Janet Anstee

Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors

The focus of this chapter lies in describing digi-tal multispectral and hyperspectral remote sensingdevelopments and applications in the mapping andmonitoring of seagrass ecosystems. Multispectralrefers to a sensor that registers light in a limitednumber of relatively broad spectral bands (band-widths of 20–60 nm); hyperspectral (also referred toas imaging spectrometry) is defined for sensors thatmeasure the entire spectrum under consideration incontiguous narrow spectral bands (bandwidths be-tween 2 and 20 nm).Currently, seagrass maps are still predominantlybeingproducedfromtheinterpretationofaerialpho-tographyalthoughitislikelythatairborneandspace-borne remote sensing methods will rapidly take overthisrolegiventheadvantagestheypresentintermsofaccuracy, repeatability, versatility, and informationcontent. Nevertheless, retrospective studies of sea-grass change using the more modern methodologieswillstillneedtomakeuseofresultsgeneratedbythemore traditional methods since aerial photographsare the dominant archival source of historical spatial

Preliminary assessment of the performance of Hyperion in coastal waters. Cal/Val activities in Moreton Bay, Queensland, Australia

Moreton Bay is the Australian EO1-Hyperion coastal site used for Cal/Val activities. Moreton Bay shows spatial gradients in optical depth, bathymetry, and substrate composition. The turbid and humic river inputs, as well as the open ocean flushing, determine the water quality of the bay. Lyngbya toxic algae blooms have become a serious environmental and health concern. The field campaigns, carried out to coincide with Hyperion overpasses, focussed on the retrieval of inherent optical properties, apparent optical properties, substrate reflectance spectra and water quality parameters. Spectra from a 12 January 2001 Hyperion image show very close agreement to in situ upwelling radiance spectra.

Hyperspectral imaging for benthic species recognition in shallow coastal waters

Airborne hyperspectral data was collected in April 1999 over 16 square kilometres of coastal waters adjacent to the South Australian Bolivar Wastewater Treatment Plant (near Adelaide). Concurrent in situ measurements of benthic reflectances were collected. A subsequent field-work mission to validate the 1999 image analysis was completed in February 2000. The aim was to map substrate type accurately, differentiating species and canopy density, if possible. The analysis approach was based on the coupling of radiative transfer models using in situ atmospheric measurements and inwater measurements to remove atmospheric and water column effects from the image data. After removal of atmospheric effects, the spectral variation is a function of the water columns constituents and spatial related effects along the image. Water samples were collected at the time of the flights and analysed for the retrieval of inherent optical properties (IOPs). The SpecTool software was used to derive benthic reflectance models based on the IOPs. The atmospheric and in-water radiative transfer corrections applied to the imagery enabled delivery of environmental baseline data of the substrate and a basis for accurate multitemporal data analysis.

Bio-Optical Properties of Two Neigboring Coastal Regions of Tropical Northern Australia: The Van Diemen Gulf and Darwin Harbour

This study focuses on the seasonal and spatial characterization of inherent optical properties and biogeochemical concentrations in the Van Diemen Gulf and Darwin Harbour, two neighboring tropical coastal environments of Northern Australia that exhibit shallow depths (~20 m), large (> 3 m) semi-diurnal tides and a monsoonal climate. To gain insight in the functioning of these optically complex coastal ecosystems, a total of 23 physical, biogeochemical and optical parameters were sampled at 63 stations during three field campaigns covering the 2012 wet and dry seasons, and the 2013 dry season. The total light absorption budget in the Van Diemen Gulf was dominated by non-algal particles (aNAP; >45%) during the dry season (May-October) and colored dissolved organic matter (aCDOM; 60%) during the wet season (November-April). The combined absorption by aNAP and aCDOM generally exceeded 70% of the total absorption budget from 400 to 620 nm, with phytoplankton, aPhy, accounting for less than 20%. In Darwin Harbour, where only the dry season conditions were sampled, the total absorption budget was dominated by an equivalent contribution of aCDOM, aNAP and phytoplankton. The major processes explaining the seasonal variability observed in the Van Diemen Gulf are resuspension from seasonal south-easterly trade winds in combination with the tidal energy and shallow bathymetry during the dry season months, and mostly terrestrial river runoff during the monsoon which discharge terrestrial CDOM from the surrounding wetlands. Due to light-limited conditions all year round, the particulate scattering coefficient (bp(555)) contributed significantly (90%) to the beam attenuation coefficient c(555), thus strongly limiting phytoplankton growth (Chlorophyll a~1 mg.m-3). Spatially, the Van Diemen Gulf had higher total suspended solids and nutrient concentrations than Darwin Harbour, with dissolved organic carbon and aCDOM subjected to photobleaching during the dry season. Key bio-optical relationships derived from this comprehensive set of parameters, the first ever to be collected in this tropical coastal environment, were successfully used for a region-specific seasonal parameterization of a Linear Matrix Inversion-based ocean color algorithm. Challenges related to the parameterization, and the use, of ocean color remote sensing algorithms for these optically complex waters are discussed.

Optimizing classification accuracy of estuarine macrophytes: By combining spatial and physics-based image analysis

Accurate baseline data of macrophyte extent is vital in estuarine monitoring. Previous techniques have often been laborious and subjective, while a purely empirical methodology often precludes transferring the method to other systems. The development of objective physics-based inversions models allows for the retrieval of; water depth, substratum composition and concentration of the water constituents from hyperspectral imagery. This paper describes approaches required to apply this method to QuickBird multispectral data from 2003 and 2008 over an estuarine lake. The addition of the inversion models quality control, improved the classification accuracy.

Operational Forecasting in Ecology by Inferential Models and Remote Sensing

This chapter addresses the demand of environmental agencies and water industries for tools enabling them to prevent and mitigate events of rapid deterioration of environmental assets such as contamination of air, soils and water, declining biodiversity, desertification of landscapes. Getting access to reliable early warning signals may avoid excessive ecological and economic costs.

Inland water quality monitoring in Australia

Consistent and accurate information on inland water quality over wider areas of the Australian continent are required to assess current condition and trends in response to key environmental and climatic impacts. Optical remote sensing offers a method to objectively assess this over multiple spatial scales provided retrieval algorithms are accurate. Here, we present the results of initial research aimed at exploring the optical variability in Australian inland waters and of linear matrix inversion algorithms applied to both in situ reflectance spectra and high resolution satellite data to retrieve water inland water quality parameters. In situ sampling reveals a high degree of optical variability both within and between lakes across the regions sampled with regional patterns evident; sub-tropical and tropical lakes exhibited greater optical complexity than deep lakes in mid-latitude regions. Clustering analysis indicated the presence of 8 different optical water types in the water bodies measured. The ability of the linear matrix inversion algorithm to map water quality, tested on in situ reflectance and WorldView2 image datasets, showed relative accuracy when parameter sets were sufficient to achieve algorithm closure. Improved algorithm parameterization will be required to account for the high degree in spatial and temporal optical variability observed in Australian inland waters.