Determination of the Degradation Degree of Pasture Lands in the West Kazakhstan Region Based on Monitoring Using Geoinformation Technologies

Yesmagulova, Bayan; Assetova, Assem; Tassanova, Zhadyra; Zhildikbaeva, Aizhan; Molzhigitova, Dinara

doi:10.12911/22998993/155167

Cited by 16 publications

(15 citation statements)

References 4 publications

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“…As a rule, dependencies with GWL are determined (Yesmagulova et al 2023) and average calculation parameters are taken, which further increases the percentage of error in determining the filtrationcapacitance properties of formations.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Porosity Parameter in Sediments Obtained Using Traditional and Precision Methods (Cenomanian Deposits in Western Siberia, Russia)

Naumenko,

Zavatskij,

Skudar

2023

QAJ

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The study focuses on porosity, an important parameter for calculating reserves. Traditionally, weight-based methods are used in calculation, which implies a simple approach to estimating hydrocarbon reserves. However, this study is based on the need to show that effective reserve assessment and hydrocarbon production planning require a combination of various methods supplemented by dynamic modeling. The Cenomanian deposit of the Yamburgskoe and Yubileynoe deposits in Western Siberia, Russia is the object of the study. A comparison is made based on the data obtained during the direct analysis using Preobrazhensky's method and microtomography of the core of the Cenomanian deposit. The study is carried out to evaluate the representativeness of various methods of measuring porosity and to investigate directly the reservoir of the Cenomanian deposit

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Section: Discussionmentioning

confidence: 99%

Comparison of the Porosity Parameter in Sediments Obtained Using Traditional and Precision Methods (Cenomanian Deposits in Western Siberia, Russia)

Naumenko,

Zavatskij,

Skudar

2023

QAJ

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“…Microelements were determined by the atomic absorption method on a Japanese-made device "Shimadzu-6800". The main criterion for assessing the quality of soils in forage lands is their genetic and agro-production properties, as the most objective indicators that, in local conditions, affect soil fertility and the yield of grassland forage [21], [25], [26]. These indicators are the gross reserves of humus, nitrogen, phosphorus, potassium, and absorption capacity in 0-20; 0-50; and 0-100 cm layers of the main types and subtypes of soils, as properties that determine their fertility.…”

Section: Methodsmentioning

confidence: 99%

Ecological assessment of pastures semi-deserts and dry steppes of Azerbaijan

Oqtay,

Nazim,

Dursun

2024

IJAAS

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The absence in the scientific literature of criteria for assessing the ecological status of pasture lands, insufficient knowledge of the use of predictive methods and technology for carrying out special agrochemical measures, as well as issues of permissible loads, served as the basis for choosing the topic of research work. For the first time, in the conditions of pastures in Azerbaijan, an environmental and energy assessment of soil-landscape complexes was carried out. A detailed and final quality assessment was drawn up on soil scale, and the coefficient of their comparative merit was determined. A scientifically based system of agrochemical measures for the superficial and fundamental improvement of pastures has been developed. The final bonitet scale, reflecting the level of fertility of soil varieties, showed that the soils of the Jeyranchol massif turned out to be the most fertile at 62 points, compared with them, the pasture soils of Ajinohur on average across the massif received 53 points, Gobustan 51 points, and the Kura-Araz lowland 55 points. On average, pasture lands in Azerbaijan are valued at 55 points, which indicates the need for agro-reclamation measures. The types of forage plants are distributed as follows: i) Cereals 116, 12%; ii) Asteraceae 109, 11.2%; iii) Legumes 82, 8.4%; iv) Brassicas 59, 6.2%; 6%; v) Cloves 50, 5.3%; vi) Lamiaceae 42, 4.6%; vii) Linear 40, 4.2%; viii) Goosefoot 6, ix) Gimletaceae 32 species, 3.4%; x) Umbrellas 38, 4.4%; xi) Other 339 types, 35.5%.

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“…Efforts to predict forest fire behaviors have been ongoing for some time, with RS and GIS playing pivotal roles globally in studying forest fires and associated risk zones [3][4][5]. The advantages of these methods include their rapid, noninvasive nature and their ability to offer expansive coverage, thereby surpassing traditional field observations [6][7][8].…”

Section: Current Techniques and Limitationsmentioning

confidence: 99%

Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

Sagynbayeva,

Mambetov,

Kalachev

et al. 2023

IJDNE

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This study addresses the pressing and pertinent issue of establishing a space-based monitoring system to track forest condition changes. Utilizing geographic information system technologies and the Normalized Difference Vegetation Index (NDVI), it examines the evolution and dynamics of pine forest regeneration in the Semey Ormany State Forest Natural Reserve (Zhanasemey branch), Kazakhstan. Landsat-5 satellite data were employed to map and identify terrestrial vegetation changes within the Zhanasemey branch. Satellite imagery for June 2007 and September 2008 were specifically utilized for this investigation. Post-acquisition, the satellite images were subjected to corrections, followed by radiometric adjustments to minimize atmospheric interference. Subsequently, NDVI classifications, an established method for plant classification, were applied to each image. The study focused on a 15-year timespan (2008 to 2022) to monitor changes in vegetation intensity, classified according to NDVI, and to track the reforestation process in areas affected by fire. Analysis of the regeneration of the pine forest post the 2008 fire, conducted at five-year intervals, revealed an increase in the restored forest area from 6,773.49 ha in 2013 to 10,721.07 ha in 2018, and finally to 14,742.54 ha in 2022. In 2008, the fire-impacted area with reduced vegetation spanned 7,015.41 ha, while by 2022, the restored area encompassed 14,742.54 ha, indicating a significant enhancement in the pine forest restoration process. The findings underscore the critical role of remote sensing in forest fire modelling and identifying areas at risk, suggesting its potential utility in large-scale monitoring and management efforts. The study demonstrates the feasibility and efficiency of using space-based technologies to monitor long-term changes in forest conditions and to track reforestation dynamics in fire-affected regions.

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Determination of the Degradation Degree of Pasture Lands in the West Kazakhstan Region Based on Monitoring Using Geoinformation Technologies

Cited by 16 publications

References 4 publications

Comparison of the Porosity Parameter in Sediments Obtained Using Traditional and Precision Methods (Cenomanian Deposits in Western Siberia, Russia)

Comparison of the Porosity Parameter in Sediments Obtained Using Traditional and Precision Methods (Cenomanian Deposits in Western Siberia, Russia)

Ecological assessment of pastures semi-deserts and dry steppes of Azerbaijan

Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

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