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Remote Sensing of Urban Impervious Surfaces: Mapping, Monitoring, and Modeling the Dynamics of Urban Impervious Surfaces with Multisource Remote Sensing Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 27378

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Dr. Wenliang Li

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Department of Geography, Environment, and Sustainability, The University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Interests: spectral unmixing analysis; environmental planning; land use and land cover change modelling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Changshan Wu

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Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
Interests: remote sensing; spatial modeling; population; urban analysis; spatial epidemiology
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Dear Colleagues,

Impervious surfaces, a major component of urbanized areas, have concurrently increased with rapid urbanization. Urban impervious surfaces have been widely considered as an important index for analyzing urban growth patterns and quantifying the development of urban and suburban areas. Meanwhile, urban impervious surface has been widely applied in the corresponding physical and socio-economic fields, such as urban hydrology study, urban heat island effect, population estimation, population distribution pattern analysis, and its impact on housing prices. Considering the important role that urban impervious surfaces play, the accurate estimation and dynamic monitoring of impervious surfaces have become essential.

The availability of multisource remote sensing data, such as LiDAR, SAR, hyperspectral, and UAV with diverse spectral, spatial, and temporal resolution provides a great opportunity for the comprehensive understanding of urban impervious surfaces. This Special Issue focuses on new techniques for mapping, monitoring, and modeling urban impervious surfaces. Moreover, we are also interested in studies investigating the impact of urban impervious surfaces on the urban environment. Please find the main topics below (but papers need not be limited to this list):

Urban impervious surfaces estimation and change analysis
Applications of new sensors, such as LiDAR, SAR, UAV in urban impervious surfaces analysis
New innovative algorithms in modeling urban impervious surfaces
Evaluation of the impact of urban impervious surfaces on the environment
Multisource remote sensing fusion for monitoring urban impervious surfaces

Dr. Wenliang Li
Prof. Dr. Changshan Wu
Guest Editors

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Keywords

Remote sensing
Urban impervious surfaces
Physical and socio-economic applications
Multisource remote sensing
Image processing algorithm
Urban impervious surfaces change analysis

Published Papers (11 papers)

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21 pages, 3507 KiB

Open AccessArticle

Dominant Factors in the Temporal and Spatial Distribution of Precipitation Change in the Beijing–Tianjin–Hebei Urban Agglomeration

by Feili Wei, Ze Liang, Weijing Ma, Jiashu Shen, Yueyao Wang, Dahai Liu and Shuangcheng Li

Remote Sens. 2022, 14(12), 2880; https://doi.org/10.3390/rs14122880 - 16 Jun 2022

Cited by 2 | Viewed by 2172

Abstract

Urbanization has a significant influence on precipitation, but existing studies lack the spatial and temporal heterogeneity analysis of its impact on precipitation in urban areas at different levels. This study investigates the spatial heterogeneity of precipitation and the influencing factors on six dimensions in 156 urban areas in the Beijing–Tianjin–Hebei urban agglomeration from 2000 to 2018, utilizing a mixed-methods analytical approach. The results show that the change in the natural factor layer caused by urbanization was the most important factor, affecting urban precipitation variation in summer and over the whole year, accounting for 34.5% and 10.7%, respectively. However, the contribution of the urban thermal environment in summer cannot be ignored, and the change in the urban thermal environment caused by human activities in winter is an important influencing factor. When considering the optimal combination of factors, relative humidity was shown to be significant in the spatial variations in precipitation during summer, which contributed 26.2%, followed by human activity as indicated by night-time light intensity. Over the whole year, aerosol optical depth makes the substantial contribution of 21.8% to urban precipitation change. These results provide benchmarks for improving the adaptability of urban-environment change and urban planning in the context of urbanization. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Impervious Surfaces: Mapping, Monitoring, and Modeling the Dynamics of Urban Impervious Surfaces with Multisource Remote Sensing Data)

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21 pages, 10108 KiB

Open AccessArticle

A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products

by Yichen Yang and Xuhui Lee

Remote Sens. 2022, 14(4), 983; https://doi.org/10.3390/rs14040983 - 17 Feb 2022

Cited by 1 | Viewed by 2044

Abstract

The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape. Full article

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27 pages, 7723 KiB

Open AccessArticle

Hierarchical Urban Land Mappings and Their Distribution with Physical Medium Environments Using Time Series of Land Resource Images in Beijing, China (1981–2021)

