scholarly journals Mapping of vegetation communities of the subzone of dark coniferous forests of the South Sakhalin based on space surveys

2020 ◽  
Vol 26 (4) ◽  
pp. 60-72
Author(s):  
Alexey Verkhoturov ◽  
Vyacheslav Melkiy
Keyword(s):  
Plant Communities ◽  
Vegetation Cover ◽  
Spectral Characteristics ◽  
Research Area ◽  
Coniferous Forests ◽  
Landsat 8 ◽  
The South ◽  
Landsat Images ◽  
Vegetation Communities ◽  
Significant Difference

Research was carried out improve efficiency of thematic mapping based on the recognition of plant communities in the subzone of dark coniferous forests for South of Sakhalin on multi-time satellite images of average resolution Landsat 8. We used reference samples of sites where geobotanical studies were conducted, for improve the quality of recognition during automated decryption. Experiments were conducted decode vegetation on singlechannel, synthesized multi-zone images obtained in different seasons of year. Spectral characteristics allow us identify plant communities in images based on morphological and physiological properties of various plants, which were quantified by reflection of vegetation in the spring image, and an integral indicator of photosynthetic activity of vegetation, which was evaluated by NDVI index calculated from spring and autumn images. Conceptual and methodological aspects of direct expert interpretation of vegetation from Landsat images by classification methods using ESRI ArcGIS raster algebra tools are considered. On example of study of vegetation communities of subzone of dark-coniferous forests of the South of Sakhalin with sufficient level of reliability, dark-coniferous forests, stone birch forest, cedar elfin formation, valley forests, thickets of Kuril bamboo, as well as residential zones, agricultural lands, areas devoid of vegetation as result of gravitational slope processes, wetlands, windfalls and man-made wasteland were identified. Decoding of vegetation cover from Landsat images showed that use of seasonal time series can significantly increase the reliability of the interpretation of most species of plant communities for the South of island. The research area is characterized by significant difference in altitude from 0 to 1100 m, as a result presence of high-altitude zone in the vegetation cover, which must be taken into account when decoding. Mapping is completed by performing automatic vectorization of raster layers and further generalization of vector polygons in accordance with selected map scale.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

2019 ◽  
Vol 12 (4) ◽  
pp. 175-187
Author(s):  
Thanh Tien Nguyen
Keyword(s):  
Rural Areas ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification Method ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images

2020 ◽  
Vol 42 (3) ◽  
pp. 161
Author(s):  
H. Sun ◽  
Q. Wang ◽  
G. X. Wang ◽  
P. Luo ◽  
F. G. Jiang
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat Images ◽  
Modelling Method ◽  
Semiarid Areas ◽  
Arid And Semiarid Areas ◽  
Land Degradation And Desertification ◽  
Arid And Semiarid

Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.

Remote Sensing Aplication and Geochemical Studies for Hydrocarbon-Induced Alterations Discoveries in Western Kendeng Zone

2020 ◽  
Author(s):  
F., R. Maulana
Keyword(s):  
Xrd Analysis ◽  
Research Area ◽  
Geological Mapping ◽  
Hydrocarbon Exploration ◽  
Distribution Area ◽  
Landsat 8 ◽  
Test Results ◽  
Iron Sulfides ◽  
The South ◽  
The West

