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Remote Sensing Applications for Interaction between Urbanization and Eco-Environment
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Remote Sensing Applications for Interaction between Urbanization and Eco-Environment

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 27278

Special Issue Editors

Dr. Guangdong Li

E-Mail Website
Guest Editor
Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing 100101, China
Interests: urban land use; urban spatial structure landscape pattern; sustainability; urbanization; interaction between urbanization and eco-environment
Dr. Sanwei He

E-Mail Website
Guest Editor
School of Public Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: urban and regional development; geographic intelligent modeling of urban growth; the integrated development of land use and transport
Dr. Zhiqi Yang

E-Mail Website
Guest Editor
School of Political Science and Public Administration, Soochow University, Suzhou, China
Interests: urban green space; urban land use change; remote sensing of urban environment; heat island effect

Special Issue Information

Dear Colleagues,

The world’s population is projected to reach 8.5–9.9 billion by 2050, with 55–78% of the population living in urban areas. This global explosion of the urban population will undoubtedly cause an increasing demand for urban land and natural resources. Although urban land only covers 0.2–2.4% of the global terrestrial surface, the conversion of Earth’s land surface to urban uses is one of the most irreversible human impacts on the global biosphere, which leads to a series of environmental pressures and ecological problems. For instance, it hastens the loss of farmland, affects energy demands, modifies the climate, alters hydrologic and biogeochemical cycles, fragments habitats, and decreases biodiversity. Moreover, the eco-environmental impacts of urban expansion reach far beyond urban areas themselves. Correspondingly, problems related to the ecological environment also affect the healthy development of urbanization and reduce the well-being of urban residents. There is a complex interaction between urbanization and the eco-environment. To achieve the UN’s Sustainable Development Goals (SDGs), the interactive rate, magnitude, and spatial distribution between urbanization and the eco-environment urgently need to be understood. Such information will be crucial for policymakers to formulate a sustainable scheme for future urbanization. Indeed, there is an increasing interest in developing novel data and methods to measure this inter-relationship. In particular, using remote sensing data to characterize this complex interaction is increasingly recognized as an important step in advancing our understanding of the interaction between urbanization and eco-environment.

This Special Issue calls for high-quality and creative review or empirical papers that cover different uses of remote sensing in the interaction between urbanization and eco-environment. Topics may cover the classical analysis of urban development and eco-environmental quality at multi-scales. Hence, articles regarding multi-source data integration, multi-scale approaches, or urbanization process and urban environmental changes, among other issues, are welcome.

Articles may address, but are not limited to, the following topics:

  • Environmental impacts of urban growth;
  • Environmental effects of demographic changes in urban areas;
  • Environmental effects of urban land use changes;
  • Urban development and water resource use;
  • Urbanization and soil pollution;
  • Urban expansion and protected areas;
  • Urban growth boundary;
  • Urban expansion and ecological conservation;
  • Eco-environmental constraints of urban development;
  • Urban green space and urban development;
  • Urban development and climate change;
  • Urbanization and biodiversity.

Dr. Guangdong Li
Dr. Sanwei He
Dr. Zhiqi Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urbanization
  • interaction between urbanization and eco-environment
  • eco-environment effects of urbanization
  • urban development
  • ecological conservation
  • eco-environment quality
  • urban land use change
  • urban growth boundary

Published Papers (16 papers)

