Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale

Barbierato, Elena; Bernetti, Iacopo; Capecchi, Irene; Saragosa, Claudio

doi:10.1080/22797254.2019.1646104

Cited by 19 publications

(9 citation statements)

References 40 publications

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“…LST Extraction and Mapping at the Regional Scale A QGIS plugin implemented by Ndossi and Avdan [35] was used in this study. The plugin has been employed to extract LST from Landsat images in various studies, mainly at the city level, to assess the significance of urban trees in mitigating temperatures [36], to investigate UHI phenomena 2018 land cover data, classed following the CORINE third-level nomenclature [32], were retrieved from the Land Monitoring Service [33] of the European Union's Copernicus Earth Observation Program. CLC is an EU program that has been providing consistent information about land covers across Europe over time since 1990.…”

Section: Methodsmentioning

confidence: 99%

“…A QGIS plugin implemented by Ndossi and Avdan [35] was used in this study. The plugin has been employed to extract LST from Landsat images in various studies, mainly at the city level, to assess the significance of urban trees in mitigating temperatures [36], to investigate UHI phenomena [37], or to estimate the impact of urban land-use change on LST [38]. For each image listed in Table 1, a five-step process was therefore performed through the plugin; the process is fully explained in Appendix A and can be summarized as follows.…”

Section: Lst Extraction and Mapping At The Regional Scalementioning

confidence: 99%

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Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

2020

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Land surface temperature (LST) is a key climate variable that has been studied mainly at the urban scale and in the context of urban heat islands. By analyzing the connection between LST and land cover, this study shows the potential of LST to analyze the relation between urbanization and heating phenomena at the regional level. Land cover data, drawn from Copernicus, and LST, retrieved from Landsat 8 satellite images, are analyzed through a methodology that couples GIS and regression analysis. By looking at the Italian island of Sardinia as a case study, this research shows that urbanization and the spatial dynamics of heating phenomena are closely connected, and that intensively farmed areas behave quite similarly to urban areas, whereas forests are the most effective land covers in mitigating LST, followed by areas covered with Mediterranean shrubs. This leads to key policy recommendations that decision-makers could implement to mitigate LST at the regional scale and that can, in principle, be exported to regions with similar climate and land covers. The significance of this study can be summed up in its novel approach to analyzing the relationship between LST and land covers that uses freely available spatial data and, therefore, can easily be replicated in other regional contexts to derive appropriate policy recommendations.Land cover changes, and, in particular, transitions from natural and semi-natural areas to artificial land covers, affect regional and local temperatures [6]. In fact, although urban areas and their surroundings receive the same amount of solar radiation, the local temperatures differ because different surface materials have different heat capacity [7]. From this perspective, land surface temperature (LST) is a significant parameter for investigating the effects of land covers on local temperatures.Hofierka et al.[8] define LST as "the radiative skin temperature of the ground", affected by solar reflectance, thermal emissivity, and heat capacity. In other words, LST combines interactions between land surface and atmosphere with ground-atmosphere energy fluxes. LST, measured through satellite thermal infrared sensors [9], represents a key climate variable, and its study allows researchers to analyze the behavior of the Earth's environmental system [10].Therefore, understanding how land cover changes affect climate represents a key element in international debates [11] that highlight that land cover can significantly affect quality of life, that is human health and safety, through its influence on LST [12,13]. The impacts on climate conditions generated by land-cover change processes can be effectively analyzed by assessing the relations between the spatial distribution of LST and that of land covers. The influence of land use/land cover changes on LST variation has been studied by various authors [6,[14][15][16][17][18]. Feizizadeh et al. [6] analyze the relations between LST and land use/land cover in Maraqeh County (Iran), using a method based on the application of the Surface Energy Bal...

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Section: Methodsmentioning

confidence: 99%

Section: Lst Extraction and Mapping At The Regional Scalementioning

confidence: 99%

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

2020

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“…Both, UHI and SUHI phenomena have been examined in the literature at different geographical scales, including at the level of continents [38][39][40], countries [41], cities and metropolitan areas [13,42] and increasingly at micro-scales [43,44]. To illustrate, [45] examines the impacts of urban functional zones and spatial heterogeneity on LST in Beijing, China; Temporal changes in LST as a result of urban redevelopments in Lyon, France where investigated by [46]; and the impacts of different types of LULC patterns on SUHI variations in Bangkok, Thailand, where evaluated by [47].…”

Section: Surface Urban Heat Island (Suhi) and Land Surface Temperature (Lst)mentioning

confidence: 99%

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

Goldblatt

Addas

Crull

et al. 2021

Land

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Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Landsat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

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“…A QGIS plugin implemented by Ndossi and Avdan [35] was used in this study. The plugin has been employed to extract LST from Landsat images in various studies, mainly at the city level, to assess the significance of urban trees in mitigating temperatures [36], to investigate UHI phenomena 2018 land cover data, classed following the CORINE third-level nomenclature [32], were retrieved from the Land Monitoring Service [33] of the European Union's Copernicus Earth Observation Program. CLC is an EU program that has been providing consistent information about land covers across Europe over time since 1990.…”

Section: Lst Extraction and Mapping At The Regional Scalementioning

confidence: 99%

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

Lai

Leone

Zoppi

2020

Computational Science and Its Applications – ICCSA 2020

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Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale

Cited by 19 publications

References 40 publications

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

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