The development of satellite imaging technology that has spectral capability has the potential to be utilized in hydrocarbon exploration. The presence of hydrocarbons can be detected through spectral recording of hydrocarbon seepage. Over a long period, hydrocarbon seepage will change the chemical structure and mineralogy of the surrounding soil and rocks, so that it will cause spectral anomalies that are key to the existence of active hydrocarbons and petroleum systems. The West Kendeng zone was chosen as a research location because several hydrocarbon seepage sites were found to contain, either oil or gas in the area. Based on hydrocarbon seepage spectral theory, Landsat 8 imagery has a wavelength spectrum capability that is sensitive to the anomalous object of hydrocarbon seepage. Therefore, this research was conducted to determine the distribution of hydrocarbon seepage areas in the West Kendeng zone by using Landsat 8 imagery. In addition to using Landsat 8, to strengthen the research results a surface geological mapping process was also carried out at the seepage location. Then the samples obtained were analyzed by XRD and XRF. XRD analysis was carried out to determine the types of minerals that became an anomaly around the seepage location. In addition, the XRF analysis is carried out to determine the chemical composition of rocks that have undergone alteration. Based on the results of Landsat 8 data calibration, an altered rock which is an anomaly of hydrocarbon seepage is found in the south and southwest of the study site. These results are confirmed by the location of the discovery of several points of seepage of hydrocarbons in the research area. The XRD test results also showed anomalous clay mineral content in the form of halloysite, albite, and augite in the southwest and south of the study site. Besides this anomaly, magnetite and pyrite were also found at that location. While the XRF test results from the sample also showed the presence of Fe2O3 element at 9.21% and CaO at 7.42% in the south and southwest of the study location. This indicates a reaction between hydrocarbons and rocks that affect the acidity conditions around them, so they will form clay minerals, iron oxides, and iron sulfides. Therefore, based on Landsat 8 image analysis, XRD, and XRF, a hydrocarbon seepage distribution area accumulated in the Bancak, Boto, Wonokerto, and Nyemoh areas in the Semarang Regency.

Large-scale geobotanical map of the Tuzla Spit and Tuzla Island (the Kerch Strait)

2019 ◽  
pp. 57-67
Author(s):  
T. A. Sokolova
Keyword(s):  
Plant Communities ◽  
Vegetation Cover ◽  
Biological Diversity ◽  
Ambrosia Artemisiifolia ◽  
Northwestern Part ◽  
The South ◽  
Vegetation Map ◽  
The North ◽  
Kerch Strait ◽  
High Resolution Satellite Images

During an ecological expertise the vegetation of Tuzla Spit and Tuzla Island, located in the middle part of the Kerch Strait (Fig. 1), was studied. This area is unique in terms of biological diversity and a presence of rare species (Ermolaeva et al., 2018). The study is based on 150 geobotanical relevés. Field data, topographic maps, and high-resolution satellite images were used in the vegetation mapping. The total area of the study is 383 hectares. There are the following hierarchical levels in the legend to the vegetation map: types of vegetation and classes of associations. A mapping unit is an association described according to the Braun-Blanquet system (Braun-Blanquet, 1964). The highest divisions of the legend are the types of vegetation: aquatic, coastal-aquatic, halophytic, psammophytic, steppe; they are given according to the ecological-phytocoenotic classification. Within the types of vegetation, classes of associations are given according to the ecological-floristic classification. 26 main numbers of the legend display the vegetation cover on the map. Geobotanical map reflects the state of vegetation in 2015 (Fig. 2). The vegetation of the island is heterogeneous. Plant communities as narrow stripes replace each other depending on the degree of moisture, salinity and orography. The sea currents have a great influence on the vegetation. In the southern part of the Taman Bay, suspension flows are directed from the South to the North and round the island, which leads to the “washing-up” of the southeastern part of the island represented by shallow waters and estuaries. It is occupied mainly by halophytic vegetation, the main dominants of plant communities are Juncus maritimus, Phragmites australis, Puccinellia distans, Bassia hirsuta, Salicornia pe­rennans, S. prostrata, Suaeda salsa, Elaeagnus angustifolia, Elytrigia elongata, Tripolium vulgare. The northwestern part of the strait is occupied by the area of jet streams of suspensions coming from the North to the South from the Sea of Azov. This caused the accumulation of sand-shell material in the northern and northwestern parts of the island forming raised areas co­vered by psammophytic and steppe communities. The main dominant species here are Crambe maritima var. pontica, Cakile euxina, Eryngium maritimum, Lactuca tatarica, Salsola tragus, Leymus sabulosus, Artemisia arenaria, Gypsophila perfoliata. As a result of the transport crossing construction, the vegetation cover was heavily transformed. The vegetation map of Tuzla Spit and Island for 2019 shows the changes that have occurred — the drainage of the territory and the reduction of the vegetated area (Fig. 3). Distribution of weed species, in particular Ambrosia artemisiifolia, is noted. The remained vegetation in the southern part of the Tuzla Spit and the southern part of the Tuzla Island has a great nature conservation value; there are unique plant communities and rare plant species listed in the Red books of different ranks (Red..., 2007, 2008, 2015): Cakile euxina, Crambe maritime, Glaucium flavum, Euphorbia paralias, E. peplis, Eryngium mari­timum, Astrodaucus littoralis, Asparagus maritimus, Centaurea arenaria, Argusia sibirica, Astragalus varius, Verbascum pinnatifidum, Leymus racemosus subsp. sabulosus, Secale sylvestre. There is an obvious need to organize a specially protected natural area in these areas.

