Abstract: This article investigates the legacy of ancient Phoenician, Greek and Etruscan colonialism in shaping the economic geography of the Mediterranean region. Utilising historical data on ancient colonies and current data on population density and night light emissions (as a proxy for economic activity), we find that geographical areas colonised by these ancient civilisations have higher population density and economic activity in the present day. We also find that ancient colonialism affected the origin and evolution of the urban system of cities and settlements prevalent in the Mediterranean region.
Keywords: Remote Sensing
Abstract: The scale and extent of global urbanization are unprecedented and increasing. As urbanization generally encroaches on natural habitats and the urban ecological footprint reaches far beyond the city limits, how urbanization affects biodiversity has received increasing attention from the scientific community. Nonetheless, the comprehensive syntheses of urbanization consequences for biodiversity, including diverse taxonomic groups, across multiple spatial scales and spanning a wide gradient range of urbanization intensity are still insufficient. Here, based on the urban-rural gradient approach, we assessed the effects of urbanization on Chinese mammal and amphibian richness across the entire urbanization gradient (i.e., urbanization level from 0 to 1) at the national, regional and urban agglomeration scales. We used the global mammal and amphibian distribution data along with corresponding background climate, habitat conditions and socioeconomic activities data for analysis. Our results revealed a detailed and diverse pattern of Chinese mammal and amphibian richness along the entire spectrum of urbanization gradient across three spatial scales. And an approximately monotonic decrease only existed in certain urban agglomerations. The imprint of urbanization on mammal and amphibian richness were largely masked by the overall primacy of background climate at the national and regional scales. As the scale of analysis shifting from the country to urban agglomerations, urbanization-associated variables and locally specific limiting factors started to play important roles in driving the richness patterns. Moreover, the environmental Kuznets curve hypothesis can explain the relationship between biodiversity pressure and urbanization activities in certain Chinese urban agglomerations. However, the findings of urbanization effects on biodiversity using the urban-rural gradient analysis should be interpreted with caution because many possible driving forces simultaneously present along the urban-rural gradient and are very challenging to attribute.
Keywords: Animals; Remote Sensing
Abstract: The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.
Keywords: Remote Sensing; COVID-19
Abstract: ’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g., NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from 14 March until 31 May 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.
Keywords: Remote Sensing; COVID-19; skyglow
Abstract: Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas).
Keywords: Remote Sensing