A comparison of spatially explicit and classic regression modelling of live coral cover using hyperspectral remote-sensing data in the Al Wajh lagoon, Red Sea

Live coral is a key component of the Al Wajh marine reserve in the Red Sea. The management of this reserve is dependent on a sound understanding of the existing spatial distribution of live coral cover and the environmental factors influencing live coral at the landscape scale. This study uses remote-sensing techniques to develop ordinary least squares and spatially lagged autoregressive explanatory models of the distribution of live coral cover inside the Al Wajh lagoon, Saudi Arabia. Live coral was modelled as a response to environmental controls such as water depth, the concentration of suspended sediment in the water column and exposure to incident waves. Airborne hyperspectral data were used to derive information on live coral cover as a response (dependent) variable at the landscape scale using linear spectral unmixing. Environmental controls (explanatory variables) were derived from a physics-based inversion of the remote-sensing dataset and validated against field-collected data. For spatial regression, cases referred to geographical locations that were explicitly drawn on in the modelling process to make use of the spatially dependent nature of coral cover controls. The transition from the ordinary least squares model to the spatially lagged model was accompanied by a marked growth in explanatory power (R 2 = 0.26 to 0.76). The theoretical implication that follows is that neighbourhood context interactions play an important role in determining live coral cover. This provides a persuasive case for building geographical considerations into studies of coral distribution

Abrupt transitions between macrobenthic faunal assemblages across seagrass bed margins

The nature of the transition from one contrasting macrobenthic assemblage to another across interfaces between intertidal seagrass and unvegetated sand was investigated in the subtropical Moreton Bay Marine Park, eastern Australia, via six two-dimensional core lattices. The same pattern of transition was manifested in each lattice. Macrofaunal abundance, species density (both observed and estimated total) and assemblage composition did not vary with distance away from the interface within the 0.75 m wide marginal bands of each habitat type. Neither were there significant differences in assemblage metrics or composition between the marginal and non-edge regions of either habitat. There were, however, very marked differences in assemblage composition, abundance and species density across the 25 cm wide strip on either side of the actual interface, the interacting assemblages reacting symmetrically. All these differences therefore took place over an ecotone distance of only 0.5 m at most. Spatial trends in assemblage metrics across the boundary zone were captured accurately by second and third order polynomial regression models. It also appeared that edge effects on individual species within the seagrass were a variable local response not a consistent effect of closeness to the bare sand

A geospatial appraisal of ecological and geomorphic change on Diego Garcia Atoll, Chagos Islands (British Indian OceanTerritory)

This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This study compiled a wide range of modern and historic geospatial datasets to examine ecological and geomorphic change at Diego Garcia Atoll across a 38-year period (1967–2005). This remarkable collection of spatially referenced information offered an opportunity to advance our understanding of the nature and extent of environmental change that has taken place with the construction of the military airbase at Diego Garcia. Changes assessed included movements of the lagoon rim shorelines, changes in the terrestrial vegetation on the lagoon rim and amendments to the bathymetry of the lagoon basin through dredging activities. Data compiled included detailed shoreline and vegetation maps produced as part of the H.M.S. Vidal Indian Ocean Expedition (1967), three Ikonos satellite images acquired in 2005 that collectively covered the complete Atoll area, a ground truthing field dataset collected in the northern section of the lagoon for the purpose of seafloor mapping (2005), observational evidence of shoreline erosion including photographs and descriptions of seawater inundations and bathymetric soundings from five independent surveys of the lagoon floor (1967, 1985, 1987, 1988 and 1997). Results indicated that much of the change along the lagoon rim is associated with the expansion of the inner lagoon shoreline as a result of the construction of the military airbase, with an estimated increase in land area of 3.01 km2 in this portion of the atoll rim. Comparisons of 69 rim width transects measured from 1967 and 2005 indicated that shorelines are both eroding (26 transects) and accreting (43 transects). Within a total vegetated area of 24 km2, there was a notable transition from Cocos Woodland to Broadleaf Woodland for a land area of 5.6 km2. From the hydrographic surveys, it was estimated that approximately 0.55 km3 of carbonate sediment material has been removed from the northwest quadrant of the lagoon, particularly in the vicinity of the Main Passage. As no previous record of benthic character exists, a complete benthic habitat map of the atoll was derived through classification of the three IKONOS satellite images. Management implications arising from this overall appraisal of geomorphic and ecological change at Diego Garcia included the need for ongoing monitoring of shoreline change at a representative set of sites around the atoll rim, monitoring of the water flow regime through the northern channels between the open ocean and the lagoon basin and an ongoing mapping campaign to record periodic changes in the character of the benthic surface ecology.BLUE Marine Foundatio

Comparing the information content of coral reef geomorphological and biological habitat maps, Amirantes Archipelago (Seychelles), Western Indian Ocean