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Research

20 pages, 11361 KiB  
Article
Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models
by Yuqing Li, Jiangbo Gao, Jie Yin, Lulu Liu, Chuanwei Zhang and Shaohong Wu
Remote Sens. 2024, 16(2), 219; https://doi.org/10.3390/rs16020219 - 05 Jan 2024
Cited by 1 | Viewed by 953
Abstract
In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the [...] Read more.
In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the city scale. This study proposes an integrated approach based on remote sensing data using CA, Markov, and simplified hydrodynamic (FloodMap) models to accurately and effectively assess flood risk under urbanization. Taking Chongqing City as a case study, this paper analyzes the temporal and spatial variations in land use/land cover (LULC) in 2010, 2015, and 2018 and predicts the LULC for 2030, based on historic trends. Flood risk is assessed by combining the hazard, exposure, and modified vulnerability. The results suggest that the area of built-up land will increase significantly from 19.56% in 2018 to 25.21% in 2030. From 2010 to 2030, the area of medium and high inundation depths will increase by 10 and 16 times, respectively. Flood damage varies remarkably according to the LULC and return period. The expected annual damage (EAD) has been estimated to increase from USD 68 million in 2010 to USD 200 million in 2030. Flood risk is proportional to population and is significantly inversely proportional to socioeconomic level. The approach used here can provide a comprehensive understanding of flood risk and is significant for land-use policymaking and the management of flood control facilities. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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16 pages, 5302 KiB  
Article
Estimation for Refined Carbon Storage of Urban Green Space and Minimum Spatial Mapping Scale in a Plain City of China
by Nan Li, Liang Deng, Ge Yan, Mengmeng Cao and Yaoping Cui
Remote Sens. 2024, 16(2), 217; https://doi.org/10.3390/rs16020217 - 05 Jan 2024
Viewed by 1043
Abstract
Current cities are not concrete jungles and deserts with sparse vegetation. Urban green space (UGS) appears widely in human activity areas and plays an important role in improving the human living environment and accumulates carbon storage. However, given the scattered distribution of UGS, [...] Read more.
Current cities are not concrete jungles and deserts with sparse vegetation. Urban green space (UGS) appears widely in human activity areas and plays an important role in improving the human living environment and accumulates carbon storage. However, given the scattered distribution of UGS, studies on both the refined spatial estimation of carbon storage and appropriate mapping scale are still lacking. Taking the downtown area of Kaifeng, China, as the study area, this study verified the i-Tree Eco model on the basis of a field survey and accurately estimated the spatial carbon storage of UGS by combining it with remote sensing data, and finally, we obtained the minimum spatial mapping scale of UGS carbon storage by scaling. The results showed that (1) the total area of UGS in study area was 26.41 km2, of which the proportion of total area of residential area and park green spaces was about 50%. The area of UGS per capita in the study area is 40.49 m2. (2) Within the 123 survey samples, the proportion of communities with tree–shrub–herbs structure was the highest, 51.22%. The average carbon density was 5.89 kg m−2, among which the park, protective and square green spaces had the highest carbon density in all land use types. (3) The total carbon storage of UGS in the study area was 114,389.17 t, and the carbon storage of UGS per capita was 175.39 kg. Furthermore, the scaling analysis showed that 0.25 km spatial resolution was the minimum spatial scale for UGS carbon storage mapping. This study improves our understanding of urban carbon storage, highlights the role and potential of UGS in carbon neutrality, and clarifies the importance of estimating urban carbon storage at appropriate scales. This study is also of great significance for rationally understanding the terrestrial carbon cycle in urban areas and improving regional climate simulations. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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20 pages, 9037 KiB  
Article
Spatiotemporal Evaluation of Regional Land Use Dynamics and Its Potential Ecosystem Impact under Carbon Neutral Pathways in the Guangdong–Hong Kong–Macao Greater Bay Area
by Haoming Chen, Na Dong, Xun Liang and Huabing Huang
Remote Sens. 2023, 15(24), 5749; https://doi.org/10.3390/rs15245749 - 15 Dec 2023
Cited by 1 | Viewed by 838
Abstract
The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon [...] Read more.
The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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19 pages, 12278 KiB  
Article
Multi-Scale Influence Analysis of Urban Shadow and Spatial Form Features on Urban Thermal Environment
