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Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis

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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 (15 December 2021) | Viewed by 56508

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Special Issue Editors

Prof. Dr. Dengsheng Lu

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Guest Editor

School of Geographical Sciences, Fujian Normal University, No. 18 Middle Wulongjiang Avenue, Fuzhou 350117, China
Interests: forest aboveground biomass mapping; classification; change detection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wenhui Kuang

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Guest Editor

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: urban land-system dynamic and ecological remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chi Zhang

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Guest Editor

1. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3. Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 270600, China
Interests: carbon cycle; urban ecosystem; temperate desert; climate change; land-use change; machine learning; ecological modelling

Dr. Tao Pan

E-Mail Website
Guest Editor

1. Department of Geography, Ghent University, 9000 Ghent, Belgium
2. University of Chinese Academy of Sciences, Beijing 100039, China
3. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4. Institute of Geographic Sciences and natural resources, Chinese Academy of Sciences, Beijing, China
5. Royal Meteorological Institute, Brussels, Belgium
Interests: remote sensing and ecological process in cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The significance of urban land-system synergies and spatial governance are increasingly emerging toward sustainable target and livable environment in cities. Satellite remote sensing, process-based models and big data are playing the pivotal roles for obtaining spatially explicit knowledge for better planning or managing city. This session expects to provide an opportunity for urban land-system synergies and governance with remote sensing, modeling and big data integration. Remote sensing, modeling and big data technologies as well as the improvement of mapping algorithms, such as impervious surface area, surface radiation and heat fluxes, heat island, and surface runoff associated with urbanization will be expected to share and exchange in this session. In addition, optimized schemes on urban-land system, i.e. low impact development in cities, carbon emission reduction in cities and so on, is suitable for this session.

The fourth Open Science Meeting of the Global Land Programme (GLP 4th OSM 2019) will be held from the 24-26 of April 2019 in Bern, Switzerland. In this conference, we will organize a session on above theme, which will attribute to the conference theme “How do we support transformation? New frontiers in studying and governing land systems”. As a follow-up to the workshop, we are calling for papers on the work presented at the session of GLP 4th OSM 2019. In addition to this, we welcome papers from the global research community actively involved in this session. As such, the special issue is open to anyone doing research in this field. The selection of papers for publication will depend on quality and rigor of research. The potential topics may include the followings:

Data integration or fusion methods from remote sensing, process-based modeling, or big data
High-spatial resolution mapping of urban land cover/land use (i.e., impervious surface, green space)
Spatial mapping and exploratory analysis on urban heat island, urban hydrological process, and other ecological factors.
Knowledge mining or discovery from available fine-scale spatially explicit information for urban governance

Prof. Dr. Dengsheng Lu
Dr. Wenhui Kuang
Prof. Dr. Chi Zhang
Dr. Tao Pan
Guest Editors

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Keywords

Urban-land system synergies
Urban eco-regulation
Urban remote sensing
Urban ecological process modeling
Geo-big data mining
Urban land/cover change
Urban impervious surface mapping
Urban heat island mitigation
Low impact development in cities
Urban green infrastructure
Hot spots for urban governance
Urban climate adaption
Carbon emission reduction in cities
Sustainable cities planning
Optimized urban-land system design

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Published Papers (11 papers)

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21 pages, 7219 KiB

Open AccessArticle

Synergies between Urban Heat Island and Urban Heat Wave Effects in 9 Global Mega-Regions from 2003 to 2020

by Chunzhu Wei, Wei Chen, Yang Lu, Thomas Blaschke, Jian Peng and Desheng Xue

Remote Sens. 2022, 14(1), 70; https://doi.org/10.3390/rs14010070 - 24 Dec 2021

Cited by 23 | Viewed by 5300

Abstract

Global urbanization significantly impacts the thermal environment in urban areas, yet urban heat island (UHI) and urban heat wave (UHW) studies at the mega-region scale have been rare, and the impact study of urbanization is still lacking. In this study, the MODIS land surface temperature (LST) product was used to depict the UHI and UHW in nine mega-regions globally between 2003 and 2020. The absolute and percentile-based UHW thresholds were adopted for both daily and three-day windows to analyze heat wave frequency, and UHW magnitude as well as frequency were compared with UHI variability. Results showed that a 10% increase in urban built-up density led to a 0.20 °C to 0.95 °C increase in LST, a 0.59% to 7.17% increase in hot day frequency, as well as a 0.08% to 0.95% increase in heat wave number. Meanwhile, a 1 °C increase in UHI intensity (the LST differences between the built-up and Non-built-up areas) led to a 2.04% to 92.15% increase in hot day frequency, where daytime LST exceeds 35 °C and nighttime LST exceeds 25 °C, as well as a 3.30% to 33.67% increase in heat wave number, which is defined as at least three consecutive days when daily maximum temperature exceeds the climatological threshold. In addition, the increasing rates of UHW magnitudes were much faster than the expansion rates of built-up areas. In the mega-regions of Boston, Tokyo, São Paulo, and Mexico City in particular, the increasing rates of UHW hotspot magnitudes were over 2 times larger than those of built-up areas. This indicated that the high temperature extremes, represented by the increase in UHW frequency and magnitudes, were concurrent with an increase in UHI under the context of climate change. This study may be beneficial for future research of the underlying physical mechanisms on urban heat environment at the mega-region scale. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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The 9 mega-regions selected and their built-up (BU) areas from 2000 to 2018. Full article ">Figure 2
Schematic of the percolation-based clustering method; (x1,y1) and (x2,y2) define the percola tion threshold at which the giant component emerges. Full article ">Figure 3
Time series of mean LST, CHT, HWN90th and HWN97.5th in the summer daytime from 2003 to 2020 in the 9 mega-regions in the built-up and Non-built-up areas. Error bars are graphical representations of the standard deviation of four indicators in the mega-region (B = Built-up area, N = Non-built-up area). Full article ">Figure 4
Daytime of Mean LST, CHT, HWN90th, and HWN97.5th within each UDI level during the study period. Each class is plotted from 18 data points which represent the areal mean LST of the class in different mega-regions from 2003–2020, respectively. Full article ">Figure 5
Spatial distribution of mean LST, CHN, and HWN over the study period in the 9 mega-regions. Full article ">Figure 6
Spatial distribution of percolation-based hotspots in the 9 mega-regions in three time periods. Full article ">Figure 7
Time series of mean LST, CHT, and HWN in the 9 mega-regions. Full article ">

15 pages, 5030 KiB

Open AccessArticle

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

by Shouzhi Chang, Zongming Wang, Dehua Mao, Fusheng Liu, Lina Lai and Hao Yu

Remote Sens. 2021, 13(22), 4512; https://doi.org/10.3390/rs13224512 - 10 Nov 2021

Cited by 12 | Viewed by 3508

Abstract

The urban functional area is critical to an understanding of the complex urban system, resource allocation, and management. However, due to urban surveys’ focus on geographic objects and the mixture of urban space, it is difficult to obtain such information. The function of a place is determined by the activities that take place there. This study employed mobile phone signaling data to extract temporal features of human activities through discrete Fourier transform (DFT). Combined with the features extracted from the point of interest (POI) data and Sentinel images, the urban functional areas of Changchun City were identified using a random forest (RF) model. The results indicate that integrating features derived from remote sensing and social sensing data can effectively improve the identification accuracy and that features derived from dynamic mobile phone signaling have a higher identification accuracy than those derived from POI data. The human activity characteristics on weekends are more distinguishable for different functional areas than those on weekdays. The identified urban functional layout of Changchun is consistent with the actual situation. The residential functional area has the highest proportion, accounting for 33.51%, and is mainly distributed in the central area, while the industrial functional area and green-space are distributed around. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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23 pages, 36272 KiB

Open AccessArticle

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

by Lu Niu, Zhengfeng Zhang, Zhong Peng, Yingzi Liang, Meng Liu, Yazhen Jiang, Jing Wei and Ronglin Tang

Remote Sens. 2021, 13(21), 4428; https://doi.org/10.3390/rs13214428 - 3 Nov 2021

Cited by 43 | Viewed by 4342

Abstract

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and ?fine particulate matter are typically global variables, while ?NDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ?NDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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21 pages, 5597 KiB

