A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Processing
2.2.1. Nighttime Light Data Sources
- (1)
- SNPP–VIIRS Monthly Composite Data
- (2)
- Luojia-1 Data
- (3)
- DMSP/OLS Data
2.2.2. Urban Built-Up Area Extraction Methods
- (1)
- Thresholding Method
- (2)
- Multi-Temporal Image Fusion Method
- (3)
- Support Vector Machine (SVM) Method
2.2.3. Cross-Dataset Evaluation Protocol
2.3. Methodological Framework Overview
3. Results
3.1. Comparison of Extraction Accuracy Across Methods and Datasets
3.2. Purpose and Scope of Data Source Applicability Analysis
3.3. Consistency Evaluation Conclusion
4. Discussion
4.1. Influence of Dataset Characteristics on Extraction Accuracy
4.2. Methodological Comparison
4.3. Evaluation of Inter-Method Agreement and Future Advancements
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhou, Y.; Smith, S.J.; Zhao, K.; Imhoff, M.L.; Thomson, A.M.; Bond-Lamberty, B.; Asrar, G.R.; Zhang, X.; He, C.; Elvidge, C.D. A global map of urban extent from nightlights. Environ. Res. Lett. 2014, 9, 054009. [Google Scholar] [CrossRef]
- Xiao, Y.; Wang, Q.; Zhou, D. Exploring the spatial heterogeneity of urban land expansion and its driving forces in China using geographically weighted regression. Appl. Geogr. 2018, 94, 89–98. [Google Scholar]
- Chen, Y.; Zheng, Z.; Wu, Z.; Qian, Q. Review and prospect of nighttime light remote sensing data applications. Prog. Geogr. 2019, 38, 205–223. [Google Scholar]
- Ye, X.; Liu, X.; Li, X. Spatiotemporal patterns of global urbanization and its relationship with economic development using nighttime light data. Sustainability 2021, 13, 8609. [Google Scholar]
- Huang, X.; Shi, K.; Cui, Y.; Li, Y. A saturated light correction method for DMSP-OLS nighttime stable light data. IEEE JSTARS 2021, 14, 1885–1894. [Google Scholar]
- Chen, Z.; Yu, B.; Yang, C.; Zhou, Y.; Yao, S.; Qian, X.; Wang, C.; Wu, B.; Wu, J. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data. Earth Syst. Sci. Data 2021, 13, 889–906. [Google Scholar] [CrossRef]
- Zhong, L.; Liu, X. Application potential analysis of new-type nighttime light data from Luojia-1. Bull. Surv. Mapp. 2019, 7, 132–137. [Google Scholar]
- Hong, Y.; Wu, B.; Song, Z.; Li, Y.; Wu, Q.; Chen, Z.; Liu, S.; Yang, C.; Wu, J.; Yu, B. A monthly night-time light composite dataset of NOAA-20 in China: A multi-scale comparison with S-NPP. Int. J. Remote Sens. 2021, 42, 7931–7951. [Google Scholar] [CrossRef]
- Mantero, P.; Moser, G.; Serpico, S.B. A simple and effective method for the classification of remote sensing images using SVM. IEEE Trans. Geosci. Remote Sens. 2005, 43, 598–608. [Google Scholar]
- Liu, Q.; Zhan, Q.; Li, J.; Yang, C.; Liu, W. Application of Luojia-1 nighttime light images in built-up land extraction: A case study of Wuhan. Geomat. Inf. Sci. Wuhan Univ. 2021, 46, 30–39. [Google Scholar]
- Turen, Y.; Sanli, D.U. Accuracy of Deformation Rates from Campaign GPS Surveys Considering Extended Observation Session and Antenna Set-Up Errors. Remote Sens. 2019, 11, 1225. [Google Scholar] [CrossRef]
- Huang, X.; Wang, Y. Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China. ISPRS J. Photogramm. Remote Sens. 2019, 152, 119–131. [Google Scholar] [CrossRef]
- Zhang, Q.; Schaaf, C.; Seto, K.C. The vegetation adjusted NTL urban index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sens. Environ. 2013, 129, 32–41. [Google Scholar] [CrossRef]
