PREDICTION AND RISK MANAGEMENT OF SPREADING FOREST PEST INFESTATIONS USING SATELLITE DATA

Abstract

The article is devoted to predicting the risk of occurrence of large foci of infection in a pine forest with bark beetles, pathogenic fungi, and nematodes. The areas of disease observed on satellite images have a spotted, clustered structure of drying forest. An important statistical characteristic of the infestation structure is the power law of distribution of infestation clusters in size. Large, catastrophic events have a significant probability in processes with power laws of distributions. The given methods of computer identification and analysis of cluster distributions make it possible to form a statistical percolation model of prediction an d risk management of forest infestation based on information captured (read out) from space images. The only effective means of combating the bark beetle is sanitary felling of the forest. The sanitary cuttings area is considered a control parameter in the model. The model uses forest observation on a lattice of satellite image pixels, similar to the lattice of a percolation system. The universality of the theory is explained by the fact that it considers the interaction of elements of infection clusters, which, near the critical state of a forest ecosystem, obey a power-law distribution. The value of the power-law indicator indicates the formation of large clusters and is used in the model for the risk prediction of infestation development. In the model, risk prediction is understood as a statistical assessment of risk in the future, taking into account changes in the conditions for its manifestation. Changes are determined based on the results of satellite imagery, and the effectiveness of sanitary tree cuttings is considered. An example of a prediction of the development of forest infestations (drying) using images from the Sent inel-2 satellites is presented. Model identification methods are c onsidered, and a test verification of the model is performed. Using scale-invariant indicators of power-law distributions made it possible to abandon expensive high-precision images and replace them with images of average spatial resolution. The approach to synthesizing a prediction and risk management model from space images discussed in the article is based on the theory of self-organized criticality. The model is quite universal and can be used in space geoinformation technologies to orga- nize effective environmental management.