CC BY
0Hydrometeorology and ecology №1 2024
UDC 911.2 IRSTI 39.03.19
DETERMINING THE SPECIES COMPOSITION OF FOREST VEGETATION IN THE
KOSTANAY REGION USING REMOTE SENSING DATA
Zh.O. Ozgeldinova1 PhD, A.A. Zhanguzhina1* PhD, Zh.T. Mukayev2 PhD, M.M. Ulykpanova1,
Berdenov Zh.G.1 PhD
1L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
2Shakarim University, Semey, Kazakhstan
*e-mail:altyn8828@mail.ru
During the scientific investigation, woody species of forest vegetation were identified
and a map of forest vegetation in the Kostanay region was produced using various data
sources: field materials, Earth remote sensing data, and ArcGIS10.9 software. An algorithm
was developed to detect tree species based on Landsat 9 satellite imagery, characterized by
high spatial resolution. Recognition of dominant tree species was performed using various
combinations of spectral bands from Landsat 9 imagery, analysis of vegetation indices
(NDVI, EVI) across different seasons, and supervised local adaptive classification. The
obtained data were validated against field research materials (August-September 2023) and
forest management records. The chosen algorithm implements contemporary approaches
to acquiring and processing necessary data from satellite remote sensing imagery.
Further differentiation and creation of the forest vegetation map of the Kostanay region
were based on the established map of tree species, digital elevation model, geological-
geomorphological features, field research, thematic maps, and physical geography of the
region. As a result of the conducted research, six classes of forest stands were delineated
in the Kostanay region, including light-coniferous and deciduous tree species such as pine,
birch, aspen, larch, shrubbery, and meadow vegetation. These research findings and the
algorithm developed can be applied to other study areas and hold practical significance.
Keywords: forest vegetation, tree species, Kostanay Region, spectral channels, decoding, geographic
information systems.
Accepted: 30.12.23
DOI: 10.54668/2789-6323-2024-112-1-133-143
INTRODUCTION and their decoding. The intensity and visibility
Remote sensing data is the main and of pixels in different spectral ranges depend
essential means and mechanism for assessing on the specific reflective characteristics of the
the state of forest resources. The development territory object by electromagnetic waves in a
of the field of space monitoring of the natural particular range. It should be noted that a zonal
environment in the modern world helps address image is capable of depicting the characteristics
many important scientific tasks related to the of landscape elements, which is utilized in
conservation and restoration of natural resources. interpreting land surface classes and monitoring
Modern space technologies are capable the natural environment. For decoding and
of providing data through Earth remote sensing representing forest vegetation and tree species,
in visible and infrared channels of spectral the optimal solution would be the application
visibility. Remote sensing methods serve as an of red, infrared, or green spectral channels.
indispensable tool that allows obtaining timely It should be noted that data from
information about the areas, quality status Landsat 9 A - B satellites exhibit the
of forest resources, and forest-forming tree best combination of spatial, spectral, and
species. The reliability of information obtained temporal characteristics among open-
from aerial and satellite images depends on the access Earth remote sensing satellite data.
accuracy of photogrammetric data processing Currently, one of the modern processing
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Scientific article Ozgeldinova, Zhanguzhina et al., Determining the species...
methods involves transforming raw The region of ancient pine forest belts
images using vegetation indices and of the ancient Tobolsk depression occupies the
subsequently creating index images. northern part of the steppe zone in the Kostanay
The decoding and mapping of tree Region. Pine forests here grow on the tops of
species in forests are important applications high sandy ridges and the upper parts of their
of space imagery data. Low-resolution slopes. Birch and aspen forests are associated
satellite images (Terra/Aqua MODIS, SPOT- with the lower parts of the slopes of sandy ridges
Vegetation) and medium-resolution images and often adjoin the shores of saline lakes.
