Section: Methodology and research methods. Models and forecasts
The possibilities of using multispectral data of remote sensing of the Earth and field research to highlight plant communities
using the example of the Pizhemsky State Nature Reserve (SNR) of the Kirov region are shown. The Pizhemsky
SNR is defined as a complex (landscape) reserve. It is especially valuable for maintaining the integrity, protection and
restoration of aquatic biogeocenoses, preserving in the natural state of the unique natural objects of the region.
Selection of plant communities in the Pizhemsky reserve with the use of remote sensing data was carried out
in several stages: pre-field cameral, field expeditionary and office generalizing. The pre-field cameral stage included
the selection of satellite images from the Landsat 7 and Sentinel 2 satellites and their interpretation to isolate areas
of vegetation that are homogeneous in interpretation. At this stage, several areas that were homogeneous in terms of
certain features (color, microtexture of the pattern, phototones, etc.) were identified, caused by various natural objects
and plant communities of the region. In the field expeditionary stage, work was carried out to identify vegetation types
on the ground. The characteristic of plant communities was carried out according to generally accepted geobotanical
techniques.
The peculiarities of the coenotic composition of forests in the reserve were studied. The composition of the plant
communities of the Pizhemsky SNR reflects the characteristic zonal features of the vegetation of the studied region and
is associated with certain landscape elements. Based on the analysis of the available cartographic material and satellite
images, it has been established floodplain meadows occupy more than 60% of the study area, forests – about 20%. In
order to identify plant communities, the most characteristic and most accessible direct interpretation features (phototone,
shape, structure) were selected. In addition, we used the synthesis of standard combinations of “artificial colors”
channels from the Landsat 7 and Sentinel 2 satellites, which made it possible to identify grassy communities, deciduous
and coniferous forests. The completed classification with training provided important information on the distribution
of the majority of plant communities typical for the region. The NDVI vegetation index made it possible to isolate pine,
deciduous forests and meadow phytocenoses, as well as to recognize water bodies and open soils.
ISSN 1995-4301
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