{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T07:01:02Z","timestamp":1769238062314,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41474010"],"award-info":[{"award-number":["41474010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61401509"],"award-info":[{"award-number":["61401509"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42201492"],"award-info":[{"award-number":["42201492"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters.<\/jats:p>","DOI":"10.3390\/rs15123096","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:01:40Z","timestamp":1686708100000},"page":"3096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR"],"prefix":"10.3390","volume":"15","author":[{"given":"Qihao","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Guowang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Xin","family":"Xiong","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Hao","family":"Ye","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Yuzhi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.2478\/jlecol-2022-0017","article-title":"The Use of Remote Sensing Data for Investigation of Environmental Consequences of Russia-Ukraine War","volume":"15","author":"Serhii","year":"2022","journal-title":"J. Landsc. Ecol."},{"key":"ref_2","unstructured":"ICRC (2015). Urban Services during Protracted Armed Conflflict: A Call for a Better Approach to Assisting Affected People, International Committee of the Red Cross."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.apgeog.2019.05.004","article-title":"Remote sensing-based mapping of the destruction to Aleppo during the Syrian Civil War between 2011 and 2017","volume":"108","author":"Lubin","year":"2019","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, S., Jendryke, M., Li, D., and Wu, C. (2018). Night-Time Light Dynamics during the Iraqi Civil War. Remote Sens., 10.","DOI":"10.3390\/rs10060858"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jiang, W., He, G., Long, T., and Liu, H. (2017). Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light. Remote Sens., 9.","DOI":"10.3390\/rs9080798"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5934","DOI":"10.1080\/01431161.2017.1331476","article-title":"Intercalibration between DMSP\/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria\u2019s major human settlement during Syrian Civil War","volume":"38","author":"Li","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","unstructured":"Human Rights Watch (2023, April 24). Burma: 40 Rohingya Villages Burned Since October. Hum. Rights Watch. [WWW Document]. Available online: https:\/\/www.hrw.org\/news\/2017\/12\/17\/burma-40-rohingya-villagesburned-october."},{"key":"ref_8","first-page":"119","article-title":"Detecting village burnings with high-cadence smallsats: A case-study in the Rakhine State of Myanmar","volume":"14","author":"Marx","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_9","unstructured":"United Nations Satellite Centre UNOSAT|UNITAR (2023, April 24). Available online: https:\/\/www.unitar.org\/sustainable-development-goals\/united-nations-satellite-centre-UNOSAT."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1080\/01431160902953909","article-title":"Relating violence to MODIS fire detections in Darfur, Sudan","volume":"31","author":"Bromley","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1080\/01431160701730110","article-title":"Use of low cost Landsat ETM+ to spot burnt villages in Darfur, Sudan","volume":"29","author":"Prins","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","first-page":"30","article-title":"Detecting urban destruction in Syria: A Landsat-based approach","volume":"4","author":"Marx","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"478","DOI":"10.2747\/1548-1603.48.4.478","article-title":"Detecting the effects of wars in the Caucasus regions of Russia and Georgia using radiometrically normalized DMSP-OLS nighttime lights imagery","volume":"48","author":"Witmer","year":"2011","journal-title":"GIScience Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6648","DOI":"10.1080\/01431161.2014.971469","article-title":"Can night-time light images play a role in evaluating the Syrian Crisis","volume":"35","author":"Li","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.3390\/rs5063057","article-title":"Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China","volume":"5","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, L.-L., Liang, P., Jiang, S., and Chen, Z.-Q. (2022). Multi-Scale Dynamic Analysis of the Russian\u2013Ukrainian Conflict from the Perspective of Night-Time Lights. Appl. Sci., 12.","DOI":"10.3390\/app122412998"},{"key":"ref_17","first-page":"33","article-title":"Mapping of nighttime light trends and refugee population changes in Ukraine during the Russian\u2013Ukrainian War","volume":"11","author":"Huang","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.15407\/ugz2022.02.011","article-title":"Use of Satellite Information for Evaluation of Socio-Economic Consequences of the War in Ukraine","volume":"2","author":"Yelistratova","year":"2022","journal-title":"Ukr. Geogr. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, L., Ren, C., Liu, L., Zhang, D., Ma, J., and Shi, Y. (2021). Spatiotemporal Inversion and Mechanism Analysis of Surface Subsidence in Shanghai Area Based on Time-Series InSAR. Appl. Sci., 11.","DOI":"10.3390\/app11167460"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Aimaiti, Y., Sanon, C., Koch, M., Baise, L.G., and Moaveni, B. (2022). War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images. Remote Sens., 14.","DOI":"10.3390\/rs14246239"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Washaya, P., Balz, T., and Mohamadi, B. (2018). Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas. Remote Sens., 10.","DOI":"10.3390\/rs10071026"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Boloorani, A.D., Darvishi, M., Weng, Q., and Liu, X. (2021). Post-War Urban Damage Mapping Using InSAR: The Case of Mosul City in Iraq. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030140"},{"key":"ref_23","unstructured":"Corey, S., and Jamon, V. (2022, January 12\u201316). Decentralized, nation-wide, high-frequency war damage mapping using InSAR time series data. Proceedings of the AGU Fall Meeting 2022, Chicago, IL, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing: Challenges and Opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.15184\/aqy.2022.14","article-title":"Remote sensing and ground survey of archaeological damage and destruction at Nineveh during the ISIS occupation","volume":"96","author":"Campana","year":"2022","journal-title":"Antiquity"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2018.03.001","article-title":"Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study","volume":"94","author":"Ali","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_27","unstructured":"(2023, April 24). Mariupol. Available online: https:\/\/en.wikipedia.org\/wiki\/Mariupol."},{"key":"ref_28","unstructured":"CNN (2023, April 24). CNN (Ukraine-Satellite-Images). Available online: https:\/\/edition.cnn.com\/interactive\/2022\/03\/world\/ukraine-satellite-images\/."},{"key":"ref_29","unstructured":"CNN (2023, April 24). What Does Putin Want in Ukraine? The Conflict Explained. Available online: https:\/\/edition.cnn.com\/2022\/02\/24\/europe\/ukraine-russia-conflict-explainer-2-cmd-intl\/index.html."},{"key":"ref_30","unstructured":"TASS (Military Operation in Ukraine) (2023, April 24). Putin Declares Beginning of Military Operation in Ukraine. Available online: https:\/\/tass.com\/politics\/1409329."},{"key":"ref_31","unstructured":"Neta, C. (The Conversation, 2022). Crawford Reliable Death Tolls from the Ukraine War Are Hard to Come by\u2014The Result of Undercounts and Manipulation, The Conversation."},{"key":"ref_32","unstructured":"Copernicus Open Access Hub (2023, April 24). Available online: https:\/\/scihub.copernicus.eu."},{"key":"ref_33","unstructured":"USGS (2023, February 13). EROS Archive\u2014Sentinel-2|U.S Geological Survey, Available online: https:\/\/www.usgs.gov\/centers\/eros\/science\/usgseros-archive-sentinel-2?qt-science_center_objects=0#qt-science_center_objects."},{"key":"ref_34","unstructured":"(2023, February 13). European Space Agency Sentinel-2 User Handbook. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/userguides\/document-library\/-\/asset_publisher\/xlslt4309D5h\/content\/sentinel-2-user-handbook."},{"key":"ref_35","unstructured":"(2023, April 24). USGS EarthExplorer, Available online: http:\/\/earthexplorer.usgs.gov."},{"key":"ref_36","unstructured":"(2023, February 13). Landsat 8 Data Users Handbook|U.S. Geological Survey, Available online: https:\/\/www.usgs.gov\/landsat-missions\/landsat-8-data-users-handbook."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering open science and applications through continuity","volume":"248","author":"Maseka","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"(2023, February 13). User Guides\u2014Sentinel-1 SAR\u2014Sentinel Online\u2014Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-1-sar."},{"key":"ref_39","unstructured":"(2023, April 24). Alaska Satellite Facility. Available online: https:\/\/search.asf.alaska.edu."},{"key":"ref_40","unstructured":"(2023, February 13). Open Street Map. Available online: www.openstreetmap.org."},{"key":"ref_41","unstructured":"(2023, April 11). Satellite Images Map of Mariupol (Mapping.jp). Available online: https:\/\/ukraine.mapping.jp\/mariupol.html."},{"key":"ref_42","unstructured":"(2022, July 23). Twitter Search Image \u201cMariupol\u201d. Available online: https:\/\/twitter.com\/search?q=mariupol&src=typed_query&f=image."},{"key":"ref_43","unstructured":"(2022, July 23). Facebook Search \u201cMariupol\u201d. Available online: https:\/\/m.facebook.com\/profile.php?id=108052062548731."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/10106049109354290","article-title":"Mapping Burns and Natural Reforestation Using Thematic Mapper Data","volume":"6","author":"Garcia","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_45","unstructured":"Key, C., and Benson, N. (2005). FIREMON: Fire Effects Monitoring and Inventory System, USDA Forest Service, Rocky Mountain Research Station."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1193\/1.1774182","article-title":"Use of Satellite SAR Intensity Imagery for Detecting Building Areas Damaged Due to Earthquakes","volume":"20","author":"Matsuoka","year":"2004","journal-title":"Earthq. Spectra"},{"key":"ref_47","unstructured":"Takeuchi, S., Suga, Y., Yonezawa, C., and Chen, A.J. (2000, January 24\u201328). Detection of Urban Disaster Using InSAR: A Case Study for the 1999 Great Taiwan Earthquake. Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., and Mastuoka, M. (2017). Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy. Remote Sens., 9.","DOI":"10.3390\/rs9040330"},{"key":"ref_49","unstructured":"Yelistratova, L.A., Apostolov, A., and Movchan, D.M. (2022). Natural Resource Potential, Ecology, and Sustainable Development of Administrative Units of the Republic of Latvia and Ukraine Amidst EU Legislative Requirements, Baltija Publishing."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hu, S., Feng, M., Nguyen, R.M.H., and Lee, G.H. (2018, January 18\u201322). CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00758"},{"key":"ref_51","unstructured":"(2023, April 24). Damage Assessment Overview Map\u2014Livoberezhnyi and Zhovtnevyi Districts, Mariupol City, Ukraine. Available online: http:\/\/unosat.org\/products\/3371."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3096\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:20Z","timestamp":1760126060000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":51,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123096"],"URL":"https:\/\/doi.org\/10.3390\/rs15123096","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,13]]}}}