- Wu, Z.; Liu, X.; Zhan, Q. An object-based method for detecting urban expansion using nighttime light data. Remote Sens. Lett. 2022, 13, 141–149. [Google Scholar]
- Ma, T. An Estimate of the Pixel Level Connection between Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Nighttime Lights and Land Features across China. Remote Sens. 2018, 10, 723. [Google Scholar] [CrossRef]
- Lu, H.; Zhang, M.; Sun, W.; Li, W. Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012. ISPRS Int. J. Geo-Inf. 2018, 7, 52. [Google Scholar] [CrossRef]
- Feng, X.; Shao, Z.; Huang, X.; He, L.; Lv, X.; Zhuang, Q. Integrating Zhuhai-1 hyperspectral imagery with Sentinel-2 multispectral imagery to improve high-resolution impervious surface area mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022, 15, 2410–2424. [Google Scholar] [CrossRef]
- Liu, Z.; He, C.; Zhou, Y.; Wu, J. How much of the world’s land has been urbanized, really? A hierarchical framework for avoiding confusion. Landsc. Ecol. 2020, 35, 1911–1925. [Google Scholar] [CrossRef]
- Shi, K.; Yu, B.; Huang, C.; Hu, Y.; Yin, B.; Chen, Z.; Wu, J. Evaluating the ability of NPP–VIIRS nighttime light data to extract built-up urban areas: A comparison with DMSP–OLS data. Remote Sens. Lett. 2014, 5, 358–366. [Google Scholar] [CrossRef]
- Wang, J.; Li, Y. Evaluating VIIRS nighttime light data for urban spatial analysis. ISPRS Int. J. Geo-Inf. 2021, 10, 280. [Google Scholar]
- Zhai, W.; Han, B.; Cheng, C. Evaluation of Luojia 1-01 Nighttime Light Imagery for Built-Up Urban Area Extraction: A Case Study of 16 Cities in China. IEEE Geosci. Remote Sens. Lett. 2020, 17, 1802–1806. [Google Scholar] [CrossRef]
- Ma, L.; Liu, Y.; Zhang, X.; Ye, Y.; Yin, G.; Johnson, B.A. Deep learning in remote sensing applications: A meta-review. ISPRS J. Photogramm. Remote Sens. 2018, 145, 134–153. [Google Scholar] [CrossRef]
- Goldblatt, R.; Stuhlmacher, M.F.; Tellman, B.; Clinton, N.; Hanson, G.; Georgescu, M.; Wang, C.; Serrano-Candela, F.; Balling, R.C., Jr. Mapping Urban Land Cover: A Novel Machine Learning Approach Using Landsat and Night-time Lights. Remote Sens. Environ. 2018, 217, 221–232. [Google Scholar]
- Elvidge, C.D.; Baugh, K.E.; Zhizhin, M.; Hsu, F.C. Why VIIRS data are superior to DMSP for mapping nighttime lights. Int. J. Remote Sens. 2017, 38, 5860–5879. [Google Scholar] [CrossRef]
- Li, X.; Zhou, Y. Urban mapping using nighttime light data: A review. Remote Sens. Environ. 2021, 256, 112307. [Google Scholar]
- Zhang, Q.; Pandey, B.; Seto, K.C. High-frequency VIIRS nighttime light data reveal dynamic urban expansion. ISPRS J. Photogramm. Remote Sens. 2022, 183, 321–333. [Google Scholar]
- Li, X.; Xu, H.; Chen, X.; Li, C. Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China. Remote Sens. 2013, 5, 3057–3081. [Google Scholar] [CrossRef]
- Shao, Z.; Zhang, Y.; Huang, X. Spatiotemporal patterns of urban expansion using DMSP/OLS nighttime light data in China. Cities 2020, 96, 102415. [Google Scholar]
- Small, C.; Pozzi, F.; Elvidge, C.D. Nighttime satellite imagery: Interpretation and applications for socioeconomic studies. Remote Sens. Environ. 2021, 247, 111872. [Google Scholar]
- Yang, L.; Yang, Y.; Shen, H. Comparative evaluation of Luojia-1 and VIIRS NTL data for urban analysis. Remote Sens. Environ. 2023, 284, 113330. [Google Scholar]
