{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T03:25:01Z","timestamp":1783394701655,"version":"3.54.6"},"reference-count":64,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban\/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices\u2019 thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up\/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban\/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster.<\/jats:p>","DOI":"10.3390\/rs12050895","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T11:59:36Z","timestamp":1583841576000},"page":"895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2818-0133","authenticated-orcid":false,"given":"Sahar","family":"Derakhshan","sequence":"first","affiliation":[{"name":"Hazards &amp; Vulnerability Research Institute, Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan L.","family":"Cutter","sequence":"additional","affiliation":[{"name":"Hazards &amp; Vulnerability Research Institute, Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0306-9535","authenticated-orcid":false,"given":"Cuizhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,10]]},"reference":[{"key":"ref_1","unstructured":"Quarantelli, E.L. (1998). What is a Disaster?, Routledge."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.2307\/3134998","article-title":"Disaster Recovery and Hazard Mitigation: Bridging the Intergovernmental Gap","volume":"45","author":"Rubin","year":"1985","journal-title":"Public Adm. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/028072708600400103","article-title":"Disaster impact and recovery: A comparison of black and white victims","volume":"4","author":"Bolin","year":"1986","journal-title":"Int. J. Mass Emergencies Disasters"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1061\/(ASCE)NH.1527-6996.0000077","article-title":"Disaster and recovery: Processes compressed in time","volume":"13","author":"Olshansky","year":"2012","journal-title":"Nat. Hazards Rev."},{"key":"ref_5","first-page":"51","article-title":"Disaster recovery and community renewal: Housing approaches","volume":"16","author":"Comerio","year":"2014","journal-title":"Cityscape"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cutter, S.L., Emrich, C.T., Mitchell, J.T., Piegorsch, W.W., Smith, M.M., and Weber, L. (2014). Hurricane Katrina and the Forgotten Coast of Mississippi, Cambridge University Press.","DOI":"10.1017\/CBO9781139161831"},{"key":"ref_7","unstructured":"Oliver-Smith, A. (2015). Conversations in catastrophe: Neoliberalism and the cultural construction of disaster risk. Cultures and Disasters: Understanding Cultural Framings in Disaster Risk Reduction, Routledge. Chapter 2."},{"key":"ref_8","unstructured":"Sarabandi, P., Kiremidjian, A.S., Eguchi, R.T., and Adams, B.J. (2008). Building Inventory Compilation for Disaster Management: Application of Remote Sensing and Statistical Modeling, University at Buffalo. Technical Report MCEER-08-0025."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"899","DOI":"10.14358\/PERS.77.9.899","article-title":"USGS Remote Sensing Coordination for the 2010 Haiti Earthquake","volume":"77","author":"Duda","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives","volume":"100","author":"Dellacqua","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2011.12.004","article-title":"Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake","volume":"68","author":"Tong","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.sbspro.2014.02.114","article-title":"Satellite Remote Sensing as a Tool in Disaster Management and Sustainable Development: Towards a Synergistic Approach","volume":"120","author":"Bello","year":"2014","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bevington, J.S., Eguchi, R.T., Gill, S., Ghosh, S., and Huyck, C.K. (2015). A Comprehensive Analysis of Building Damage in the 2010 Haiti Earthquake Using High-Resolution Imagery and Crowdsourcing. Time-Sensitive Remote Sens, 131\u2013145.","DOI":"10.1007\/978-1-4939-2602-2_9"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.proeps.2015.08.063","article-title":"Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies","volume":"15","author":"Menderes","year":"2015","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1126\/science.aad8728","article-title":"Global trends in satellite-based emergency mapping","volume":"353","author":"Voigt","year":"2016","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Kerle, N., and Filatova, T. (2018). Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10111760"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kerle, N., Ghaffarian, S., Nawrotzki, R., Leppert, G., and Lech, M. (2019). Evaluating Resilience-Centered Development Interventions with Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11212511"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nex, F., Duarte, D., Steenbeek, A., and Kerle, N. (2019). Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions. Remote Sens., 11.","DOI":"10.3390\/rs11030287"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1109\/JSTARS.2014.2322143","article-title":"A New Hybrid Strategy Combining Semisupervised Classification and Unmixing of Hyperspectral Data","volume":"7","author":"Dopido","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","first-page":"226","article-title":"Mapping the Distribution of Mangrove Species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, Using Hyperspectral Data and High-Resolution Data","volume":"33","author":"Jia","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","first-page":"51","article-title":"Remote Sensing and Object-Based Techniques for Mapping Fine-Scale Industrial Disturbances","volume":"34","author":"Powers","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Prosper, W., 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_23","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.patrec.2005.08.004","article-title":"Urban monitoring using multi-temporal SAR and multi-spectral data","volume":"27","author":"Calpe","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1111\/0033-0124.00232","article-title":"Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data","volume":"52","author":"Ward","year":"2000","journal-title":"Prof. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1080\/01431160110075802","article-title":"Using a Time Series of Satellite Imagery to Detect Land Use and Land Cover Changes in the Atlanta, Georgia Metropolitan Area","volume":"23","author":"Yang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1080\/01431160110104728","article-title":"Urban Built-up Land Change Detection with Road Density and Spectral Information from Multi-Temporal Landsat TM Data","volume":"23","author":"Zhang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2014.09.014","article-title":"Corrigendum to Urban Growth of the Washington, D.C.\u2013Baltimore, MD Metropolitan Region from 1984 to 2010 by Annual, Landsat-Based Estimates of Impervious Cover. [Remote Sens. Environ. 2013, 129, 42\u201353.]","volume":"155","author":"Sexton","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2015.06.007","article-title":"A 30-Year (1984\u20132013) Record of Annual Urban Dynamics of Beijing City Derived from Landsat Data","volume":"166","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Faridatul, M.I., and Wu, B. (2018). Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7120453"},{"key":"ref_30","first-page":"249","article-title":"Assessing land use\/cover changes: A nationwide multidate spatial database for Mexico","volume":"5","author":"Mas","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160310001619571","article-title":"Using ThematicMapper data for change detection and sustainable use of cultivated land: A case study in the Yellow River delta, China","volume":"25","author":"Zhao","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5721\/EuJRS20154808","article-title":"Remote Sensing based multi- temporal land cover classification and change detection in northwestern Ethiopia","volume":"48","author":"Zewdie","year":"2015","journal-title":"Eur. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ayele, G.T., Tebeje, A.K., Demissie, S.S., Belete, M.A., Jemberrie, M.A., Teshome, W.M., and Teshale, E.Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air Soil Water Res., 11.","DOI":"10.1177\/1178622117751603"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"825","DOI":"10.14358\/PERS.71.7.825","article-title":"Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques","volume":"71","author":"Caravaggi","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"167","article-title":"Enhanced Change Detection Index for Disaster Response, Recovery Assessment and Monitoring of Buildings and Critical Facilities\u2014A Case Study for Muzzaffarabad, Pakistan","volume":"63","author":"So","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1080\/2150704X.2013.763297","article-title":"Improved NDBI Differencing Algorithm for Built-up Regions Change Detection from Remote-Sensing Data: An Automated Approach","volume":"4","author":"Varshney","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_39","first-page":"35","article-title":"Built-up Index Methods and Their Applications for Urban Extraction from Sentinel 2A Satellite Data: Discussion","volume":"35","year":"2017","journal-title":"J. Opt. Soc. Am."},{"key":"ref_40","first-page":"321","article-title":"Relation between social and environmental conditions in Colombo Sri Lanka and the Urban Index estimated by satellite remote sensing data","volume":"31","author":"Kawamura","year":"1996","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01431161.2010.481681","article-title":"Improving the Normalized Difference Built-up Index to Map Urban Built-up Areas Using a Semiautomatic Segmentation Approach","volume":"1","author":"He","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1080\/01431160802039957","article-title":"A New Index for Delineating Built-up Land Features in Satellite Imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.3390\/rs4102957","article-title":"Enhanced Built-Up and Bareness Index (EBBI) for mapping built-up and bare land in an urban area","volume":"4","author":"Adnyana","year":"2012","journal-title":"Remote Sens"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.23953\/cloud.ijarsg.67","article-title":"Urban Built-up Area Extraction and Change Detection of Adama Municipal Area Using Time-Series Landsat Images","volume":"5","author":"Sinha","year":"2016","journal-title":"Int. J. Adv. Remote Sens. Gis."