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Remote Sensing Imagery for Mapping Economic Activities

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 19547

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Special Issue Editors

Dr. Naizhuo Zhao

E-Mail Website1 Website2
Guest Editor

Department of Land Resource Management, School of Humanities and Law, Northeastern University, Shenyang 110167, China
Interests: nighttime light imagery; air pollution, public health; geospatial big data

Dr. Guofeng Cao

E-Mail Website
Guest Editor

Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: GIScience; geostatistics/spatial statistics; big data and GeoAI; remote sensing; environmental health

Dr. Tilottama Ghosh

E-Mail Website
Guest Editor

Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA
Interests: nighttime light remote sensing; socio-economic studies; demography; land use and land cover change; urbanization
Special Issues, Collections and Topics in MDPI journals

Dr. Qiming Zheng

E-Mail Website
Guest Editor

Centre for Nature-based Climate Solutions, National University of Singapore, Singapore 117543, Singapore
Interests: nighttime light imagery; urbanization; climate change mitigation; time series analysis

Special Issue Information

Dear Colleagues,

Remote sensing is a powerful tool with an extensive record of successful applications in not only natural systems but also human societies. These applications, and especially the use of nighttime light imagery, make efficient and objective estimations of socio-economic factors over large areas possible. In the last decade, the advent of more advanced optical remote sensing instruments and their image products with finer spectral, spatial, and temporal resolutions, such as visible infrared imaging radiometer suites, promises more accurate evaluation of socio-economic systems across different geographic scales.

This Special Issue aims to publish studies that use remote sensing imagery to assess or map socioeconomics or socioeconomic-related activities. Studies integrating remote sensing images with other types of geo-spatial big data (e.g., location-based social media and points of interest) are particularly welcome. Papers may address, but are not limited to, the following topics: wealth production, electricity consumption, pollutant emissions, population estimates, and urban development/decline.

Dr. Naizhuo Zhao
Dr. Guofeng Cao
Dr. Tilottama Ghosh
Dr. Qiming Zheng
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

City
Energy
GDP
Human activity
Land cover/use
Nighttime lights
Population
Socio-economic systems

Published Papers (8 papers)

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Research

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25 pages, 14351 KiB

Open AccessArticle

Exploring the Spatial and Temporal Characteristics of China’s Four Major Urban Agglomerations in the Luminous Remote Sensing Perspective

by Jiahan Wang, Jiaqi Chen, Xiangmei Liu, Wei Wang and Shengnan Min

Remote Sens. 2023, 15(10), 2546; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102546 - 12 May 2023

Cited by 1 | Viewed by 1558

Abstract

This study addresses the knowledge gap regarding the spatiotemporal evolution of Chinese urban agglomerations using long time series of luminescence remote sensing data. The evolution of urban agglomerations is of great significance for the future development and planning of cities. Nighttime light data provide a window for observing urban agglomerations’ characteristics on a large spatial scale, but they are affected by temporal discontinuity. To solve this problem, this study proposes a ridge-sampling regression-based Hadamard matrix correction method and constructs consistent long-term nighttime light sequences for China’s four major urban agglomerations from 1992 to 2018. Using the Getis-Ord Gi* hot-cold spot, standard deviation ellipse method, and Baidu search index, we comprehensively analyze the directional evolution of urban agglomerations and the correlations between cities. The results show that, after correction, the correlation coefficient between nighttime light intensity and gross domestic product increased from 0.30 to 0.43. Furthermore, this study identifies unique features of each urban agglomeration. The Yangtze River Delta urban agglomeration achieved balanced development by shifting from coastal to inland areas. The Guangdong-Hong Kong-Macao urban agglomeration developed earlier and grew more slowly in the north due to topographical barriers. The Beijing-Tianjin-Hebei urban agglomeration in the north has Beijing and Tianjin as its core, and the southeastern region has developed rapidly, showing an obvious imbalance in development. The Chengdu-Chongqing urban agglomeration in the inland area has Chengdu and Chongqing as its dual core, and its development has been significantly slower than that of the other three agglomerations due to the influence of topography, but it has great potential. Overall, this study provides a research framework for urban agglomerations based on four major urban agglomerations to explore their spatiotemporal characteristics and offers insights for government urban planning. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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18 pages, 34226 KiB

