Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%).

On the impact of Urbanisation on CO2 Emissions

We use a globally consistent, time-resolved data set of CO2 emission proxies to quantify urban CO2 emissions in 91 cities. We decompose emissiontrends into contributions from changes in urban extent, population density and per capita emissions. We find that urban CO2 emissions areincreasing everywhere but that the dominant contributors differ according to development level. A cluster analysis of factors shows that developingcountries were dominated by cities with rapid area and per capita CO2 emissions increases. Cities in the developed world, by contrast, show slow area and per capita CO2 emissions growth. China is an important intermediate case with rapid urban area growth combined with slower per capita CO2 emissions growth. For many developed countries, urban per capita emissions are often lower than their national average suggesting that urbanisation may reduce overall emissions. However trends in per capita urban emissions are higher than their national equivalent almost everywhere suggesting that urbanisation will become a more serious problem in future. An important exception is China whose per capita urban emissions are growing more slowly than the national value. We also see anegative correlation between trends in population density and per capita CO2 emissions, highlighting a strong role for densification as a tool toreduce CO2 emissions.

Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling

Long term, global records of urban extent can help evaluate environmental impacts of anthropogenic activities. Remotely sensed observations can provide insights into historical urban dynamics, but only during the satellite era. Here, we develop a 1 km resolution global dataset of annual urban dynamics between 1870 and 2100 using an urban cellular automata model trained on satellite observations of urban extent between 1992 and 2013. Hindcast (1870–1990) and projected (2020–2100) urban dynamics under the five Shared Socioeconomic Pathways (SSPs) were modeled. We find that global urban growth under SSP5, the fossil-fuelled development scenario, was largest with a greater than 40-fold increase in urban extent since 1870. The high resolution dataset captures grid level urban sprawl over 200 years, which can provide insights into the urbanization life cycle of cities and help assess long-term environmental impacts of urbanization and human–environment interactions at a global scale.

An urban extent dataset in late imperial China in 15th–19th centuries

Long-term urban extent data are highly desirable for understanding urban land use patterns and achieving sustainable development goals. However, urban observation data based on remote sensing are typically confined to recent decades. In this study, we advance in this arena by reconstructing the urban extents for China that extend back from 15th century to 19th century based on multiple historical documents. Cities in late imperial China (the Ming and the Qing Dynasties, 1368–1911) generally had city walls, and these walls were usually built around the urban built-up area. By restoring the scope of the city walls, the urban extend in this period could be restored. Firstly, we collected the years of construction or reconstruction of city walls from the historical data. Specifically, the period in which the scope of the city wall keeps unchanged is recorded as a lifetime of it. Secondly, specialization of the scope of the city wall could be conducted based on the urban morphology method, and variety of documentation, including the historical literature materials, the military topographic maps of the first half of the 20th century, and the remote sensing images of the 1970s. Correlation and integration of the lifetime and the spatial data would produce China City Wall Areas Dataset (CCWAD) in late imperial. Based on the proximity to the time of most of the city walls, we generated China Urban Extent Dataset (CUED) in the 15th–19th centuries in six representative years (i.e., 1400, 1537, 1648, 1708, 1787, and 1866). These datasets are available at https://doi.org/10.6084/m9.figshare.14112968.v1

Scaling the Potential of Compact City Development: The Case of Lahore, Pakistan

With increasing urban populations, high vehicle miles have made the concept of a compact city imperative. A compact city is characterized by high-density development and mixed land use with no urban sprawl. City managers are trying hard to make their cities compact and livable. The potential conformance to a compact city development requires scaling before any significant intervention. Several studies have been conducted on the different aspects of the compact city in the developed world, but there is limited understanding in the South Asian context. This study aimed to fill this research gap and proposes a theoretical matrix to gauge the potential compactness of Lahore, Pakistan. It comprises some key attributes, such as landscape ecology, measurement of density, density distribution, transportation network, accessibility, dispersion index, and mixed-use land consumption, which were analyzed in this research. The data were analyzed using Geographical Information System (GIS) and ERDAS IMAGINE software to make a scaling matrix. The research findings show that Lahore is a semi-compact city, with high potential to become a true compact city. The paper recommends that the urban extent should not be extended until targeted colonization is achieved, and the spatial growth of the city should be managed by encouraging infilled development, high-density living, and public transport provision. This research will help policymakers, urban planners, and transport planners devising policies for compact city development.

Urban form and productivity: What shapes are Latin-American cities?

This paper examines the linkages between urban form and city productivity using seven alternative metrics for urban form and applying them to a comprehensive sample of Latin-American cities. While most of the literature has concentrated on the effects of population density (compact vs. sprawling urban development), this paper seeks to assess whether different dimensions of a city’s urban form, such as shape, structure, and land use, affect its economic performance. We found that both the shape of the urban extent and the inner-city connectedness have a statistically significant association with the productivity level of a city.

An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data

An accelerating trend of global urbanization accompanying various environmental and urban issues makes frequently urban mapping. Nighttime light data (NTL) has shown great advantages in urban mapping at regional and global scales over long time series because of its appropriate spatial and temporal resolution, free access, and global coverage. However, the existing urban extent extraction methods based on nighttime light data rely on auxiliary data and training samples, which require labor and time for data preparation, leading to the difficulty to extract urban extent at a large scale. This study seeks to develop an unsupervised method to extract urban extent from nighttime light data rapidly and accurately without ancillary data. The clustering algorithm is applied to segment urban areas from the background and multi-scale spatial context constraints are utilized to reduce errors arising from the low brightness areas and increase detail information in urban edge district. Firstly, the urban edge district is detected using spatial context constrained clustering, and the NTL image is divided into urban interior district, urban edge district and non-urban interior district. Secondly, the urban edge pixels are classified by an adaptive direction filtering clustering. Finally, the full urban extent is obtained by merging the urban inner pixels and the urban pixels in urban edge district. The proposed method was validated using the urban extents of 25 Chinese cities, obtained by Landsat8 images and compared with two common methods, the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM). The Kappa coefficient ranged from 0.687 to 0.829 with an average of 0.7686 (1.80% higher than LOT and 4.88% higher than INNL-SVM). The results in this study show that the proposed method is a reliable and efficient method for extracting urban extent with high accuracy and simple operation. These imply the significant potential for urban mapping and urban expansion research at regional and global scales automatically and accurately.

Analyzing Temporal Changes in an Urbanized Area Using Densely Staked Image Classification and Multinomial Logistic Regression (MLR) Technique

Monitoring transformation of non-built-up area to urban spread via densely-stacked Land-Use-Land-Cover (LULC) classification offers a catalogue of spatio-temporal statistics to evaluate discrepancies instigated by transition factors. Impacts of major transition apparatuses in an area persuading the haphazard urbanization pattern are evaluated for Vellore acts a major contribution to Smart city project. Implications of causative factors: i) Population density; ii) proximity from rail-road-network; and iii) commercial areas are scrutinized with respect to urbanization upsurge. Multi-variate correlation is established using trend analysis and Multinomial Regression (MLR) technique for individual and homogeneous amalgamation of the aforementioned factors. Resulting equations obtained is formally used to detect closeness of urban extent from several landscapes. Research outcomes exhibited that the built-up straggling occurs from 30 to 232 m along the landscapes with a maximum of 336 m. Illustration of this study can also be assessed for various social and economic causative factors against urbanization for other smart cities.