Next Article in Journal
The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries
Previous Article in Journal
Integrating Network Centrality and Node-Place Model to Evaluate and Classify Station Areas in Shanghai
Article

Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery

by 1,2, 1,2,*, 3 and 1,2
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China
3
Dongguan Geographic Information & Urban Planning Research Center, Dongguan 523129, China
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Monika Sester
ISPRS Int. J. Geo-Inf. 2021, 10(6), 415; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060415
Received: 13 April 2021 / Revised: 25 May 2021 / Accepted: 4 June 2021 / Published: 16 June 2021
Urban area hotspots are considered to be an ideal proxy for spatial heterogeneity of human activity, which is vulnerable to urban expansion. Nighttime light (NTL) images have been extensively employed in monitoring current urbanization dynamics. However, the existing studies related to NTL images mainly concern detection of urban areas, leaving inner spatial differences in urban NTL luminosity poorly explored. In this study, we propose an innovative approach to explore the spatiotemporal trajectory of urban area hotspots using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images. Firstly, multi-temporal VIIRS NTL intensity was decomposed by time-series analysis to obtain annual stable components after data preprocessing. Secondly, the support vector machine (SVM) regression model was utilized to identify urban area hotspots. In order to ensure the model accuracy, the grid search and cross-validation method was integrated to achieve the optimized model parameters. Finally, we analyzed the spatiotemporal migration trajectory of urban area hotspots by the center of gravity method (i.e., shift distance and angle of urban area hotspot centroid). The results indicate that our method successfully captured urban area hotspots with a regression coefficient over 0.8. Meanwhile, the findings give an intuitive understanding of coupling interaction between urban area hotspots and socioeconomic indicators. This study provides important insights for further decision-making regarding sustainable urban planning. View Full-Text
Keywords: urban area hotspot; nighttime light imagery; SVM regression; VIIRS; urbanization urban area hotspot; nighttime light imagery; SVM regression; VIIRS; urbanization
Show Figures

Figure 1

MDPI and ACS Style

Ruan, Y.; Zou, Y.; Chen, M.; Shen, J. Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery. ISPRS Int. J. Geo-Inf. 2021, 10, 415. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060415

AMA Style

Ruan Y, Zou Y, Chen M, Shen J. Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery. ISPRS International Journal of Geo-Information. 2021; 10(6):415. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060415

Chicago/Turabian Style

Ruan, Yuling, Yanhong Zou, Minghui Chen, and Jingya Shen. 2021. "Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery" ISPRS International Journal of Geo-Information 10, no. 6: 415. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060415

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop