Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30 m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used – Pleiades (2 m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

Therapeutic hypothermia minimizes neuronal injury in animal models of hypoxic-ischemic encephalopathy with greater effect when used sooner after the insult. Clinical trials generally showed limited benefit but are difficult to perform in a timely manner. In this clinical study, we evaluated the association between the use of hypothermia (or not) and health-related quality of life among survivors of pediatric cardiac arrest as well as overall mortality.DESIGN: Single-center, retrospectively identified cohort with prospective assessment of health-related quality of life.SETTING: PICU of a pediatric hospital.

Some of the authors of this publication are also working on these related projects: Predicting energy and water consumption on dairy farms through statistical analysis and machine-learning methods View project GrassQ View project

Airborne remote sensing, whether performed from conventional aerial survey platforms such as light aircraft or the more recent Remotely Piloted Airborne Systems (RPAS) has the ability to compliment mapping generated using earth-orbiting satellites, particularly for areas that may experience prolonged cloud cover. Traditional aerial platforms are costly but capture spectral resolution imagery over large areas. RPAS are relatively low-cost, and provide very-high resolution imagery but this is limited to small areas. We believe that we are the first group to retrofit these new, low-cost, lightweight sensors in a traditional aircraft. Unlike RPAS surveys which have a limited payload, this is the first time that a method has been designed to operate four distinct RPAS sensors simultaneously-hyperspectral, thermal, hyper, RGB, video. This means that imagery covering a broad range of the spectrum captured during a single survey, through different imaging capture techniques (frame, pushbroom, video) can be applied to investigate different multiple aspects of the surrounding environment such as, soil moisture, vegetation vitality, topography or drainage, etc. In this paper, we present the initial results validating our innovative hybrid system adapting dedicated RPAS sensors for a light aircraft sensor pod, thereby providing the benefits of both methodologies. Simultaneous image capture with a Nikon D800E SLR and a series of dedicated RPAS sensors, including a FLIR thermal imager, a four-band multispectral camera and a 100-band hyperspectral imager was enabled by integration in a single sensor pod operating from a Cessna c172. However, to enable accurate sensor fusion for image analysis, each sensor must first be combined in a common vehicle coordinate system and a method for triggering, time-stamping and calculating the position/pose of each sensor at the time of image capture devised. Initial tests were carried out over agricultural regions with geometric tests designed to assess the spatial accuracy of the fused imagery in terms of its absolute and relative accuracy. The results demonstrate that by using our innovative system, images captured simultaneously by the four sensors could be geometrically corrected successfully and then co-registered and fused exhibiting a root-mean-square error (RMSE) of approximately 10m independent of inertial measurements and ground control.