by Tao Pan, Wenhui Kuang, Ruoyi Pan, Zhenguo Niu and Yinyin Dou

Remote Sens. 2022, 14(3), 580; https://doi.org/10.3390/rs14030580 - 26 Jan 2022

Cited by 4 | Viewed by 1709

Abstract

Rapid urban expansion and structural changes are taking place in China’s capital city, Beijing, but without an update of urban land features in a timely manner our understanding of the new urban heterogeneity is restricted, as land-background data is indispensable for bio-geophysical and bio-geochemical processes. In this plain region, the investigations of multi-scale urban land mappings and physical medium environmental elements such as slope, aspect, and water resource services are still lacking, although Beijing can provide an exemplary case for urban development and natural environments in plains considering the strategic function of China’s capital city. To elucidate these issues, a remote-sensing methodology of hierarchical urban land mapping was established to obtain the urban land, covering structure and its sub-pixel component with an overall accuracy of over 90.60%. During 1981–2021, intense and sustained urban land expansion increased from 467.13 km² to 2581.05 km² in Beijing, along with a total growth rate of 452.53%. For intra-urban land structures, a sharp growth rate of over 650.00% (i.e., +1649.54 km²) occurred in terms of impervious surface area (ISA), but a greening city was still evidently observed, with a vegetation-coverage rate of 8.43% and 28.42% in old and newly expanded urban regions, respectively, with a more integrative urban ecological landscape (Shannon’s Diversity Index (SHDI) = −0.164, Patch Density (PD) = −8.305). We also observed a lower rate of ISA (0.637 vs. 0.659) and a higher rate of vegetation cover (0.284 vs. 0.211) in new compared to old urban regions, displaying a higher quality of life during urban expansion. Furthermore, the dominant aspect of low, medium, and high density ISA was captured with the north–south orientation, considering the sunlight conditions and traditional house construction customs in North China, Over 92.00% of the ISA was distributed in flat environment regions with a slope of less than 15°. When the water-resource service radius shifted from 0.5 km to 0.5–1 km and 1–2 km, high density vegetation displayed a dependence on water resources. Our results provide a new survey of the evolution of hierarchical urban land mapping during 1981–2021 and reveals the relationship with physical medium environments, providing an important reference for relevant research. Full article

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29 pages, 22349 KiB

Open AccessArticle

Capability of Remote Sensing Images to Distinguish the Urban Surface Materials: A Case Study of Venice City

by Rosa Maria Cavalli

Remote Sens. 2021, 13(19), 3959; https://doi.org/10.3390/rs13193959 - 02 Oct 2021

Cited by 7 | Viewed by 1674

Abstract

Many countries share an effort to understand the impact of growing urban areas on the environment. Spatial, spectral, and temporal resolutions of remote sensing images offer unique access to this information. Nevertheless, their use is limited because urban surface materials exhibit a great diversity of types and are not well spatially and spectrally distinguishable. This work aims to quantify the effect of these spatial and spectral characteristics of urban surface materials on their retrieval from images. To avoid other sources of error, synthetic images of the historical center of Venice were analyzed. A hyperspectral library, which characterizes the main materials of Venice city and knowledge of the city, allowed to create a starting image at a spatial resolution of 30 cm and spectral resolution of 3 nm and with a spectral range of 365–2500 nm, which was spatially and spectrally resampled to match the characteristics of most remote sensing sensors. Linear spectral mixture analysis was applied to every resampled image to evaluate and compare their capabilities to distinguish urban surface materials. In short, the capability depends mainly on spatial resolution, secondarily on spectral range and mixed pixel percentage, and lastly on spectral resolution; impervious surfaces are more distinguishable than pervious surfaces. This analysis of capability behavior is very important to select more suitable remote sensing images and/or to decide the complementarity use of different data. Full article

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24 pages, 6825 KiB

Open AccessArticle

A Framework for Generating High Spatiotemporal Resolution Land Surface Temperature in Heterogeneous Areas

by Xinming Zhu, Xiaoning Song, Pei Leng, Xiaotao Li, Liang Gao, Da Guo and Shuohao Cai

Remote Sens. 2021, 13(19), 3885; https://doi.org/10.3390/rs13193885 - 28 Sep 2021

Cited by 12 | Viewed by 2001

Abstract

Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time t_p in Zhangye and Beijing of China. Further, the predicted LST at t_p was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at t_p. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas. Full article

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19 pages, 15233 KiB

Open AccessArticle

Fine-Scale Urban Heat Patterns in New York City Measured by ASTER Satellite—The Role of Complex Spatial Structures

by Bibhash Nath, Wenge Ni-Meister and Mutlu Özdoğan

Remote Sens. 2021, 13(19), 3797; https://doi.org/10.3390/rs13193797 - 22 Sep 2021

Cited by 3 | Viewed by 3311

Abstract

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing population’s demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) and different urban components based on high-resolution QuickBird satellite imagery classification. We further investigated the relationships between ASTER-derived surface temperature and building footprint and land use information acquired by the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The impervious-medium and dark surfaces, along with bright covers, generate higher surface temperatures. Even with highly reflective urban surfaces, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows cast by buildings effectively reduce urban heat; on the contrary, grassland does not reduce or amplify urban heat. The data aggregated to the census tract reveals high-temperature hotspots in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability showed cooling effects, consistent with shadows cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the impact of shadow generated by building heights variability and trees on small-scale surface temperature patterns since recent image reveals similar hotspot locations. This study further helps identify the risk areas to protect public health. Full article

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23 pages, 6463 KiB

Open AccessArticle

Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data

by Jiaqi Shen, Yanmin Shuai, Peixian Li, Yuxi Cao and Xianwei Ma

Remote Sens. 2021, 13(18), 3666; https://doi.org/10.3390/rs13183666 - 14 Sep 2021