(Landsat) provide the opportunity to create It should be noted that the forests of
cartographic materials that assist in identifying the Kostanay Region include unique relict
tree species (Shikhov A.N. et al., 2020). pine groves interspersed with birch and aspen
The analysis of literature in the clusters, such as the Arakaragai forest massif,
field of space monitoring of the natural the Kazanbasy and Amankaragai groves,
environment, including forests, has revealed a and the groves of the Naurzum State Nature
vast body of work dedicated to the application Reserve (the small protected strip forest Tersyk-
of remote sensing data (development of Karagai and the pine grove Naurzum-Karagai)
methodological approaches to decoding forest- (Pugachev P.G., 1994, Vilesov E.N. et al., 2009).
forming tree species) in the study of forests. The goal of this study is an
The issues of conducting various types of experimental assessment of the possibilities
forest monitoring and methods of decoding forest for recognizing tree species in the forests of
cover are described in the works of Lupyana E. A. et the Kostanay Region based on the analysis of
al.(2011), Bartalev S. A., Egorov V. A. et al. (2016, seasonal changes in their spectral reflectance
2007). Marchukova V. S. et al. (2012), Zharko V. characteristics using Landsat 9 satellite data.
O. et al. (2014), Zhirina V. M. (2014), Isaeva A. S.
et al.(2014), Rouse J.W. et al. (1973). Czaplewski MATERIALS AND METHODS
R. (1994). Epting, J. et al. (2005) and so on. To determine the tree species in the
In the studied region, two large forest forest vegetation of the studied region, we
provinces can be identified – the Trans-Urals- systematically carried out the following tasks:
Obagan Forests and the Turgai Belted Pine – The existing possibilities of using Earth
Forests, which are located within the boundaries remote sensing data have been investigated
of the forest-steppe and steppe natural zones. as the primary foundational information
The area of the state forest fund in the for modern monitoring and the creation of
Kostanay Region is 1 million 146 thousand cartographic representations of forest vegetation;
hectares. The regional akimat manages 457 – An action algorithm has been
thousand hectares of forest resources, with the developed for identifying tree species in
majority of the forest resource fund falling under forests based on Landsat 9 satellite imagery
the Republican administration. Out of the land characterized by high spatial resolution;
designated for the regional management’s forest – The use of vegetation indices NDVI
fund, the projected forest cover constitutes about (Normalized Difference Vegetation Index),
240 thousand hectares. The remaining portion of EVI (Enhanced vegetation index) has been
the fund consists of agricultural lands (arable land, investigated, and their values have been
hayfields, pastures), transportation routes, etc. analyzed to determine tree species in the
The forest cover of the Kostanay Region forest vegetation of the researched region;
is predominantly composed of birch, aspen- - The data for identifying tree species
birch forests, and pine groves. White poplar, in the Kostanay Region have been verified
weeping willow, bird cherry, wild apple, and using field research and forestry materials.
larch are also found. Deciduous forests form The Kostanay Region was chosen
large continuous expanses in the central part as the study area to investigate the potential
between the Tobol and Obagan rivers, aspen use of spectral channels for identifying tree
groves thrive in moist depressions to the west, species in forest vegetation based on Landsat
and birch groves occupy drier terrain depressions. 9 A-B satellite data. According to field
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Hydrometeorology and ecology №1 2024
research data (August-September 2023) for different types of forests vary differently
and an analysis of specialized literature on across different parts of the electromagnetic
the studied region, it was identified that the spectrum depending on the season.
Kostanay Region is mainly characterized by Figure 1 displays various combinations
coniferous (light-coniferous) and deciduous of Landsat 9 image channels. During the
(small-leaved) forests, with dominant experiment, a visual assessment was conducted
tree species being pine, birch, and aspen. to determine the most suitable combination
The main tree-forming species in the forest for distinguishing different tree species.