Increasing the use of geomorphological map products in marine spatial planning has the potential to greatly enhance return on mapping investment as they are commonly two orders of magnitude cheaper to produce than biologically-focussed maps of benthic communities and shallow substrates. The efficacy of geomorphological maps derived from remotely sensed imagery as surrogates for habitat diversity is explored by comparing two map sets of the platform reefs and atolls of the Amirantes Archipelago (Seychelles), Western Indian Ocean. One mapping campaign utilised Compact Airborne Spectrographic Imagery (19 wavebands, 1 m spatial resolution) to classify 11 islands and associated reefs into 25 biological habitat classes while the other campaign used Landsat 7 þ ETM imagery (7 bands, 30 m spatial resolution) to generate maps of 14 geomorphic classes. The maps were compared across a range of characteristics, including habitat richness (number of classes mapped), diversity (ShannoneWeiner statistic) and thematic content (Cramer’s V statistic). Between maps, a strong relationship was revealed for habitat richness (R2 ¼ 0.76), a moderate relationship for class diversity and evenness (R2 ¼ 0.63) and a variable relationship for thematic content, dependent on site complexity (V range 0.43 e0.93). Geomorphic maps emerged as robust predictors of the habitat richness in the Amirantes. Such maps therefore demonstrate high potential value for informing coastal management activities and conservation planning by drawing on information beyond their own thematic content and thus maximizing the return on mapping investment

Reef-scale assessment of intertidal large benthic foraminifera populations on one tree Island, great barrier reef and their future carbonate production potential in a warming ocean

Populations of large benthic foraminiferans (LBFs) that inhabit coral reef platforms are major producers of calcium carbonate (CaCO3) in reef ecosystems. This study documented the population density of living intertidal LBF populations at One Tree Reef (OTR), southern Great Barrier Reef, in a community dominated by Marginopora vertebralis and Baculogypsina sphaerulata. Densities of 7.7 × 103 M. vertebralis individuals (ind.)/m2 and 4.5 × 105 B. sphaerulata ind./m2 were estimated for these populations in May 2011. We applied remote-sensing technology to determine reef-scale estimates of suitable Foraminifera habitats and used these to estimate overall stocks of LBF populations on the intertidal algal flat at OTR of ca. 2800 metric tons. The growth rate of M. vertebralis was determined in a laboratory study, and the data were used to calculate the annual CaCO3 production of the reef flat by the LBF population. The response of M. vertebralis to ocean warming was investigated using 3-week incubations at temperatures ranging from ambient sea surface temperature to +6°C. There were significant decreases in growth and concomitant CaCO3 production in 6°C warmer water, which resulted in shell dissolution of M. vertebralis. These results indicate that climate-driven ocean warming projected for the region will result in significant decreases in CaCO3 production in overall foraminiferan populations, although species-specific effects should be further investigated

Attainable standards of accuracy in the determination of Holocene sea levels in the Central Pacific: Introductory note

The research underpinning Stoddart and Murphy’s paper ‘Attainable standards of accuracy in the determination of Holocene sea-levels in the Central Pacific’ was undertaken in 1990–1993. This was a time when topographic survey was on the cusp of moving from traditional ground-based levelling surveys, from fixed, sea level-related benchmarks, to methods based on satellite altimetry. The former approach required surveys to be related to a well-defined tidal record (as detailed by Stoddart, 1978), while the latter effectively removed the need for a local datum by providing a common, global reference point, essentially the centre of the earth. As Stoddart and Murphy themselves perceptively noted ‘New mobile global positioning systems (GPS) and satellite altimetry surveys hold out the prospect of relatively high accuracy surveying even on remote islands’

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected

Chapter 2 ‘A CLOSE AND FRIENDLY ALLIANCE’: BIOLOGY, GEOLOGY AND THE GREAT BARRIER REEF EXPEDITION OF 1928–1929

oceanography, climate change, reefs, marine science, marine conservation, marine researc

Fathom

[Extract] Terrestrial, bipedal, air breathing, and poorly waterproofed, how can humans fathom the bottom of the sea? This article was composed by an anthropologist, a cultural theorist, a philosopher, a coastal geographer, a cultural geographer, a feminist studies scholar, an artist, a spatial scientist, an ecocritic, a free diver, an STS scholar, a spear fisher, a biologist, a marine ecologist, a poet, a dancer, and a swimmer. (If the math does not add up, we remind you that we are always more than one.) Our insights emerged from a one-day workshop at Clovelly Beach in Sydney, Australia, on land and in the water, where we shared our perspectives and practices in researching ocean environments. Our collaboration is an experiment in multidisciplinary practice-based inquiry, where differences and tensions need not preclude collaborative understanding. In this article we combine emerging critical ocean studies and blue humanities perspectives to propose fathoming as a vital, embodied practice that gathers technoscientific acts of measurement together with practices of immersion, imagination, and speculation. Through collaborative multi-situated inquiry1 we learn new things not only about the sea but also about the limits of epistemological mastery and the rewards of knowing with