Climate Change is now an undisputed fact (IPCC, 2007). There is a broad consensus on fact that cities have a special role in Climate Change, occupying an especially relevant role in Urban Heat Island (Oke, 1973). This scientific and technical consensus, however, does not seem to have influenced urban planning practice. The analysis of the UHI is today a fundamental element for the proper understanding of the primary factors of the contribution of cities to CC. The analysis of the structure of climate in Metropolitan Areas should enable the adoption of measures to mitigate the adverse effects of CC[J1].<p> </p>This paper proposes the construction of a set of explanatory models of the UHI of the Metropolitan Region of Barcelona (MRB) aimed at assisting planners in taking measures that serve, at the level of territorial and urban planning, to mitigate the effects of climate change. The general objective of the research is to study, using remote sensing techniques as well as "in situ" measurements, how urban design affects in the generation of the Urban Heat Island (UHI), as well as the urban microclimate in general. Specifically, this paper seeks to clarify whether the design of green areas can mitigate the UHI.<p> </p>The hypothesis is that morphology of public space plays a key role to control UHI. The research methodology consisted in: a) studying the urban and climatic parameters of selected areas; b) analyzing the spatial distribution of the LST using remote sensing technologies (Landsat 8); c) obtaining LST and LSAT through field work, during day and night time; and d) constructing a model of surface and air temperatures as a function of the different types of land cover, combining Remote Sensed data and in situ measurements, for each of the areas of analysis.
The gradual spread of urbanization, the phenomenon known under the term urban sprawl, has become one of the paradigms that have characterized the urban development since the second half of the twentieth century and early twenty-first century. The arrival of electrification to nearly every corner of the planet is certainly the first and more meaningful indicator of artificialization of land. In this sense, the paper proposes a new methodology designed to identify the highly impacted landscapes in China based on the analysis of the satellite image of nighttime lights.<p> </p>The night-lights have been used widespread in scientific contributions, from building human development indices, identifying megalopolis   or analyzing the phenomenon of urbanization and sprawl , but generally they have not been used to forecast the urbanization in the near future. This paper proposes to study the urbanization impact in China between 1992 and 2013, and models a hypothesis of future scenarios of urbanization (2013-2025). For this purpose, the paper uses DMSP-OLS Nighttime Lights (1992 – 2013). After obtaining a homogeneous series for the whole period 1992- 2013, we proceed to model the spatial dynamics of past urbanization process using the "urbanistic potential" of each of the 13.7 millions of analyzed cells. This model allows to design a probable growth of the urbanization phenomenon between 2013 and 2025 as well to predict a progressive displacement of the urbanization from east coast to mainland and west, in congruence with the current demographic models .
The separation between the countryside and the city, from rural and urban areas, has been one of the central themes of the literature on urban and territorial studies. The seminal work of Kingsley Davis  in the 1950s introduced a wide and fruitful debate which, however, has not yet concluded in a rigorous definition that allows for comparative studies at the national and subnational levels of a scientific nature. In particular, the United Nations (UN) definition of urban and rural population is overly linked to political and administrative factors that make it difficult to use data adequately to understand the human settlement structure of different countries. The present paper seeks to define a more rigorous methodology for the identification of rural and urban areas. For this purpose it uses the night lights supplied by the SNPP satellite, and more specifically by the VIIRS sensor for the determination of the urbanization gradient, and by means of the same construct a more realistic indicator than the statistics provided by the UN. The arrival of electrification to nearly every corner of the planet is certainly the first and most meaningful indicator of artificialization of land. In this sense, this paper proposes a new methodology designed to identify highly impacted (urbanized) landscapes worldwide based on the analysis of satellite imagery of night-time lights. The application of this methodology on a global scale identifies the land highly impacted by light, the urbanization process, and allows an index to be drawn up of Land Impacted by Light per capita (LILpc) as an indicator of the level of urbanization. The methodology used in this paper can be summarized in the following steps: a) a logistic regression between US Urban Areas (UA), as a dependent variable, and night-time light intensity, as an explanatory variable, allows us to establish a nightlight intensity level for the determination of Areas Highly Impacted by Light (AHIL); b) the delimitation of the centers and peripheries is made by setting a threshold of night-time light intensity that allows the inclusion of most of the centers and sub-centers; c) once identified urbanized areas, or AHIL, it is necessary to delimit the rural areas, or Areas Little Impacted by Light (ALIL), which are characterized by low intensity night light; d) finally, rurban landscapes are those with nightlight intensities between ALIL and AHIL. The developed methodology allows comparing the degree of urbanization of the different countries and regions, surpassing the dual approach that has traditionally been used. This paper enables us to identify the different typologies of urbanized areas (villages, cities and metropolitan areas), as well as “rural”, “rurban”, “periurban” and “central” landscapes. The study identifies 186,134 illuminated contours (urbanized areas). 404 of these contours have more than 1,000,000 inhabitants and can be considered real “metropolitan areas”; on the other hand there are 161,821 contours with less than 5,000 inhabitants, which we identified as “villages”. Finally, the paper shows that 40.26% live in rural areas, 15.53% in rurban spaces, 26.04% in suburban areas and only 18.16% in central areas.