The article investigates risk management components and specific industry risks of forest management. The risk management model is offered in forestry on the basis of the managed parameter.

Identification of fires by satellite methods and means is one of the main tasks of the modern forest fire monitoring system. The article presents a method for mapping forest fires based on the data of the MODIS Spectroradiometer, based on the analysis of the obtained images using the ScanMagic software package. In September 2009, a blocking anticyclone was established in Australia, contributing to an abnormal increase in temperatures, lack of precipitation, and as a result, the emergence of massive forest fires that led to smoke in the region. Studies have been conducted on the example of this phenomenon. As a result, the results of the study are considered, the analysis of changes in the number of fire centers and the assessment of smoke development is carried out. Currently, this method is well established and is used for detecting and analyzing fire situations, including in Australia with a wider range of functions presented in this paper.

The description of model of optimization of processing of the diverse space distributed data is provided in work. Approach to implementation of consolidation of information from different sources is considered. The control algorithm of diverse data taking into account filtering of fragments of all arrays of information is provided. Key information and technical aspects of application of model of processing of diverse geodata for the solution of tasks of management of forestry are described.

In this paper, we study the spatial and temporal variability of the Bering sea ice cover. The Bering sea is a major transportation importance as a link in the Northern sea route in turn every activity at sea, meeting the challenges of hydro-meteorological research and predictions in this region largely depend on the knowledge about ice conditions. Observing changes in the ice environment can also serve to assess climate change. For the study, the methods of mathematical statistics applied to data on the amount of ice as a percentage for the Bering sea area in the 1x1 degree grid for the period November 1981goda-April 2014. In the course of the work, the trend component of the time variability of sea ice was identified, spectral and harmonic analysis was performed, and autocorrelation analysis was performed. As a result, for long-term variability, the existence of a trend in the development of ice conditions was revealed, and the presence of a linear trend is unlikely. Analysis of local trends showed a decrease in ice cover in the period 1992-2003, after 2003 there was a large positive trend. Spectral analysis 4 peaks of the spectrum for each were calculated characteristics of harmonics. The sum of harmonics gives a good result when combined with the original data. Long-term variability is low-inertia. when performing an auto forecast for average annual values with a 10-year lead time, it was noted that the prognostic model does not give good results.

No abstract