The paper considers the approaches and possibilities of using two types of simulation: the species distribution model and the ecological niche model. The study aimed to simulate favorable habitats and the potential spread of non-gregarious locust pests in North Kazakhstan based on satellite and ground data for preventive measures. The MaxEnt software was used to conduct the simulation. According to the species distribution model, high indicators of the habitat are predicted in the Pavlodar and Kostanay regions, on 69.9-100% of the studied territory. With the simulation of ecological niches for non-gregarious locust pests, the following class boundaries were determined for the transition from quantitative to qualitative indicators from I (85-100%) to IV (0-50%), which indicates the zones of the probability of pest attack from a higher indicator to a lower one. According to the fundamental model, high indicators of the area of pest occurrence, that is, zones I and II, are located in the central and northern parts of the Pavlodar region. Here, the probability of non-gregarious locust occurrence of zone I and II with a ratio of 1:1 is observed in a slightly arid, moderately warm agro-climatic zone. In the southern part of the Kostanay region, the simulation predicts the probability of occurrence on zones I and II with a ratio of 1:2 in the moderately arid warm agro-climatic zone of this region. In the southern and southeastern parts of the Akmola region, the model predicts the probability of occurrence in zones I and II with a ratio of 1:3 in a slightly humid, moderately warm agro-climatic zone of the region. The considered species distribution model can be used as a modern tool for long-term forecasting of the spread of non-gregarious locust pests since it takes into account the peculiarities of the agricultural landscape. The fundamental niche model can be used in a long-term population forecast since it focuses more on the theoretical conditions of pest habitats.

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.