Oceanus.

v. 38, no.1 (1995

TreeWatch.net : a water and carbon monitoring and modeling network to assess instant tree hydraulics and carbon status

TreeWatch.net is an initiative that has been developed to watch trees grow and function in real-time. It is a water- and carbon-monitoring and modeling network, in which high quality measurements of sap flow and stem diameter variation are collected on individual trees. Automated data processing using a cloud service enables instant visualization of water movement and radial stem growth. This can be used to demonstrate the sensitivity of trees to changing weather conditions, such as drought, heat waves, or heavy rain showers. But TreeWatch.net's true innovation lies in its use of these high precision harmonized data to also parameterize process-based tree models in real-time, which makes displaying the much needed mechanisms underlying tree responses to climate change possible. Continuous simulation of turgor to describe growth processes and long-term time series of hydraulic resistance to assess drought-vulnerability in real-time are only a few of the opportunities our approach offers. TreeWatch.net has been developed with the view to be complementary to existing forest monitoring networks and with the aim to contribute to existing dynamic global vegetation models. It provides high-quality data and real-time simulations in order to advance research on the impact of climate change on the biological response of trees and forests. Besides its application in natural forests to answer climate-change related scientific and political questions, we also envision a broader societal application of TreeWatch.net by selecting trees in nature reserves, public areas, cities, university areas, schoolyards, and parks to teach youngsters and create public awareness on the effects of changing weather conditions on trees and forests in this era of climate change

Oceanographic Weather Maps: Using Oceanographic Models to Improve Seabed Mapping Planning and Acquisition

In a world of high precision sensors, one of the few remaining challenges in multibeam echosounding is that of refraction based uncertainty. A poor understanding of oceanographic variability can lead to inadequate sampling of the water mass and the uncertainties that result from this can dominate the uncertainty budget of even state-of-the-art echosounding systems. Though dramatic improvements have been made in sensor accuracies over the past few decades, survey accuracy and efficiency is still potentially limited by a poor understanding of the “underwater weather”. Advances in the sophistication of numerical oceanographic forecast modeling, combined with ever increasing computing power, allow for the timely operation and dissemination of oceanographic nowcast and forecast model systems on regional and global scales. These sources of information, when examined using sound speed uncertainty analysis techniques, have the potential to change the way hydrographers work by increasing our understanding of what to expect from the ocean and when to expect it. Sound speed analyses derived from ocean modeling system’s three-dimensional predictions could provide guidance for hydrographers during survey planning, acquisition and post-processing of hydrographic data. In this work, we examine techniques for processing and visualizing of predictions from global and regional operational oceanographic forecast models and climatological analyses from an ocean atlas to better understand how these data could best be put to use to in the field of hydrograph

Preparing for landing, ready for take-off: zoning noise pollution as spatio-temporal practices at Berlin-Tegel and Berlin-Tempelhof Airport (1965-1975)

"The article outlines the spatio-temporal dimensions of air traffic noise and its relation to urban planning and the presumed conditions of public health. Placed in the setting of Cold War Berlin, the measuring and the localization of noise pollution becomes, step by step, a social and political issue. Emerging environmental awareness started challenging the well-established top-down planning procedures concerning inner-city territories. In this essay, the implementation of noise pollution zones at West Berlin airports is linked to modes of policing an urban soundscape, in particular when it comes to the spatial and temporal annoyance induced by noise. When combating air traffic noise, new social movements and environmental experts face different sets of hegemonic rules that organize airspace. This struggle requires a different logic of gaining and 'doing territory' than on the city's street level." (author's abstract

Horizontal distribution and overlap of planktivorous fish stocks in the Norwegian Sea during summers 1995–2006

The Norwegian Sea harbours several large pelagic fish stocks, which use the area for feeding during the summer. The period 1995–2006 had some of the highest biomass of pelagic fish feeding in the Norwegian Sea on record. Here we address the horizontal distribution and overlap between herring, blue whiting and mackerel in this period during the summers using a combination of acoustic, trawl and LIDAR data. A newly developed temperature atlas for the Norwegian Sea is used to present the horizontal fish distributions in relation to temperature. The centre of gravity of the herring distribution changed markedly several times during the investigated period. Blue whiting feeding habitat expanded in a northwestern direction until 2003, corresponding with an increase in abundance. Strong year classes of mackerel in 2001 and 2002 and increasing temperatures throughout the period resulted in an increased amount of mackerel in the Norwegian Sea. Mackerel was generally found in waters warmer than 8°C, while herring and blue whiting were mainly found in water masses between 2 and 8°C. The horizontal overlap between herring and mackerel was low, while blue whiting had a large horizontal overlap with both herring and mackerel. The changes in horizontal distribution and overlap between the species are explained by increasing stock sizes, increasing water temperature and spatially changing zooplankton densities in the Norwegian Sea

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks