Geoinformatic assessment of urban heat island and land use/cover processes: a case study from Akure

Makinde, E. O.; Agbor, C.F.

doi:10.1007/s12665-019-8433-7

Cited by 20 publications

(15 citation statements)

References 28 publications

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“…In an investigation of the Chinese city of Wuhan, in which water accounts for over 25% of the total area of the central district-including two rivers (the Yangtze River and Han River), East Lake (the largest urban lake in China), and dozens of other lakes-Wu et al found that both the area and the spatial distribution of water bodies contributed to significant distributions in the effects caused by SUHIs [70]. Moreover, the findings reported in multiple studies have affirmed that water bodies exhibit the lowest LSTs [11,20,41,45,47,60,73,78,79,88,90,93,97,98,111,112,115,138,139,[151][152][153], along with vegetation cover. Furthermore, water has generally been found to have a negative correlation with LST [20,111,154], except for a few circumstances due to various reasons primarily related to the climate characteristics of cities.…”

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confidence: 99%

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

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confidence: 99%

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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“…Temperature increases of 2-3 K could already be measured between forest and farmland. This is most likely due to the different land use and the resulting absence of natural cooling processes by evapotranspiration and photosynthesis activity [45]. In our study, fallow land, bare soil, and farmland reach temperatures between 11-13 • C. Likewise, we could observe that smaller villages can also develop underground temperatures of approximately 13 • C. This shows that the phenomenon of the suburban heat island cannot be reduced to the city's effect alone.…”

Section: Resultsmentioning

confidence: 46%

Calculating Energy and Its Spatial Distribution for a Subsurface Urban Heat Island Using a GIS-Approach

et al. 2021

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In urban areas, the human influence on the city-ecosystem often results in a Subsurface Urban Heat Island (SUHI), which can be used geothermally. Unfortunately, a model of a SUHI does not consider the geology and hydrogeology of the subsoil. These can vary significantly over short distances, and are of considerable importance for the energy balance. In this work, we calculated the energy and its density stored in the subsoil via a SUHI. For this so-called energy-SUHI (e-SUHI), we evaluated the geology and its physical parameters for the first 20 m below ground level in the German city of Nuremberg and linked them to measured underground temperatures in a GIS application. This approach revealed stored energy of 1.634 × 1010 MJ within the soil and water for the study area with an area of 163 km2 and a volume of 3.26 × 109 m3. It corresponds to an average energy density of 5.0 MJ/m3. The highest energy density of 16.5 MJ/m3 was found in the city center area and correlated well to increases in subsurface temperature. As expected, our model reacts sensitively to thickness changes in the geological layers and the unsaturated zone.

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“…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%

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

et al. 2021

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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.

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Geoinformatic assessment of urban heat island and land use/cover processes: a case study from Akure

Cited by 20 publications

References 28 publications

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Calculating Energy and Its Spatial Distribution for a Subsurface Urban Heat Island Using a GIS-Approach

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

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