Evaluation of the structure and composition of forests in Moscow region based on field and remote sensing data

Abstract

The sequence and content of the main stages of determining the indicators of the structure and composition of forest communities based on satellite imagery of the Landsat system are described. It is shown that the application of quantitative processing methods for the interpolation of point data from field research, in particular canonical discriminant analysis, allows obtaining characteristics of the vegetation cover and investigating the factors of its biodiversity formation. The presented methods and results of the assessment of various parameters of the state of forests, their structure and typological composition can be integrated into the international network of the National Forest Inventory. Despite the difference in methodological approaches, there is a principal possibility of harmonizing the data obtained with the data of the Global Earth Observation System of Systems. For the test territory in the central partof the Russian plain (western sector of the Moscow oblast), the results of a joint analysis of field research data, remote sensing dataand a digital elevation model are presented. A series of maps of the medium scale characterizing the spatial structure and composition of the forest cover of the study area was obtained.

Modeling of Forest Communities’ Spatial Structure at the Regional Level through Remote Sensing and Field Sampling: Constraints and Solutions

“…It varied from 20% to 100% for 38 association groups based on 1025 field sample plots. However, the number of sample plots per each association group varied from 4 to 114, which is unlikely in terms of sample size [54].…”

Spatial differentiation of forests in the Russian Federation based on the author's algorithm for assessing the level of adaptation to climate change

“…Результаты настоящего исследования показывают, что субъекты европейской части России, имеющие хвойные леса в качестве преобладающей группы пород, в основном, адаптированы к климатическим изменениям, то есть, в них на 2022 год площадь хвойных пород выше, чем в среднем за предшествующие 10 лет. В Центральном федеральном округе отмечают отсутствие неморализации видов ельников и сосняков в связи с современными изменениями климата [28] 3. Scheme of spatial differentiation of the constituent entities of the Russian Federation according to the level of adaptation to the risk of changes in the species composition of forests Источник: собственная композиция авторов Source: author's composition Согласно полученным результатам, индикативный показатель для риска пожаров более 0 (сниженный и недостаточный уровень адаптации лесов) типичен для Южного, Уральского и Сибирского федеральных округов (рис.…”