Semi-Automated Object-Based Classification of Coral Reef Habitat Using Discrete Choice Models

As for terrestrial remote sensing, pixel-based classifiers have traditionally been used to map coral reef habitats. For pixel-based classifiers, habitat assignment is based on the spectral or textural properties of each individual pixel in the scene. More recently, however, object-based classifications, those based on information from a set of contiguous pixels with similar properties, have found favor with the reef mapping community and are starting to be extensively deployed. Object-based classifiers have an advantage over pixel-based in that they are less compromised by the inevitable inhomogeneity in per-pixel spectral response caused, primarily, by variations in water depth. One aspect of the object-based classification workflow is the assignment of each image object to a habitat class on the basis of its spectral, textural, or geometric properties. While a skilled image interpreter can achieve this task accurately through manual editing, full or partial automation is desirable for large-scale reef mapping projects of the magnitude which are useful for marine spatial planning. To this end, this paper trials the use of multinomial logistic discrete choice models to classify coral reef habitats identified through object-based segmentation of satellite imagery. Our results suggest that these models can attain assignment accuracies of about 85%, while also reducing the time needed to produce the map, as compared to manual methods. Limitations of this approach include misclassification of image objects at the interface between some habitat types due to the soft gradation in nature between habitats, the robustness of the segmentation algorithm used, and the selection of a strong training dataset. Finally, due to the probabilistic nature of multinomial logistic models, the analyst can estimate a map of uncertainty associated with the habitat classifications. Quantifying uncertainty is important to the end-user when developing marine spatial planning scenarios and populating spatial models from reef habitat maps

Markov Models for Linking Environments and Facies in Space and Time (Recent Arabian Gulf, Miocene Paratethys)

If, as comparative sedimentology maintains, knowledge of the Recent can sometimes be helpful to explain the past (and vice-versa), common quantitative denominators might exist between Recent and fossil systems. It may also be possible to describe dynamics and find linkages between space and time with a unique set of quantitative tools. To explore such conceptual links, spatial facies patterns mapped using satellite imagery were compared with temporal patterns in analogous ancient outcropping facies using Markov chains and graphs. Landsat and Ikonos satellite imagery was used to map benthic facies in a nearshore carbonate ramp (Ras Hasyan) and offshore platform system (Murrawah, Al Gharbi) in the Recent Arabian Gulf (United Arab Emirates), and results were compared to the Fenk quarry outcrop in Burgenland, Austria, a carbonate ramp of the Miocene (Badenian) Paratethys. Facies adjacencies (i.e. Moore neighbourhood of colour-coded image pixels of satellite image or outcrop map) were expressed by transition probability matrices which showed that horizontal (spatial) facies sequences and vertical (temporal) outcrop sequences had the Markov property (knowledge of t-th state defines likelihoods of t+1st state) and that equivalent facies were comparable in frequency. We expressed the transition probability matrices as weighted digraphs and calculated fixed probability vectors which encapsulate information on both the spatial and temporal components (size of and time spent in each facies). Models of temporal functioning were obtained by modifying matrices (digraphs) of spatial adjacency to matrices (digraphs) of temporal adjacency by using the same vertices (facies) but adjusting transitions without changing paths. With this combined spatio-temporal model, we investigated changes in facies composition in falling and rising sea-level scenarios by adjusting transition likelihoods preferentially into shallower (falling sea-level) or deeper (rising sea-level) facies. Our model can also be used as a numerical analogue to a Ginsburg-type autocyclic model. The fixed probability vector was used as a proxy for final facies distribution. Using Markov chains it is possible to use vertical outcrop data to evaluate the relative contribution of each facies in any time-slice which can aid, for example, in estimation of reservoir sizes and to gain insight into temporal functioning as derived from spatial pattern

The Emerging Role of LiDAR Remote Sensing in Coastal Research and Resource Management Full Access

Knowledge of coastal elevation is an essential requirement for resource management and scientific research. Recognizing the vast potential of lidar remote sensing in coastal studies, this Special Issue includes a collection of articles intended to represent the state-of-the-art for lidar investigations of nearshore submerged and emergent ecosystems, coastal morphodynamics, and hazards due to sea-level rise and severe storms. Some current applications for lidar remote sensing described in this Special Issue include bluegreen wavelength lidar used for submarine coastal benthic environments such as coral reef ecosystems, airborne lidar used for shoreline mapping and coastal change detection, and temporal waveform-resolving lidar used for vegetation mapping