scholarly journals Modelling and Delineation of Hydrocarbon Micro - Seepage Prone Zones on Soil and Sediment in Ugwueme, South Eastern Nigeria with Soil Adjustment Vegetation Index (SAVI)

2020 ◽  
pp. 13-33
Author(s):  
Mfoniso Asuquo Enoh ◽  
Uzoma Chinenye Okeke ◽  
Needam Yiinu Barinua
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat Images ◽  
Landsat 7 ◽  
Soil And Sediment

Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study.  The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover.  The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016.  The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage.  The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030.  The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.

scholarly journals Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ksenia S. Yankovich ◽  
Elena P. Yankovich ◽  
Nikolay V. Baranovskiy
Keyword(s):  
Lake Baikal ◽  
Forest Fire ◽  
Vegetation Cover ◽  
Spectral Characteristics ◽  
Forest Vegetation ◽  
Fire Danger ◽  
Landsat 8 ◽  
Agricultural Crop ◽  
Forest Fire Danger

The vegetation cover of the Earth plays an important role in the life of mankind, whether it is natural forest or agricultural crop. The study of the variability of the vegetation cover, as well as observation of its condition, allows timely actions to make a forecast and monitor and estimate the forest fire condition. The objectives of the research were (i) to process the satellite image of the Gilbirinskiy forestry located in the basin of Lake Baikal; (ii) to select homogeneous areas of forest vegetation on the basis of their spectral characteristics; (iii) to estimate the level of forest fire danger of the area by vegetation types. The paper presents an approach for estimation of forest fire danger depending on vegetation type and radiant heat flux influence using geographic information systems (GIS) and remote sensing data. The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis Support System (GRASS) software were used to process satellite images. The area’s forest fire danger estimation and visual presentation of the results were carried out in ArcGIS Desktop software. Information on the vegetation was obtained using the analysis of the Landsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area. The maps (schemes) of the Gilbirinskiy forestry were also used in the present article. The unsupervised k-means classification was used. Principal component analysis (PCA) was applied to increase the accuracy of decoding. The classification of forest areas according to the level of fire danger caused by the typical ignition source was carried out using the developed method. The final information product was the map displaying vector polygonal feature class, containing the type of vegetation and the level of fire danger for each forest quarter in the attribute table. The fire danger estimation method developed by the authors was applied to each separate quarter and showed realistic results. The method used may be applicable for other areas with prevailing forest vegetation.

scholarly journals STUDY OF INFLUENCE OF THE FAULT ZONES ON VEGETATION COVER OF STATE TERRITORY OF THE NATURE SANCTUARY "DOLINSKY" (SAKHALIN ISLAND) USING EARTH’S REMOTE SENSING DATA

2021 ◽  
Vol 26 (5) ◽  
pp. 75-85
Author(s):  
Olesya V. Kuptsova ◽  
◽  
Inna I. Lobishcheva ◽  
Alexey A. Verhoturov ◽  
Vyacheslav A. Melkiy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Communities ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Fault Zones ◽  
Sakhalin Island ◽  
The State ◽  
Likelihood Method ◽  
Landsat 8 ◽  
Sensing Data

