Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat island effect. This phenomenon has recently been taken into account by local elected officials. As a result, developers have decided to use solutions in redevelopment projects to combat high temperatures in urban areas. Consequently, the objective is to study the land-surface temperature evolution of six main urban redevelopments in Lyon, France, from 2000 to 2017. Three of them (the Confluence, Kaplan, and Museum sites) were composed of industrial areas that have undergone major transformations and are now tertiary or residential areas. Two sites have been more lightly transformed, particularly by increasing vegetation to reduce heat stress and urban flooding (Dock and Garibaldi Street). Finally, the Groupama Stadium has been built into agricultural and wooded areas. Changes in vegetation cover (NDVI), water (MNDWI), and moisture (NDMI) content, built areas (NDBI) and bare soil (NDBaI) are also monitored. The results show that the Confluence and Kaplan sites were accompanied by a decrease in surface temperature and an increase in vegetation and moisture, whereas the Groupama Stadium displayed a rise in surface temperature and a decrease in vegetation. On the other hand, the Museum, Dock, and Garibaldi sites did not exhibit clear and uniform trends, although an increase in surface temperature was shown in some statistical tests. The disparity of the results shows the necessity to include a significant amount of vegetation during redevelopment operations in order to reduce heat stress.

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 30 August 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.

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.

The widespread use of low-cost environmental monitoring systems, together with recent developments in the design of Internet of Things architectures and protocols, has given new impetus to smart city applications. Such progress should, in particular, considerably improve the fine characterization of a wide range of physical quantities within our cities. Indeed, the cost-effectiveness of these emerging sensors combined with their reduced size allows for high density deployments resulting in a higher spatial granularity. In this paper, we briefly present the 3M’Air project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature and then bridge the gap between individual exposure and regional measurements. We present then the design, implementation and evaluation of our low-cost, small-size WSN-based participatory monitoring system. This system is composed of mobile sensing nodes measuring temperature, humidity and a number of pollutants (NO2, PM1, PM2.5 and PM10). The collected data are sent to a server for analysis and building temperature and air quality maps. To validate our platform, we have carried out multiple tests to compare our sensor nodes to reference stations and to each other. We have also evaluated the energy consumption of our nodes under different configurations. The results are satisfactory and show that our nodes can be used in environmental participatory monitoring.

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.

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.