High-performance Intelligent Computations for Environmental and Disaster Monitoring

At present, global climate changes on the Earth made a rational land use, environmental monitoring, prediction of natural and technological disasters, etc the tasks of great importance. The basis for the solution of these crucial problems lies in the integrated use of data of different nature: modeling data, in‐situ measurements and observations, and indirect observations such as airborne and space borne remote sensing data [GEOSS, 2010]. In particular, models can be used to fill in the gaps in the data by extrapolating and estimating necessary parameters to the site of interest; to better understand and predict different processes occurring in the atmosphere, land, ocean and sea, etc; they can help to interpret measurements and to design new observing systems. In‐situ measurements are often used for assimilation into models, calibration, and validation of both modeling and remote sensing data. Satellite observations have an advantage of acquiring data for large and hard‐to‐reach territories, as well as providing continuous and human‐independent measurements. Many important applications such as monitoring and predictions of natural disasters, environmental monitoring, etc. heavily rely on the use of Earth observation (EO) data from space. For example, the satellite‐derived flood extent is very important for calibration and validation of hydraulic models to reconstruct what happened during the flood and determine what caused the water to go where it did [Horritt, 2006]. Information on flood extent provided in the near real‐time (NRT) can also be used for damage assessment and risk management, and can benefit to rescuers during flooding [Corbley, 1999]. Both space borne microwave and optical data can provide means to detect drought conditions, estimate drought extent and assess the damage caused by the drought events [Kogan et al, 2004], [Wagner et al, 2007]. To assess vegetation health/stress, which is extremely important for agriculture applications, optical remote sensing data can be used to derive biophysical and biochemical variables such as pigment concentration, leaf structure, water content at leaf level and leaf area index (LAI), fraction of photosynthetically active radiation absorbed by vegetation (FPAR) at canopy level etc. [Liang, 2004]. The EO domain is characterized by the large volumes of data that should be processed, catalogued, and archived [Fusco et al, 2003], [Shelestov et al, 2006]. For example, GOME instrument onboard Envisat satellite generates nearly 400 Tb data per year [Fusco et al, 2003]. The processing of satellite data is carried out not by the single application with a monolithic code, …