by Liqun Lin, Yangyan Deng, Man Peng, Longxiang Zhen and Shuwei Qin
Remote Sens. 2023, 15(20), 4902; https://doi.org/10.3390/rs15204902 - 10 Oct 2023
Cited by 1 | Viewed by 1309
Abstract
In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day [...] Read more.
In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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21 pages, 11020 KiB  
Article
Effects of Production–Living–Ecological Space Patterns Changes on Land Surface Temperature
by Han Liu, Ling Qin, Menggang Xing, Haiming Yan, Guofei Shang and Yuanyuan Yuan
Remote Sens. 2023, 15(14), 3683; https://doi.org/10.3390/rs15143683 - 24 Jul 2023
Viewed by 1098
Abstract
Rapid economic and social development has triggered competition for limited land space from different industries, accelerating the evolution of Beijing’s urban landscape types. The increase in impermeable surfaces and the decrease in ecological land have led to an increase in the impact on [...] Read more.
Rapid economic and social development has triggered competition for limited land space from different industries, accelerating the evolution of Beijing’s urban landscape types. The increase in impermeable surfaces and the decrease in ecological land have led to an increase in the impact on the urban thermal environment. Since previous studies have mainly focused on the impact of a single urban landscape on the urban thermal environment and lacked an exploration of the combined impact of multiple landscapes, this study applied standard deviation ellipses, Pearson correlation analysis, land surface temperature (LST) profile analysis, and hot spot analysis to comprehensively explore the influence of the evolving production–living–ecological space (PLES) pattern on LST. The results show that the average LST of various spaces continued to increase before 2009 and decreased slowly after 2009, with the highest average temperature being living space, followed by production space, and the lowest average temperature being ecological space for each year. The spatiotemporal shift path of the thermal environment is consistent with the shift trajectory of the living space center of gravity in Beijing; LST is positively correlated with living space (LS) and negatively correlated with production space (PS) and ecological space (ES). LST is positively correlated with LS and negatively correlated with PS and ES. Influenced by the change in bedding surface type, the longitudinal thermal profile curve of LST shows a general trend of “low at both ends and high in the middle”. With the change in land space type, LST fluctuates significantly, and the horizontal thermal profile curve shows a general trend of “first decreasing, followed by increasing and finally decreasing”. In addition, the hot spot analysis shows that the coverage area of very hot spots, hot spots, and warm spots increased by 0.72%, 1.13%, and 2.03%, respectively, in the past 30 years, and the main expansion direction is southeast, and very cold spots and cold spots are distributed in the northwest ecological space, and the area change first decreases and then increases. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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27 pages, 6593 KiB  
Article
Suitability of NASA’s Black Marble Daily Nighttime Lights for Population Studies at Varying Spatial and Temporal Scales
by Juan Fernando Martinez, Kytt MacManus, Eleanor C. Stokes, Zhuosen Wang and Alex de Sherbinin
Remote Sens. 2023, 15(10), 2611; https://doi.org/10.3390/rs15102611 - 17 May 2023
Viewed by 2474
Abstract
This paper investigates the potential link between changes in NASA’s Black Marble VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500 m Linear Lat Lon Grid (VNP46A2) nighttime lights product (NTL) and human dynamics, particularly population counts and changes at a variety [...] Read more.
This paper investigates the potential link between changes in NASA’s Black Marble VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500 m Linear Lat Lon Grid (VNP46A2) nighttime lights product (NTL) and human dynamics, particularly population counts and changes at a variety of spatial and temporal scales. We conducted analyses in four case studies at varying resolutions to explore the relationship of NTL data for population studies, including demographic research, disaster mitigation and adaptation planning, and infrastructure development. The analyses were conducted using different administrative geographies, including a refugee camp, a subnational region, and a country. We compared changes in population counts, density, migration, and displacement against changes in daily, weekly, monthly, and annual NTL values. Our case study results demonstrate that out-migration does not always lead to a decrease in NTL. We found that rural population decline did not correspond to a decrease in NTL. Despite significant out-migration in many rural areas NTL remained largely unchanged. NTL provided essential information on infrastructure damage in the short-term aftermath of this disaster; however, NTL alone was not correlated to the location of displaced individuals. Through news reports, we were able to corroborate the NTL changes to downtimes of the electrical systems. Monthly NTL averages were highly correlated to population counts, but a pixel-level analysis showed that the changes in NTL were primarily attributed to economic diversification. In summary, NTL is the product of several factors including demographic, environmental, economic, and political forces that shape electricity infrastructure, and we suggest that NTL data must first be parameterized with ancillary ground-level information in order to be effectively applied to population models. