City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road

The configuration of urban land-covers is essential for improving dwellers' environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000-2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km(2) to 29,257.51 km(2), with an average growth rate of 370.75 km(2)/y during 2000-2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km(2)/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km(2)/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (oShannon's Diversity Index (SHDI) = -0.063; oPatch Density (PD) = 0.054) and presented better connectivity (oConnectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas

The state-of-the-art of urban climate change modeling and observations

As an effect of climate change, cities need detailed information on urban climates at decision scale that cannot be easily delivered using current observation networks, nor global and even regional climate models. A review is presented of the recent literature and recommendations are formulated for future work. In most cities, historical observational records are too short, discontinuous, or of too poor quality to support trend analysis and climate change attribution. For climate modeling, on the other hand, specific dynamical and thermal parameterization dedicated to the exchange of water and energy between the atmosphere and the urban surfaces have to be implemented. Therefore, to fully understand how cities are impacted by climate change, it is important to have (1) simulations of the urban climate at fine spatial scales (including coastal hazards for coastal cities) integrating global climate scenarios with urban expansion and population growth scenarios and their associated uncertainty estimates, (2) urban climate observations, especially in Global South cities, and (3) spatial data of high resolution on urban structure and form, human behavior, and energy consumption

Induction of protective and therapeutic anti-pancreatic cancer immunity using a reconstructed MUC1 DNA vaccine

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is a common, highly lethal disease with a rising incidence. MUC1 is a tumor-associated antigen that is over-expressed in pancreatic adenocarcinoma. Active immunotherapy that targets MUC1 could have great treatment value. Here we investigated the preventive and therapeutic effect of a MUC1 DNA vaccine on the pancreatic cancer.</p> <p>Methods</p> <p>MUC1-various tandem repeat units(VNTR) DNA vaccine was produced by cloning one repeat of VNTR and inserting the cloned gene into the pcDNA3.1. In the preventive group, female C57BL/6 mice were immunized with the vaccine, pcDNA3.1 or PBS; and challenged with panc02-MUC1 or panc02 cell. In the therapeutic group the mice were challenged with panc02-MUC1 or panc02 cell, and then immunized with the vaccine, pcDNA3.1 or PBS. The tumor size and the survival time of the animals were compared between these groups.</p> <p>Results</p> <p>The DNA vaccine pcDNA3.1-VNTR could raise cytotoxic T lymphocyte (CTL) activity specific for MUC1. In the preventive experiment, the mice survival time was significantly longer in the vaccine group than in the control groups (<it>P </it>< 0.05). In the therapeutic experiment, the DNA vaccine prolonged the survival time of the panc02-MUC1-bearing mice (<it>P </it>< 0.05). In both the preventive and therapeutic experiments, the tumor size was significantly less in the vaccine group than in the control groups (<it>P </it>< 0.05). This pcDNA3.1-VNTR vaccine, however, could not prevent the mice attacked by panc02 cells and had no therapeutic effect on the mice attacked by panc02 cells.</p> <p>Conclusion</p> <p>The MUC1 DNA vaccine pcDNA3.1-VNTR could induce a significant MUC1-specific CTL response; and had both prophylactic and therapeutic effect on panc02-MUC1 tumors. This vaccine might be used as a new adjuvant strategy against pancreatic cancer.</p

Identifying Urban Flood Regulation Priority Areas in Beijing Based on an Ecosystem Services Approach

Climate change and rapid urbanization have severe impacts on urban flood regulation ecosystem services (UFRES). Quantifying the UFRES has attracted increasing attention for urban sustainable development. However, few studies have focused on how to identify urban flood regulation priority areas. In this study, we simulated urban surface runoff by using the soil conservation services-curve number model, and quantified UFRES supply and demand by using relative indicators (i.e., runoff reduction ratio and urban vulnerability) at the subdistrict scale in Beijing, China. Then, an urban flood regulation priority index was developed by integrating UFRES demand and supply, and further used to identify priority areas. The results show that the mean runoff reduction ratio in Beijing decreased from 38.70% (for a 1-year rainfall return period) to 24.74% (for a 100-year rainfall return period). Subdistricts with low UFRES supply were mainly located in the urban central area and the southeastern zone, while subdistricts with high UFRES demand were mainly located in the urban central region. Meanwhile, places with high priority for flood regulation were mainly located in the inner city, and low priority areas were mainly located in northwestern, southwestern, and northeastern Beijing. Our results also imply that the urban flood regulation priority index is an effective indicator to identify urban flood regulation priority areas. These findings could provide urban planners with a comprehensive understanding of UFRES and scientific guidance to improve them

Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis

Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis as a case study to analyze spatial patterns and dynamic changes based on time-series urban impervious surface area (ISA) datasets. ISA was developed from Landsat imagery between 1991 and 2014 using a hybrid approach consisting of linear spectral mixture analysis, decision tree classifiers, and post-processing. The spatial patterns of ISA distribution and its dynamic changes in eight directions—east, southeast, south, southwest, west, northwest, north, and northeast—at the temporal scale were analyzed with a buffer zone-based approach. This research indicated that ISA can be extracted from Landsat imagery with both producer and user accuracies of over 90%. ISA in Hangzhou metropolis increased from 146 km2 in 1991 to 868 km2 in 2014. Annual ISA growth rates were between 15.6 km2 and 48.8 km2 with the lowest growth rate in 1994–2000 and the highest growth rate in 2005–2010. Urban ISA increase before 2000 was mainly due to infilling within the urban landscape, and, after 2005, due to urban expansion in the urban-rural interfaces. Urban expansion in this study area has different characteristics in various directions that are influenced by topographic factors and urban development policies

Comparison of Changes in Urban Land Use/Cover and Efficiency of Megaregions in China from 1980 to 2015

Urban land use/cover and efficiency are important indicators of the degree of urbanization. However, research about comparing their changes at the megaregion level is relatively rare. In this study, we depicted the differences and inequalities of urban land and efficiency among megaregions in China using China&rsquo;s Land Use/cover Dataset (CLUD) and China&rsquo;s Urban Land Use/cover Dataset (CLUD-Urban). Furthermore, we analyzed regional inequality using the Theil index. The results indicated that the Guangdong-Hong Kong-Macao Great Bay Area had the highest proportion of urban land (8.03%), while the Chengdu-Chongqing Megaregion had the highest proportion of developed land (64.70%). The proportion of urban impervious surface area was highest in the Guangdong-Hong Kong-Macao Great Bay Area (75.16%) and lowest in the Chengdu-Chongqing Megaregion (67.19%). Furthermore, the highest urban expansion occurred in the Yangtze River Delta (260.52 km2/a), and the fastest period was 2000&ndash;2010 (298.19 km2/a). The decreasing Theil index values for the urban population and economic density were 0.305 and 1.748, respectively, in 1980&ndash;2015. This study depicted the development trajectory of different megaregions, and will expect to provide a valuable insight and new knowledge on reasonable urban growth modes and sustainable goals in urban planning and management

Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City

Lack of research into the complexity in urban land conversion, and paucity of observational data of soil organic carbon (SOC) beneath impervious surface area (ISA) limit our understanding of the urbanization effects on carbon (C) pools in dryland cities. Employing Landsat TM images acquired in 1990 and 2010, a hybrid classification method consisting of Linear Spectral Mixture Analysis and decision tree classification was applied to retrieve the land cover (water, ISA, greenspace, cropland, and remnant desert) of the largest dryland city in China—Urumqi. Based on vegetation carbon (VEGC) and SOC density data determined through field observations and literature reviews, we developed Urumqi’s C pool maps in 1990 and 2010, and assessed the urbanization impacts on ecosystem C. Our results showed that ISA tripled from 1990 to 2010 displacing remnant desert and cropland. The urban landscape, especially the greenspaces, became obviously fragmented. In 2010, more than 95% of the urban ecosystem C was SOC, 48% of which under the ISA. The city lost 19% of C stock from 1990 to 2010. About 82% of the ecosystem C loss was caused by the conversion of remnant desert and cropland into ISA, mainly in the northern city

Mapping impervious surface distribution in China using multi-source remotely sensed data

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling