Analysis of Land Use Land Cover Change
Detection of Bostanlik District, Uzbekistan

Juliev, Mukhiddin; Pulatov, Alim; Fuchs, Sven; Hübl, Johannes

doi:10.15244/pjoes/94216

Cited by 41 publications

(31 citation statements)

References 15 publications

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“…Land use and land cover are two different terms that are usually evaluated in combination since the former (physical properties of surface elements) and the latter (human use of land cover) cannot be considered independent of each other [21]. Thus, LULC is the result of human interaction with the environment, which is affected by changes in socioeconomic processes.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan

et al. 2020

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In this study, the spatiotemporal dynamics of land use and land cover (LULC) were evaluated in the peri-urban area of the Arshaly district, which borders the capital of the Republic of Kazakhstan. Landsat multispectral images were used to study the changes in LULC. The analysis of LULC dynamics was carried out using supervised classification with a multi-temporal interval (1998, 2008, and 2018). During the study period, noticeable changes occurred in LULC. There was an increase in the area of arable land and forests and a reduction in the pastures. There was a sharp increase in the built-up area; that is, there was an intensification of land use through an increase in the share of arable land as well as the transformation of agricultural land for development. However, in general, the influence of urban sprawl in this peri-urban area has so far been accompanied by only a slight focus on its sustainable development.

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Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan

et al. 2020

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“…All the data were projected to the same reference system UTM (zone 38N) and WGS 84 datum, the preprocessing steps included the assignment of the coordinate system, layer stacking of the separate bands of the datasets and splitting the images based on the polygon of the study area. Supervised classi cation methods and maximum likelihood algorithm were used for producing LULC maps (Juliev et al 2019…”

Section: Data Sources and Methodsmentioning

confidence: 99%

Seasonal effects of Land cover changes on ecological of migratory birds in Al-Hammar Marsh, southern Iraq

Ali

Ramahi

2021

Preprint

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Al-Hammar Marsh in Iraq stands out for hosting the largest concentrations of coastal migratory birds along the migration path between the Arabian Gulf and the Mediterranean Sea. Despite this importance, there is no complete review of the dynamics of these birds. In this study, we collected and analyzed the results of six species of migratory birds (Mallard duck, Graylag goose, White pelican, Barn swallow, Common gull, White stork) in the Region, in addition to the results of the land cover in October of 2000 to 2020, The results show the presence across the past two decades of a change in the population makeup of migratory birds. Complete numbers of migratory birds indicated a decline in the residual types between 2000 and 2020. The disproportion among numbers was also too large for trends to be observed. The region also showed a decrease in the areas of vegetation cover during the study period was equal to 5.0%, 3.5%, and 15.6%, And an increase in the areas of water bodies during the study period was equal to 7.8%, 21.0%, and 62.6%, respectively, Finally, A multivariate analysis suggests a general decline in types that depend on the Wetland for feeding and breeding an in the sublittoral and the offshore zones. Use satellite data can be used for monitoring several variables such as water bodies, vegetation cover, Climate factors that influence the activity of Migratory birds.

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“…In the classification of maximum likelihood, the algorithm is based on probability and it assumes that the statistics of the studied data are distributed over each spectral range and class. The pixels were calculated based on the probability that each m class is defined, and then the pixel probability was assigned according to the highest class (Juliev, Pulatov, Fuchs, and Hübl, 2019).…”

Section: Methodsmentioning

confidence: 99%

Application of Geoinformation Technologies and Remote Sensing to Detect Land Use and Changes in the Soil Cover Caused by the Drying of the Aral Sea

Safarov¹,

Prenov²,

Bekanov³

et al. 2020

PTQ

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It is established that the Aral Sea problem, which is due to the negative impact of human activity over time, has led to environmental degradation in the Aral Sea region and changes in the soil cover. The extent of this ecological problem can be detected using geoinformation technologies and remote sensing methods. In this study, the maximum likelihood classification algorithm was applied in the presentation of Landsat images to detect changes in the soil cover based on multispectral satellite data obtained from Landsat 7 and Landsat 8 for 2008 and 2018, respectively. The resulting land-use maps indicate a significant increase in saline land from 18% to 22% during the period under study. These land transformations pose a severe threat to the land resources of the area. Consequently, proper management of the land resources is required to preserve them and to ensure that they continue to play their part in the socio-economic development of the region.

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Analysis of Land Use Land Cover Change Detection of Bostanlik District, Uzbekistan

Cited by 41 publications

References 15 publications

A Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan

A Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan

Seasonal effects of Land cover changes on ecological of migratory birds in Al-Hammar Marsh, southern Iraq

Application of Geoinformation Technologies and Remote Sensing to Detect Land Use and Changes in the Soil Cover Caused by the Drying of the Aral Sea

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