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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31 pages, 14747 KiB  
Article
Mapping and Influencing the Mechanism of CO2 Emissions from Building Operations Integrated Multi-Source Remote Sensing Data
by You Zhao, Yuan Zhou, Chenchen Jiang and Jinnan Wu
Remote Sens. 2023, 15(8), 2204; https://doi.org/10.3390/rs15082204 - 21 Apr 2023
Viewed by 1695
Abstract
Urbanization has led to rapid growth in energy consumption and CO2 emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing [...] Read more.
Urbanization has led to rapid growth in energy consumption and CO2 emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing data. However, there is still a lack of research on estimating long-term BCEs by integrating multi-source remote sensing data and applications in different regions. We selected the Beijing–Tianjin–Hebei (BTH) urban agglomeration and the National Capital Region of Japan (NCRJ) as research areas for this study. We also built multiple linear regression (MLR) models between prefecture-level BCEs and multi-source remote sensing data. The prefecture-level BCEs were downscaled to grid scale at a 1 km2 resolution. The estimation results verify the method’s difference and accuracy at different development stages. The multi-scale BCEs showed a continuous growth trend in the BTH urban agglomeration and a significant downward trend in the NCRJ. The decrease in energy intensity and population density were the main factors contributing to the negative growth of BCEs, whereas GDP per capita and urban expansion significantly promoted it. Through our methods and analyses, we contribute to the study of estimating greenhouse gas emissions with remote sensing and exploring the environmental impact of urban growth. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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18 pages, 2862 KiB  
Article
The Vanishing and Renewal Landscape of Urban Villages Using High-Resolution Remote Sensing: The Case of Haidian District in Beijing
by Hubin Wei, Yue Cao and Wei Qi
Remote Sens. 2023, 15(7), 1835; https://doi.org/10.3390/rs15071835 - 30 Mar 2023
Cited by 2 | Viewed by 2018
Abstract
How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this [...] Read more.
How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km2, showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km2. The scale of urban village blocks in 2020 decreased to 1.02 km2, 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: “urban village–blue–green space”, “urban village–real estate”, and “urban village–municipal facilities”. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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18 pages, 5497 KiB  
Article
Spatiotemporal Evolution of Residential Exposure to Green Space in Beijing
by Yue Cao, Guangdong Li and Yaohui Huang
Remote Sens. 2023, 15(6), 1549; https://doi.org/10.3390/rs15061549 - 12 Mar 2023
Cited by 3 | Viewed by 2398
Abstract
Urban green space has a critical impact on the urban ecological environment, residents’ health, and urban sustainability. Quantifying residential exposure to green space and proposing targeted enhancement strategies in urban areas is helpful to rationally plan urban green space construction, reduce the inequality [...] Read more.
Urban green space has a critical impact on the urban ecological environment, residents’ health, and urban sustainability. Quantifying residential exposure to green space and proposing targeted enhancement strategies in urban areas is helpful to rationally plan urban green space construction, reduce the inequality in residential exposure to green space, and promote environmental equity. However, the long-time evolution analysis of residential exposure to green space at different scales and the influence of green space quality on residential exposure to green space are rarely reported. Here we produced a long-time series dataset of urban green space from 1990 to 2020 based on the 30 m Landsat data and used the Normalized Difference Vegetation Index (NDVI) as a representation of the green space quality to comprehensively analyze residential exposure to green space at the city and block scales within the 5th ring of Beijing, China. We found that the urban green space in Beijing is mainly distributed in urban areas between the 4th and 5th rings (i.e., 153.4 km2 in 2020), and there is little green space within the 2nd ring area (i.e., 12.6 km2 in 2020). There is clear spatial inequality in residential exposure to green space, and about 2.88 million (i.e., ~27%) residents have experienced different degrees of decline in residential exposure to green space from 2015 to 2020. However, the degree of inequality in residential exposure to green space has gradually weakened from a high level (Palma ratio = 2.84) in 1990 to a relatively low level (Palma ratio = 0.81) in 2020. In addition, the spatial-temporal analysis method of residential exposure to green space based on green space quality has certain advantages that can help explore the degraded and lost areas of green space. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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18 pages, 25624 KiB  
Article
Construction of Urban Thermal Environment Network Based on Land Surface Temperature Downscaling and Local Climate Zones
by Xueling Zhang, Alimujiang Kasimu, Hongwu Liang, Bohao Wei, Yimuranzi Aizizi, Yongyu Zhao and Rukeya Reheman
Remote Sens. 2023, 15(4), 1129; https://doi.org/10.3390/rs15041129 - 18 Feb 2023
Cited by 3 | Viewed by 1612
Abstract
It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the [...] Read more.
It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the perspective of influencing factors. However, there are few studies on mitigating LST from the standpoint of regional networks. This paper first combines Landsat 8 with Sentinel-2 imagery for LST downscaling based on the Google Earth engine as a way to match local climate zone (LCZ) with 17 classification types. Then, the thermal environment resistance surface is constructed according to LCZ, and the essential cold sources are identified using morphological spatial pattern analysis (MSPA) and circuit theory to form the thermal environment green corridor and obtain the pinch point and barrier point areas. The results show that (1) The downscaling of LST based on random forest (RF) for the Urumqi–Changji–Wujiaqu metropolitan area has an R2 of 0.860 and an RMSE of 3.23, with high downscaling accuracy. (2) High temperature (HT), medium temperature (MT), and low temperature (LT) have the largest proportions in the study area; HT dominates in Urumqi, LT in Changji, and MT in Wujiaqu. (3) The natural types (LCZ-D, LCZ-C, and LCZ-F) in the LCZ classification occupy a large area, and the building types are mainly concentrated in Urumqi; LCZ-D, LCZ-G, and LCZ-A contribute the most to the cooling of LST, and LCZ-F, LCZ-C, and LCZ-10 contribute the most to the warming of LST. (4) After identifying critical cold source patches according to MSPA to arrive at 253 green corridors, subsensitive corridors and sensitive corridors need to take certain measures to prevent corridor blockage; pinch point areas, as well as barrier point areas, need to be protected and repaired according to their respective characteristics. In summary, corresponding cooling measures to specific areas can improve the connectivity between cooling sources and slow down the temperature increase of the whole area. This study and experimental approach can provide new insights for urban planners and climate researchers. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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22 pages, 15525 KiB  
Article
Study on Regional Eco-Environmental Quality Evaluation Considering Land Surface and Season Differences: A Case Study of Zhaotong City
by Jianwan Ji, Zhanzhong Tang, Linlin Jiang, Tian Sheng, Fei Zhao, Rui Zhang, Eshetu Shifaw, Wenliang Liu, Huan Li, Xinhan Liu and Huiyuan Lu
Remote Sens. 2023, 15(3), 657; https://doi.org/10.3390/rs15030657 - 22 Jan 2023
Cited by 6 | Viewed by 2015
Abstract
Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed [...] Read more.
Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed to consider the influence of land surface and season elements in evaluating regional EEQ. Therefore, this study aimed to promote an accurate EEQ-evaluating framework for cloudy areas. Zhaotong city, a typical karst and cloudy region, was chosen as the study area. First, we integrated multi-source spatiotemporal datasets and constructed a novel eco-environmental comprehensive evaluation index (ECEI) to assess its EEQ from 2000 to 2020. Next, standard deviation ellipse (SDE) and trend analysis methods were applied to investigate regional EEQ’s change trends. Finally, ecological index (EI) values for different years were calculated to validate the effectivity of the ECEI. The main findings were as follows: (1) The EEQ of Zhaotong showed an upward-fluctuating trend (0.0058 a−1), with average ECEI values of 0.729, 0.693, 0.722, 0.749, and 0.730. (2) The spatial distribution pattern of the EEQ showed high values in the north and low values in the south, with Zhaoyang district having the lowest ECEI value. (3) From 2000 to 2020, the standard deviation of the major axis of the ellipse moved northeast of Zhaotong city with θ of SDE changing from 57.06° to 62.90°, thus, indicating the improvement of northeastern regions’ EEQ. (4) The coefficients of the determinant (R2) between the EI and ECEI were 0.84, which was higher than that of EI–RSEI (R2 = 0.56). This indicated that our promoted framework and the ECEI could acquire more accurate EEQ results and provide suggestions for relevant policymakers. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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21 pages, 3627 KiB  