zone of the Kostanay Region (dark-coniferous, We analyzed the synthesis of Landsat 9
light-coniferous, and small-leaved) are sufficiently channels in the SWIR1(Shortwave Infared 1)-
distinguishable in the spectra on medium and NIR (Near Infared) -RED range, where dark
high-spatial resolution satellite images, especially coniferous and light coniferous forests appear
in the presence of near-and mid-infrared channels. in dark green, while deciduous and small-
The existence of differences in the leaved forests take on a bright green color hue.
phenological dynamics of tree species allows us to The mixed composition of forests can be
hypothesize the possibility of detecting variations distinguished by various color transitions, primarily
in the dynamics of their spectral reflectance depending on the percentage of coniferous
characteristics based on regular satellite imagery forests in the forest-forming tree species, as well
acquired with sufficiently high frequency. as the age-related characteristics of the trees.
The success of decoding satellite The synthesis of spectral channels in
imagery also largely depends on the seasonal the NIR-RED-GREEN range takes on a reddish
acquisition period. With the appropriate selection color tone. In summer season images, light-
of satellite images from different seasons, it is coniferous and dark-coniferous forests stand out
possible to identify forest-forming tree species. with dark-red hues, deciduous forests primarily
with a bright red color, and mixed forests acquire
RESULTS AND DISCUSSION transitional colors (from red to bright red).
During the experiment, the changes in Combining SWIR-NIR-RED channels allows
spectral reflectance characteristics for different for the differentiation of pine tree species, which
tree species were investigated (Figure 1). exhibit a distinctive reddish tone, enabling their
Overall, the coefficients of spectral brightness distinction from deciduous tree species (Figure 1).
a b c
Fig.1. Composite images from combinations of spectral channels: a) SWIR1-NIR-RED; b) Composite image from
combinations of spectral channels NIR-RED-GREEN; c) Composite image from combinations of spectral channels
SWIR2 (Shortwave 2)-SWIR1(Shortwave 1)-NIR
A distinctive feature of pine forests is Pine forests mainly grow on sandy soils,
often a higher brightness in the mid-infrared range and the ground cover there is relatively sparse.
of the spectrum, as well as in the visible channels. Areas devoid of vegetation often affect spectral
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Scientific article Ozgeldinova, Zhanguzhina et al., Determining the species...
visibility and imagery, resulting in pronounced Thus, during the summer months,
brightness in the mid-infrared channel and to a lesser there is a general sharp increase in the near-
extent in the red channel of the visible spectrum. infrared channel for all tree species, but
Thus, when using a combination the difference between species remains.
of channels involving infrared, red, and It should be noted that in the visible
green channels, coniferous forests in July spectrum, the differences in spectral
images appear red, as they are the only brightness between different tree species
ones containing chlorophyll in their leaves. in images during the autumn period are
The combination of spectral channels more informative than in the summer.
SWIR1-NIR-SWIR2 does not include any The study of vegetation indices has shown
channel from the visible range, and coniferous that they also effectively reflect the stages of
forests appear blue. This combination most phenological development. However, for the
distinctly highlights coniferous vegetation. most effective recognition, it is necessary to use
For reliable decoding of tree species in the vegetation index not as a static feature. A
forests from satellite images during the summer characteristic feature, especially for deciduous
period, data from the near-infrared channel is tree species, is not the vegetation index itself but
sufficiently reliable. In this channel, the crowns of its temporal changes, which reflect the succession
leaves from small-leaved trees are visible in the of phenological phases. Since the difference in
images and have a higher reflectance coefficient the rate of phenological development between
compared to, for example, dark-coniferous and different tree species can be about a week, it is
light-coniferous trees. It is also worth noting that the advisable to select May and October images
Red Edge spectral channel (transitional between with a one-week interval, which is possible for
red and near-infrared) (Shikhov A.N. et al., 2020). satellites like Landsat or, for example, Landsat 9.
Table 1
The changes in NDVI values characterize tree species in different months (Munzer Nur., 2021).
