This paper is dedicated to the study of social vulnerability in risk analysis task using multi-source data statistics. Formal KPCA-algorithm for quantitative analysis of social factors of disaster risk is proposed. Modified form of damage function basing on the prospect theory and decision making under uncertainty on cognitive bias and handling of risk is proposed. Formal relations for analysis of damage with age, education and income of population are calculated. Analysis demonstrates that at least half of disaster damage might be caused by social factors: education, long-term experience and social behaviour. Using algorithm and approach proposed it was shown a way to estimate quantitative parameters of the losses distributions in view of dynamics of social drivers.

The problem of uncertainty analysis in multi-component systems is considered. As an example a problem of decision making under uncertainty in task of modeling of carbon balance and analysis of greenhouse gases (GHG) emissions using satellite tools was considered. Approaches to decision making under uncertainty are described: interval, fuzzy, and stochastic assessments. Different approaches and algorithms to calculate carbon and GHG emissions are described. For every algorithm (deterministic inventory, ecological modeling, and satellite control of emissions) errors and uncertainties are analyzed and estimated. Algorithms for uncertainty analysis are presented. Algorithm for analysis of components of uncertainty of vegetation productivity assessment using satellite data is proposed. Uncertainty component analysis allows understand important properties of the system studied and its feedback to anthropogenic load and climate impact. It was demonstrated that the comprehensive analysis of uncertainties not only reduces errors but also obtains new knowledge about the systems studied.

This paper is dedicated to the study of risk perception issues. A formal algorithm for the quantitative analysis of social factors of disaster risk is proposed. A modified form of the damage function based on the prospect theory and decision making under uncertainty due to cognitive bias and the handling of risk is proposed. Relationships for analysis of the most probable damage according to age, education and income of the population are proposed. Using multi-source statistics, the interconnected influence of education and long-term experience (education function) on the one hand, and short-term information (risk perception function) on the other hand to develop the damage function as a measure of vulnerability toward disasters has been demonstrated. As the result of the data analysis demonstrates, up to half of the damage function might be caused by social factors: education, long-term experience and responsible behaviour of information agents. It was concluded that public efforts based on ethical principles and directed toward empowering people and developing the human capacity through education can have a positive response in reducing vulnerability and enhancing the adaptive capacity toward the disasters including climate-related threats.

Climate change has the potential to affect terrestrial ecosystems and, thereby, the carbon cycle. Various vegetation biomes are likely to respond differently to changes in climatic factors. The purpose of this study was to analyse the trends of the terrestrial vegetation productivity and climate drivers on regional levels and relations between ones. The gross primary productivity from the global satellite-based terrestrial production efficiency model (PEM) MOD17 as the vegetation productivity indicator and meteorological data from the weather station network as climatic indicators were used. The analysis covered a period from 2000 to 2012. Correlation analysis was used to quantify the association between the vegetation productivity and climatic indicators for different growing seasons and landscape-climatic zones of Ukraine. The Mann–Kendall trend test was applied to take into account seasonal features. Multiple linear regression models for corresponding seasons and zones have been simulated using the principal component analysis. The results showed no detectable limiting effect of the climatic drivers on plant productivity for forest areas. The limiting effect of the temperature increasing and precipitation amount decreasing for the steppe zone and eastern forest-steppe subzone of Ukraine for summer was observed.

This paper aims to study the method of satellite spectral reflectance indexes harnessed for crop productivity control and forecasting. The general method is proposed and discussed in the framework of a general stochastic model of socio-economic regional development. The set of spectral indexes have been selected for control of bio-productivity and its variability analysis. The method for analysis of uncertainties of the method of productivity assessment by remote sensing data has been proposed. The separate components of uncertainties and corresponding errors have been estimated. The way to control the threshold productivity toward the harmful impact (for example, droughts) as the approach to risk assessment is proposed. Spectral reflectance indexes NDVI, VCI, VHI, TCI, PDI, SAVI have been proposed for assessment of crop productivity and control of critical values of productivity. Other functions of used indexes - for example, FPAR, LAI - are effective tools for control of modeling variables.