Open AccessArticle

Stability and Changes in the Spatial Distribution of China’s Population in the Past 30 Years Based on Census Data Spatialization

by Xiaofan Xu, Minghong Tan, Xiaoyu Liu, Xue Wang and Liangjie Xin

Remote Sens. 2023, 15(6), 1674; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061674 - 20 Mar 2023

Cited by 1 | Viewed by 2173

Abstract

As the world’s most populous country, China has experienced massive population growth and dramatic regional migration over the past 30 years. From 1990 to 2020, the national population increased by 24.4%, the urban population tripled, and the rural population declined by 41.0%. Combined with complex topographic features, unique characteristics of the population distribution have emerged. Many studies have examined changes in the spatial distribution of the population. However, few studies have examined the stability of certain aspects of this distribution over the last 30 years, particularly at the raster scale, which may provide important information for future research and development plans. Based on land use maps and nighttime light images, China’s census data from 1990 to 2020 was scaled down to a resolution of 1 km using a method called multiple linear regression based on spatial covariates. The results show that there were some striking features of both stability and change in the spatial distribution of China’s population over the past three decades. The population shares divided by the Hu line, the Qinling-Huaihe line, and the three-step staircase have remained almost unchanged. In contrast, the population share of the coastal region has risen from 23.7% to 29.0% during the study period. The urban areas have expanded by 1.35 times and their population has doubled. In addition, for every 1 km² increase in the urban areas, an area of 29.4 km² has been depopulated on average. This suggests that urbanization can alleviate population pressure in larger areas. However, the coastal regions and urban and peri-urban areas were the main areas of population density growth, so they required a great deal of attention for ecological protection. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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17 pages, 34466 KiB

Open AccessArticle

Refined Estimation of Potential GDP Exposure in Low-Elevation Coastal Zones (LECZ) of China Based on Multi-Source Data and Random Forest

by Feixiang Li, Liwei Mao, Qian Chen and Xuchao Yang

Remote Sens. 2023, 15(5), 1285; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051285 - 26 Feb 2023

Cited by 2 | Viewed by 1401

Abstract

With climate change and rising sea levels, the residents and assets in low-elevation coastal zones (LECZ) are at increasing risk. The application of high-resolution gridded population datasets in recent years has highlighted the threats faced by people living in LECZ. However, the potential exposure of gross domestic product (GDP) within LECZ remains unknown, due to the absence of refined GDP datasets and corresponding analyzes for coastal regions. The climate-related risks faced by LECZ may still be underestimated. In this study, we estimated the potential exposure of GDP in the LECZ across China by overlying DEM with new gridded GDP datasets generated by random forest models. The results show that 24.02% and 22.7% of China’s total GDP were located in the LECZ in 2010 and 2019, respectively, while the area of the LECZ only accounted for 1.91% of China’s territory. Significant variability appears in the spatial-temporal pattern and the volume of GDP across sectors, which impedes disaster prevention and mitigation efforts within administrative regions. Interannual comparisons reveal a rapid increase in GDP within the LECZ, but a decline in its share of the country. Policy reasons may have driven the slow shift of China’s economy to regions far from the LECZ. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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25 pages, 18523 KiB

Open AccessArticle

Measurement Method and Influencing Mechanism of Urban Subdistrict Vitality in Shanghai Based on Multisource Data

by Yishao Shi, Jianwen Zheng and Xiaowen Pei

Remote Sens. 2023, 15(4), 932; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040932 - 08 Feb 2023

