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ASSESSMENT OF DEGRADATION IN MOUNTAIN AND
FOOTHILL AREAS USING GIS TECHNOLOGIES IN PARKENT
DISTRICT, UZBEKISTAN
Shermanov Beknazar Ortikovich
Assistant at the Alfraganus University
https://doi.org10.5281/zenodo.14865910
ARTICLE INFO
ABSTRACT
Qabul qilindi:11-yanvar 2025 yil
Ma’qullandi: 12-yanvar 2025 yil
Nashr qilindi: 13-yanvar 2025 yil
In today's world, analyzing the soils of mountain
and foothill regions and examining degradation
processes using remote sensing and Geographic
Information System (GIS) data analysis is essential. These
techniques serve as powerful tools for land-use planning,
including assessments of land cover, forests, and water
resources. This study investigates land cover changes and
degradation processes in the mountainous and foothill
regions of the Tashkent region, situated in the western
part of the eastern Tien Shan mountains. Despite
substantial precipitation in the area due to its climate,
human encroachment has led to the misuse of pastures,
causing significant land use and cover changes. Vacant
land and sparse forests have been converted into open
land, exacerbating soil degradation due to rainfall. GIS
technologies play a crucial role in monitoring these
changes and formulating effective strategies for land
management.
KEY WORDS
Landslides, Anthropogenic
Impact, Ecological Balance, Land
Use
Planning,
Mountainous
Regions, Sub-Mountainous Regions,
Terrain Changes, Climate Impact.
Introduction
Numerous researchers have analyzed land cover change dynamics in Uzbekistan's
mountainous and foothill regions [1]. These areas have been significantly affected by
inefficient land use due to human and livestock activities, as well as the mismanagement of
pastures. To mitigate these challenges, Geographic Information System (GIS) and remote
sensing technologies can enhance machine learning applications to effectively monitor land
use and land cover changes [2, 3]. These tools have also introduced innovative measurement
techniques and interactive mapping methods to assess recent shifts in natural resources, such
as rangelands and forests, in mountain and foothill areas [4,5].
In the Parkent district, land cover has undergone substantial transformations over the past
two decades [6]. These changes can be studied using satellite imagery, such as Landsat 8 OLI
data [7]. The classification of land cover into distinct categories—based on vegetation type,
water bodies, and soil composition—enables the identification of degraded or at-risk areas [8].
Developing land cover maps using data from 2004 to 2024 provides a foundation for
monitoring and assessing vacant lands and agricultural zones, thereby enhancing the
interpretation of land degradation through remote sensing analysis [9,10].
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Understanding land use changes is essential for effective land management and planning. The
creation of monitoring maps using remote sensing and GIS technology allows for the rapid
generation and storage of time-stamped geospatial data for extensive areas [11]. These
technologies also offer predictive capabilities, equipping decision-makers with valuable
insights for machine learning-based analysis [12,13]. Therefore, accurate information on land
use and resource monitoring is crucial for implementing strategies to mitigate land
degradation [14].
Study Area
The Parkent district is situated on the northern slopes of the Eastern Tien-Shan mountain
range, forming the eastern part of Tashkent. This study involved analyzing remotely sensed
land cover images spanning a decade (2014-2024) to propose potential land improvement
measures based on observed changes. The study area extends between latitudes 41°00' N to
41°30' N and longitudes 69°51' E to 71°23' E, encompassing a significant portion of the Ugam-
Chotkal Biosphere Reserve.
The reserve features a well-defined zonation ranging from 550 to 3,500 meters above sea
level, covering the steep slopes of the Chotkal ridge, which are deeply cut by gorges [15]. The
vegetation of the area consists of three main types of fir trees—spruce, hemispherical, and
kurman—forming mixed forests. The understory includes shrubs such as common hawthorn,
Fedchenko's rose, Korolkov’s annali, oblong zirk, many-flowered green, and Tien-Shan
mountain ash.
More than 700 vascular plant species, belonging to 70 families and 280 genera, have been
recorded in the reserve. These include 13 species listed in Uzbekistan's Red Book and 48
endemic species specific to the western region of the Turkistan ridge. Additionally,
researchers have documented 216 species of capped fungi and over 20 medicinal plants, such
as aconite, colchicum, immortelle, valerian, ziziphora, and snake’s head. The area also hosts
over 15 decorative plant species, including veronica, carnations, primrose, tulips, eremurus,
crocuses, iris, and delphinium [16].
Fig. 1.
Study area Parkent districts, Tashkent region
he presence of numerous rare plant species in the study area, along with signs of land
degradation, indicates that this ecosystem is undergoing significant changes. Monitoring land
cover changes is a crucial criterion for assessing degradation levels [17,18]. This process
requires the appropriate application of remote sensing techniques, which enable detailed
analysis by modeling specific remote sensing data indices tailored to areas of interest [19,20].
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Advancements in this technology have contributed to the development of GIS, leading to
automation in land cover studies, the creation of measurement systems for collecting land use
information, and a better understanding of land cover transformations [21]. Conducting a
proper analysis of land cover change is essential for evaluating and monitoring the long-term
consequences of land use modifications [22].
Materials and Methods
This study utilizes satellite imagery from the years 2014 and 2024, obtained from the United
States Geological Survey (USGS) Earth Explorer database. The datasets include images from
the Landsat 5 TM (Thematic Mapper) and Landsat 8 OLI (Operational Land Imager), both with
a spatial resolution of 30 meters.
