Data Assimilation Technique For Flood Monitoring and Prediction

This paper focuses on the development of methods and cascade of models for flood monitoring and forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and models in the Grid infrastructure and related projects are discussed

Multi-Agent Security System based on Neural Network Model of User's Behavior

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”

Spatial distribution of arable and abandoned land across former Soviet Union countries

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others

Efficiency assessment of using satellite data for crop area estimation in Ukraine

The knowledge of the crop area is a key element for the estimation of the total crop production of a country and therefore the management of agricultural commodities markets. Satellite data and derived products can be effectively used for stratification purposes and a-posteriori correction of area estimates from ground observations. In this paper we assess the efficiency of using satellite data for crop area estimation in Ukraine. Results of several pilot studies carried out from 2010 to 2012 in Ukraine are described. First, we describe results of the study conducted in 2010 to explore the specific difficulties and efficiency of crop area estimation assisted by satellite remote sensing. The study was carried out on 3 oblasts in Ukraine with a total area of 78500 km2. The efficiency of several image types (MODIS, Landsat TM, AWiFS, LISS-III and RapidEye) combined with a field survey on a stratified sample of square segments is assessed. Second, we present results of integrating data acquired by Ukrainian remote sensing satellite Sich-2 with EO-1 and Landsat-TM/ETM+ for crop area estimation in the Lvivska oblast in 2012. Only optical satellite images were used in both these studies, and results show particular difficulties in discriminating summer crops in Ukraine such as maize, soy beans, sunflower and sugar beet. Therefore, we incorporate SAR satellite images (Radarsat-2 quadpolarization) in order to improve discrimination between summer crops. Obtained results show that omission and commission classification errors can be reduced by adding radar imagery to the optical ones.JRC.H.4-Monitoring Agricultural Resource

Crop area estimation in Ukraine using satellite data within the MARS project

In the paper we describe the results of a pilot project on crop area estimation with satellite images in Ukraine. The best results were obtained with MLP classifiers and TM images that demonstrated to give a cost-efficient contribution to the improvement of the estimators. MODIS images also demonstrated to be cost-efficient. This study provided useful insights in the framework of Geoland2-Crop CIS and updates the conclusions of the MARS project in the mid 90’s: at that time the approach had proved to be technically solid, but did not reach the cost-efficiency threshold with the costs of 15 years ago.JRC.D.5-Food Securit

On Possible ACD Application for Optimization of Cutting and Assembly in Mechanical Engineering

Application of adaptive critic designs for optimization of real-world processes require a model of the process under optimization or feedback from the real process. For optimization of mechanical manufacturing it is often too expensive and time consuming to use real equipment for the model. However, mathematical models adequately describing real manufacturing processes with realistic noise and interference assumptions may be too difficult to create. We propose to use micro machine tools and micro manipulators as the physical models of real mechanical engineering equipment. They allow us to reduce the cost of experiments and accelerate their speed. We have created prototypes of micro machine tools and work on their use for adaptive critic based optimal control. We describe possible use of adaptive critic designs for optimization of two typical problems of mechanical engineering: shaft cutting and gear fitting on an axle

Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences

In this paper, we propose a new approach to pixel and parcel-based classification of multi-temporal optical satellite imagery. We first restore missing data due to clouds and shadows based on vector and raster data fusion in different phases of classification methodology. Pixel-based classification maps are derived from an ensemble of neural networks, in particular multilayer perceptrons (MLPs).. The proposed approach is applied for regional scale crop classification using multi-temporal Landsat-8 images for the JECAM site in the Kyivska oblast of Ukraine in 2013. The obtained results on crop area estimates are also compared to official statistics.JRC.D.5-Food Securit

Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequence

In this paper, we propose a new approach to parcel-based classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows based on vector and raster data fusion in different phase of classification methodology in Ukraine within the JECAM project. For obtaining pixel-based classification map, an ensemble of neural networks, in particular multilayer perceptron (MLPs), is used. The proposed approach is applied for regional scale crop classification using multi-temporal Landsat-8 images for the Kyivska oblast in Ukraine in 2013. The obtained results are also validated through comparison to official statisticsJRC.D.5-Food Securit

Crop area estimation combining remote sensing and ground survey in Ukraine

This short paper describes an operational technique to collect unbiased crop area statistics by combining field area measurements from ground survey and satellite data in Ukraine.JRC.H.4-Monitoring Agricultural Resource