- Chen, Z.; Yin, H.; Fang, S. Assessing the performance of high-resolution Luojia-1 imagery in urban studies. Int. J. Remote Sens. 2022, 43, 3224–3240. [Google Scholar]
- Zhao, N.; Cao, G.; Samson, E.L.; Wang, J. Challenges in validating NTL-based urban extent maps due to limited ground truth data. Urban Stud. 2020, 57, 498–517. [Google Scholar]
- Huang, Q.; Lu, Y. A framework for selecting appropriate NTL data for urban studies. Remote Sens. Lett. 2019, 10, 875–884. [Google Scholar]
- Ma, T.; Zhou, Y.; Pei, T. Comparison of threshold and SVM for nighttime light-based urban classification. Sensors 2018, 18, 1138. [Google Scholar]
- Shi, K.; Chen, Y.; Yu, B.; Xu, T.; Chen, Z.; Wu, J. Multi-temporal image fusion for improving nighttime light-based urban area extraction. Remote Sens. 2020, 12, 2125. [Google Scholar]
- Levin, N.; Zhang, Q. A global analysis of threshold-based urban boundary detection using nighttime light data. ISPRS J. Photogramm. Remote Sens. 2020, 167, 86–99. [Google Scholar]
- Zhang, J.; Li, Z.; Ma, L. Nighttime light fusion and temporal stability for improved urban monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022, 15, 2111–2123. [Google Scholar]
- Yao, X.; Sun, X.; Liu, H. Overestimation of urban extents in SVM-based classification of NTL data. Comput. Environ. Urban Syst. 2021, 85, 101543. [Google Scholar]
- Wang, W.; Gong, J.; Lin, L. Hybrid approaches to enhance nighttime urban detection. Int. J. Appl. Earth Obs. Geoinf. 2020, 90, 102123. [Google Scholar]
- Ghamisi, P.; Plaza, J.; Benediktsson, J.A. Validation protocols for classification algorithms in remote sensing. IEEE Geosci. Remote Sens. Mag. 2019, 7, 54–65. [Google Scholar]
- Bai, Y.; Zhou, Y.; Chen, H. Evaluating spatial agreement using IoU for urban area extraction. J. Urban Technol. 2023, 30, 54–71. [Google Scholar]
- Zhang, Q.; Pandey, B. The influence of radiometric resolution on method agreement in urban extraction. Remote Sens. Environ. 2020, 248, 111983. [Google Scholar]
- Wu, W.; Xu, H.; Zhang, X. Artifact-induced spatial inconsistencies in Luojia-1 temporal fusion. ISPRS J. Photogramm. Remote Sens. 2021, 175, 92–105. [Google Scholar]
- Xie, Y.; Fang, S.; Yin, H. Sensitivity of machine learning methods to radiometric heterogeneity in NTL data. Remote Sens. 2022, 14, 2135. [Google Scholar]
- Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015; Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F., Eds.; Springer: Cham, Switzerland, 2015; pp. 234–241. [Google Scholar]
- Zhang, Y.; Wang, J.; Wu, J. Urban feature extraction using U-Net from high-resolution remote sensing imagery. Remote Sens. 2021, 13, 712. [Google Scholar]
- Xie, H.; Liu, Y.; Gao, B. Comparative study of CNN-based and traditional classification for urban land use. ISPRS J. Photogramm. Remote Sens. 2022, 187, 235–248. [Google Scholar]
- Bischke, B.; Helber, P.; Borth, D. Deep learning for pixel-level mapping in urban environments. IEEE Geosci. Remote Sens. Lett. 2019, 16, 1128–1132. [Google Scholar]
- Liu, J.; Wang, S.; Hu, M. DeepLabV3+ for accurate urban boundary extraction from remote sensing data. Remote Sens. Environ. 2023, 290, 113378. [Google Scholar]
- Chen, L.C.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Computer Vision–ECCV 2018; Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y., Eds.; Springer: Cham, Switzerland, 2018; pp. 801–818. [Google Scholar]