Open AccessArticle

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

by Zherui Yin, Wenhui Kuang, Yuhai Bao, Yinyin Dou, Wenfeng Chi, Friday Uchenna Ochege and Tao Pan

Remote Sens. 2021, 13(21), 4288; https://doi.org/10.3390/rs13214288 - 25 Oct 2021

Cited by 6 | Viewed by 2879

Abstract

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. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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Figure 1
Geographic information map of Africa. Notes: the yellow and green in the upper right corner of the location diagram represent climatic regions of Africa. The main map represents elevation and administrative divisions of Africa. Full article ">Figure 2
Examples of the verified points for the fractional land covers within urban regions throughout Africa in 2020. Note: the density levels from 1 to 10 represent the fractions of impervious surface area or vegetation space at 10% intervals from 0.01% to 100%, separately. The buffer region represents an outward buffer distance radius of 100 km from the edge of each city. Full article ">Figure 3
Spatial distribution and statistical map of urban expansion in Africa during 2000–2020. (a) Urban land distribution in 2020 (dark yellow) in sub-Africa regions of Middle Africa, Eastern Africa, Southern Africa, Northern Africa, and Western Africa; and the urban land statistics at the national scale in years 2000, 2010, and 2020, respectively. (b) The sample cities of urban expansion in Cairo ((b1), Egypt), Abuja ((b2), Nigeria), and Bangui ((b3), Central Africa). (c) Total urban expansion changes at the national scale from 2000 to 2020, and we only marked the number when a total urban expansion area was more than 1000 km2 in each country throughout the studied period. Full article ">Figure 4
Spatial statistics of the fractional urban land structure in Africa during 2000–2020. (a–c) Show the average density of the fractional intra-urban impervious surface area (UISA), the fractional intra-urban vegetation space (UVS), and the fractional intra-urban other land-cover (UOL) at the country scale in 2000, 2010, and 2020, respectively, and the size of the circle represents the total area of urban land on a national scale. (d) Statistics of the changes in average density of the fractional UISA, UVS, and UOL in sub-African regions of Middle Africa, Eastern Africa, Southern Africa, Northern Africa, and Western Africa in 2000, 2010, and 2020, respectively. Full article ">Figure 5
Regional examples of impervious surface area fractions and vegetation space fractions in Tanzania (i.e., the first line) and Egypt (i.e., the second line) of Africa. In each line, we provide 2-m Google images, 30-m Landsat images, 30-m fractional impervious surface area, and vegetation space, respectively. Notes: the regional example in the first line is located in humid/semi-humid areas and the regional example in the second line is located in arid/semi-arid areas. Full article ">Figure 6
Statistics of the functional relationship values between the urban impervious surface area and gross domestic product, and between urban impervious surface area and urban population in Africa during 2000–2020. Notes: “Δ” represents the words “dynamic change”, ISA: impervious surface area, GDP: gross domestic product. Full article ">

16 pages, 6208 KiB

Open AccessArticle

Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine

by Wenhui Kuang, Yali Hou, Yinyin Dou, Dengsheng Lu and Shiqi Yang

Remote Sens. 2021, 13(20), 4187; https://doi.org/10.3390/rs13204187 - 19 Oct 2021