Google Earth Pro was used for preliminary analysis and visual interpretation of the study
area's mountainous landscape. Image classification was carried out using ArcGIS 10.6
software, following a structured data processing workflow. This included assigning borders,
defining the coordinate system, and sub-setting images based on the study area’s polygon
boundaries.
Land classification was performed using supervised classification techniques and the
maximum likelihood algorithm in ArcGIS. The Land Cover Change Index (LCCI) algorithm, a
widely used and recognized method for assessing Landsat satellite imagery, was applied (Fig.
3) [23].
The study identified several land classification categories, including bare land, forests, water
bodies, agricultural areas, and built-up areas. To enhance classification accuracy, 20 training
samples were selected from each land cover category within the study area. The final
classification maps were generated using these training samples to ensure precise land cover
representation (Fig. 2).
Fig. 2.
Flow chart for adopted using ArcGIS methods
Results
A land use and land cover (LULC) index study was conducted using remote sensing data to
map foothill areas and open lands. The index successfully distinguished between these land
types using ArcGIS software, leveraging the distinct spectral responses captured in all bands
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of Landsat 5 TM+ and Landsat 8 OLI. The classification process proved to be highly effective in
differentiating between foothill areas and open lands.
The land cover change analysis for the period 2014-2024 identified a combination of forested
and bare land areas undergoing transformation. While significant land cover shifts were
observed in certain regions, large sections of forest and wasteland remained unchanged. Some
of the noticeable land cover changes were linked to agricultural activities. Areas affected by
deforestation were specifically highlighted in the land cover change map to illustrate the
impact of human activities on landscape transformation over the 10-year period (Fig. 4).
Additionally, a relief map of the study area was generated using a Digital Elevation Model
(DEM) file obtained from
earthdata.nasa.gov
. Elevation zones were classified to analyze
changes in land cover based on altitude. The results indicated that forests within the study
area predominantly declined and were replaced by open lands at elevations ranging from
1,000 to 2,500 meters between 2014 and 2024 (Fig. 3).
Fig. 3.
Relief map research area using DEM file. (Source: earthdata.nasa.gov)
The
Land Cover Change Index (LCCI)
offers valuable insights into the current state of land
use and land cover. LCCI algorithms assess land conditions at various stages of the growing
season, including early, mid, and late summer. By analyzing LCCI variations throughout the
growing season, it becomes possible to track changes in vegetation and tree cover over time,
providing a clearer understanding of long-term environmental transformations (Fig. 4).
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Fig. 4.
Land cover changing map.
However, it is essential to validate high Land Cover Change Index (LCCI) algorithm results by
confirming land cover classifications using Google Earth Pro in the study area. The identified
land cover categories revealed variations in the univariate statistical values of radiation heat
flux parameters, as illustrated in Fig. 3
.
The spatial parameters demonstrated a gradual shift
in values for each factor, as shown in Fig. 2.
The maps in
Fig. 2
depict the distribution range of each parameter under study, with the
average and standard deviation values providing the most accurate results for land cover
classification. Fig. 4 presents a comparative analysis of land cover classes and classification
accuracy. The findings indicate a relative improvement in classification accuracy for all
forested areas, whereas bare land categories exhibited fluctuations in accuracy, with some
showing an increase and others a decrease.
A ten-year period, from 2012 to 2022, was analyzed to examine patterns of land cover change.
In 2012, forests covered approximately 61% of the total study area, while bare and open land
accounted for nearly 28%, and agricultural land made up around 12%. Over the decade, there
was a notable expansion of bare and open land, which increased to 45% of the total area.
Conversely, forest cover declined significantly from 61% in 2012 to 42% in 2022, marking a
19% reduction over the study period. The most pronounced shift was observed in fallow land.
Notably, in 2012, about 32% of the total area consisted of bare and open land, which
expanded by roughly 7% over the decade. These substantial changes emphasize the
importance of continuous monitoring and effective land management to preserve ecological
balance and sustain natural resources.
Conclusions
This study highlights significant land cover transformations in the study area over the past
decade. The application of remote sensing techniques and high-accuracy Landsat data has
proven instrumental in tracking these changes and assessing their impact. The findings
indicate that remote sensing data is valuable in evaluating land cover dynamics. The 10-year
assessment of the mountainous and highland regions reveals a marked decline in forested
areas alongside an expansion of open land, increasing the risk of environmental degradation.
The challenging topography of mountainous and sub-mountainous regions accelerates these
processes. Poor management of open lands may lead to further degradation, including the
depletion of fertile soil layers. Such land cover alterations may contribute to future soil
erosion, flooding, and landslides, potentially becoming recurrent hazards.
The land cover change maps generated in this study can serve as a basis for future landslide
susceptibility mapping in the region. The deforested areas identified highlight the influence of
dynamic anthropogenic activities on land cover changes over the 10-year period. These maps
can aid public authorities and stakeholders in developing natural hazard warnings and land
use planning strategies at the local government level. Additionally, they can support landslide
susceptibility assessments and help policymakers implement land use measures to mitigate
the risks associated with natural hazards. Overall, the results underscore the critical role of
remote sensing methods and precise Landsat data in monitoring and assessing land cover
changes, particularly in mountainous and sub-mountainous regions where such
transformations can lead to severe environmental hazards.
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