Mapping Urban Green Spaces Based on an Object-Oriented Approach

Gülçin, Derya; Akpınar, Abdullah

doi:10.30516/bilgesci.486893

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…The perception of overheating in cities is central key as far as the urban liveability and human health are concerned. According to the state of the art [4][5][6][7][8], different methodologies, integrated with a GIS tool, are available to evaluate green roofs and urban green areas and to analyze the liveability of outdoor spaces, considering the relationship between the climate and the characteristics of the urban environment.…”

Section: Gis-based Methodology To Evaluate Green Roofs and Green Urbamentioning

confidence: 99%

“…GIS-based methodology to evaluate green roofs and green urban areas 9 [4][5][6][7][8][9][10][11][12] Outdoor thermal comfort: UHI mitigation strategies and thermal comfort indexes 20 [2,4,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Energy savings and Indoor thermal comfort from Green roofs 5 [1,[31][32][33][34] 2.…”

Section: No Of Papers Sourcementioning

confidence: 99%

“…Covering roofs with vegetation helps to mitigate the UHI effect by modifying microclimate and local climate [6]. A map can be created, through an analysis of the land cover, to identify green urban spaces, such as parks, playgrounds and residential green areas [7]. Moreover, through the use of a geographic information system (GIS) tool, it is possible to analyze the presence of vegetation according to the availability of the data.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

Mutani

Todeschi

2020

Atmosphere

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There is growing attention to the use of greenery in urban areas, in various forms and functions, as an instrument to reduce the impact of human activities on the urban environment. The aim of this study has been to investigate the use of green roofs as a strategy to reduce the urban heat island effect and to improve the thermal comfort of indoor and outdoor environments. The effects of the built-up environment, the presence of vegetation and green roofs, and the urban morphology of the city of Turin (Italy) have been assessed considering the land surface temperature distribution. This analysis has considered all the information recorded by the local weather stations and satellite images, and compares it with the geometrical and typological characteristics of the city in order to find correlations that confirm that greenery and vegetation improve the livability of an urban context. The results demonstrate that the land-surface temperature, and therefore the air temperature, tend to decrease as the green areas increase. This trend depends on the type of urban context. Based on the results of a green-roofs investigation of Turin, the existing and potential green roofs are respectively almost 300 (257,380 m2) and 15,450 (6,787,929 m2). Based on potential assessment, a strategy of priority was established according to the characteristics of building, to the presence of empty spaces, and to the identification of critical areas, in which the thermal comfort conditions are poor with low vegetation. This approach can be useful to help stakeholders, urban planners, and policy makers to effectively mitigate the urban heat island (UHI), improve the livability of the city, reduce greenhouse gas (GHG) emissions and gain thermal comfort conditions, and to identify policies and incentives to promote green roofs.

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Section: Gis-based Methodology To Evaluate Green Roofs and Green Urbamentioning

confidence: 99%

Section: No Of Papers Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

Mutani

Todeschi

2020

Atmosphere

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“…The updated information on certain spatial areas (regarding the current statuses of UGSs) is crucial for land users and land use planning decision-makers [44]. Remote sensing is also very common in UGS mapping, in conjunction with various other methods, such as the object-oriented approach, support vector machines, deep neural networks, and maximum likelihood [45][46][47]. In this study, we used ArcGIS software to analyze and manage the spatial database, including the raster and vector data.…”

Section: Geographical Information System and Remote Sensingmentioning

confidence: 99%

The Application of Geographical Information Systems and the Analytic Hierarchy Process in Selecting Sustainable Areas for Urban Green Spaces: A Case Study in Hue City, Vietnam

Linh

Pham

Chương

et al. 2022

Climate

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In recent years, there has been growing awareness about the roles and benefits of urban green spaces (UGSs), particularly in the context of mitigating the negative effects of climate change, which have become increasingly serious. In Vietnam, the government has allocated considerable resources to the development of UGSs in many cities. However, regarding implementation, UGS development in Vietnam faces many challenges; many cities find it difficult to meet the set criterion regarding the number of green spaces per capita. This research was conducted in Hue City, which is known as one of the greenest cities in Vietnam. The results show that there are twenty-one UGSs in Hue City (with a total area of 88.67 ha). These are located primarily along the Huong River and around the Hue Imperial Citadel. However, under government stipulations, the current number of UGSs is not considered sufficient in proportion to the local population, and will not accommodate the future growth of the population. We applied the analytic hierarchy process (AHP) along with the participation of local residents, using six criteria to map potential areas for future UGS planning. In this, the distance from existing residential areas to potential UGS locations is the most important criterion. The suitability map identified 684 hectares of Hue City as highly suitable for UGSs. This research also proposes a scenario for UGS planning in Hue based on retaining the existing green spaces combined with creating another 35 green spaces, comprising a total area of 167 hectares. This is to meet the needs of local residents by 2030.

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“…Image classification is necessary to map land use/land cover based on remotely sensed data. Machine learning has been utilized effectively in analyzing and processing remote sensing data [10]. According to [11], the machine learning method can be categorized as supervised, unsupervised technique, and reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effectiveness of Support Vector Machine and Random Forest Classifiers in Delineating the Green Area

Nadzri,

Khalid,

Wahab

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Due to human limitations in exploring the world, the existence of remote sensing technology has made it possible and affordable for humans to study the green cover in the modern world, especially over a large region. This is so that details of the objects can be captured and monitored by satellites or other aircraft by measuring the wavelengths of radiation that are both emitted and reflected from the area. For the past decades, various approaches have been utilized by researchers to detect green areas such as deep learning, machine learning, object based and pixel-based classification. Thus, this study aims to evaluate the effectiveness of machine learning classifiers such as Support Vector Machine (SVM) and Random Forest (RF) in detecting and delineating the green area in Universiti Teknologi Mara (UiTM) Shah Alam. Based on the study, the overall accuracy obtained by the SVM classifier is 80% with a 0.75 kappa coefficient, whereas the RF classifier managed to get 79% with a 0.74 kappa. Even though the result of both classifiers is almost the same, the accuracy of detecting green area by the SVM classifier is 93% which outperforms the RF classifier with an accuracy of 88%. This shows that the SVM classifier is more effective than the RF classifier. The detection and delineation of the green area using both machine learning approaches also showed using a map so that it is easier to be analyzed and observed.

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Mapping Urban Green Spaces Based on an Object-Oriented Approach

Cited by 9 publications

References 41 publications

The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

The Application of Geographical Information Systems and the Analytic Hierarchy Process in Selecting Sustainable Areas for Urban Green Spaces: A Case Study in Hue City, Vietnam

Analyzing the Effectiveness of Support Vector Machine and Random Forest Classifiers in Delineating the Green Area

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