Urban Impervious Surface Area Research Articles

Development of Soil-Suppressed Impervious Surface Area Index for Automatic Urban Mapping

Expanding urban impervious surface area (ISA) mapping is crucial to sustainable development, urban planning, and environmental studies. Multispectral ISA mapping is challenging because of the mixed-pixel problems with bare soil. This study presents a novel approach using spectral and temporal information to develop a Soil-Suppressed Impervious Surface Area Index (SISAI) using the Landsat Operational Land Imager (OLI) data set, which reduces the soil but enhances the ISA signature. This study mapped the top 12 populated megacities using SISAI and achieved an over-all accuracy of 0.87 with an F1-score of 0.85. It also achieved a higher Spatial Dissimilarity Index between the ISA and bare soil. However, it is limited by bare gray soil and shadows of clouds and hills. SISAI encourages urban dynamics and inter-urban compari- son studies owing to its automatic and unsupervised methodology.

Expansion of Urban Impervious Surfaces in Lahore (1993–2022) Based on Gee and Remote Sensing Data

Impervious surfaces are an essential component of our environment and are mainly triggered by human developments. Rapid urbanization and population expansion have increased Lahore's urban impervious surface area. This research is based on estimating the urban imper- vious surface area ( uisa ) growth from 1993 to 2022. Therefore, we aimed to generate an accurate urban impervious surfaces area map based on Landsat time series data on Google Earth Engine ( gee ). We have used a novel global impervious surface area index ( gisai ) for impervious surface area ( uisa ) extraction. The gisai accomplished significant results, with an average overall accuracy of 90.93% and an average kappa coefficient of 0.78. We also compared the results of gisai with Global Human Settlement Layer-Built and harmonized nighttime light ( ntl ) isa data products. The accuracy assessment and cross-validation of uisa results were performed using ground truth data on ArcGIS and gee. Our research findings revealed that the spatial extent of uisa increased by 198.69 km2 from 1993 to 2022 in Lahore. Additionally, the uisa has increased at an average growth rate of 39.74 km2. The gisai index was highly accurate at extract- ing uisa and can be used for other cities to map impervious surface area growth. This research can help urban planners and policymak- ers to delineate urban development boundaries. Also, there should be controlled urban expansion policies for sustainable metropolis and should use less impermeable materials for future city developments.

Evaluating trends, profits, and risks of global cities in recent urban expansion for advancing sustainable development

In recent decades, the world has witnessed a remarkable surge in urban expansion, leading to profound consequences for the global climate, biodiversity, and ecological environment. Despite the existence of numerous case studies, assessing the precise contributions of individual cities to this global trend has proven challenging, primarily due to the daunting task of collecting census data for thousands of cities. The study conducted a comprehensive analysis of the recent urban expansion in more than 6000 cities, utilizing state-of-the-art long-term datasets of urban impervious surface area (ISA), population, gross domestic product (GDP) data, and a global city boundary. By tracking the changes in ISA, population, and GDP, we assessed the magnitude and direction (increasing or decreasing) of these variables over time at both the global level and across different regions and countries. Through correlation analysis, we established statistical relationships between urban expansion and GDP growth as well as population change. Furthermore, we examined the profits of these cities and evaluated the projected flood risks they faced. Through the study, we conducted a comprehensive analysis and revealed detailed characteristics, patterns, and disparities in city development, economic output, population density, as well as the profits and projected flood risks across cities, countries, and regions. This in-depth understanding of recent urban expansion provides valuable insights for decision-makers and stakeholders to actively promote sustainable urban development and effectively manage associated risks in diverse geographical contexts.

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.

A New Technique for Impervious Surface Mapping and Its Spatio-Temporal Changes from Landsat and Sentinel-2 Images

Accurately mapping and monitoring the urban impervious surface area (ISA) is crucial for understanding the impact of urbanization on heat islands and sustainable development. However, less is known about ISA spectra heterogeneity and their similarity to bare land, wetland, and high-rise-building shadows. This study proposes a feature-based approach using decision tree classification (FDTC) to map ISAs and their spatio-temporal changes in a coastal city in southeast China using Landsat 5 TM, Landsat 8 OLI/TIRS, and Sentinel-2 images from 2009 to 2021. Atmospheric correction using simplified dark object subtraction (DOS) was applied to Landsat imagery, which enabled faster computation. FDTC’s performance was evaluated with three sensors with different spectral and spatial resolutions, with parameter thresholds held constant across remote-sensing images. FDTC produces a high average overall accuracy (OA) of 94.53%, a kappa coefficient (KC) of 0.855, and a map-level image classification efficacy (MICE) of 0.851 for ISA mapping over the studied period. In comparison with other indices such as BCI (biophysical composition index), PISI (automated built-up extraction index), and ABEI (perpendicular impervious surface index), the FDTC demonstrated higher accuracy and separability for extracting ISA and bare land as well as wetland and high-rise buildings. The results of FDTC were also consistent with those of two open-source ISA products and other remote sensing indices. The study found that the ISA in Xiamen City increased from 16.33% to 26.17% over the past 13 years due to vegetation occupation, encroachment onto bare land, and reclamation of coastal areas. While the expansion significantly reduced urban vegetation in rapidly urbanizing areas of Xiamen, ambitious park greening programs and massive redevelopment of urban villages resulted in a modest but continuous increase in urban green space.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries.

Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90.

Evaluation of urban expansion and the impacts on vegetation in Chinese Loess Plateau: a multi-scale study.

Vegetation degradation caused by rapid urban expansion is a pressing global challenge. Focusing on the Chinese Loess Plateau (CLP), we use satellite observations from 2000 to 2017 to evaluate the spatiotemporal pattern of urban expansion, and its imprint on vegetation across old urban, new urban, urban, non-urban areas as well as the entire urbanization intensity (UI) gradient (from 0 to 100%). We found a massive increase of urban impervious surface area (UISA) in the CLP from 2000 to 2017, and an uneven expansion of UISA at different urban agglomerations and cities. Less green were found in urban and new urban areas, while old urban and non-urban areas generally showed an improved greening pattern. In addition, the annual maximum EVI (EVImax) differences between urban and non-urban areas were - 0.0995 on average from 2000 to 2017. The Guanzhong Plain urban agglomeration (GPUA) witnessed the most significant EVImax differences (- 0.120), and the Ningxia Yanhuang urban agglomeration (NYUA) witnessed the lowest EVImax differences (- 0.012). The EVImax showed significantly decreased trends along the entire spectrum of urbanization gradient for 97.4% (38 of 39) cities and five urban agglomerations. The most significant decrease was found in the GUPA (slope = - 0.0197/10a, p < 0.01), while the smallest drop was found in the NYUA (slope = - 0.011/10a, p < 0.01). This study offered a fundamental support for understanding the vegetation variation along the urban-rural gradient, which may help stakeholders to make better ecological management policies for urban vegetation in ecologically fragile areas.

TEMPORAL AND SPATIAL ANALYSIS OF CLASSIFICATION TREE FOR IMPERVIOUS SURFACE MAPPING FROM SENTINEL-2 MSI DATA

Abstract. For studies of urban development, it is an important method for obtaining the distribution of impervious surface (IS) areas and their dynamic change from remote sensing data. The dilemma of the same spectrum for different features and different spectrums for the same features, posed by the complexity of the IS objects, is the fundamental obstacle encountered in the extraction of urban IS areas. In this study, an automatic extraction method for urban IS areas is proposed and analyzed, based on classification and regression tree (CART) and ensemble learning strategies. The Sentinel-2 MSI data of 30 cities in China from 2018 to 2020 were selected for IS extraction experiments. We perform temporal and spatial modeling of the splitting threshold offset in the classification model to explore the effect of time and space on IS extraction. The obtained offset models show that the temporal variation is not significant, while the spatial offsets have more obvious linear relationships.

A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas

Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely.

Integrating Zhuhai-1 Hyperspectral Imagery With Sentinel-2 Multispectral Imagery to Improve High-Resolution Impervious Surface Area Mapping

Mapping impervious surface area (ISA) in an accurate and timely manner is essential for a variety of fields and applications, such as urban heat islands, hydrology, waterlogging, and urban planning and management. However, the large and complex urban landscapes pose great challenges in retrieving ISA information. Spaceborne hyperspectral (HS) remote sensing imagery provides rich spectral information with short revisit cycles, making it an ideal data source for ISA extraction from complex urban scenes. Nevertheless, insufficient single-band energy, the involvement of modulation transfer function (MTF), and the low signal-to-noise ratio (SNR) of spaceborne HS imagery usually result in poor image clarity and noises, leading to inaccurate ISA extraction. To address this challenge, we propose a new deep feature fusion-based classification method to improve 10 m resolution ISA mapping by integrating Zhuhai-1 HS imagery with Sentinel-2 multispectral (MS) imagery. We extract deep features that include spectral and spatial features, respectively, from MS and HS imagery via a 2-D convolutional neural network (CNN), aiming to increase feature diversity and improve the model’s recognition capability. The Sentinel-2 imagery is used to enhance the spatial information of the Zhuhai-1 HS image, improving the urban ISA retrieval by reducing the impact of noises. By combining the deep spatial features and deep spectral features, we obtain joint spatial-spectral features, leading to high classification accuracy and robustness. We test the proposed method in two highly urbanized study areas that cover Foshan city and Wuhan city, China. The results reveal that the proposed method obtains an overall accuracy of 96.72% and 96.75% in the two study areas, 18.78% and 8.66% higher than classification results with only HS imagery as input. The final ISA extraction overall accuracy is 95.42% and 95.50% in the two study areas, the highest among the comparison methods.

Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

The urban thermal environment is impacted by changes in urban landscape patterns resulting from urban expansion and seasonal variation. In order to cope effectively with urban heat island (UHI) effects and improve the urban living environment and microclimate, an analysis of the heating effect of impervious surface areas (ISA) and the cooling effects of vegetation is needed. In this study, Landsat 8 data in four seasons were used to derive the percent ISA and fractional vegetation cover (FVC) by spectral unmixing and to retrieve the land surface temperature (LST) from the radiative transfer equation (RTE). The percent ISA and FVC were divided into four different categories based on ranges 0–25%, 25–50%, 50–75%, and 75–100%. The LST with percent ISA and FVC were used to calculate the surface heating rate (SHR) and surface cooling rate (SCR). Finally, in order to analyze the heating effect of ISA and the cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in the four seasons. The results showed the following: (1) In summer, SHR decreases as percent ISA increases and SCR increases as FVC increases in the study area. (2) Unlike the dependence of LST on percent ISA and FVC, the trends of SHR/SCR as a function of percent ISA/FVC are more complex for different value ranges, especially in spring and autumn. (3) The SHR (heating capacity) decreases with increasing percent ISA in autumn. However, the SCR (cooling capacity) decreases with increasing FVC, except in summer. This study shows that our methodology to analyze the variation and change trends of SHR, SCR, and LST based on different ISA and FVC categories in different seasons can be used to interpret urban ISA and vegetation cover, as well as their heating and cooling effects on the urban thermal environment. This analytical method provides an important insight into analyzing the urban landscape patterns and thermal environment. It is also helpful for urban planning and mitigating UHI.

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.

A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018

Abstract. Accurate and timely maps of urban underlying land properties at the national scale are of significance in improving habitat environment and achieving sustainable development goals. Urban impervious surface (UIS) and urban green space (UGS) are two core components for characterizing urban underlying environments. However, the UIS and UGS are often mosaicked in the urban landscape with complex structures and composites. The “hard classification” or binary single type cannot be used effectively to delineate spatially explicit urban land surface property. Although six mainstream datasets on global or national urban land use and land cover products with a 30 m spatial resolution have been developed, they only provide the binary pattern or dynamic of a single urban land type, which cannot effectively delineate the quantitative components or structure of intra-urban land cover. Here we propose a new mapping strategy to acquire the multitemporal and fractional information of the essential urban land cover types at a national scale through synergizing the advantage of both big data processing and human interpretation with the aid of geoknowledge. Firstly, the vector polygons of urban boundaries in 2000, 2005, 2010, 2015 and 2018 were extracted from China's Land Use/cover Dataset (CLUD) derived from Landsat images. Secondly, the national settlement and vegetation percentages were retrieved using a sub-pixel decomposition method through a random forest algorithm using the Google Earth Engine (GEE) platform. Finally, the products of China's UIS and UGS fractions (CLUD-Urban) at a 30 m resolution were developed in 2000, 2005, 2010, 2015 and 2018. We also compared our products with six existing mainstream datasets in terms of quality and accuracy. The assessment results showed that the CLUD-Urban product has higher accuracies in urban-boundary and urban-expansion detection than other products and in addition that the accurate UIS and UGS fractions were developed in each period. The overall accuracy of urban boundaries in 2000–2018 are over 92.65 %; and the correlation coefficient (R) and root mean square errors (RMSEs) of UIS and UGS fractions are 0.91 and 0.10 (UIS) and 0.89 and 0.11 (UGS), respectively. Our result indicates that 71 % of pixels of urban land were mosaicked by the UIS and UGS within cities in 2018; a single UIS classification may highly increase the mapping uncertainty. The high spatial heterogeneity of urban underlying covers was exhibited with average fractions of 68.21 % for UIS and 22.30 % for UGS in 2018 at a national scale. The UIS and UGS increased unprecedentedly with annual rates of 1605.56 and 627.78 km2 yr−1 in 2000–2018, driven by fast urbanization. The CLUD-Urban mapping can fill the knowledge gap in understanding impacts of the UIS and UGS patterns on ecosystem services and habitat environments and is valuable for detecting the hotspots of waterlogging and improving urban greening for planning and management practices. The datasets can be downloaded from https://doi.org/10.5281/zenodo.4034161 (Kuang et al., 2020a).