Hedgehog Signalling in Androgen Independent Prostate Cancer

Objectives: Androgen-deprivation therapy effectively shrinks hormone-naïve prostate cancer, both in the prostate and at sites of distant metastasis. However prolonged androgen deprivation generally results in relapse and androgen-independent tumour growth, which is inevitably fatal. The molecular events that enable prostate cancer cells to proliferate in reduced androgen conditions are poorly understood. Here we investigate the role of Hedgehog signalling in androgen-independent prostate cancer (AIPC). Methods: Activity of the Hedgehog signalling pathway was analysed in cultured prostate cancer cells, and circulating prostate tumour cells were isolated from blood samples of patients with AIPC. Results: AIPC cells were derived through prolonged culture in reduced androgen conditions, modelling hormone therapy in patients, and expressed increased levels of Hedgehog signalling proteins. Exposure of cultured AIPC cells to cyclopamine, which inhibits Hedgehog signalling, resulted in inhibition of cancer cell growth. The expression of the Hedgehog receptor PTCH and the highly prostate cancer-specific gene DD3PCA3 was significantly higher in circulating prostate cancer cells isolated from patients with AIPC compared with samples prepared from normal individuals. There was an association between PTCH and DD3PCA3 expression and the length of androgen-ablation therapy. Conclusions: Our data are consistent with reports implicating overactivity of Hedgehog signalling in prostate cancer and suggest that Hedgehog signalling contributes to the androgen-independent growth of prostate cancer cells. As systemic anti-Hedgehog medicines are developed, the Hedgehog pathway will become a potential new therapeutic target in advanced prostate cancer.Peer reviewedFinal Accepted Versio

Fractal Patterns of Coral Communities: Evidence from Remote Sensing (Arabian Gulf, Dubai, U.A.E.)

In this study, the spatial character of benthic communities is investigated in an Arabian Gulf shallow subtidal carbonate ramp setting, using IKONOS satellite imagery. The patchy distribution of three assemblages of live and dead corals on extensive (but also fragmented) hardground pavements was investigated using a variety of spatial statistics. It was found that the spatial expression of the benthic groups display characteristics that approximate to power-law distributions over several orders of magnitude to an extent that suggests fractal behaviour. Pronounced anisotropy was observed between the spatial patterns in the near-shore and off-shore region which is attributed to different mechanisms of patch formation controlled by the local hydrodynamic regime. The study area is know to be subjected to recurrent and cyclic thermal induced mass mortality events on a decadal time scale, inhibiting reef framework development and likely to be a controlling mechanism in the patchiness of the benthic communities

Spatial Patterns in Arabian Gulf Coral Assemblages (Jebel Ali, Dubai, U.A.E.) in Response to Temperature-Forcing

We evaluated spatial and temporal patterns using maps from Ikonos satellite imagery in combination with 8 years of line transects and photosquares and the HadISST1 sea-surface temperature data set to explain why coral assemblages in the southern Arabian Gulf (Dubai) are impoverished and mostly do not build framework reefs. Analysis of archive sea surface temperature (SST) data confirms that the area is subjected to recurrent temperature anomalies. Frequencies of anomalies might suggest at least a partial link to the El Niño Southern Oscillation possibly via the Indian Ocean Zonal Mode. The dominant driver of local temperature was oscillations in the position of the subtropical jetstream. Classification of IKONOS satellite data showed that the spatial expression of four coral assemblages was consistent with reef development on a (multi-)decadal time-scale following recurring episodes of coral mass mortality induced by severe SST anomalies. Merging a remotely sensed map of substrate distribution with a detailed bathymetric digital elevation model revealed no evidence of significant framework development, suggesting that the cycle of temperature induced mortality has been operating for a considerable time

Complexes of Group 2 dications with soft thioether- and selenoether-containing macrocycles

A new route to cationic complexes of Mg, Ca, Sr and Ba with 18-membered ring O4S2, O4Se2 and O2S4 donor macrocycles from metal acetonitrile complexes with weakly coordinating [BArF]? anions is described. The precursors used were [M(MeCN)x][BArF]2 (M = Mg, x = 6; M = Ca, x = 8) and [M?(acacH)(MeCN)5][BArF]2 (M? = Sr or Ba). Reaction of these with the heterocrowns, [18]aneO4S2 (1,4,10,13-tetraoxa-7,16-dithiacyclooctadecane), [18]aneO4Se2 (1,4,10,13-tetraoxa-7,16-diselenacyclooctadecane) or [18]aneO2S4 (1,10-dioxa-4,7,13,16-tetrathiacyclooctadecane) in anhydrous CH2Cl2 solution gave [M(heterocrown)(MeCN)2][BArF]2 for M = Mg, Ca or Sr, whilst the larger Ba forms [Ba(heterocrown)(acacH)(MeCN)][BArF]2. The complexes have been characterised by microanalysis, IR, 1H and 13C{1H} NMR spectroscopy. X-ray crystal structures are reported for [Ca([18]aneO2S4)(MeCN)2][BArF]2, [Ca([18]aneO4Se2)(MeCN)2][BArF]2, [Sr([18]aneO4S2)(MeCN)2][BArF]2, and [Sr([18]aneO4Se2)(MeCN)2][BArF]2 which contain 8-coordinate metal centres with trans-nitrile ligands and ?6-heterocrowns, and for the 9-coordinate [Ba([18]aneO4Se2)(acacH)(MeCN)][BArF]2. Adventitious hydrolysis of the magnesium complexes in solution results in six-coordinate complexes, [Mg(?3-[18]aneO4Se2)(OH2)2(MeCN)][BArF]2 and [Mg(?3-[18]aneO4S2)(OH2)2(MeCN)][BArF]2, whose structures were determined. X-ray crystal structures are also reported for [Mg(MeCN)6][BArF]2, [M(MeCN)8][BArF]2 (M = Ca, Sr) and [Ca(18-crown-6)(MeCN)2][BArF]

NeMO-Net The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%