Nighttime light imagery of the earth are a useful way to study the urbanization process. Satellite nocturnal images have been used to identify metropolitan areas as well as urban growth. However, the study of the extent and internal structure of urban systems by nighttime lights has had a fundamental limitation to date: the low spatial resolution of satellite sensors. DMSP Operational Linescan System (OLS), with its 2.7 km/pixel footprint, and Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, with a spatial resolution of 742 m/pixel, still have considerable limitations for the in-depth study of the internal structure of urban systems. The launch of Luojia 1-01 in June 2018 has increased expectations. Its high-resolution nocturnal images (130 metres/pixel) allows a better in-depth study of the landscape impacted by the urbanization. Nevertheless, the areas resulting from urban sprawl process are characterized by weak night lighting, which makes identification extremely difficult. Breaking the rigid boundary that historically distinguished the urban from the rural, the topological inversion of the landscape produced by urban sprawl, makes difficult to identify the territories impacted by dispersed, fragmented and low density urbanization processes. The identification of the sprawled zones and their segmentation of the agricultural covers as well as the rest of the open spaces is especially complex, given the spatial resolution of Luojia 1-01. In this sense, the consideration of the NDVI, altitude, orientation, slope as well as the information provided by the thermal bands of Landsat8 can help to carry out a finer identification of the different urban landscapes, and specifically of the Urban Sprawl. The aim of this paper is to analyse the capacity of Luojia 1-01 to identify different types of urban landscapes, especially the results of the urban dispersion process known as Urban Sprawl. The case study is Barcelona Metropolitan Area (636 km² and 3.2 million inhabitants).
The study of urban heat islands (UHI) is of great importance in the context of climate change (CC) and global warming. Cities accumulate heat in urban land covers as well as in built infrastructures, representing true islands of heat in relation to their rural (less artificialized) environment. The densest urban spaces as well as the industrial and commercial areas are characterized by accumulating more heat during the day. On the other hand, the areas of lower density, the Urban Sprawl, tend to have a better climatic behaviour. The lower density as well as the greater amount of vegetation in the Urban Sprawl areas reduce the UHI during daylight hours. Nevertheless, the literature on urban climate has highlighted the singular importance of the nighttime UHI. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems regarding the availability of land surface and air temperature data for night time. This paper aims to develop a model for nocturnal UHI using data from Landsat thermal bands (with spatial resolution of 30 square meters per pixel) and contrasting Landsat's very limited nighttime images with daytime ones. The contrast allows the construction of “cooling” models of the LST based on geographical (longitude, latitude, distance to the sea, DTM, slope, orientation, etc.) and land covers characteristics (density, vegetation and building indexes, impervious surface, and others parameters). Said models will allow evaluating the nighttime LST of the different urban landscapes: historical centers, "ensanches", discontinuous urban fabrics (of different densities), scattered building areas and industrial and commercial areas, trying to clarify the nightly UHI of Urban Sprawl. The case study is the Metropolitan Area of Barcelona (636 km2, 3.3 million inhabitants).
Nighttime light imagery of the earth are a useful way to study the urbanization process. Satellite nocturnal images have been used to identify metropolitan areas as well as urban growth. However, the study of the extent and internal structure of urban systems by nighttime lights has had a fundamental limitation to date: the low spatial resolution of satellite sensors. DMSP Operational Linescan System (OLS), with its 2.7 km/pixel footprint, and Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, with a spatial resolution of 742 m/pixel, still have considerable limitations for the in-depth study of the internal structure of urban systems. The launch of Luojia 1-01 in June 2018 has increased expectations. Its high-resolution nocturnal images (130 metres/pixel) allows a better in-depth study of the landscape impacted by the urbanization. Nevertheless, the areas resulting from urban sprawl process are characterized by weak night lighting, which makes identification extremely difficult. Breaking the rigid boundary that historically distinguished the urban from the rural, the topological inversion of the landscape produced by urban sprawl, makes difficult to identify the territories impacted by dispersed, fragmented and low density urbanization processes.
The study of urban heat islands (UHI) is of great importance in the context of climate change (CC). The use of satellite images has helped considerably to understand UHI, especially in analyses of land surface temperature (LST). However, available sensors have a major limitation: their low spatial resolution (100 meters, Landsat; 1000 meters, MODIS) does not allow detailed analysis of UHI. Moreover, most remote sensors are limited to daytime data collection, while UHI mainly appear during the night. The images provided by satellites that study nocturnal thermal radiation, such as MODIS, have very low spatial resolution. There is abundant literature about the fusion of images from several satellites and sensors, especially information from MODIS and Landsat. However, most of these studies have concentrated on studying the combination of congruent images in the temporal plane, to extrapolate the results obtained to other temporal instances for which there is no detailed information. In general, few studies have focused on increasing the resolution of thermal images beyond the 100 meters/pixel of Landsat.
The objective of this paper is to combine information from various sensors (Modis, Landsat 8 and Sentinel 2) by constructing a set of OLS models of daytime and nighttime LST. These models provide a detailed view of daytime UHI (10 meters) and a robust evaluation of the range of cooling produced during the night. A modelling exercise at 1 meter/pixel of resolution has also been developed, using information from more detailed sensors installed on aircraft in the Barcelona Metropolitan Area.
Satellite nocturnal images of the earth are a useful way to identify urbanisation. Nighttime lights have been used in a range of scientific contributions, including studies on building human development indices and on the identification of megalopolises and impacted landscapes. However, the study of the area and internal structure of urban systems by nighttime light imagery has had a fundamental limitation to date: the low spatial resolution of satellite sensors. Although the DMSP Operational Linescan System (OLS) has been gathering global low-light imaging data for over 40 years, its 2.7 km/pixel footprint has limited its use for in-depth studies of urban development. The 2011 launch by NASA and the NOAA of the Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, has led to a significant improvement. This instrument has better spatial resolution (742 m/pixel), on-board calibration, a greater radiometric range, and fewer saturation and blooming problems than DMSPOLS data. However, it still has considerable limitations for the in-depth study of the area and internal structure of urban systems.
The launch of Luojia 1-01 in June 2018 has increased expectations. LJ1-01 is a nano satellite weighing 20 kg that can obtain high-resolution nocturnal images (130 metres/pixel). The aim of this paper is to analyse, and compare with previous satellites, the new instrument’s capacity to delimit the urbanised area and its efficiency in identifying types of urban landscape (compact, dispersed and urban). The case study is Barcelona Metropolitan Region (3,200 km2, 4.7 million inhabitants).
Literature widely recognize the strong influence of urban green areas in the microclimatic regulation and its potential to mitigate warming in cities. To promote viable actions to climate change adaptation for cities through vegetation and therefore help to palliate the urban heat island effect (UHI) and to reduce health risk during extreme heat episodes requires accurate criteria for each context in its different scales. This study presents a multi-scale approach to quantify the influence of urban green spaces at two different scales: global (Barcelona Metro Area) and detailed (studying the environments of seven specific parks) in the urban continuum of the cities of Gavà, Viladecans and Castelldefels. For this purpose, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 and Sentinel 2 data imagery are analyzed. The study confirms the significance of the NDVI to moderate the LST, as well as the intensity and extent of the cooling effect of the parks. In conclusion, the models and methods applied in this study suggest effective planning measures to moderate UHI.
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.
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 .
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].
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.
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 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.