Cited by 18 | Viewed by 5975

Abstract

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 10⁴ km² and 17.16 × 10⁴ km², respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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The hierarchical scale principle and subpixel metric method for mapping ISA and GS fractions. Full article ">Figure 2
The strategy for mapping urban impervious surface area (ISA) and green space (GS) products (NSDI, Normalized Settlement Density Index; NGSI, Normalized Green Space Index). Full article ">Figure 3
(a) shows the selection principle of the spatial distribution of sample points; (b1,b2) show the sampling areas in a Google Earth image; whereas (b3,b4) show the digitized proportions of the impervious (pink) and vegetation (green) areas. Full article ">Figure 4
(a) shows the distribution of global urban ecoregions and sampled cities; (b1,b2) show the impervious surface area fraction in sampled cities; (c1,c2) show the urban and non-urban land in sampled cities; (b3,b4,c3,c4) show the digitized the impervious and vegetation areas. Full article ">Figure 5
Examples of spatial distribution of NSDI and NGSI in selected regions circa 2020. (a1,b1,c1,d1,e1) show the NSDI in selected regions; (a2,b2,c2,d2,e2) show the NGSI in selected regions. Full article ">Figure 6
Examples of urban impervious surface area fraction in selected cities. (a1,b1,c1,d1,e1) show the urban impervious surface area fraction in 2005; (a2,b2,c2,d2,e2) show the urban impervious surface area fraction circa 2020. Full article ">Figure 7
Spatial distribution of global ISA (a1) and GS (a2) circa 2020. (b1,c1,d1,e1,f1,g1) show the areas of ISA in different regions; (b2,c2,d2,e2,f2,g2) show the areas of GS in different regions. Full article ">Figure 8
The percentages of impervious surface areas and green spaces in urban land areas in selected cities. Full article ">Figure 9
Comparisons between our dataset and other existing datasets. (GUE, global urban expansion [<a href="#B21-remotesensing-13-04187" class="html-bibr">21</a>]; GUL, global urban land [<a href="#B25-remotesensing-13-04187" class="html-bibr">25</a>]; GHSL, global human settlement layer [<a href="#B23-remotesensing-13-04187" class="html-bibr">23</a>]; GAIA, global artificial impervious area [<a href="#B9-remotesensing-13-04187" class="html-bibr">9</a>]; and MSMT, global ISA [<a href="#B27-remotesensing-13-04187" class="html-bibr">27</a>]). Full article ">

24 pages, 8503 KiB

Open AccessArticle

Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data

by Likai Zhu, Yuanyuan Guo, Chi Zhang, Jijun Meng, Lei Ju, Yuansuo Zhang and Wenxue Tang

Remote Sens. 2020, 12(24), 4026; https://doi.org/10.3390/rs12244026 - 9 Dec 2020

Cited by 26 | Viewed by 4344

Abstract

With rapid urbanization, retrieving livability information of human settlements in time is essential for urban planning and governance. However, livability assessments are often limited by data availability and data update cycle, and this problem is more serious when making an assessment at finer spatial scales (e.g., community level). Here we aim to develop a reliable and dynamic model for community-level livability assessment taking Linyi city in Shandong Province, China as a case study. First, we constructed a hierarchical index system for livability assessment, and derived data for each index and community from remotely sensed data or Internet-based geospatial data. Next, we calculated the livability scores for all communities and assessed their uncertainties using Monte Carlo simulations. The results showed that the mean livability score of all communities was 59. The old urban and newly developed districts of our study area had the best livability, and got a livability score of 62 and 58 respectively, while industrial districts had the poorest conditions with an average livability score of 48. Results by dimension showed that the old urban district had better conditions of living amenity and travel convenience, but poorer conditions of environmental health and comfort. The newly developed districts were the opposite. We conclude that our model is effective and extendible for rapidly assessing community-level livability, which provides detailed and useful information of human settlements for sustainable urban planning and governance. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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19 pages, 8184 KiB

Open AccessArticle

Recording Urban Land Dynamic and Its Effects during 2000–2019 at 15-m Resolution by Cloud Computing with Landsat Series

by Yulin Dong, Zhibin Ren, Yao Fu, Zhenghong Miao, Ran Yang, Yuanhe Sun and Xingyuan He

Remote Sens. 2020, 12(15), 2451; https://doi.org/10.3390/rs12152451 - 30 Jul 2020

