Abstract

The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012–2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user’s accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

Details

Title
Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021
Author
Ma, Caihong 1   VIAFID ORCID Logo  ; Li, Tianzhu 2 ; Sui, Xin 3 ; Liao, Ruilin 3 ; Xie, Yanmei 4 ; Zhang, Pengyu 3 ; Wu, Mingquan 5 ; Wang, Dacheng 5 

1  Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); Beijing Forestry University, College of Information, Beijing, China (GRID:grid.66741.32) (ISNI:0000 0001 1456 856X); China University of Mining and Technology, College of Environment and Spatial Informatics, Xuzhou, China (GRID:grid.411510.0) (ISNI:0000 0000 9030 231X) 
2  Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419) 
3  Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); Beijing Forestry University, College of Information, Beijing, China (GRID:grid.66741.32) (ISNI:0000 0001 1456 856X) 
4  Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); China University of Mining and Technology, College of Environment and Spatial Informatics, Xuzhou, China (GRID:grid.411510.0) (ISNI:0000 0000 9030 231X) 
5  Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
Pages
631
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-024-03461-3
ProQuest document ID
3068273062
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.