{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T03:23:03Z","timestamp":1772508183848,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T00:00:00Z","timestamp":1575936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS201908"],"award-info":[{"award-number":["OFSLRSS201908"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The heavy industry in India has witnessed rapid development in the past decades. This has increased the pressures and load on the Indian environment, and has also had a great impact on the world economy. In this study, the Preparatory Project Visible Infrared Imaging Radiometer (NPP VIIRS) 375-m active fire product (VNP14IMG) and night-time light (NTL) data were used to study the spatiotemporal patterns of heavy industrial development in India. We employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-term VNP14IMG data and artificial heat-source objects. Next, the initial heavy industry heat sources were distinguished from normal heat sources using a threshold recognition model. Finally, the maximum night-time light data were used to delineate the final heavy industry heat sources. The results suggest, that this modified method is a much more accurate and effective way of monitoring heavy industrial heat sources, and the accuracy of this detection model was higher than 92.7%. The number of main findings were concluded from the study: (1) the heavy industry heat sources are mainly concentrated in the north-east Assam state, east-central Jharkhand state, north Chhattisgarh and Odisha states, and the coastal areas of Gujarat and Maharashtra. Many heavy industrial heat sources were also found around a line from Kolkata on the Eastern Indian Ocean to Mumbai on the Western Indian Ocean. (2) The number of working heavy industry heat sources (NWH) and, particularly, the total number of fire hotspots for each working heavy industry heat source area (NFHWH) are continuing to increase in India. These trends mirror those for the Gross Domestic Product (GDP) and total population of India between 2012 and 2017. (3) The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha whereas the smallest negative values, the     S l o p e _ N W H     in Jharkhand and Chhattisgarh were also the two largest values in the whole country. The smallest negative values of     S l o p e _ N W H     and     S l o p e _ N F H W H     were in Haryana. The     S l o p e _ N F H W H     in the mainland Gujarat had the second most negative value, while the value of the     S l o p e _ N W H     was the third-highest positive value.<\/jats:p>","DOI":"10.3390\/ijgi8120568","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T10:52:41Z","timestamp":1575975161000},"page":"568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018"],"prefix":"10.3390","volume":"8","author":[{"given":"Caihong","family":"Ma","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5959-9351","authenticated-orcid":false,"given":"Zheng","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Ma","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Chen","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"ref_1","unstructured":"The World Bank (2019, December 09). World Bank Open Data. Available online: https:\/\/data.worldbank.org\/country\/russian-federation."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, J., Chen, F., Ma, Y., Liu, J., Li, X., Duan, J., and Guo, R. (2018). Assessing Heavy industry heat source Distribution in China Using Real-Time VIIRS Active Fire\/Hotspot Data. Sustainability, 10.","DOI":"10.3390\/su10124419"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41893-017-0003-y","article-title":"Targeted emission reductions from global super-polluting power plant units","volume":"1","author":"Tong","year":"2018","journal-title":"Nat. Sustain."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhao, F., Wang, S., Liu, W., and Wang, L. (2018). A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites. Sustainability, 10.","DOI":"10.3390\/su10061935"},{"key":"ref_5","unstructured":"BP (British Petroleum) (2019, December 09). BP Home-Page. Available online: https:\/\/www.bp.com\/."},{"key":"ref_6","unstructured":"International Energy Agency (IEA) (2019, December 09). Shaping a Secure and Sustainable Energy Future for All. Available online: https:\/\/www.iea.org\/#statistics-data."},{"key":"ref_7","unstructured":"MEIC (2019, December 09). Global Power Emissions Database(GPED). (In Chinese)."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7463","DOI":"10.1021\/es300831w","article-title":"Increase in NOx emissions from Indian thermal power plants during 1996\u20132010: Unit-based inventories and multisatellite observations","volume":"46","author":"Lu","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_9","first-page":"221","article-title":"Review of change detection techniques from remotely sensed images","volume":"10","author":"Deilami","year":"2015","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ins.2014.01.037","article-title":"A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system","volume":"269","author":"Roy","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2017.10.019","article-title":"Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach","volume":"204","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/0034-4257(88)90110-1","article-title":"Monitoring