Change of impervious surface area and its impacts on urban landscape: an example of Shenyang between 2010 and 2017

ABSTRACT Introduction One of the most striking features of urbanization is the replacement of the original natural land cover type by artificial impervious surface area (ISA). However, the extent of the contribution of various environmental factors, especially the growth of 3D space to ISA expansion, and the scope and mechanism of their influences in dramatically expanding cities, are yet to be determined. The boosted regression tree (BRT) model was adopted to analyze the main influencing factors and driving mechanisms of ISA change in Shenyang, China between 2010 and 2017. Outcomes The nearly complete-coverage ISA (≥0.7) increased from 42% in 2010 to 47% in 2017. The percentage of landscape with a high ISA fraction increased, while the landscape evenness and diversity of ISA decreased. The BRT analysis revealed that elevation, regional population density, and landscape class had the largest influences on the change of urban ISA, contributing 22.55%, 18.16%, and 11.18% to the model, respectively. Conclusion Overall, topographic and socioeconomic factors had the greatest influence on urban ISA change in Shenyang, followed by land use type and building pattern indices. The trend of high aggregation was strong in large commercial and residential areas. The 3D expansion of the city had an influence on its areal expansion.

Using a Multimedia Aquivalence Model to Evaluate the Environmental Fate of Fe, Mn and Trace Metals in an Industrial City, China

The rapid expansion of urban impervious surface areas complicates urban-scale heavy metal circulation among various environmental compartments (air, soil, sediment, water, and road dust). Herein, a level III steady-state aquivalence model evaluated the fate of heavy metals in Nanjing, China. Iron was the most abundant heavy metal in all environmental compartments, while cadmium was the rarest. Most simulated concentrations agreed with measured values within three logarithmic residuals. In the simulated heavy metal cycle, industrial emission contributed almost the entire input, whereas sediment burial was the dominant output pathway. The transfer fluxes between bottom sediment and water were the highest. Thereinto, the contribution of sediment resuspension for Fe and Mn was significantly higher than that to the other metal elements, which could partly explain why Fe and Mn are the major blackening ingredients in malodorous black rivers. Road dust was also an important migration destination for heavy metals, accounting for 3–45%, although soil and sediment were the main repositories of heavy metals in the urban environment. The impact of road dust on surface water should not be neglected, with its contribution reaching 4–31%. The wash-off rate constant W for road dust–water process was proved to be consistent with that for film–water and was independent of the type of heavy metals. Sensitivity analysis highlighted the notable background value effect on Fe and Mn.

Assessing spatio-temporal changes in forest cover and fragmentation under urban expansion in Nanjing, eastern China, from long-term Landsat observations (1987–2017)

Assessing changes in forest cover and fragmentation under urban expansion and the correlation analysis between these changes can provide decision support for urban forest managers. In this study, forest cover and morphological spatial patterns (cores, islets, perforations, edges, loops, bridges, and branches) were firstly mapped by the vegetation change tracker (VCT) algorithm and morphological spatial pattern analysis (MSPA) respectively in Nanjing from 1987 to 2017. Next, the visible red and near-infrared-based built-up index (VrNIR-BI) was derived to extract urban impervious surface (UIS) areas, and the neighbourhood-based urban expansion model was proposed to describe the expansion types (edge-expansion, infilling and outlying). Finally, the relationships among the forest cover, fragmentation changes and urban expansion were determined using the Pearson correlation coefficient (r). The results showed that the distribution of forest cover in Nanjing was relatively scattered and it decreased by 94 km2, while the UIS area increased by 893 km2 from 1987 to 2017, resulting in some forests being gradually surrounded or encroached upon by UIS areas. The main forest morphological spatial pattern was identified as cores, with many small-area cores. Moreover, islets exhibited a higher proportion of 0.18, and these suggested that the fragmentation of Nanjing's forests was severe. Forest net changes had the higher correlation with cores, edges and branches. In addition, there were strong correlations between edge-expansion and cores, edges, branches with the r values greater than 0.75, which meant that most of the urban areas extended to core regions from the forest edges or branches. The derived information will help forest managers monitor forest dynamics in response to urban expansion and achieve sustainable development.

Monitoring Three-Decade Expansion of China’s Major Cities Based on Satellite Remote Sensing Images

As the largest developing country, China has experienced dramatic urban expansion since the “reform and opening-up” policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China’s 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China’s cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP).

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 × 0.5 km to 2 × 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model.

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’s Land Use/cover Dataset (CLUD) and China’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 2000–2010 (298.19 km2/a). The decreasing Theil index values for the urban population and economic density were 0.305 and 1.748, respectively, in 1980–2015. 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.