Article
Early Warning of the Carbon-Neutral Pressure Caused by Urban Agglomeration Growth: Evidence from an Urban Network-Based Cellular Automata Model in the Greater Bay Area
by Sanwei He, Shifa Ma, Bin Zhang, Guangdong Li and Zhenjie Yang
Remote Sens. 2023, 15(2), 338; https://doi.org/10.3390/rs15020338 - 06 Jan 2023
Cited by 3 | Viewed by 1505
Abstract
Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of [...] Read more.
Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of small-scale urban growth simulation to model urban agglomeration growth to assist in carbon-neutral planning, ignoring the significant characteristics of the process to achieve carbon neutrality: large-scale and long-term. This paper proposes a framework to model large-scale and long-term urban growth, which couples a quantity module and a spatial module to model the quantity and spatial allocation of urban land, respectively. This framework integrates the inertia of historical land-use change, the driving effects of the urbanization law (S-curve), and the traction of the urban agglomeration network to model the long-term quantity change of urban land. Moreover, it couples a partitioned modeling framework, spatially heterogeneous rules derived by geographically weighted regression (GWR), and quantified land-use planning orientations to build a cellular automata (CA) model to accurately allocate the urbanized cells in a large-scale spatial domain. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) as an example, the proposed framework is calibrated by the urban growth from 2000 to 2010 and validated by that from 2010 to 2020. The figure of merit (FoM) of the results simulated by the framework is 0.2926, and the simulated results are also assessed by some evidence, which both confirm the good performance of the framework to model large-scale and long-term urban growth. Coupling with the coefficients proposed by the Intergovernmental Panel on Climate Change (IPCC), this framework is used to project the carbon emissions caused by urban growth in the GHMGBA from 2020 to 2050. The results indicate that Guangzhou, Foshan, Huizhou, and Jiangmen are under great pressure to achieve the carbon-neutral targets in the future, while Hong Kong, Macao, Shenzhen, and Zhuhai are relatively easy to bring up to the standard. This research contributes to the ability of land-use models to simulate large-scale and long-term urban growth to predict carbon emissions and to support the carbon-neutral planning of the GHMGBA. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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16 pages, 6917 KiB  
Article
The Supply–Demand Budgets of Ecosystem Service Response to Urbanization: Insights from Urban–Rural Gradient and Major Function-Oriented Areas
by Zuzheng Li, Baoan Hu and Yufei Ren
Remote Sens. 2022, 14(22), 5670; https://doi.org/10.3390/rs14225670 - 10 Nov 2022
Cited by 4 | Viewed by 1481
Abstract
The differentiation in the urbanization level’s impact on the supply–demand budgets of ecosystem services (ESs) from the perspective of the major function-oriented areas is of great significance for formulating sustainable development strategies at the regional level. This study first constructed the research framework [...] Read more.
The differentiation in the urbanization level’s impact on the supply–demand budgets of ecosystem services (ESs) from the perspective of the major function-oriented areas is of great significance for formulating sustainable development strategies at the regional level. This study first constructed the research framework of the supply, demand, and supply–demand ratios (ESDRs) of ESs responding to urbanization from the perspective of major function-oriented zoning, and then took the rapidly urbanized Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) of China as a case from 2000 to 2020. The relationships between three urbanization indicators, gross domestic production (GDP), population density (PD), and artificial land proportion (ALP), as well as ESDRs of ESs were investigated using Pearson Correlation analysis across three major functional areas. The sensitivity of ESDRs to urbanization was further evaluated using the Random Forest model. The results showed that the supply of carbon fixation, water provision, and food provision increased, whereas their demands far exceeded their supplies, resulting in an increased imbalance between ES supply and demand. With the exception of soil conservation, significantly negative relationships were observed between urbanization indicators and the other three ES supply–demand budgets. The ESDRs of water provision, carbon fixation, and food provision were the most sensitive variables that depended on the population density (PD) in almost all functional areas, whereas the ESDR of carbon fixation exhibited the highest sensitivity to GDP in developed urban areas and rural areas within the preferred development area (PDA) and key development area (KDA). This study could provide comprehensive information for decision making and ES management in different functional areas. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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21 pages, 7835 KiB  
