Evaluating the Dynamic Changes of Urban Land and Its Fractional Covers in Africa from 2000–2020 Using Time Series of Remotely Sensed Images on the Big Data Platform

Dramatic urban land expansion and its internal sub-fraction change during 2000–2020 have taken place in Africa; however, the investigation of their spatial heterogeneity and dynamic change monitoring at the continental scale are rarely reported. Taking the whole of Africa as a study area, the synergic approach of normalized settlement density index and random forest was applied to assess urban land and its sub-land fractions (i.e., impervious surface area and vegetation space) in Africa, through time series of remotely sensed images on a cloud computing platform. The generated 30-m resolution urban land/sub-land products displayed good accuracy, with comprehensive accuracy of over 90%. During 2000–2020, the evaluated urban land throughout Africa increased from 1.93 × 104 km2 to 4.18 × 104 km2, with a total expansion rate of 116.49%, and the expanded urban area of the top six countries accounted for more than half of the total increments, meaning that the urban expansion was concentrated in several major countries. A turning green Africa was observed, with a continuously increasing ratio of vegetation space to built-up area and a faster increment of vegetation space than impervious surface area (i.e., 134.43% vs., 108.88%) within urban regions. A better living environment was also found in different urbanized regions, as the newly expanded urban area was characterized by lower impervious surface area fraction and higher vegetation fraction compared with the original urban area. Similarly, the humid/semi-humid regions also displayed a better living environment than arid/semi-arid regions. The relationship between socioeconomic development factors (i.e., gross domestic product and urban population) and impervious surface area was investigated and both passed the significance test (p < 0.05), with a higher fit value in the former than the latter. Overall, urban land and its fractional land cover change in Africa during 2000–2020 promoted the well-being of human settlements, indicating the positive effect on environments.

Assessing the quantitative relationships between the impervious surface area and surface heat island effect during urban expansion

As an important component of underlying urban surfaces, the distribution pattern and density of the impervious surface area (ISA) play an important role in the generation of surface urban heat island (SUHI) effects. However, the quantitative and localized exploration of the ISA’s influence on SUHIs in the process of urban expansion from the perspective of temporal and spatial changes is still not clear. Based on multisource remote sensing data, the SUHI effect of urban expansion is revealed by using geospatial analysis methods such as profile, difference and regression analysis. The results show the following: (1) urban expansion plays a significant role in aggravating SUHIs. Overall, the ISA and land surface temperature (LST) have obvious consistency in terms of spatial distribution patterns. However, local spatial differentiation is significant. The areas with the highest LST were not concentrated in the downtown area with the highest ISA but were scattered in the cultivated land and exposed surface areas under development in the northern part of the city. (2) In general, the ISA can explain the spatial distribution of LST well, there is an obvious positive correlation between them, and the quadratic polynomial function is the best fitting model between them. (3) The density and spatial allocation of ecological elements, such as green space and water bodies, play an important role in alleviating SUHIs. This study found that the urban center with the highest ISA coverage rate has no significant SUHI due to the reasonable allocation of green space and water bodies. The research results can provide a scientific basis for future urban planning and ecological environment construction.

Stormwater Utility Fee Estimation Method for Individual Land Use Areas

In South Korea, a reasonable rate system that can be domestically applied to calculate the sewage and stormwater separately from the domestic sewage fee system is needed. This study proposes a phased pricing scenario to separate sewage and stormwater in Bupyeong-gu, Incheon, and the rate changes are compared based on a simulated calculation of the stormwater utility fee. In this investigation, stormwater runoff cases from other countries and the current domestic system are analyzed. A stormwater utility fee introduction scenario is presented that considers the impervious surface area. Water and sewage usage statistics and hydrant spatial data were collected from the Incheon Metropolitan City Waterworks Authority, and the total amount of water and sewage fees from the land use area were calculated. The stormwater utility fee was calculated, and the rates of each step were compared. The total sewage fee of Bupyeong-gu during 2014 was 21,685,446,578 won and the phased stormwater utility fee was calculated, assuming that 40% represents the stormwater cost. The sewage fee for the residential area in phase 3 decreased by 0.77% compared to phase 1. For the commercial areas, the sewage fee decreased by 36.87%. Because the impervious surface ratio was high, the overall area contributing to the impervious surface area was small. In the industrial area, the sewage fee increased by 8.35%. In the green area, the sewage fee increased by 37.46%. The sewage fee for the apartment complexes decreased by 10.6%. Finally, the possibility of estimating the actual stormwater utility fee is confirmed.

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

A Soil and Impervious Surface Adjusted Index for Urban Impervious Surface Area Mapping

Index-based methods are widely applied to urban impervious surface area (ISA) mapping, but the confusion between ISA and soil remains unsolved. In this article, the near-infrared (NIR)-blue bands were selected as feature space by analyzing the spectra from the US Geological Survey spectral library, and a simple impervious surface ratio index (ISRI) was developed by shifting the NIR-blue coordinate origin toward the convergence point of the fitting lines of ISA and soil. The ISRI was then validated for threshold simulation, separability, and correlation analysis. Results demonstrated that ISRI had a good performance for ISA mapping in four cities in China with different geographic environments, with all extraction accuracies all above 90%. ISRI had a high separability between ISA and soil and was better than other indices (normalized difference built-up index and biophysical composition index). Further, ISRI has a close relationship with the ISA proportion. Therefore, ISRI would be a simple and reliable index for urban ISA mapping.

Urbanization impacts on flood risks based on urban growth data and coupled flood models

Urbanization increases regional impervious surface area, which generally reduces hydrologic response time and therefore increases flood risk. The objective of this work is to investigate the sensitivities of urban flooding to urban land growth through simulation of flood flows under different urbanization conditions and during different flooding stages. A sub-watershed in Toronto, Canada, with urban land conversion was selected as a test site for this study. In order to investigate the effects of urbanization on changes in urban flood risk, land use maps from six different years (1966, 1971, 1976, 1981, 1986, and 2000) and of six simulated land use scenarios (0%, 20%, 40%, 60, 80%, and 100% impervious surface area percentages) were input into coupled hydrologic and hydraulic models. The results show that urbanization creates higher surface runoff and river discharge rates and shortened times to achieve the peak runoff and discharge. Areas influenced by flash flood and floodplain increases due to urbanization are related not only to overall impervious surface area percentage but also to the spatial distribution of impervious surface coverage. With similar average impervious surface area percentage, land use with spatial variation may aggravate flash flood conditions more intensely compared to spatially uniform land use distribution.