Cited by 6 | Viewed by 1570

Abstract

Most previous studies on urban vitality focused on the analysis and evaluation of the overall vitality of urban agglomerations or single cities, while there are few related studies at the micro scale, such as subdistricts and even blocks. Based on multisource data and using the kernel density analysis and entropy methods, the economic vitality, social vitality, cultural vitality, ecological vitality and comprehensive vitality of each subdistrict in Shanghai were measured. Additionally, correlation analysis, the ordinary least squares (OLS) regression model, the spatial lag model (SLM) and the spatial error model (SEM) were used for fitting analysis to reveal the influencing mechanism of urban subdistrict vitality. The results showed that (1) the spatial distribution of economic vitality and social vitality in Shanghai showed the spatial characteristics of radiating outward from the center, and the types of social activity location corresponding to different levels of hotspot areas are different. Cultural vitality showed the spatial distribution characteristics of “gathering in the centre and dispersing around, with Puxi higher than Pudong”, but the cultural vitality values of different subdistricts vary greatly. Ecological vitality showed an increasing trend from the center to the surrounding areas. (2) The overall urban vitality of Shanghai also showed a decreasing circular structure from the center to the periphery. (3) Among the three regression models, i.e., the OLS regression model, SLM and SEM, the model with the best explanation of urban vitality is the SLM, which had an R² of 0.6984, indicating that it can explain 69.84% of the spatial distribution pattern of urban vitality. (4) The factors that have significant effects on urban vitality are functional mix, metro station accessibility, metro station density, bus station density and intersection density, and all of them are positively correlated. The order of the strength of the effects is bus station density > metro station density > intersection density > metro station accessibility > functional mix. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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30 pages, 9127 KiB

Open AccessArticle

Analysis of the Spatial and Temporal Evolution of the GDP in Henan Province Based on Nighttime Light Data

by Zongze Zhao, Xiaojie Tang, Cheng Wang, Gang Cheng, Chao Ma, Hongtao Wang and Bingke Sun

Remote Sens. 2023, 15(3), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030716 - 25 Jan 2023

Cited by 10 | Viewed by 2117

Abstract

The collection of traditional administrative unit-based gross domestic product (GDP) data is time-consuming and laborious, and the data lacks accurate spatial information. Long-term series nighttime light (NTL) data can provide effective spatiotemporal GDP change information, which can be used to analyze economies’ spatial distributions and development trends. In this study, we generated a spatial model of the relationship between NTL indices and GDP parameters, based on NPP-VIIRS-like NTL data for the period 2001 to 2020, conducted a multitemporal and multilevel connectivity analysis of the GDP spatialization data, and constructed a tree structure for horizontal and vertical analysis. Standard deviation ellipses and economic centers of the first-level economic connected components at the provincial and municipal levels were generated, and the economic center distribution range and development direction at the provincial and municipal levels were analyzed. The results show that GDP spatialization data, based on NPP-VIIRS-like NTL data, can intuitively reflect the GDP spatial distribution. In Henan Province, the economic levels of different regions vary, and the economic regions represented by Zhengzhou have developed rapidly, driving surrounding regional economic rapid development. Henan Province’s development trend from single-city economic centers to multicity economic centers is obvious, and the economic center has shifted to the southeast. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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23 pages, 12683 KiB

Open AccessArticle

Spatiotemporal and Multiscale Analysis of the Coupling Coordination Degree between Economic Development Equality and Eco-Environmental Quality in China from 2001 to 2020

by Jianwan Ji, Zhanzhong Tang, Weiwei Zhang, Wenliang Liu, Biao Jin, Xu Xi, Futao Wang, Rui Zhang, Bing Guo, Zhiyu Xu, Eshetu Shifaw, Yibing Xiong, Jinming Wang, Saiping Xu and Zhenqing Wang

Remote Sens. 2022, 14(3), 737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030737 - 04 Feb 2022

Cited by 37 | Viewed by 3583

Abstract

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a⁻¹), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a⁻¹), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a⁻¹), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R²of the fitted curve at different scales ranged from 0.8183 to 0.9915. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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19 pages, 7657 KiB

Open AccessArticle

Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China

by Zhiwei Yong, Kun Li, Junnan Xiong, Weiming Cheng, Zegen Wang, Huaizhang Sun and Chongchong Ye

Remote Sens. 2022, 14(3), 600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030600 - 26 Jan 2022

Cited by 21 | Viewed by 3945

Abstract

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R² = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R² = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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12 pages, 2795 KiB

Open AccessTechnical Note

China’s Wealth Capital Stock Mapping via Machine Learning Methods

by Lulu Ren, Feixiang Li, Bairu Chen, Qian Chen, Guanqiong Ye and Xuchao Yang

Remote Sens. 2023, 15(3), 689; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030689 - 24 Jan 2023

Viewed by 1364

Abstract

The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R² = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data. Full article

(This article belongs to the Special Issue Remote Sensing Imagery for Mapping Economic Activities)

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