Defining scales of the land use effect to map the urban heat island in a mid-size European city: Rennes (France)

Foissard, Xavier; Dubreuil, Vincent; Quénol, Hervé

doi:10.1016/j.uclim.2019.100490

Cited by 55 publications

(40 citation statements)

References 56 publications

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“…On the other hand, the buffer size for the bare-soil surfaces was between 50 m and 1000 m depending on the indicator (respectively density of bare-soil and BI, and NDBaI, and Enhanced Built-Up and Bareness Index (EBBI)). In this latter case, this size is similar to that of previous studies [46].…”

Section: Implication Of Important Predictors In Urban Air Temperaturesupporting

confidence: 91%

“…However, in this study, it was found that the buffers with the best correlation between air temperature and building density, based on LiDAR data, were 5 and 10 m. Furthermore, the vegetation density obtained from LiDAR, which explains air temperature in an optimum way, was within a radius of 50 to 200 m, regardless of its height (low, medium, or high; Figure 5). This also corresponds to a smaller buffer size than that used in previous studies [46]. On the other hand, the buffer size for the bare-soil surfaces was between 50 m and 1000 m depending on the indicator (respectively density of bare-soil and BI, and NDBaI, and Enhanced Built-Up and Bareness Index (EBBI)).…”

Section: Implication Of Important Predictors In Urban Air Temperaturementioning

confidence: 72%

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A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

Alonso¹,

Renard²

2020

Remote Sensing

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Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of August 30th, 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.

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Section: Implication Of Important Predictors In Urban Air Temperaturesupporting

confidence: 91%

Section: Implication Of Important Predictors In Urban Air Temperaturementioning

confidence: 72%

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

Alonso¹,

Renard²

2020

Remote Sensing

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“…In addition, it is forecast that by 2050 Lyon will have the climate of Canberra (Australia), with an increase in annual temperatures of 1.8 • C compared to the reference period 1971-2000 [66]. The number of summer days, which is defined according to the ETCCDI indices as days with maximum temperatures above 25 • C [67,68], tends to increase over the same period. The number of heat waves has increased sharply since the decade 1994-2003 [23].…”

Section: Study Area: the Metropolis Of The Greater Lyon Areamentioning

confidence: 99%

A Comparative Study of the Physiological and Socio-Economic Vulnerabilities to Heat Waves of the Population of the Metropolis of Lyon (France) in a Climate Change Context

Alonso

Renard

2020

IJERPH

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Increases in the frequency and intensity of heat waves are direct consequences of global climate change with a higher risk for urban populations due to the urban heat island effect. Reducing urban overheating is a priority, as is identifying the most vulnerable people to establish targeted and coordinated public health policies. There are many ways of understanding the concept of vulnerability and multiple definitions and applications exist in the literature. To date, however, nothing has been done on the territory of this study, the metropolis of Lyon (France). The objective is thus to construct two vulnerability indices: physiological, focusing on the organism’s capacities to respond to heat waves; and socio-economic, based on the social and economic characteristics and capacities of the community. To this end, two complementary methodologies have been implemented: the AHP (Analytic Hierarchy Process) and the PCA (Principal Component Analysis) with Varimax rotation, respectively. The results were then spatialized to the smallest demographic census unit in France. The areas highlighted differed due to conceptual and methodological differences: the highest physiological vulnerabilities are in the center while the socio-economic ones are in the eastern periphery of the urban area. The location of these areas will enable prevention campaigns to be carried out, targeted according to the publics concerned.

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“…En outre, il est nécessaire d'estimer au mieux la température de l'air en tous lieux, notamment face à la rationalisation actuelle du réseau de stations météorologiques fixes de Météo France (Journal officiel du Sénat, 2012). En France, les agglomérations qui disposent de leur propre réseau de stations météorologiques fixes ne sont que peu nombreuses, comme à Rennes ou à Dijon (Foissard et al, 2019 ;Richard et al, 2018).…”

Section: Introductionunclassified

Compréhension du microclimat urbain lyonnais par l’intégration de prédicteurs complémentaires à différentes échelles dans des modèles de régression

Alonso¹,

Renard²

2020

Climatologie

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Le changement climatique est un phénomène majeur actuel générant de multiples conséquences. En milieu urbain, il exacerbe celui de l’îlot de chaleur urbain. Ces deux manifestations climatiques engendrent des conséquences sur la santé des habitants et sur la sensation d’inconfort thermique ressenti en milieu urbain. Ainsi, il est nécessaire d’estimer au mieux la température de l’air en tout point d’un territoire, notamment face à la rationalisation actuelle du réseau de stations météorologiques fixes de Météo France. La connaissance spatialisée de la température de l’air est de plus en plus demandée pour alimenter des modèles quantitatifs liés à un large éventail de domaines, tels que l’hydrologie, l’écologie ou les études sur les changements climatiques. Cette étude se propose ainsi de modéliser la température de l’air, mesurée durant 4 campagnes mobiles réalisées durant les mois d’été, entre 2016 et 2019, dans Lyon par temps clair, à l’aide de modèle de régressions à partir de 33 variables explicatives issues de données traditionnellement utilisées, de données issues de la télédétection par une acquisition LiDAR (Light Detection And Ranging) ou satellitaire Landsat 8. Trois types de régression statistique ont été expérimentés, la régression partial least square, la régression linéaire multiple et enfin, une méthode de machine learning, la forêt aléatoire de classification et de régression. Par exemple, pour la journée du 30 août 2016, la régression linéaire multiple a expliqué 89% de la variance pour les journées d’étude, avec un RMSE moyen de seulement 0,23°C. Des variables comme la température de surface, le NDVI ou encore le MNDWI impactent fortement le modèle d’estimation.

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Defining scales of the land use effect to map the urban heat island in a mid-size European city: Rennes (France)

Cited by 55 publications

References 56 publications

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

A Comparative Study of the Physiological and Socio-Economic Vulnerabilities to Heat Waves of the Population of the Metropolis of Lyon (France) in a Climate Change Context

Compréhension du microclimat urbain lyonnais par l’intégration de prédicteurs complémentaires à différentes échelles dans des modèles de régression

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