Spatio-temporal association of light pollution and urban sprawl using remote sensing imagery and GIS: A simple method based in Otsu's algorithm
Introduction
The effects urban expansion has on the environmental, economic and even life expectancy has increased the research in the built environment [70,53,27]. In the US, there has been a debate about urban expansion since the 1970s, which has increased in intensity in a directly proportional relationship with the disorderly growth experienced by the cities of that country [26]. Urban expansion and the growth of cities are a key factor that triggers economic and social processes in the territory. There are great scientific efforts to study the causes and consequences of such expansion, and to expect and prioritize the challenges that cities will face. It is important to provide to the scientific community with tools to monitor urban areas so urban managers could make better decisions. Several urban studies show a lack of reliable measures that integrate different spatial dimensions into a single metric ([22]; Besussi et al. 2003; [31,32]). Fortunately, it has been a diversification in the technology to identify and measure the expansion of urban land [18,64,33].
One of the most used techniques in the last two decades to observe changes in urban spaces is remote sensing [17,56]. We relate this technique to all those activities of observation and perception (detection) of objects or events in distant (remote) places. In remote sensing, the sensors are not in direct contact with the objects or events observed. Such sensors may be terrestrial or orbit around the earth on board satellites. The latter are the most used to determine different geographical and ecological particularities of the territories, such as the use of land, vegetation and the presence and expansion of cities [1,58,65]. The data got by such observations can be analyzed by geographic information software (GIS) [61,28]. Among the large number of features and information from satellite images, we can mention artificial light emitted by cities and built environment resulting from nighttime measurements.
Light pollution is a present phenomenon with greater intensity in urban areas (Schernhammer 2004; [57,19,44,39]), which increases when artificial lighting from different sources interacts with particles suspended in the atmosphere through radiative transfer. ([24,38]; Aube [4,42,43,13,49,45,35,5]). We can define this phenomenon as the intensification of artificial light in night environments produced by urbanization. In this sense, different authors mention light pollution as a phenomenon linked to urban expansion and population growth [46,7,71,15,54]. The amount of light pollution emitted from any city depends, to a large extent, on the actions of the human population. Among the activities that are most related to the phenomenon of light pollution are economic, cultural, consumer habits, urban structure and territorial expansion (the generation of anthropogenic elements). It is important then to find the relationship between urban growth and light pollution.
The area of influence of artificial night light has exploded in recent decades, along with the growth of the built environment that cities have experienced mainly in the Northern Hemisphere of planet Earth. [12,47,23,48,59,19,50,63]. Night images captured from different satellites represent a valuable source of information for all researchers interested in the study of urban phenomena. There is a great advantage in using remote night perception data [76,11], because local measures require too much time to cover an important area. In addition, with a correct method of pixel subtraction, it is helpful to distinguish image pixels that belong to anthropogenic elements and discard those that do not represent cities [66,25,75,74].
Through this work, we show a simple method to simulate the expansion of the built environment in Mexico in recent years. This method is intended to support urban planners who seek to make a simple analysis of the built environment (any artificial environment that provides a scenario for human activity) based on georeferenced data. Hence, one novelty of this research is that we can characterize this built environment: including buildings, transport systems, communication routes, and all those anthropogenic elements within an urban area with few computational resources. We determined the amount of land area of the country covered by anthropogenic elements between the years 1992–2013. We should note that, despite determining an expansion for a specific territory, we can replicate the model to any country if the data is available. The method can provide insight into future pressures in urbanization processes, and it can be useful in urban planning to avoid light pollution. Therefore, we spatially quantified trends.
Section snippets
Methodology
We obtained the data from satellite images composed of night lights available from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce, (NOAA). The composite images used for the emissions analysis are part of a worldwide nighttime light collection from six satellites from 1992 to 2013, downloaded from the spatio-temporal series of DMSP/OLS nighttime light data, Earth Observation Group(EOG, [16]), http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html. The product
Results and discussions
We present the results arranged at the national level, to check the method used and to analyze the extent of the built environment between the years of study (1992–2013). We transformed the results of the 22 years analyzed into vector layers and re-projected to the MEXICO ITRF2008 CCL coordinate system with which we calculated the approximate coverage area of the two resulting classes.
As expected, the correlation between the two variables (unconstructed pixels versus urbanized pixels) was
Conclusions
The method presented addresses the representation and spatial distribution of elements that are not only urban if not of an artificial (anthropogenic) environment. The aim was to manage the information of nocturnal images got via remote sensing through statistical techniques and image processing and to observe the changes in the built environment. Also, one purpose was to figure how these elements cover the surface of the country. We discovered increases and decreases in the growth rate of the
Author statement
All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript.
Declaration of Competing Interest
The Authors declare that there is no conflict of interest.
Acknowledgement
This work was supported by the Slovak Research and Development Agency under Project No. APVV-18–0014.
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