LAV@HAZARD: A Web-Gis interface for volcanic hazard assessment

Satellite data, radiative power of hot spots as measured with remote sensing, historical records, on site geological surveys, digital elevation model data, and simulation results together provide a massive data source to investigate the behavior of active volcanoes like Mount Etna (Sicily,Italy) over recent times. The integration of these eterogeneous data into a coherent visualization framework is important for their practical exploitation. It is crucial to fill in the gap between experimental and numerical data, and the direct human perception of their meaning. Indeed, the people in charge of safety planning of an area need to be able to quickly assess hazards and other relevant issues even during critical situations. With this in mind, we developed LAV@HAZARD, a web-based geographic information system that provides an interface for the collection of all of the products coming from the LAVA project research activities. LAV@HAZARD is based on Google Maps application programming interface, a choice motivated by its ease of use and the user-friendly interactive environment it provides. In particular, the web structure consists of four modules for satellite applications (time-space evolution of hot spots, radiant flux and effusion rate), hazard map visualization, a database of ca. 30,000 lava-flow simulations, and real-time scenario forecasting by MAGFLOW on Compute Unified Device Architecture

Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because weather and climate are of particular interest for almost all living organisms on earth, the efficient classification of clouds is one of the most important problems. Geostationary satellites such as Meteosat Second Generation (MSG) offer the only possibility to generate long-term cloud data sets with high spatial and temporal resolution. This work, therefore, addresses research problems on efficient and parallel processing of MSG data to enable new applications and insights. First, we address the lack of a suitable processing chain to generate a long-term Fog and Low Stratus (FLS) time series. We present an efficient MSG data processing chain that processes multiple tasks simultaneously, and raster data in parallel using the Open Computing Language (OpenCL). The processing chain delivers a uniform FLS classification that combines day and night approaches in a single method. As a result, it is possible to calculate a year of FLS rasters quite easy. The second topic presents the application of Convolutional Neural Networks (CNN) for cloud classification. Conventional approaches to cloud detection often only classify single pixels and ignore the fact that clouds are highly dynamic and spatially continuous entities. Therefore, we propose a new method based on deep learning. Using a CNN image segmentation architecture, the presented Cloud Segmentation CNN (CS-CNN) classifies all pixels of a scene simultaneously. We show that CS-CNN is capable of processing multispectral satellite data to identify continuous phenomena such as highly dynamic clouds. The proposed approach provides excellent results on MSG satellite data in terms of quality, robustness, and runtime, in comparison to Random Forest (RF), another widely used machine learning method. Finally, we present the processing of raster time series with a system for Visualization, Transformation, and Analysis (VAT) of spatio-temporal data. It enables data-driven research with explorative workflows and uses time as an integral dimension. The combination of various raster and vector data time series enables new applications and insights. We present an application that combines weather information and aircraft trajectories to identify patterns in bad weather situations

Thermal-Based Evaporative Stress Index for Monitoring Surface Moisture Depletion

The standard suite of indicators currently used in operational drought monitoring reflects anomalous conditions in several major components of the hydrologic budget—representing deficits in precipitation, soil moisture content, runoff, surface and groundwater storage, snowpack, and streamflow. In principle, it is useful to have a diversity of indices because drought can assume many forms (meteorological, agricultural, hydrological, and socioeconomic), over broad ranges in timescale (weeks to years), and with varied impacts of interest to different stakeholder groups. Farmers, for example, may be principally interested in soil moisture deficits, river forecasters will focus on streamflow fluctuations, and water managers will be concerned with longer-term stability in municipal water supply and reservoir levels. Only recently has actual evapotranspiration (ET) been considered as a primary indicator of drought conditions (e.g., Anderson et al., 2007b; Labedzki and Kanecka- Geszke, 2009; Li et al., 2005; Mo et al., 2010). ET is a valuable drought indicator because it reflects not only moisture availability but also the rate at which water is being consumed. Because transpiration (T) and carbon uptake by vegetation are tightly coupled through stomatal exchange, ET anomalies are indicative of vegetation health and growing conditions. In addition, the importance of so-called flash droughts is becoming increasingly evident, where hot, dry, and windy atmospheric conditions can lead to unusually rapid soil moisture depletion and, in some cases, devastating crop failure. Such events cannot be easily identified using local precipitation anomalies but should have a detectable ET signature

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015