Improvement of heart rate recovery after exercise training in older people.

Twenty-four subjects aged 70 and older were retrospectively selected from our archives and screened for symptoms of cardiovascular disease. Baseline exercise test was negative for myocardial ischemia in all subjects. All subjects had completed an 8-week program, performed for a variety of indications and consisting of an aerobic physical training program including 30 minutes of cycling three times per week at 65% to 75% of maximum heart rate achieved at peak exercise test performed at enrollment, an educational intervention, dietary advice, and psychological support. All subjects underwent a cardiopulmonary exercise test (CPX) before and at the end of exercise training. At the end of each CPX, peak oxygen uptake (VO2peak), the rate of increase of ventilation per unit of increase of carbon dioxide production (VE/VCO2slope), and HRR were recorded. Twenty-five healthy subjects younger than 60 with no evidence of exercise-induced myocardial ischemia and not enrolled in any exercise training program were also retrospectively selected from our archives and used as a control group for analyzing HRR. These patients performed two exercise tests several weeks apart. Several studies have shown that changes in vagal tone can be used as an outcome tool that helps identify patients or subjects with or without cardiovascular disease at risk for a cardiovascular event, although the evidence of a prognostic value of HRR in older subjects without cardiovascular disease is rather poor. In this study, exercise training resulted in HRR improvement in healthy elderly subjects, suggesting that exercise training improves vagal/sympathetic balance in older subjects without cardiovascular disease as well. Whether the observed improvement in HRR may have long-term beneficial prognostic effects was not the aim of the study, although a beneficial effect might be postulated, in light of the Framingham dat

A Monte Carlo Event Generator for W Off-shell Pair Production including Higher Order Electromagnetic Radiative Corrections

We present the Monte Carlo event generator {\tt WOPPER} for pair production of $W$'s and their decays at high energy $e^+e^-$ colliders. {\tt WOPPER} includes the effects from finite $W$ width and focusses on the calculation of higher order electromagnetic corrections in the leading log approximation including soft photon exponentiation and explicit generation of exclusive hard photons.Comment: Contribution to the Second Workshop -- Munich, Annecy, Hamburg: $e^+e^-$ Collisions at 500~GeV: The Physics Potential, November 20, 1992, to April 3, 1993. LaTeX, 6 pages + 4 uuencoded EPS figures, IKDA 93/28, SI-93-

Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

1. Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). 2. We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. 3. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. 4. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate

Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta

Estimation of evapotranspiration is always a major component in water resources management. The reliable estimation of daily evapotranspiration supports decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are used in conjunction with Surface Energy Balance (SEB) models to overcome difficulties in obtaining daily evapotranspiration measurements on a regional scale. In this study the SEB System (SEBS) is used to estimate daily evapotranspiration and evaporative fraction over the Nile Delta along with data acquired by the Advance Along Track Scanning Radiometer (AATSR) and the Medium Spectral Resolution Imaging Spectrometer (MERIS), and six in situ meteorological stations. The simulated daily evapotranspiration values are compared against actual ground-truth data taken from 92 points uniformly distributed all over the study area. The derived maps and the following correlation analysis show strong agreement, demonstrating SEBS' applicability and accuracy in the estimation of daily evapotranspiration over agricultural areas

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.This study is supported and funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 641762, ECOPOTENTIAL