The quality of pre-announcement communication and the accuracy of estimated arrival time in critically ill patients, a prospective observational study.

BACKGROUND: Efficient communication between (helicopter) emergency medical services ((H)EMS) and healthcare professionals in the emergency department (ED) is essential to facilitate appropriate team mobilization and preparation for critically ill patients. A correct estimated time of arrival (ETA) is crucial for patient safety and time-management since all team members have to be present, but needless waiting must be avoided. The aim of this study is to investigate the quality of the pre-announcement and the accuracy of the ETA. METHODS: A prospective observational study was conducted in potentially critically ill/injured patients transported to the ED of a Level I trauma center by the (H)EMS. Research assistants observed time slots prior to arrival at the ED and during the initial assessment, using a stopwatch and an observation form. Information on the pre-announcement (including mechanisms of injury, vital signs, and the ETA) is also collected. RESULTS: One hundred and ninety-three critically ill/injured patients were included. Information in the pre-announcement was often incomplete; in particular vital signs (86%). Forty percent of the announced critically ill patients were non-critical at arrival in the ED. The observed time of arrival (OTA) for 66% of the patients was later than the provided ETA (median 5:15 min) and 19% of the patients arrived sooner (3:10 min). Team completeness prior to the arrival of the patient was achieved for 66% of the patients. CONCLUSIONS: The quality of the pre-announcement is moderate, sometimes lacking essential information on vital signs. Forty percent of the critically ill patients turned out to be non-critical at the ED. Furthermore, the ETA was regularly inaccurate and team completeness was insufficient. However, none of the above was correlated to the rate of complications, mortality, LOS, ward of admission or discharge location.

Estimating photosynthetic capacity from leaf reflectance and Chl fluorescence by coupling radiative transfer to a model for photosynthesis.

In photosynthesis models following the Farquhar formulation, the maximum carboxylation rate Vcmax is the key parameter. Remote-sensing indicators, such as reflectance ρ and Chl fluorescence (ChlF), have been proven as valuable estimators of photosynthetic capacity and can be used as a constraint to Vcmax estimation. We present a methodology to retrieve Vcmax from leaf ρ and ChlF by coupling a radiative transfer model, Fluspect, to a model for photosynthesis. We test its performance against a unique dataset, with combined leaf spectral, gas exchange and pulse-amplitude-modulated measurements. Our results show that the method can estimate the magnitude of Vcmax estimated from the far-red peak of ChlF and green ρ or transmittance τ, with values of root-mean-square error below 10 µmol CO2  m-2  s-1 . At the leaf level, the method could be used for detection of plant stress and tested against more extensive datasets. With a similar scheme devised for the higher spatial scales, such models could provide a comprehensive method to estimate the actual photosynthetic capacity of vegetation.

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir.

Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH4 and CO2 gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60 µmol mol-1, respectively, for CH4 (using a flame ionization or FID detector) and CO2 (using a thermal conductivity or TCD detector). Our results show that CO2 emissions are significantly (an order of 5.102-103) higher than those of CH4 in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82 × 103 for CH4 and 1.19 × 106 for CO2. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Assessing temporal stability for coarse scale satellite moisture validation in the Maqu area, Tibet.

This study evaluates if the temporal stability concept is applicable to a time series of satellite soil moisture images so to extend the common procedure of satellite image validation. The area of study is the Maqu area, which is located in the northeastern part of the Tibetan plateau. The network serves validation purposes of coarse scale (25-50 km) satellite soil moisture products and comprises 20 stations with probes installed at depths of 5, 10, 20, 40, 80 cm. The study period is 2009. The temporal stability concept is applied to all five depths of the soil moisture measuring network and to a time series of satellite-based moisture products from the Advance Microwave Scanning Radiometer (AMSR-E). The in-situ network is also assessed by Pearsons's correlation analysis. Assessments by the temporal stability concept proved to be useful and results suggest that probe measurements at 10 cm depth best match to the satellite observations. The Mean Relative Difference plot for satellite pixels shows that a RMSM pixel can be identified but in our case this pixel does not overlay any in-situ station. Also, the RMSM pixel does not overlay any of the Representative Mean Soil Moisture (RMSM) stations of the five probe depths. Pearson's correlation analysis on in-situ measurements suggests that moisture patterns over time are more persistent than over space. Since this study presents first results on the application of the temporal stability concept to a series of satellite images, we recommend further tests to become more conclusive on effectiveness to broaden the procedure of satellite validation.

High-resolution methods for fluorescence retrieval from space.

The retrieval from space of a very weak fluorescence signal was studied in the O(2)A and O(2)B oxygen atmospheric absorption bands. The accuracy of the method was tested for the retrieval of the chlorophyll fluorescence and reflectance terms contributing to the sensor signal. The radiance at the top of the atmosphere was simulated by means of a commercial radiative-transfer program at a high resolution (0.1 cm(-1)). A test data set was generated in order to simulate sun-induced chlorophyll fluorescence at the top of the canopy. Reflectance terms were spectrally modeled using cubic splines and fluorescence by means of the sum of two Voigt functions. Sensor radiance residual minimization was performed in the presence of a multiplicative noise, thus ensuring that the sensor simulations were realistic. The study, which focused on the possibility of retrieving fluorescence with an accuracy better than 10%, was performed for instrument resolutions ranging from about 0.4 cm(-1) to 2 cm(-1) in order to test the algorithm for the characteristics of existing and planned hyper-spectral sensors. The algorithm was also used to retrieve fluorescence in the single O(2)A band at the OCO and TANSO-FTS instrument spectral resolutions.

Suppression of local haze variations in MERIS images over turbid coastal waters for retrieval of suspended sediment concentration.

Atmospheric correction over turbid waters can be problematic if atmospheric haze is spatially variable. In this case the retrieval of water quality is hampered by the fact that haze variations could be partly mistaken for variations in suspended sediment concentration (SSC). In this study we propose the suppression of local haze variations while leaving sediment variations intact. This is accomplished by a multispectral data projection (MDP) method based on a linear spectral mixing model, and applied prior to the actual standard atmospheric correction. In this linear model, the hazesediment spectral mixing was simulated by a coupled water-atmosphere radiative transfer (RT) model. As a result, local haze variations were largely suppressed and transformed into an approximately homogenous atmosphere over the MERIS top-of-atmosphere (TOA) radiance scene. The suppression of local haze variations increases the number of satellite images that are still suitable for standard atmospheric correction processing and subsequent water quality analysis.