A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas

Urbanization can be a challenge and an opportunity for earthquake risk mitigation. However, little is known about the changes in exposure (for example, population and urban land) to earthquakes in the context of global urbanization, and their impacts on fatalities in earthquake-prone areas. We present a global analysis of the changes in population size and urban land area in earthquake-prone areas from 1990 to 2015, and their impacts on earthquake-related fatalities. We found that more than two thirds of population growth (or 70% of total population in 2015) and nearly three quarters of earthquake-related deaths (or 307,918 deaths) in global earthquake-prone areas occurred in developing countries with an urbanization ratio (percentage of urban population to total population) between 20 and 60%. Holding other factors constant, population size was significantly and positively associated with earthquake fatalities, while the area of urban land was negatively related. The results suggest that fatalities increase for areas where the urbanization ratio is low, but after a ratio between 40 and 50% occurs, earthquake fatalities decline. This finding suggests that the resistance of building and infrastructure is greater in countries with higher urbanization ratios and highlights the need for further investigation. Our quantitative analysis is extended into the future using Shared Socioeconomic Pathways to reveal that by 2050, more than 50% of the population increase in global earthquake-prone areas will take place in a few developing countries (Pakistan, India, Afghanistan, and Bangladesh) that are particularly vulnerable to earthquakes. To reduce earthquake-induced fatalities, enhanced resilience of buildings and urban infrastructure generally in these few countries should be a priority

Rapid Population Growth throughout Asia’s Earthquake-Prone Areas: A Multiscale Analysis

Assessing the changes of the population living throughout the most seismically hazardous area (MSHA) constitutes an important foundation for seismic risk assessment. However, the changes of the population living in the MSHA of Asia, which exhibits the highest number of earthquake related fatalities, were poorly understood. Therefore, this study analyzed the changes of the population in the MSHA between 2000 and 2015 at the continental, subcontinental, and national scales. We found that the population, especially the vulnerable population (i.e., children under or equal to the age of 14 and elderly people over or equal to the age of 65), in Asia’s MSHA increased rapidly between 2000 and 2015. The population in the MSHA increased by 185.88 million with a growth rate of 20.93%, which was 3.38% greater than that in the non-MSHA region. Meanwhile, the vulnerable population in the MSHA increased by 63.65 million with a growth rate of 19.73%. The increase of the vulnerable population in the MSHA was 19.93% greater than that in the non-MSHA region. We also found that urban population growth was a major factor impacting the increase in both the population and the vulnerable population throughout Asia’s MSHA. Therefore, attention should be paid to the changes of the population in Asia’s MSHA, whilst it is imperative to execute strict building codes and select the development location more carefully in the MSHA

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales

Investigating the Patterns and Dynamics of Urban Green Space in China’s 70 Major Cities Using Satellite Remote Sensing

Urban green space (UGS) plays a pivotal role in improving urban ecosystem services and building a livable environment for urban dwellers. However, remotely sensed investigation of UGS at city scale is facing a challenge due to the pixels’ mosaics of buildings, squares, roads and green spaces in cities. Here we developed a new algorithm to unmix the fraction of UGS derived from Landsat TM/ETM/8 OLI using a big-data platform. The spatiotemporal patterns and dynamics of UGSs were examined for 70 major cities in China between 2000 and 2018. The results showed that the total area of UGS in these cities grew from 2780.66 km2 in 2000 to 6764.75 km2 in 2018, which more than doubled its area. As a result, the UGS area per inhabitant rose from 15.01 m2 in 2000 to 18.09 m2 in 2018. However, an uneven layout of UGS occurred among the coastal, western, northeastern and central zones. For example, the UGS percentage in newly expanded urban areas in the coastal zone rose significantly in 2000–2018, with an increase of 2.51%, compared to the decline in UGS in cities in the western zone. Therefore, the effective strategies we have developed should be adopted to show disparities and promote green infrastructure capacity building in those cities with less green space, especially in western China

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales

How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis.

Effective and timely quantification of the spatiotemporal pattern of urban expansion in China is important for the assessment of its environmental effects. However, the dynamics of the most recent urban expansions in China since 2012 have not yet been adequately explained due to a lack of current information. In this paper, our objective was to quantify spatiotemporal patterns of urban expansion in China between 1992 and 2015. First, we extracted information on urban expansion in China between 1992 and 2015 by integrating nighttime light data, vegetation index data, and land surface temperature data. Then we analyzed the spatiotemporal patterns of urban expansion at the national and regional scales, as well as at that of urban agglomerations. We found that China experienced a rapid and large-scale process of urban expansion between 1992 and 2015, with urban land increasing from 1.22 × 104 km2 to 7.29 × 104 km2, increasing in size nearly fivefold and with an average annual growth rate of 8.10%, almost 2.5 times as rapid as the global average. We also found that urban land in China expanded mainly by occupying 3.31 × 104 km2 of cropland, which comprised 54.67% of the total area of expanded urban land. Among the three modes of growth-infilling, edge expansion, and leapfrog-edge expansion was the main cause of cropland loss. Cropland loss resulting from edge expansion of urban land totalled 2.51 × 104 km2, accounting for over 75% of total cropland loss. We suggest that effective future management with respect to edge expansion of urban land is needed to protect cropland in China

Rapid Urban Land Expansion in Earthquake-Prone Areas of China

Abstract A timely understanding of urban expansion in earthquake-prone areas is crucial for earthquake risk assessment and urban planning for earthquake mitigation. However, a comprehensive evaluation of urban expansion in earthquake-prone areas is lacking in China, especially in the context of rapid urbanization. Based on time series urban land data and seismic ground-motion parameter zonation maps, this study analyzed urban expansion in the most seismically hazardous areas (MSHAs) of China from 1992 to 2015 on the national, regional, and city scales. The results show that urban land area in the MSHAs expanded by 6767 km2 from 1992 to 2015, with a gain of 350%. Specifically, the increase in urban land area of small cities in the MSHAs of western China during this period was the fastest, 6.24 times greater than that at the national level. In terms of spatial patterns, the urban land patches in the MSHAs in 2015 were more fragmented than those in 1992 on all scales. The percentage of change in the number of patches and the landscape shape index of the urban land patches of small cities in the MSHAs of western China were the highest across all cities. Therefore, we believe that special attention should be paid to the cities in the MSHAs that exhibit the most rapid increases in both urban land area and fragmentation, especially the small cities in western China. It is imperative to integrate earthquake mitigation into the urban planning of these cities

Examining the impacts of urbanization on surface radiation using Landsat imagery

Metropolitan Beijing is facing many environmental problems such as haze and urban heat island due to the rapid urbanization. Surface shortwave, longwave, and net radiations are key components of the surface-atmosphere radiation budget. Since megacities are affected by the thermal radiation of complex landscape structures and atmospheric environments, quantitative and spatially explicit retrieval from remotely sensed data remains a challenge. We collected the surface radiation fluxes from seven fixed sites representing different land-use types to calibrate the local parameters for remotely sensed retrieval of net radiation. We proposed a remote sensing–based surface radiation retrieval method by embedding the underlying land covers and integrating the observational data. The improved method is feasible to accurately retrieve surface radiation and delineate spatial characteristics in metropolitan areas. The accuracy evaluation indicated that the difference between remotely sensed and in situ observed net radiation ranged within 0~± 40 W· m−2. The root mean squared error of the estimated net surface radiation was 32.71 W· m−2. The strongly spatial heterogeneity of surface radiation components in metropolitan Beijing was closely related to land-cover patterns from urban area to outskirts. We also found that the surface net radiation had a decreasing trend from 1984 to 2014, and the net radiation in the urban area was lower than that in the outskirts. According to the surface radiation budgets, urbanization resulted in the cooling effect in net radiation flux in the daytime, which was stemmed from low atmospheric transmittances from massive aerosol concentration and high surface albedo from light building materials

The population in China’s earthquake-prone areas has increased by over 32 million along with rapid urbanization

Accurate assessments of the population exposed to seismic hazard are crucial in seismic risk mapping. Recent rapid urbanization in China has resulted in substantial changes in the size and structure of the population exposed to seismic hazard. Using the latest population census data and seismic maps, this work investigated spatiotemporal changes in the exposure of the population in the most seismically hazardous areas (MSHAs) in China from 1990 to 2010. In the context of rapid urbanization and massive rural-to-urban migration, nearly one-tenth of the Chinese population in 2010 lived in MSHAs. From 1990 to 2010, the MSHA population increased by 32.53 million at a significantly higher rate of change (33.6%) than the national average rate (17.7%). The elderly population in MSHAs increased by 81.4%, which is much higher than the group’s national growth rate of 58.9%. Greater attention should be paid to the demographic changes in earthquake-prone areas in China

Urban expansion in China between 1992 and 2015.

<p>Urban expansion in China between 1992 and 2015.</p