- Hu, X.; Xu, H.; Fang, S. Fusion of nighttime light and multispectral data for refined urban area extraction. Int. J. Remote Sens. 2021, 42, 5004–5021. [Google Scholar]
- Zhang, H.; Li, X.; Wang, L. Integrating socioeconomic data into NTL-based urban delineation. Comput. Environ. Urban Syst. 2020, 80, 101452. [Google Scholar]
- Yu, B.; Liu, H.; Wu, J.; Zhang, L. Urban form recognition from fused optical and nighttime light data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 2135–2147. [Google Scholar]
- Tang, J.; Zhang, Y.; Yang, X. High-resolution monitoring of urban microstructures using fused remote sensing data. Remote Sens. Environ. 2022, 281, 113197. [Google Scholar]
- Gong, P.; Li, X.; Wang, J.; Zhang, C. Annual maps of global artificial impervious area (GAIA) from 1985 to 2018. Nat. Sustain. 2020, 3, 529–540. [Google Scholar]
Satellite | DMSP/OLS | SNPP/VIIRS | Luojia-1 |
Country | United States | United States | China |
Data Acquisition Period | 1992–2012, data not available after 2013 | December 2011–present | June 2018–present |
Spatial Resolution | 2.7 km | 700 m | 130 m |
Temporal Resolution | 12 h | 12 h | 15 days |
Pixel Saturation | Oversaturated | Unsaturated | Unsaturated |
Coverage Area | Global | 75°N–60°S | China |
Method | Accuracy | Robustness | Scalability | Processing Time |
---|---|---|---|---|
Thresholding | Low–Moderate | Low | High | Fast (3 s) |
Image Fusion + K-means | Moderate | Moderate | Moderate | Medium (15–20 s) |
SVM + Features | High | High | Moderate–Low | Slowest (65–90 s) |
Indicator | Definition | Computation Basis | Interpretation |
---|---|---|---|
Consistency Index (CI) | Spatial agreement between methods/datasets | Intersection-over-Union (IoU) | Higher CI = greater cross-sensor robustness |
Stability Score (SS) | Temporal consistency under seasonal variation | Inverse of monthly standard deviation | Higher SS = more temporally stable extraction |
Processing Efficiency (PE) | Execution speed under high-resolution conditions | Runtime on 10M-pixel benchmark dataset | Lower time = more efficient, scalable method |
Method | Advantages | Disadvantages | Best-Fit Dataset |
---|---|---|---|
Thresholding | Simple; Stable in urban core; Low computational cost | Sensitive to noise; Fixed threshold; Poor edge detection | SNPP/VIIRS |
Multi-temporal fusion | Captures temporal dynamics; Good urban continuity | Overestimation risk; Temporal inconsistency issues | SNPP/VIIRS (temporal-rich) |
SVM | High adaptability; Good at suburban expansion | Requires labeled data; Risk of overfitting; Needs tuning | DMSP/OLS |
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Tu, S.; Zhan, Q.; Qiu, R.; Yu, J.; Qubi, A. A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City. Remote Sens. 2025, 17, 2879. https://doi.org/10.3390/rs17162879
Tu S, Zhan Q, Qiu R, Yu J, Qubi A. A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City. Remote Sensing. 2025; 17(16):2879. https://doi.org/10.3390/rs17162879
Chicago/Turabian StyleTu, Shiqi, Qingming Zhan, Ruihan Qiu, Jiashan Yu, and Agamo Qubi. 2025. "A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City" Remote Sensing 17, no. 16: 2879. https://doi.org/10.3390/rs17162879
APA StyleTu, S., Zhan, Q., Qiu, R., Yu, J., & Qubi, A. (2025). A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City. Remote Sensing, 17(16), 2879. https://doi.org/10.3390/rs17162879