For the most effective recognition, it is NDMI) were calculated for differentiating the
necessary to use a combination of different vegetation species composition of forest vegetation, and
indices. More comprehensive information about they differ in the complexity of computational
the on-site situation is provided by index images. operations. In the process of analyzing the results
Among the numerous spectral indices, vegetation of mapping index calculations, it was found
indices are of the greatest interest for identifying that the most indicative indices for determining
tree species in forest vegetation (Munzer Nur., tree species in forest vegetation at the level of
2021). Based on the analysis of literary sources, individual indicators, based on Landsat 9 A-B
spectral index indicators (NDVI, EVI, SAVI, data, are the normalized NDVI and EVI indices.
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Hydrometeorology and ecology №1 2024
We analyzed the NDVI (Normalized drawn for the territory of the Kostanay region.
Difference Vegetation Index), which is one of the These indices provide the ability to make
most common vegetation indices widely used in a detailed distinction between deciduous forests
remote sensing tasks. The advantage of using this against the dominance of coniferous tree species
index lies in having spectral brightness coefficients and classify deciduous species into broad-leaved
measured in each of the channels in both the (birch) and other broad-leaved. NDVI and
numerator and denominator, allowing a significant other spectral vegetation indices are not perfect
reduction in the influence of the atmosphere indicators of plant biomass, but with careful
and observation geometry. This enables a more analysis, they can be effective in differentiating the
accurate comparison of measurements taken species composition of forest vegetation (Figure 4).
at different times. The seasonal dynamics The research revealed that during the
of chlorophyll content in tree leaves can be period of leaf unfolding and canopy establishment
visually represented through graphs showing the from May to September, the NDVI values for
seasonal dynamics of NDVI. Table 1 presents the deciduous trees increase. The NDVI values for
average NDVI values by month for tree species. deciduous trees range from 0.55 to 0.60, while
The change in average values of the coniferous forests, such as pine, have values
Normalized Difference Vegetation Index (NDVI) ranging from 0.48 to 0.35 according to our
allows for the classification of the forested area calculations. During the summer months, the
into classes based on the values of this index. NDVI values for deciduous trees show an increase.
This is because different tree species have distinct Clearly, during the period of leaf fall
periods of vegetation, leaf unfolding, and leaf fall, and leaf unfolding from October to April,
which is well reflected in the NDVI vegetation coniferous vegetation will have the highest
index and allows for the identification of certain NDVI values, allowing for the differentiation
tree species. Based on the analysis of NDVI of coniferous species from deciduous ones.
map values, the following conclusions were
Fig. 2. Indicators of the normalized vegetation index (NDVI) for the Kostanay region based on the
materials of the Landsat 9 satellite survey, July 2023
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Scientific article Ozgeldinova, Zhanguzhina et al., Determining the species...
Closer to October, a decline in the NDVI difference in phenological development can be
values of deciduous trees is observed. When about a week. This underscores the requirement
analyzing the seasonal dynamics of NDVI, it for timely imaging, which should coincide with
was found that the values of the index are higher these periods. Open satellite data that meet such
in the summer months (June-July) than in the timeliness requirements include images from
autumn months (September and October). This the Copernicus project’s Landsat 9 satellites.
can be explained by the seasonal dynamics of Materials from high spatial resolution
the vegetation index, as plants go through all satellite imagery provided the opportunity to
stages of vegetation during the season. As the ensure the high accuracy of reference data.
phases of vegetative development change, the They are an integral part of the stage
composition and content of pigments in plant of performing a controlled (with training)
leaves change, and the biomass increases, along classification necessary for the further creation
with the amount of chlorophyll in green leaves. of a map of woody species of forest vegetation
As chlorophyll accumulates, the of the Kostanay region according to the
brightness of plants decreases in the visible data of the Landsat 9 A-B satellite survey.
part of the spectrum, increases in the red, Image classification is an important part
and especially in the infrared region. This of remote sensing, image analysis, and pattern
explains the increase in the value of the recognition. Among the possible options for
normalized difference vegetation index (NDVI). supervised classification, the Mahalanobis
With the breakdown of chlorophyll distance method was chosen. The advantage
in the autumn months, the opposite picture is of this classification method lies in taking into
observed – brightness increases in the red zone, account multiple variables that are correlated
and decreases in the near-infrared, as seen in the with each other. Based on the data of the
analysis of the October image. NDVI values in performed classification, a map of tree species
October for deciduous forests are significantly of forest vegetation in the Kostanay region was
lower than in the summer months. The decrease in compiled and prepared. Further differentiation
index values is associated with vegetation drying and grouping into forest cover classes in the
out and, consequently, low chlorophyll content. Kostanay region were carried out according
This fact indicates the reliability of the applied to the data of the digital elevation model, the
methods in analyzing vegetation conditions. geological-geomorphological features of the
It is known that forests of different research region, field studies conducted in 2023,
species can demonstrate different dynamics thematic maps, and the physical geography of
of phenological development. For example, the studied area (Pugachev P.G., 1994, Vilesov
the appearance of leaves on birch trees E.N. et al. 2009, A.R. Medeu et al., 2010).
usually occurs earlier than on other trees. May As a result of the conducted research,
images generally confirm this fact, as birch, we identified 6 classes of forest stands in the
willow, maple, and rowan have the highest Kostanay region, associated with specific
index values compared to other species. geological-geomorphological areas and
The existence of differences in the characterized by zonal features (Figure 3).
phenological dynamics of tree species allows According to the geological-
for the possibility of detecting differences geomorphological features, the forest stands in
in the dynamics of their spectral reflectance the Kostanay region were differentiated into the
characteristics based on regular high-frequency following classes: 1) Forests of residual, erosion-
satellite observations. This dynamic is denudation small watersheds (hilly watershed and
particularly evident in early May, September, hilly-valley plains) (300...450 meters above sea
and early October images, as confirmed by level); 2) Forests of abrasion-denudation layer-
principal component images where vegetation pedestal plains (low-undulating and hollow-
differentiation is maximal. It is also worth basin with eluvial cover) (250...350 meters above
noting that the most significant differences sea level); 3) Forests of accumulative-denudation
in dynamics occur in these months, and the layer plains (200...300 meters above sea level);
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Hydrometeorology and ecology №1 2024
Fig. 3. Map of tree species of forest vegetation in the Kostanay region based on the materials of
the Landsat 9 satellite imagery as of July 15, 2023
4) Forests of aeolian plains and convex watersheds of one pixel is a complex combination of
(150...200 meters above sea level); 5) Forests of radiation reflected from several trees, possibly
river alluvial plains (low river terraces), terraces, of different species, and the underlying surface.
and slope-valley locations (100...150 meters The results obtained by us provide grounds
above sea level); 6) Flat floodplain terraces to conclude that by applying this methodological
of low level (0...130 meters above sea level). approach to higher spatial resolution satellite
imagery, a more reliable classification of tree
CONCLUSION species can be achieved.
Analyzing the Forest Tree Species Map of Thus, the methods we employed for
the Kostanay Region compiled during the study, identifying tree species in the forest vegetation
obtained through Landsat 9 satellite imagery, of the Kostanay Region allowed for the
it can be noted that areas with birch vegetation differentiation of classes of forest-forming tree
have a higher vegetation index compared to species in the region through the processing and
pine forest areas. Assessing the applicability of analysis of remote sensing data, field research
this methodological approach, which involves data (July 2023), and specialized literature.
the use of an algorithm for forest mapping, it The synthesis method of simultaneous index
is necessary to emphasize that the accuracy of and multispectral images proposed in the study
the generated maps depends on the quality and enables the identification of tree species and their
quantity of the training dataset. In images with a qualitative condition.
resolution of 10 meters, the spectral information
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Scientific article Ozgeldinova, Zhanguzhina et al., Determining the species...
As a result of the conducted research in of the recognizability of woody forest species
the Kostanay Region, we identified coniferous based on satellite data on seasonal changes
and deciduous forest-forming tree species in in their spectral-reflective characteristics //
the studied region, such as pine, birch, aspen, Modern problems of remote sensing of the Earth
larch, shrub thickets, and meadow vegetation. from space. 2014. Vol. 11. No. 3., pp. 159-170.
Further differentiation of forest stands based 7. Zhirin V. M., Dynamics of spectral
on physiogeographical features (landscape brightness of the breed-age structure of
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into 6 classes of forest stands and conduct their images/ V. M. Zhirin, S. V. Knyazeva, S. P.
mapping. Eidlina // Forest science. 2014. No.5. pp. 3-12.
8. Isaev A. S., Satellite sensing ― a
This study was undertaken as part of grant unique tool for monitoring forests in Russia/
funding for scientists awarded for scientific and (or) A. S. Isaev, S. A. Bartalev, E. A. Lupyan, N. V.
scientific and technical projects from 2023 to 2025 Lukina// Bulletin of the Russian Academy of
by the Ministry of Science and Higher Education of Sciences. 2014. Vol. 84. No. 12. pp. 1073-1079.
the Republic of Kazakhstan (IRN № AP19678305). 9. Rouse J.W., Haas R.H., Schell J.A.,
Deering D.W. Monitoring vegetation systems
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ҚОСТАНАЙ ОБЛЫСЫНЫҢ ОРМАНДАРЫНДАҒЫ ОРМАНТҮЗУШІ ТҰҚЫМДАР
ҚҰРАМЫН ЖЕРДІ ҚАШЫҚТЫҚТАН ЗОНДТАУ ДЕРЕКТЕРІ АРҚЫЛЫ АНЫҚТАУ
Ж.О. Озгединова1 PhD, А.А. Жангужина1* PhD, Ж.Т. Мукаев2 PhD, М.М. Улыкпанова1,
Берденов Ж.Г.1 PhD
1Л.Н. Гумилев атындағы Еуразия ұлттық университеті, Қазақстан, Астана қ.
2 Шәкәрім Университеті, Қазақстан, Семей қ.
Е-mail: altyn8828@mail.ru
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Hydrometeorology and ecology №1 2024
Ғылыми зерттеу барысында Қостанай облысының орман өсімдіктерінің ағаш түрлері
анықталды және орман өсімдіктерінің картасы құрастырылды әр түрлі деректер бой-
ынша: далалық материалдар, Жерді қашықтықтан зондтау мәліметтері және ArcGIS10.9
бағдарламалық құралын пайдалану арқылы және арнайы әдебиеттер материалдары не-
гізінде. Жоғары кеңістіктік ажыратылымдықпен сипатталынатын Landsat 9 спутниктік
суреттері негізінде орман ағаштарының түрлерін анықтау бойынша әрекеттер алгоритмі
жасалды. Орман құраушы негізгі ағаш түрлерін анықтау Landsat 9 спектрлі каналда-
рының комбинациясы негізінде, жылдың әртүрлі маусымындағы өсімдіктердің индек-
стерін (NDVI, EVI) зерттеу және бақыланатын жергілікті бейімделу классификациясы
негізінде жүзеге асырылды. Алынған деректер далалық зерттеулердің (2023 жылғы та-
мыз-қыркүйек) және орман шаруашылығының материалдарымен тексерілді.Таңдалған
әрекеттер алгоритмі Жерді қашықтықтан зондтау арқылы алынған ғарыштық сурет-
терінің деректерінен қажетті материалды алу және өңдеудің ең өзекті тәсілдерін жүзеге
асырады. Қостанай облысының орман өсімдіктерінің картасын одан әрі саралау және
құрастыру орман өсімдіктерінің ағаш түрлері картасы, цифрлық рельеф үлгісінің дерек-
тері, зерттелетін аймақтың геологиялық және геоморфологиялық ерекшеліктері, жүр-
гізілген далалық зерттеулер , тақырыптық карталардың материалдары және зерттелетін
аймақтың физикалық географиясы негізінде жүзеге асырылды. Зерттеу нәтижесінде Қо-
станай облысының аумағында орман алқаптарының 6 класын анықталып сонымен қатар
аймақта ақшылқылқанды, жапырақты орман құраушы түрлері анықталды, оның ішінде
қарағай, қайың, көктерек, қарағай, бұта және шалғынды өсімдіктер. Бұл зерттеу алго-
ритмін басқа зерттеу объектілеріне қолдануға болады және практикалық маңызы бар.
