification.Variability in the deeper parts of the imagery was able to be detected through the GEOBIA statisticalanalysis process to establish boundaries of homogeneity which were then used to inform both the Tier1 and Tier 2 classifications. Stretching the spectral properties of the imagery assisted in discriminatingthe dark and light substrates and the integration of the field data enabled the association of featuresto substrate types.Overall, the GEOBIA approach minimised the impacts of noise across the data and defined regions ofhomogeneity statistically and objectively for further integration with field data observations. Both Tier 1and Tier 2 classifications relied on the outputs from the GEOBIA segmentation process and the detailof the field data to map the substrates to the detail achieved in this project.DocumentAuthor Reviewer Dateversion1.0 K.Wouters A.Eustace 29 March 20122.0 K.Wouters A.Eustace 02 April 20123.0 A.Eustace N.Teffer 03 April 20124.0 A.Eustace K.Wouters 04 April 2012Specialists in Satellite Imagery and Geospatial Solutions | Commercial in confidence Page | 11
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Fremantle Port Inner Harbour and Ch
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Client: Fremantle PortsDocument his
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ContentsExecutive Summary .........
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Executive SummaryProject StatusFrem
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1. Introduction1.1 Background and p
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Figure 1.1Dredge areas, reclaimed a
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Table 2.2 Audit tableAuditCode801:M
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AuditCodeSubject Action How Evidenc
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3. Annual Survey Footprint of Offsh
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Figure 3.2 Differences in bathymetr
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A one in five year storm event (as
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5. SummaryThere were zero non-compl
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6. ReferencesDEC (2009) Guidelines
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STATUS OF THIS DOCUMENTThis documen
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Report shall be 15 months from the
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6-3 If the Water Quality Monitoring
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Table 2: Summary of key proposal ch
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Fremantle Ports1 Cliff StreetFREMAN
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Fremantle Ports - Gage RoadsOffshor
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Client: Fremantle PortsRevisions hi
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Figure 1.1Figure 1.2Figure 1.3Figur
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Executive SummaryOceanica Consultin
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1. Introduction1.1 BackgroundThe Ga
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Figure 1.2Gage Roads habitat map in
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Figure 1.3Bathymetry for Gage Roads
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Figure 1.4 Benthic habitat within G
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2. Methods2.1 Benthic habitat surve
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2.2 Seagrass recovery2.2.1 Permanen
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Page 87 and 88: Table 3.4PERMANOVA table of results
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Page 99: Appendix AGeo Oceans Report
Page 102 and 103: Benthic Habitat AssessmentTowed Vid
Page 104 and 105: 1. IntroductionGeo Oceans was commi
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Page 108 and 109: Figure 1 Substrate and biota habita
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Page 112 and 113: Appendix 1: Video analysis biologic
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Page 118 and 119: Table of ContentsIntroduction......
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Page 129: Appendix CSeagrass Monitoring Site
Page 147: Appendix ESeagrass Shoot Count Data
Page 155: Appendix GSediment Field Log
Page 169: Appendix INutrient and Metals Data
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MarineandFreshwaterResearchLaborato
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