Cited by 4 | Viewed by 1910

Abstract

It is necessary to understand the relationship between the impervious surface area (ISA) distribution, variation trends and potential driving forces over Dongying, Shandong Province. We extracted ISA information from Landsat images with 3–5 year intervals during 1995 to 2018 using Minimum Noise Fraction (MNF) transform, Pixel Purity Index (PPI), and Linear Spectral Mixture Analysis (LSMA), followed by the analysis on three driving forces of ISA expansion (physical geography, socioeconomic factors, and urban cultural features). Our results show the retrieved ISA thematic map fit the limited requirement of root mean square error (RMSE). The correct classification accuracy of ISA is greater than 83.08%. Further, the cross–comparison exhibits the general consistent with the ISA distribution of the land use classification map published by the National Basic Geographic Information Center. The gradual increasing trend can be captured on the expansion of ISA from 1995 to 2018. Despite of the central region always shown as the high ISA density, it still keeps increasing annually and radiating the surrounding region, especially in the southward which has formed into a new large–scale and high intensity of ISA in 2015–2018. Though the ISA patches scattered in the west region or along the northern and eastern part of the ocean coastline are still small, the expansion trend of ISA can be detected. The expansion intensity index (EII) of ISA measuring the situation of its expansion changes from the lowest value 0.12% between 1995 and 2000 up to the highest 0.73% between 2000 and 2005. Richly endowed by nature, the city’s natural geographical environment provides an elevated chance of further urbanization. The rapid increase of regional economy provides a fundamental driving force for expanding ISAs. The development of urban culture promotes the sustainable development of ISAs. Our results provide a scientific basis for future urban land use management, construction planning, and environmental protection in Dongying. Full article

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20 pages, 103083 KiB

Open AccessArticle

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

by Yingbin Deng, Renrong Chen, Yichun Xie, Jianhui Xu, Ji Yang and Wenyue Liao

Remote Sens. 2021, 13(14), 2840; https://doi.org/10.3390/rs13142840 - 20 Jul 2021

Cited by 9 | Viewed by 3486

Abstract

This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns. Full article

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14 pages, 3019 KiB

Open AccessArticle

Improving Urban Impervious Surfaces Mapping through Integrating Statistical Methods and Spectral Mixture Analysis

by Wenliang Li

Remote Sens. 2021, 13(13), 2474; https://doi.org/10.3390/rs13132474 - 25 Jun 2021

Cited by 2 | Viewed by 1758

Abstract

Impervious surfaces have been widely considered as the key indicator for evaluating urbanization and environmental quality. As one of the most widely applied methods, spectral mixture analysis (SMA) has been commonly used for mapping urban impervious surface fractions. When implementing SMA, the original multispectral remote-sensing reflectance images are served as the foundation and key to successful SMA. However, the limited spectral variances among different land covers from the original reflectance images make it challenging in information extraction and results in unsatisfactory mapping results. To address this issue, a new method has been proposed in this study to improve urban impervious surface mapping through integrating statistical methods and SMA. In particular, two traditional statistical methods, principal component analysis (PCA) and minimum noise fraction rotation (MNF) were applied to highlight the spectral variances among different land covers. Three endmember classes (impervious surface, soil, and vegetation) and corresponding spectra were identified and extracted from the vertices of the 2-D space plots generated by the first three components of each of the statistical analysis methods, PCA and MNF. A new dataset was generated by stacking the first three components of the PCA and MNF (in a total of six components), and a fully constrained linear SMA was implemented to map the fractional impervious surfaces. Results indicate that a promising performance has been achieved by the proposed new method with the systematic error (SE) of −3.45% and mean absolute error (MAE) of 11.52%. Comparative analysis results also show a much better performance achieved by the proposed statistical method-based SMA than the conventional SMA. Full article

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18 pages, 17202 KiB

Open AccessArticle

Extraction and Analysis of Finer Impervious Surface Classes in Urban Area

by Wenyue Liao, Yingbin Deng, Miao Li, Meiwei Sun, Ji Yang and Jianhui Xu

Remote Sens. 2021, 13(3), 459; https://doi.org/10.3390/rs13030459 - 28 Jan 2021

Cited by 3 | Viewed by 2558

Abstract

Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology. Full article

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12 pages, 2746 KiB

Open AccessTechnical Note

Wetland Mapping Using HJ-1A/B Hyperspectral Images and an Adaptive Sparse Constrained Least Squares Linear Spectral Mixture Model

by Xiaodong Na, Xingmei Li, Wenliang Li and Changshan Wu

Remote Sens. 2021, 13(4), 751; https://doi.org/10.3390/rs13040751 - 18 Feb 2021

Cited by 5 | Viewed by 2961

Abstract

In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation. Full article

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Remote Sensing of Urban Impervious Surfaces: Mapping, Monitoring, and Modeling the Dynamics of Urban Impervious Surfaces with Multisource Remote Sensing Data

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