Vegetation condition assessment based on long-term satellite monitoring data

Автор: Glagolev V.A., Zubareva A.M.

Журнал: Региональные проблемы @regionalnye-problemy

Рубрика: Геология. Геоэкология

Статья в выпуске: 4 т.26, 2023 года.

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In this study, MODIS satellite imagery data was processed for 2016 to identify areas with high fire danger, taking into account vegetation characteristics in 2016-2017. To assess the biodiversity of the territory and damage from vegetation fires, it is proposed an algorithm for the spatial and temporal analysis of vegetation fires distribution, taking into account their influence on vegetation transformation in the Russian Far East. The processing algorithm includes: the network imposition on the study area; obtaining satellite image fragments for the territory and converting the satellite image into operational-territorial units of a given size, with the transfer of attribute data over a multi-year period; forming vector layers of individual vegetation fires on the base of point placement or fires area, determining the nesting of individual fires in operational-territorial units and adding data on the condition of vegetation they arose on. The materials of the study contained both annual and daily vegetation data on the location of vegetation fires, based on MODIS satellite images, Terra spectroradiometer and Aqua Land Cover Modeling Grid Version 6. In the course of data analysis, 88 variants of vegetation condition transformation were found in the presence of from 1 to 11 fires. In 8604 cases, changes in the flora structure and quality were caused by an anthropogenic factor. In 40 variants, the transformation did not depend on fires. The maximum transformation is recorded in the following vegetation cover classes: small-leaved and broad-leaved forests, forest-steppes, steppes, meadows and arable land. Most cases of vegetation transformation in operational-territorial units (from 30 to 70%) are recorded when forest- steppe is turning to steppe.

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Vegetation, fires, earth remote sensing data, vegetation index, modeling options

Короткий адрес: https://sciup.org/143180917

IDR: 143180917   |   DOI: 10.31433/2618-9593-2023-26-4-71-76

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