Fault zones on the territory of Nature Sanctuary “Dolinsky” (Sakhalin Island), which are characterized by high geodynamic activity, are generally well distinguished when analyzing satellite imagery materials. In any territory, it is not difficult to identify the various plant communities that occupy it, as well as to determine their state by the content of phytomass determined by the vegetation index NDVI. The aim of the study is to test the validity of the hypothesis about the formation of abundant vegetation cover within the fault zones by analyzing the state of various plant communities by the volume of phytomass. Methods: decryption and analysis of Earth remote sensing data from Sentinel, Landsat and SRTM generation, geoinformation mapping on the ArcGIS platform. Results. In the course of the study, the state of the Nature Sanctuary “Dolinsky” analyzed by Landsat-8, Sentinel-2A satellite sur-veys, as well as SRTM data. Fault zones identified using the software systems ArcGIS, QGIS, and PyLEFA by lineament analysis, vegetation was classified by the maximum likelihood method, and its condition was determined by the values of the NDVI index, which reflects the content of phytomass in the study area. As result of the work carried out, an increase in phytomass revealed, and, consequently, good conditions for the growth of plant communities confined to the zones of distribution of faults of the earth's crust, and the reliability of the working hypothesis confirmed.

scholarly journals Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

2020 ◽  
Vol 13 (1) ◽  
pp. 51
Author(s):  
Bryn E. Morgan ◽  
Jonathan W. Chipman ◽  
Douglas T. Bolger ◽  
James T. Dietrich
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Temporal Trends ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Aerial Vehicle

Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

STUDYING OF TERSKO-SULAKSKAYA LOWLAND VEGETATION UNDER THE RESULTS OF GROUND-BASED RESEARCH AND AUTOMATED INTERPRETATION

1970 ◽  
pp. 29-32
Author(s):  
M. I. Dzhalalova ◽  
A. B. Biarslanov ◽  
D. B. Asgerova
Keyword(s):  
Plant Communities ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Impacts ◽  
Species Abundance ◽  
Remote Sensing Data ◽  
Indigenous Communities ◽  
The State ◽  
Anthropogenic Factors ◽  
Internet Resource

The state of plant communities in areas located in the Tersko-Sulak lowland was studied by assessing phytocenotic indicators: the structure of vegetation cover, projective cover, species diversity, species abundance and elevated production, as well as automated decoding methods. There are almost no virgin soils and natural phytocenoses here; all of them have been transformed into agrocenoses (irrigated arable lands and hayfields, rice-trees and pastures). The long-term impact on pasture ecosystems of natural and anthropogenic factors leads to significant changes in the indigenous communities of this region. Phytocenoses are formed mainly by dry-steppe types of cereals with the participation of feather grass, forbs and ephemera, a semi-desert haloxerophytic shrub - Taurida wormwood. At the base of the grass stand is common coastal wormwood and Taurida wormwood - species resistant to anthropogenic influences. Anthropogenic impacts have led to a decrease in the number of species of feed-rich grain crops and a decrease in the overall productivity of pastures. Plant communities in all areas are littered with ruderal species. The seasonal dynamics of the land cover of the sites was estimated by the methods of automatic decoding of satellite images of the Landsat8 OLI series satellite for 2015, dated by the periods: spring - May 20, summer - July 23, autumn - October 20. Satellite imagery data obtained by Landsat satellite with a resolution in the multispectral image of 30 m per pixel, and in the panchromatic image - 10 m per pixel, which correspond to the requirements for satellite imagery to assess the dynamics of soil and vegetation cover. Lower resolution data, for example, NDVI MODIS, does not provide a reliable reflection of the state of soil and vegetation cover under arid conditions. In this regard, remote sensing data obtained from the Internet resource https://earthexplorer.usgs.gov/ was used.

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