Abstract
The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Methodology of natural geo-systems modeling for variables and indicators selections is described. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Brief conclusions on efficiency of the described methodology are proposed. Proposed methodology can applied for wide range of risk-related tasks, such as natural and technological disaster monitoring, air-water-soil pollution control, crop productivity control, etc.
Highlights
Since its origin, remote sensing demonstrates a stormy evolution (Elachi and Van Zyl, 2006; Lillesand et al, 2014)
The common methodology of remote sensing is being developed, and its basic idea is grounded on models of signals formation, i.e., models of individual indicators and methods for their monitoring (Elachi and Van Zyl, 2006)
Conclusions on the development of the individual processes in studied natural systems usually are built on such monitoring (Elachi and Van Zyl, 2006; Qiu et al, 2007)
Summary
Remote sensing demonstrates a stormy evolution (Elachi and Van Zyl, 2006; Lillesand et al, 2014). The common methodology of remote sensing is being developed, and its basic idea is grounded on models of signals formation, i.e., models of individual indicators and methods for their monitoring (Elachi and Van Zyl, 2006). Conclusions on the development of the individual processes in studied natural systems usually are built on such monitoring (Elachi and Van Zyl, 2006; Qiu et al, 2007). We understand the complex interactions between the processes and phenomena, can simulate the feedbacks in multiagent environment, model the integrated dynamics of the processes, and predict the behavior of multi-component systems. Remote sensing can and should become a source of information about behavior of the variables in these complex, interlinked models. Remote sensing should be tool for monitoring, and for predictions and forecasts
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