Article
Estimating Large-Scale Interannual Dynamic Impervious Surface Percentages Based on Regional Divisions
by Tianyu Xu, Erzhu Li, Alim Samat, Zhiqing Li, Wei Liu and Lianpeng Zhang
Remote Sens. 2022, 14(15), 3786; https://doi.org/10.3390/rs14153786 - 06 Aug 2022
Cited by 4 | Viewed by 1419
Abstract
Impervious surface information is an important indicator to describe urban development and environmental changes. The substantial increase in impervious surface area will have a significant impact on the regional landscape and environment. Therefore, the timely and accurate acquisition of large-scale impervious surface percentage [...] Read more.
Impervious surface information is an important indicator to describe urban development and environmental changes. The substantial increase in impervious surface area will have a significant impact on the regional landscape and environment. Therefore, the timely and accurate acquisition of large-scale impervious surface percentage (LISP) is of great significance for urban management and ecological assessment. However, previous LISP estimation methods often ignored the impact of regional geographic environment and climate differences on remote sensing information, resulting in low overall accuracy and obvious regional differences in the estimated results. Thus, in this study, based on the time-series characteristics of multi-temporal remote sensing images combined with the information on geographical environment and climate heterogeneity, a method of time-series remote sensing image fusion and LISP estimation based on regional divisions was proposed. Firstly, the entire region was divided into several regions according to the spatial differences of Köppen–Geiger climate data and MODIS NDVI time-series data. Subsequently, adaptive time-series image fusion methods and remote sensing feature construction methods were proposed for different regions. Finally, the proposed method was used to estimate the percentage of impervious surfaces in other years in Asia. The results indicate that the overall R2 of each region is better than 0.82, and the estimation models have a good ability to transfer across time and can directly estimate the impervious surface percentage in other years without using additional samples. In addition, compared with other existing impervious surface products, the proposed method has higher overall estimation accuracy and regional consistency. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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18 pages, 6100 KiB  
Article
Relationship between Urban Three-Dimensional Spatial Structure and Population Distribution: A Case Study of Kunming’s Main Urban District, China
by Yang Wang, Xiaoli Yue, Cansong Li, Min Wang, Hong’ou Zhang and Yongxian Su
Remote Sens. 2022, 14(15), 3757; https://doi.org/10.3390/rs14153757 - 05 Aug 2022
Cited by 4 | Viewed by 1959
Abstract
The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land [...] Read more.
The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city’s 3D spatial structure. We took 439 blocks in Kunming’s Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming’s Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city’s 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribution is the core factor affecting the 3D spatial structure of Kunming’s Main Urban District. The correlation between daytime population distribution and 3DSFD is not significant. This variability has been ignored in previous studies. The findings are informative for further understanding of the relationship between urban 3D space and population distribution, especially the difference between night and daytime populations. This study can help city managers reasonably plan urban land development intensity and construction height, guide the population layout and formulate management policies to improve urban population and space matching, enhancing the livability and attractiveness of cities. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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19 pages, 14308 KiB  
Article
A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)
by Yaoping Cui, Yiming Fu, Nan Li, Xiaoyan Liu, Zhifang Shi, Jinwei Dong and Yan Zhou
Remote Sens. 2022, 14(13), 3060; https://doi.org/10.3390/rs14133060 - 25 Jun 2022
Cited by 1 | Viewed by 1639
Abstract
The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long [...] Read more.
The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)
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