Cited by 43 | Viewed by 4718

Abstract

Cities, the core of the global climate change and economic development, are high impact land cover land use change (LCLUC) hotspots. Comprehensive records of land cover land use dynamics in urban regions are essential for strategic climate change adaption and mitigation and sustainable urban development. This study aims to develop a Google Earth Engine (GEE) application for high-resolution (15-m) urban LCLUC mapping with a novel classification scheme using pan-sharpened Landsat images. With this approach, we quantified the annual LCLUC in Changchun, China, from 2000 to 2019, and detected the abrupt changes (turning points of LCLUC). Ancillary data on social-economic status were used to provide insights on potential drivers of LCLUC by examining their correlation with change rate. We also examined the impacts of LCLUC on environment, specifically air pollution. Using this approach, we can classify annual LCLUC in Changchun with high accuracy (all above 0.91). The change detection based on the high-resolution wall-to-wall maps show intensive urban expansion with the compromise of cropland from 2000 to 2019. We also found the growth of green space in urban regions as the result of green space development and management in recent years. The changing rate of different land types were the largest in the early years of the observation period. Turning points of land types were primarily observed in 2009 and 2010. Further analysis showed that economic and industry development and population migration collectively drove the urban expansion in Changchun. Increasing built-up areas could slow wind velocity and air exchange, and ultimately led to the accumulation of PM_2.5. Our implement of pan-sharpened Landsat images facilitates the wall-to-wall mapping of temporal land dynamics at high spatial resolution. The primary use of GEE for mapping urban land makes it replicable and transferable by other users. This approach is a first crucial step towards understanding the drivers of change and supporting better decision-making for sustainable urban development and climate change mitigation. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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13 pages, 10845 KiB

Open AccessArticle

Investigating the Patterns and Dynamics of Urban Green Space in China’s 70 Major Cities Using Satellite Remote Sensing

by Wenhui Kuang and Yinyin Dou

Remote Sens. 2020, 12(12), 1929; https://doi.org/10.3390/rs12121929 - 15 Jun 2020

Cited by 51 | Viewed by 6422

Abstract

Urban green space (UGS) plays a pivotal role in improving urban ecosystem services and building a livable environment for urban dwellers. However, remotely sensed investigation of UGS at city scale is facing a challenge due to the pixels’ mosaics of buildings, squares, roads and green spaces in cities. Here we developed a new algorithm to unmix the fraction of UGS derived from Landsat TM/ETM/8 OLI using a big-data platform. The spatiotemporal patterns and dynamics of UGSs were examined for 70 major cities in China between 2000 and 2018. The results showed that the total area of UGS in these cities grew from 2780.66 km² in 2000 to 6764.75 km² in 2018, which more than doubled its area. As a result, the UGS area per inhabitant rose from 15.01 m² in 2000 to 18.09 m² in 2018. However, an uneven layout of UGS occurred among the coastal, western, northeastern and central zones. For example, the UGS percentage in newly expanded urban areas in the coastal zone rose significantly in 2000–2018, with an increase of 2.51%, compared to the decline in UGS in cities in the western zone. Therefore, the effective strategies we have developed should be adopted to show disparities and promote green infrastructure capacity building in those cities with less green space, especially in western China. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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18 pages, 3726 KiB

Open AccessArticle

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

by Shu Zhang, Chuanglin Fang, Wenhui Kuang and Fengyun Sun

Remote Sens. 2019, 11(15), 1834; https://doi.org/10.3390/rs11151834 - 6 Aug 2019

Cited by 20 | Viewed by 4019

Abstract

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. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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20 pages, 14351 KiB

Open AccessArticle

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

by Ge Lou, Qiuxiao Chen, Kang He, Yue Zhou and Zhou Shi

Remote Sens. 2019, 11(15), 1821; https://doi.org/10.3390/rs11151821 - 4 Aug 2019

Cited by 53 | Viewed by 7478

Abstract

The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China’s large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High–high clustering units were then defined as the main centers by calculating the Anselin Local Moran’s I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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22 pages, 25756 KiB

Open AccessArticle

City-Level Comparison of Urban Land-Cover Configurations from 2000–2015 across 65 Countries within the Global Belt and Road

by Tao Pan, Wenhui Kuang, Rafiq Hamdi, Chi Zhang, Shu Zhang, Zhili Li and Xin Chen

Remote Sens. 2019, 11(13), 1515; https://doi.org/10.3390/rs11131515 - 27 Jun 2019

Cited by 23 | Viewed by 5169

Abstract

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 km² to 29,257.51 km², with an average growth rate of 370.75 km²/y during 2000–2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km²/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km²/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 (?Shannon’s Diversity Index (SHDI) = −0.063; ?Patch Density (PD) = 0.054) and presented better connectivity (?Connectance 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. Full article

(This article belongs to the Special Issue Urban Land-System Synergies and Governance Using Remote Sensing, Modeling and Big Data, Analysis)

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