Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Grigoraș, Georgiana; Urițescu, Bogdan

doi:10.2478/avutgs-2018-0002

Cited by 21 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies of this scale usually use images of low or ultra-low spatial resolution (more than 1 km per pixel), but with very high temporal resolution (less than an hour between images of the same area). Examples of such systems are MODIS and AVHRR [10]. Despite all the advantages of these systems, the low spatial resolution of images makes them unsuitable for regional studies on a city or agglomeration scale.…”

Section: Methods For Obtaining Satellite Imagesmentioning

confidence: 99%

Urban climate and registration of thermal anomalies of territories using satellite imagery

2021

View full text Add to dashboard Cite

This paper describes a simplified method of mapping of the ur-ban environment surface to obtain a map of the thermal anomalies distribu-tion and study the structure of the urban heat island. Those maps allow evaluating or planning the urban microclimate optimization methods and studying the effect of land cover type on the site temperature. The article discusses the processing of five satellite images for the summer and winter from 2002 to 2019. We propose a simpler and more automated processing of thermal images for Landsat 7 and Landsat 8. The stages of automatic atmospheric correction according to the DOS1 method and calculation of the emissivity with surface classification are considered. Image processing was carried out in the QGIS software package using the Semiautomatic Classification Plugin extension. As a result, thermal anomalies in Chelya-binsk were localized and a comparison of the thermal map for the specific region before and after urbanization was made.

show abstract

Section: Methods For Obtaining Satellite Imagesmentioning

confidence: 99%

Urban climate and registration of thermal anomalies of territories using satellite imagery

2021

View full text Add to dashboard Cite

show abstract

“…Normalized Difference Vegetation Index (NDVI) is one of the most widely applied vegetation indices and an important descriptor of greenness of the biomes. NDVI is considered as land use land cover indicator to quantitatively explore the relationship between the thermal environment and urban expansion [55,56], and to investigate the greening effect on LST changes [32,35,36].…”

Section: Urban Feature Layers 241 Vegetation and Water Bodies Layers Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

“…Local statistical methods, such as Getis-Ord Gi* [25], allow the thermal spatial pattern to be analyzed by estimating the value of the feature within its spatial context of neighboring features [26]. This latter is a performant and robust method of hot-spot detection already used in previous studies [27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

et al. 2021

View full text Add to dashboard Cite

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

show abstract

“…Some studies have shown significantly higher UHI in new urban areas compared to old urban areas [31,43], which has been explained by the greater presence of many large industrial agglomerations (major anthropogenic heat sources) in new urban areas [44][45][46]. On the other hand, some other studies have shown the opposite, with higher UHI intensity in old urban areas [41,47], since old urban areas, being the center of commercial and human activities [48,49], have a higher density of impervious surfaces and lower vegetation coverage [35,50,51]. Apparently, one major weakness of these studies is the vague and nonstandard use of new vs. old urban areas, which can greatly affect the results [31,52].…”

Section: Introductionmentioning

confidence: 99%

Evolution and Built-Up Age Dependency of Urban Thermal Environment

Li,

Liu,

Liu

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The urban heat island (UHI) represents an anthropogenic modification to the earth’s surface, and its relationship with urban development, built-up age dependency in particular, is poorly understood. We integrated global artificial impervious areas to analyze the impacts of built-up age and urban development intensity (UDI) on land surface temperatures (LSTs) in Hefei, the capital of Anhui Province of China, from 2000 to 2019. A key finding was that the built-up areas with different built-up ages were strongly associated with LST, and this relationship does not change significantly over time, suggesting temporal stability of spatial patterns of LSTs. This finding puts forward a challenge to the application of the classic concept of space-for-time in LST studies because the premise of space-for-time is that spatial and temporal variation are equivalent. This result reveals the vital importance of annual development activities on the urban thermal environment. Another highlighted result was LST sensitivity to UDI, an effective measure of the impact of urbanization on LST, which increased significantly from 0.255 °C per 10% UDI to 0.818 °C per 10% UDI. The more than doubling of LST sensitivity to UDI should be a major concern for city administration. These findings have crucial theoretical and practical significance for the regulation of LSTs and UHI.

show abstract

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Cited by 21 publications

References 18 publications

Urban climate and registration of thermal anomalies of territories using satellite imagery

Urban climate and registration of thermal anomalies of territories using satellite imagery

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

Evolution and Built-Up Age Dependency of Urban Thermal Environment

Contact Info

Product

Resources

About