},{"key":"ref_46","first-page":"136","article-title":"Development of New Indices for Extraction of Built-Up Area & Bare Soil from Landsat Data","volume":"1","author":"Waqar","year":"2012","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., and Liu, J. (2017). Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sens., 9.","DOI":"10.3390\/rs9030249"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3390\/rs6042912","article-title":"Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-Spectral Satellite Images","volume":"6","author":"Wieland","year":"2014","journal-title":"Remote Sens."},{"key":"ref_49","unstructured":"(2020, January 20). U.S. Geological Survey (USGS) Earthquake Hazards Program, Available online: https:\/\/earthquake.usgs.gov\/earthquakes\/eventpage\/."},{"key":"ref_50","unstructured":"(2020, January 20). New Zealand Police Publication of Earthquake Fatalities 2011, Available online: http:\/\/www.police.govt.nz\/sites\/default\/files\/publications\/christchurch-earthquake-fatalities-locations-map.pdf."},{"key":"ref_51","unstructured":"(2020, February 16). Reserve Bank of New Zealand (February 2016), Bulletin, Volume 79, No. 3, Available online: https:\/\/www.rbnz.govt.nz\/-\/media\/ReserveBank\/Files\/Publications\/Bulletins\/2016\/2016feb79-3.pdf."},{"key":"ref_52","unstructured":"Swiss Re Institute (2020, February 16). Special feature for sigma No.2\/2019. L\u2019Aquila, 10 years on. Available online: https:\/\/www.swissre.com\/dam\/jcr:a1800651-558e-403a-b8df-cb4d8ce9e2ec\/Sigma_2_2019_feature_EN.PDF."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1016\/j.techfore.2012.12.001","article-title":"Spatial connectivity as a recovery process indicator: The L\u2019Aquila earthquake","volume":"80","author":"Contreras","year":"2013","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1177\/028072701203000210","article-title":"Social dimensions of disaster recovery","volume":"30","author":"Tierney","year":"2012","journal-title":"Int. J. Mass Emergencies Disasters"},{"key":"ref_55","unstructured":"The World Bank (2004). Iran- Bam Earthquake Emergency Response Project (English), World Bank. Available online: http:\/\/documents.worldbank.org\/curated\/en\/462231468284376813\/Iran-Bam-Earthquake-Emergency-Response-Project."},{"key":"ref_56","unstructured":"United States Geological Survey (USGS), EarthExplorer (2019, September 15). Landsat Satellite Missions, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_57","unstructured":"Jensen, J.R. (2016). Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson Education Inc.. [4th ed.]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sun, Z., Wang, C., Guo, H., and Shang, R. (2017). A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9090942"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/0034-4257(95)00213-8","article-title":"The Precision of the NDVI Derived from AVHRR Observations","volume":"56","author":"Roderick","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.14358\/PERS.73.12.1381","article-title":"Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematicoriented Index Combination Technique","volume":"73","author":"Xu","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"14019","DOI":"10.3390\/rs71014019","article-title":"Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series","volume":"7","author":"Emanuele","year":"2015","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2013.07.025","article-title":"Phenology-Assisted Classification of C3 and C4 Grasses in the U.S. Great Plains and Their Climate Dependency with MODIS Time Series","volume":"138","author":"Wang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Anderson, G.P., Pukall, B., Allred, C.L., Jeong, L.S., Hoke, M., Chetwynd, J.H., and Adler-Golden, S.M. (1999, January 7). FLAASH and MODTRAN4: State-of-the-Art Atmospheric Correction for Hyperspectral Data. Proceedings of the 1999 IEEE Aerospace Conference, Proceedings (Cat. No.99TH8403), Snowmass at Aspen, CO, USA.","DOI":"10.1109\/AERO.1999.792088"},{"key":"ref_64","unstructured":"Johnson, L.A., and Olshansky, R.B. (2017). After Great Disasters: An in-Depth Analysis of How Six Countries Managed Community Recovery, Lincoln Institute of Land Policy."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/895\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:05:53Z","timestamp":1760173553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/895"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,10]]},"references-count":64,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050895"],"URL":"https:\/\/doi.org\/10.3390\/rs12050895","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,10]]}}}