Spatiotemporal Analysis of Urban Green Areas Using Change Detection: A Case Study of Kharkiv, Ukraine

Morar, Cezar; Lukić, Tin; Valjarević, Aleksandar; Niemets, Liudmyla; Kostrikov, Sergiy; Sehida, Kateryna; Telebienieva, Ievegeniia; Kliuchko, Liudmyla; Kobylin, Pavlo; Kravchenko, Kateryna

doi:10.3389/fenvs.2022.823129

Cited by 11 publications

(3 citation statements)

References 66 publications

(67 reference statements)

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“…Land cover change is a significant aspect when analysing small urban green areas [82,84] since their surface and their often unclear legal status make them vulnerable to transformations [43]. For example, in Bucharest, in the context of a volatile legislative framework, many small green areas have been transferred into private property and transformed into other land uses [85,86].…”

Section: Methods Efficiency For Extracting Small Urban Green Areasmentioning

confidence: 99%

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Popa¹,

Onose²,

Şandric³

et al. 2022

Preprint

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The importance of small urban green areas has increased in the context of rapid urbanization and densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions – temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities, across all analysed images. The timeseries analysis showed consistency in location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region, due to the vegetation characteristics and the planning of the two cities. For the same reasons, the increase in vegetation density and quality, as a result of the distance from the city centre and the decrease in the density of built-up areas is more obvious in Athens. The proposed method provides valuable insights in the distribution and quality of small urban green areas at city level and can represent the ground basis for many analyses, currently limited by poor spatial resolution.

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Section: Methods Efficiency For Extracting Small Urban Green Areasmentioning

confidence: 99%

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Popa¹,

Onose²,

Şandric³

et al. 2022

Preprint

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“…Based on the model of least resistance, Guan et al [15] simulated urban expansion conditions under different scenarios considering resource level, environmental barriers, environmental resistance, and urban expansion, and made suggestions to guide sustainable urban development. Morar et al [23] developed and used an original integrated approach using urban remote sensing (URS) and GIS for changes detection to evaluate the current state and monitor spatial transformations of the Urban Green Areas and found out UGAs were not expanding and partially shrinking during 2000-2020 in Kharkiv, Ukraine.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal analysis of urban sprawling using change detection: a case study of Shaki district, Azerbaijan

Artunov,

Pashayev,

Gasimova

2023

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Introduction. The contemporary globalized world characterizes the rapid population growth, its significant concentration in cities, and an increase in the urban population. Currently, many socio-cultural, economic, environmental, and other challenges are arising in modern cities. Cities are therefore emerging from the common understanding and displaying new characteristics: reduced density, dispersed development, poor accessibility and monofunction. However, the concentration of population in cities also brings its own set of issues. The purpose of article. The purpose of the study was to identify Shaki City's urban development process, and how its land use has evolved over time. It examines the population growth in the region and the increase in the specific weight of the urban population between 2016 and 2023, analyzing the population growth trend over a 20-year period. The direction and extent of urban land use has been studied by determining the relationship between the growth rate of the urban population and the extension of the urban area, and by analyzing the changes which have occurred during the period of land use. Research methods. Statistical data has also been used for this purpose, together with data from the Azersky satellite. Machine Learning (ML), which is widely used in remote sensing systems, was applied, Support Vector Machine Learning (SVM), and image classification and processing were performed. On the basis of the obtained data, a comparative analysis of the previous and current conditions was carried out and the area of changes in the area between the classified areas was calculated. Simultaneously, the changes between categories during the use of the area and the recent changes in the direction of land use were shown. Classification performance has been assessed, user and producer accuracies have been determined and kappas have been calculated. Main findings. The increase in the population of the Shaki district led to an increase in the specific weight of the urban population and the extension of the town to the south and south-east where the population previously lived sparsely. It is mainly due to construction of new housing estates in region, as well as construction of a central clinic, an ASAN service, and a regional education division. A 'flight to the centre' was observed, resulting in noticeable changes in the land use structure between 2016 and 2022, in line with the growth rate of urbanisation and economic development. The decoding of the distribution images of the region shows that there has been an increase in the area of settlements over the six-year period. By 2016, settlements cover 22.4 per cent of the city, and by 2022, the figure rises to 39 per cent. From 34% to 32.9%, the total area of forest reserves decreased. Scientific novelty and practical value The article describes for the first time the urban sprawling and territorial transformations in Shaki district in the context of population growth by using change detection analyses. The practical value of the study is the possibility of using its algorithm and method to conduct similar studies in other cities of Azerbaijan. The results of the study are significant in the context of justifying regional measures to adapt urban expansion to population growth.

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“…As indicated by Gruber and Bartelt [30], potential avalanche release areas are strongly related to the slope inclination of the terrain in general. Therefore, GIS and remote sensing based techniques can be used to automatically and efficiently determine potential avalanche release areas and other natural hazards [31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Spatial Modeling of Snow Avalanches Using Analytic Hierarchy Process. A Case Study of the Šar Mountains, Serbia

et al. 2022

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Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed, where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can will help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches, and methodology and criteria used in this study can be tested in other high mountainous regions.

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Spatiotemporal Analysis of Urban Green Areas Using Change Detection: A Case Study of Kharkiv, Ukraine

Cited by 11 publications

References 66 publications

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

Spatiotemporal analysis of urban sprawling using change detection: a case study of Shaki district, Azerbaijan

GIS-Based Spatial Modeling of Snow Avalanches Using Analytic Hierarchy Process. A Case Study of the Šar Mountains, Serbia

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