Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

A method to analyze the potential of optical remote sensing for benthic habitat mapping

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

EVALUATING SATELLITE DERIVED BATHYMETRY IN REGARD TO TOTAL PROPAGATED UNCERTAINTY, MULTI-TEMPORAL CHANGE DETECTION, AND MULTIPLE NON-LINEAR ESTIMATION

Acoustic and electromagnetic hydrographic surveys produce highly-accurate bathymetric data that can be used to update and improve current nautical charts. For shallow-water surveys (i.e., less than 50m depths), this includes the use of single-beam echo-sounders (SBES), multi-beam echo-sounders (MBES), and airborne lidar bathymetry (ALB). However, these types of hydrographic surveys are time-consuming and require considerable financial and operational resources to conduct. As a result, some maritime regions are seldom surveyed due to their remote location and challenging logistics. Satellite-derived bathymetry (SDB) provides a means to supplement traditional acoustic hydrographic surveys. In particular, Landsat 8 imagery: 1) provides complete coverage of the Earth’s surface every 16 days, 2) has an improved dynamic range (12-bits), and 3) is freely-available from the US Geological Survey. While the 30 m spatial resolution does not match MBES, ALB, or SBES coverage, SDB based on Landsat 8 can be regarded as a type of “reconnaissance survey” that can be used to identify potential hazards to navigation in areas that are seldom surveyed. It is also a useful means to monitor change detection in dynamic regions. This study focused on developing improved image-processing techniques and time-series analysis for SDB from Landsat 8 imagery for three different applications: 1. An improved means to estimate total propagated uncertainty (TPU), mainly the vertical component, for single-image SDB; 2. Identifying the location and movement of dynamic shallow areas in river entrances based on multiple-temporal Landsat 8 imagery; 3. Using a multiple, nonlinear SDB approach to enhance depth estimations and enable bottom discrimination. An improved TPU estimation was achieved based on the two most common optimization approaches (Dierssen et al., 2003 and Stumpf et al., 2003). Various single-image SDB band-ratio outcomes and associated uncertainties were compared against ground truth (i.e., recent Lidar surveys). Several parameters were tested, including various types of filters, kernel sizes, number of control points and their coverage, and recent vs. outdated control points. Based on the study results for two study sites (Cape Ann, MA and Ft Myers, FL), similar performance was observed for both the Stumpf and the Dierssen models. Validation was performed by comparing estimated depths and uncertainties to observed ALB data. The best performing configuration was achieved using low-pass filter (kernel size 3x3) with ALB control points that were distributed over the entire study site. A change detection process using image processing was developed to identify the location and movement of dynamic shallow areas in riverine environments. Yukon River (Alaska) and Amazon River (Brazil) entrances were evaluated as study sites using multiple satellite imagery. A time-series analysis was used to identify probable shallow areas with no usable control points. By using an SDB ratio model with image processing techniques that includes feature extraction and a well-defined topological feature to describe the shoal feature, it is possible to create a time-series of the shoal’s motion, and predict its future location. A further benefit of this approach is that vertical referencing of the SDB ratio model to chart datum is not required. In order to enhance the capabilities of the SDB approach to estimate depth in non-uniform conditions, Dierssen’s band ration SDB algorithm was transformed into a full non-linear SDB model. The model was evaluated in the Simeonof Island, AK, using Lidar control points from a previous NOAA ALB survey. Linear and non-linear SDB models were compared using the ALB survey for performance evaluation. The multi-nonlinear SDB model provides an enhanced performance compared to the more traditional linear SDB method. This is most noticeable in the very shallow waters (0-2 m), where a linear model does not provide a good correlation to the control points. In deep-waters close to the extinction depth, the multi-nonlinear SDB method is also able to better detect bottom features than the linear SDB method. By recognizing the water column contributions to the SDB solution, it is possible to achieve a more accurate estimate of the bathymetry in remote areas

Resilience of Long-lived Mediterranean Gorgonians in a Changing World: Insights from Life History Theory and Quantitative Ecology

[eng] Temperate benthic communities face cumulative impacts from multiple stressors acting both at local and global scales. Understanding how local management and ocean warming affect the dynamics and resilience of dominant habitat-forming species is central to marine conservation. In this thesis, we combined long-term demographic surveys and large-scale distribution datasets with innovative population and spatial modeling approaches, and meta-analyses to unravel the causes and consequences of extreme life-histories. The final goal was to understand resilience patterns and mechanisms, and to assess the effectiveness of widely used conservation tools such as fishing regulations, marine protected areas (MPAs), and active restoration. A comparative analysis across marine sessile species revealed strong linkages between depth occurrence, longevity and demographic stability. These results demonstrated a fundamental role of environmental gradients in shaping the evolution of life-history strategies in the ocean and suggest that deep-sea benthic communities tend to be dominated by long-lived species that are very vulnerable to external sources of mortality. In addition, we demonstrate that life-history strategies play a role in important conservation issues such as recovery processes. For instance, recovery times after fishing or restoration actions for long-lived species can take several decades. Thus, accounting for the temporal dimension and life-history tradeoffs in conservation efforts is essential to avoid potential long-lasting impacts and enhance the recovery of damaged ecosystems. Focusing on the Mediterranean red coral Corallium rubrum, an overharvested precious coral emblematic of coralligenous assemblages, we revealed an extremely slow life-history strategy characterized by low reproduction success, high natural survival, and extended longevity. These traits drive a general pattern of slow population dynamics and suggest low resilience to human-driven stressors. We then show that local and global stressors such as overfishing and warming have strong impacts on different demographic processes of red coral populations. Harvesting causes dramatic decreases in total biomass and strong shifts in size-class distribution towards populations dominated by small colonies. More broadly, MPAs strongly enhance structural complexity of red coral populations but, contrary to prior expectations, have little effect on their long-term viability and associated extinction risk. Population recovery after harvesting is a very slow process that can take decades. Interestingly, recovery is not driven by sexual reproduction but by the capacity of harvested colonies to survive and regrow new branches. This recovery mechanism may explain the persistence of this historically overexploited long-lived coral. Unfortunately, novel impacts associated to climate change threat shallow red coral populations. Recurrent warming-driven mass mortality events had detrimental effects on affected populations, causing long-term declines and potential local extinction. While MPAs have been proposed to enhance the resilience of marine ecosystems to climate change, our simulations suggested that MPAs only have a weak buffering effect to climatic impacts. On the other hand, spatial analyses revealed that future climate change may cause extensive impacts on shallow populations of the red coral and the red gorgonian Paramuricea clavata (another emblematic species of coralligenous assemblages with an important structural role) across the Mediterranean Sea. Importantly, we found that the extent and severity of warming impacts is strongly dependent on depth and the global emissions scenarios. Altogether, our results demonstrate a high vulnerability and low demographic resilience of shallow red coral populations to climate change. The results presented in this thesis suggest that, only by simultaneously acting at both local and global scales, we can we ensure the persistence and enhance the structural role of the long-lived Mediterranean red coral. Finally, this thesis also shows the potential of combining long-term large-scale field data, quantitative tools, and principles of life-history theory to provide new perspectives to advance marine conservation in a changing ocean

Physics-based satellite-derived bathymetry for nearshore coastal waters in North America

Accurate bathymetric information is fundamental to safe maritime navigation and infrastructure development in the coastal zone, but is expensive to acquire with traditional methods. Satellite-derived bathymetry (SDB) has the potential to produce bathymetric maps at dramatically reduced cost per unit area and physics-based radiative transfer model inversion methods have been developed for this purpose. This thesis demonstrates the potential of physics-based SDB in North American coastal waters. First the utility of Landsat-8 data for SDB in Canadian waters was demonstrated. Given the need for precise atmospheric correction (AC) for deriving robust ocean color products such as bathymetry, the performances of different AC algorithms were then evaluated to determine the most appropriate AC algorithm for deriving ocean colour products such as bathymetry. Subsequently, an approach to minimize AC error was demonstrated for SDB in a coastal environment in Florida Keys, USA. Finally, an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, was demonstrated. Based on the findings of this thesis, it was concluded that: (1) Landsat-8 data hold great promise for physics-based SDB in coastal environments, (2) the problem posed by imprecise AC can be minimized by assessing and quantifying bias as a function of environmental factors, and then removing that bias in the atmospherically corrected images, from which bathymetry is estimated, and (3) an ensemble approach to SDB can produce results that are very similar to those obtained with the best individual image, but can be used to reduce time spent on pre-screening and filtering of scenes

A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef

Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km2 spatial resolution. Coupled ocean–atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L−1 within the validation range of 0.14 to 24 mg L−1, with a detection limit of 0.25 mg L−1. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site