Түйін сөздер: орман өсімдіктері, ағаш түрлері, Қостанай облысы, спектрлік арналар, дешифрлеу, гео-
ақпараттық жүйелер.
ОПРЕДЕЛЕНИЕ ПОРОДНОГО СОСТАВА ЛЕСНОЙ РАСТИТЕЛЬНОСТИ
КОСТАНАЙСКОЙ ОБЛАСТИ ПО ДАННЫМ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ
ЗЕМЛИ
Ж.О. Озгединова1 PhD, А.А. Жангужина1* PhD, Ж.Т. Мукаев2 PhD, М.М. Улыкпанова1,
Берденов Ж.Г.1 PhD
1Евразийский национальный университет имени Л.Н.Гумилева, г. Астана, Казахстан
2Университет Шакарима, г.Семей, Казахстан
Е-mail: аltyn8828@mail.ru
В процессе научного исследования были определены древесные породы и создана
карта лесной растительности Костанайской области на основе различных данных: по-
левых материалов, данных дистанционного зондирования Земли и с использованием
программного средства ArcGIS10.9, материалов специальной литературы. Создан ал-
горитм действий для выявления древесных пород лесов по материалам космических
снимков Landsat 9, характеризующихся высоким пространственным разрешением.
Распознавание преобладающих древесных пород лесообразующих пород выполня-
лось на основе различных комбинации спектральных каналов снимка Landsat 9, иссле-
дованию вегетационных индексов (NDVI, EVI) в различные сезоны года и контроли-
руемой локально-адаптивной классификации с обучением. Полученные данные были
верифицированы с материалами полевых исследований (август-сентябрь 2023) и ле-
соустройства. Выбранный алгоритм действий реализует наиболее актуальные подхо-
ды к получению и обработке необходимого материала из данных космических снимков
141
Scientific article Ozgeldinova, Zhanguzhina et al., Determining the species...
дистанционного зондирования Земли. Дальнейшая дифференциация и создание карты
лесной растительности Костанайской области осуществлялось на основе созданной кар-
те древесных пород лесной растительности, данным цифровой модели рельефа, геоло-
го-геоморфологическим особенностям региона исследования, проведенным полевым
исследованиям, материалам тематических карт и физической географии исследуемого
региона. В результате проведенных исследований на территории Костанайской области
было выделено 6 классов лесных массивов и были выделены светлохвойные, листвен-
ные лесообразующие породы, такие как сосны, березы, осины, лиственница, кустарни-
ковые заросли и луговая растительность. Данный алгоритм проведенных исследований
могут быть применен на других объектах исследования и имеет практическое значение.
Ключевые слова: лесная растительность, древесные породы, Костанайская область, спектральные
каналы, дешифрирование, геоинформационные системы.
Сведения об авторах/Авторлар туралы мәліметтер/Information about authors:
Ozgeldinova Zhanar - PhD, Acting Professor of the Department physical and economic geography of L.N. Gumilyov
Eurasian National University, Astana, ozgeldinova@mail.ru
Zhanguzhina Altyn - PhD, Acting Associate Professor of the Department physical and economic geography of L.N.