grassland dryness and fire potential in Australia with NOAA\/AVHRR data","volume":"25","author":"Paltridge","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1080\/01431160500113526","article-title":"Validation of the MODIS active fire product over Southern Africa with ASTER data","volume":"26","author":"Morisette","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"004","article-title":"Research and evaluation of the algorithm of land surface fire detection based on FY3-VIRR data","volume":"3","author":"Zhao","year":"2011","journal-title":"Fire Saf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.rse.2015.08.011","article-title":"Mapping post-fire habitat characteristics through the fusion of remote sensing tools","volume":"173","author":"Vogeler","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2015.08.032","article-title":"Active fire detection using Landsat-8\/OLI data","volume":"185","author":"Schroeder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.procs.2017.11.189","article-title":"VIIRS Nightfire Remote Sensing Volcanoes","volume":"119","author":"Trifonov","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","unstructured":"Baugh, K. (2015, January 14\u201318). Characterization of Gas Flaring in North Dakota using the Satellite Data Product, VIIRS Nightfire. Proceedings of the AGU Fall Meeting 2015, San Francisco, CA, USA."},{"key":"ref_19","first-page":"13","article-title":"Classification of Urban Industrial Heat Sources Based on Suomi-NPP VIIRS Night-time Thermal Anomaly Products: A Case Study of the Beijing-Tianjin-Hebei Region","volume":"34","author":"Sun","year":"2018","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2013.12.008","article-title":"The new VIIRS 375 m activefire detection data product: Algorithm description and initial assessment","volume":"143","author":"Schroeder","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2016.02.054","article-title":"The collection 6 MODIS active fire detection algorithm and fire products","volume":"178","author":"Giglio","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dai, Z., Hu, Y., and Zhao, G. (2017). The Suitability of Different Night-time Light Data for GDP Estimation at Different Spatial Scales and Regional Levels. Sustainability, 9.","DOI":"10.3390\/su9020305"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhao, H., and Jiang, S. (2018). A Zipf\u2019s Law-Based Method for Mapping Urban Areas Using NPP-VIIRS Night-time Light Data. Remote Sens., 10.","DOI":"10.3390\/rs10010130"},{"key":"ref_24","unstructured":"The World Bank (2019, December 09). The World Bank In India. Available online: http:\/\/www.worldbank.org\/en\/country\/india."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13299","DOI":"10.5194\/acp-15-13299-2015","article-title":"High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010","volume":"15","author":"Liu","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_26","unstructured":"(2019, April 18). VIIRS I-Band 375 m Active Fire Data, Available online: https:\/\/earthdata.nasa.gov\/earth-observation-data\/near-real-time\/firms\/viirs-i-band-active-fire-data."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Elvidge, C.D., Zhizhin, M., Baugh, K., Hsu, F.C., and Ghosh, T. (2019). Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data. Remote Sens., 11.","DOI":"10.3390\/rs11040395"},{"key":"ref_28","unstructured":"(2019, December 09). VIIRS DNB Nighttime Imagery, Available online: https:\/\/maps.ngdc.noaa.gov\/viewers\/VIIRS_DNB_nighttime_imagery\/index.html."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, X., Ma, M., Yang, H., and Ge, W. (2017). Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Night-time Light Data. Remote Sens., 9.","DOI":"10.3390\/rs9060626"},{"key":"ref_30","unstructured":"(2019, April 18). Version 1 VIIRS Day\/Night Band Nighttime Lights. Available online: https:\/\/eogdata.mines.edu\/download_dnb_composites.html."},{"key":"ref_31","unstructured":"(2019, December 09). The Chinese Academy of Sciences version of the Earth Luminous Data Set (codenamed \u201cFlint\u201d) Provides Annual Data Download Service. Available online: https:\/\/www.jianshu.com\/p\/5fde55a4d267?tdsourcetag=s_pcqq_aiomsg."},{"key":"ref_32","unstructured":"(2019, December 09). NPP_NIGHT_LIGHT. Available online: https:\/\/pan.baidu.com\/s\/17UqS7P66_6AMdr-a4sfUXA#list\/path=%2F."},{"key":"ref_33","unstructured":"(2019, April 18). GADM Data. Available online: https:\/\/gadm.org\/data.html."},{"key":"ref_34","unstructured":"India Brand Equity Foundation (IBEF) (2019, December 09). About Jharkhand: Information on Mining Industries, Economy, Agriculture & Geography. Available online: https:\/\/www.ibef.org\/states\/jharkhand.aspx."},{"key":"ref_35","unstructured":"(2019, April 18). Industrial Development & Economic Growth in Chhattisgarh. Available online: https:\/\/www.ibef.org\/industry\/chhattisgarh-presentation."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/12\/568\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:41:04Z","timestamp":1760190064000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/12\/568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,10]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["ijgi8120568"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8120568","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,10]]}}}