Gumilyov Eurasian National University, Astana, altyn@mail.ru
Mukaev Zhandos - PhD, Associate Professor, the Dean of the Faculty of Natural and Mathematical Sciences of Shakarim
University, Semey, zhandos.mukaev@mail.ru
Ulykpanova Meruert - PhD student of the Department physical and economic geography of L.N. Gumilyov Eurasian
National University, Astana, ulykpanova@mail.ru
Berdenov Zharas - PhD Associate Professor of the Department of Physical and Economic Geography of the L.N.Gumilev
Eurasian National University, Dean of the Faculty of Natural Sciences, Astana, berdenov-z@mail.ru
Озгелдинова Жанар Озгелдиновна - PhD, и. о. профессора кафедры физической и экономической географии Евра-
зийского национального университета им. Л. Н. Гумилева, Астана, ozgeldinova@mail.ru
Жангужина Алтын Амиржановна - автор-корреспондент) - PhD, и. о. доцента кафедры физической и экономиче-
ской географии Евразийского национального университета имени Л.Н.Гумилева, Астана, altyn@mail.ru
Мукаев Жандос Толеубекович – PhD, ассоц. профессор, декан естественно-математического факультета универси-
тета Шакарима, Семей, zhandos.mukaev@mail.ru
Улыкпанова Меруерт Муратовна - докторант кафедры физической и экономической географии Евразийского на-
ционального университета имени Л.Н.Гумилева, Астана, ulykpanova@mail.ru
Берденов Жарас -Галимжанович - PhD доцент кафедры физической и экономической географии Евразийского на-
ционального университета имени Л.Н.Гумилева, декан факультета естественных наук, Астана, berdenov-z@mail.ru )
Озгелдинова Жанар Озгелдиновна - PhD, Гумилев атындағы Еуразия ұлттық университетінің физикалық және
экономикалық география кафедрасының профессор м. а., Астана, ozgeldinova@mail.ru
Жангужина Алтын Амиржановна -(корреспондент-автор) - PhD, Еуразия ұлттық университетінің физикалық
және экономикалық география кафедрасының доцент м. а., Астана, altyn@mail.ru
Мукаев Жандос Толеубекович - PhD, доцент, Шәкәрім университетінің жаратылыстану-математикалық факуль-
тетінің деканы, Семей, zhandos.mukaev@mail.ru
Улыкпанова Меруерт Муратовна - Гумилев атындағы Еуразия ұлттық университетінің физикалық және экономи-
калық география кафедрасының докторанты, Астана, ulykpanova@mail.ru
Берденов Жарас Галимжанович - PhD Л.Н. Гумилев атындағы Еуразия ұлттық университетінің физикалық
және экономикалық география кафедрасының доценті, жаратылыстану ғылымдары факультетінің деканы, Астана,
berdenov-z@mail.ru
Вклад авторов/Авторлардың қосқан үлесі/Authors contribution:
Ozgeldinova Zhanar - concept development, conducting a research
Zhanguzhina Altyn - methodology development, conducting a research
Mukaev Zhandos - creating software
Ulykpanova Meruert - conducting statistical analysis, preparing and editing the text, visualization
Berdenov Zharas - resources, preparing and editing the text, visualization
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Hydrometeorology and ecology №1 2024
Озгелдинова Жанар Озгелдиновна - тұжырымдаманы әзірлеу, зерттеу жүргізу
Жангужина Алтын Амиржановна - әдістемені әзірлеу, зерттеу жүргізу
Мукаев Жандос Толеубекович – бағдарламалық жасақтама жасау
Улыкпанова Меруерт Муратовна - статистикалық талдау жүргізу, мәтінді дайындау және өңдеу, көрнекілік
Берденов Жарас Галимжанович - ресурстар, мәтінді дайындау және өңдеу, көрнекілік
,
Озгелдинова Жанар Озгелдиновна - разработка концепции, проведения исследования
Жангужина Алтын Амиржановна -разработка методологии, проведения исследования
Мукаев Жандос Толеубекович - создание программного обеспечения
Улыкпанова Меруерт Муратовна - проведение статистического анализа, подготовка и редактирование текста,
визуализация
Берденов Жарас Галимжанович - ресурсы, подготовка и редактирование текста, визуализация
143
