{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:27:45Z","timestamp":1773415665699,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSERC Discovery Program","award":["RGPIN-386183"],"award-info":[{"award-number":["RGPIN-386183"]}]},{"name":"NSERC Postgraduate Scholarship","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Centre for Urban Environments (University of Toronto Mississauga)","award":["NA"],"award-info":[{"award-number":["NA"]}]},{"name":"Department of Geography, Geomatics and Environment (University of Toronto Mississauga)","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The 2019\u20132020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe \u2018mega-fires\u2019 and how vegetation may recover from 2019\u20132020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988\u20132020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019\u20132020 bushfires were the most widespread and severe, followed by 2007\u20132008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1\u20133) faster recovery observed in areas with less surviving vegetation. Later (year 6\u201310) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.<\/jats:p>","DOI":"10.3390\/rs12233942","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T07:49:54Z","timestamp":1606895394000},"page":"3942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Contextualizing the 2019\u20132020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8195-2465","authenticated-orcid":false,"given":"Mitchell T.","family":"Bonney","sequence":"first","affiliation":[{"name":"Department of Geography, Geomatics and Environment, University of Toronto Mississauga, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada"}]},{"given":"Yuhong","family":"He","sequence":"additional","affiliation":[{"name":"Department of Geography, Geomatics and Environment, University of Toronto Mississauga, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada"}]},{"given":"Soe W.","family":"Myint","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences and Urban Planning, Arizona State University, 975 S Myrtle Ave, Tempe, AZ 85281, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,2]]},"reference":[{"key":"ref_1","unstructured":"Richards, L., Brew, N., and Smith, L. (2020, August 01). 2019\u201320 Australian Bushfires\u2014Frequently Asked Questions: A Quick Guide, Available online: https:\/\/www.aph.gov.au\/About_Parliament\/Parliamentary_Departments\/ Parliamentary_Library\/pubs\/rp\/rp1920\/Quick_Guides\/AustralianBushfires."},{"key":"ref_2","unstructured":"Royal Commission into National Natural Disaster Arrangements (2020, November 15). Interim Observations, 31 August 2020, Available online: https:\/\/naturaldisaster.royalcommission.gov.au\/publications\/interim-observations-1."},{"key":"ref_3","unstructured":"Khalil, S. (2020, August 03). Australia Fires: \u2018Apocalypse\u2019 Comes to Kangaroo Island. Available online: https:\/\/www.bbc.com\/news\/world-australia-51102658."},{"key":"ref_4","unstructured":"Tarabay, J. (2020, June 24). There\u2019s No Place Like Kangaroo Island. Can It Survive Australia\u2019s Fires?. Available online: https:\/\/www.nytimes.com\/2020\/02\/04\/world\/australia\/kangaroo-island-fire.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1071\/WF10070","article-title":"Regional signatures of future fire weather over eastern Australia from global climate models","volume":"20","author":"Clarke","year":"2011","journal-title":"Int. J. Wildland Fire"},{"key":"ref_6","unstructured":"Australian Government: Bureau of Meteorology (2020, May 15). Historical Weather Observations and Statistics, Available online: http:\/\/www.bom.gov.au\/climate\/data-services\/station-data.shtml."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.foreco.2012.09.015","article-title":"Mega-fires, inquiries and politics in the eucalypt forests of Victoria, South-Eastern Australia","volume":"294","author":"Attiwill","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1002\/2015GB005160","article-title":"Century-scale patterns and trends of global pyrogenic carbon emissions and fire influences on terrestrial carbon balance","volume":"29","author":"Yang","year":"2015","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/S0034-4257(97)00048-5","article-title":"Modeling rates of ecosystem recovery after fires by using Landsat TM data","volume":"61","author":"Viedma","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1071\/WF9960125","article-title":"Remote sensing for forest fire severity and vegetation recovery","volume":"6","author":"White","year":"1996","journal-title":"Int. J. Wildland Fire"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1071\/WF08203","article-title":"Pinus halepensis regeneration after a wildfire in a semiarid environment: Assessment using multitemporal Landsat images","volume":"20","author":"Lasanta","year":"2011","journal-title":"Int. J. Wildland Fire"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr algorithm on google earth engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bright, B.C., Hudak, A.T., Kennedy, R.E., Braaten, J.D., and Khalyani, A.H. (2019). Examining post-fire vegetation recovery with Landsat time series analysis in three Western North American forest types. Fire Ecol., 15.","DOI":"10.1186\/s42408-018-0021-9"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Quintero, N., Viedma, O., Urbieta, I.R., and Moreno, J.M. (2019). Assessing landscape fire hazard by multitemporal automatic classification of Landsat time series using the google earth engine in West-Central Spain. Forests, 10.","DOI":"10.3390\/f10060518"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2017.11.007","article-title":"Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time-series","volume":"205","author":"Frazier","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2015.10.024","article-title":"Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada mountains of California","volume":"171","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/2150704X.2015.1126375","article-title":"Forest recovery trends derived from Landsat time series for North American boreal forests","volume":"37","author":"Pickell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2017.03.035","article-title":"A Nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series","volume":"194","author":"White","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/14498596.2012.733618","article-title":"Remote sensing of post-fire vegetation recovery; a study using Landsat 5 TM imagery and NDVI in North-East Victoria","volume":"57","author":"Sever","year":"2012","journal-title":"J. Spat. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1139\/x05-060","article-title":"Landscape-level interactions of prefire vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska","volume":"35","author":"Epting","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"37572","DOI":"10.1038\/srep37572","article-title":"Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China","volume":"6","author":"Liu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.rse.2011.01.022","article-title":"Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data","volume":"6","author":"Schroeder","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A., Haywood, A., and Nguyen, T.H. (2018). Using Landsat spectral indices in time-series to assess wildfire disturbance and recovery. Remote Sens., 10.","DOI":"10.3390\/rs10030460"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2016.06.015","article-title":"Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems","volume":"184","author":"Quintano","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2017.07.022","article-title":"Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America","volume":"199","author":"Yang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1071\/WF05052","article-title":"Fire regime and post-fire normalized difference vegetation index changes in the Eastern Iberian peninsula (Mediterranean basin)","volume":"15","author":"Malak","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1080\/01431160210144732","article-title":"Influence of fire severity on plant regeneration by means of remote sensing imagery","volume":"24","author":"Lloret","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Meng, R., Dennison, P.E., D\u2019Antonio, C.M., and Moritz, M.A. (2014). Remote sensing analysis of vegetation recovery following short-interval fires in Southern California shrublands. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0110637"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1071\/WF05051","article-title":"Remote sensing of fire severity in the Blue Mountains: Influence of vegetation type and inferring fire intensity","volume":"15","author":"Hammill","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1071\/WF10099","article-title":"Influence of short-interval fire occurrence on post-fire recovery of fire-prone shrublands in California, USA","volume":"22","author":"Lippitt","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.foreco.2009.10.005","article-title":"Long-term impacts of prescribed burning on regional extent and incidence of wildfires\u2014Evidence from 50 years of active fire management in SW Australian forests","volume":"259","author":"Boer","year":"2009","journal-title":"Forest Ecol. Manag."},{"key":"ref_35","unstructured":"Dowie, D. (2006). Age Class Distributions of Seven Vegetation Groups on Kangaroo Island. Fahrenheit 451\u2014A Fire Management Program for Biodiversity and Asset Protection on Kangaroo Island."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Estes, B.L., Knapp, E.E., Skinner, C.N., Miller, J.D., and Preisler, H.K. (2017). Factors influencing fire severity under moderate burning conditions in the Klamath Mountains, northern California, USA. Ecosphere, 8.","DOI":"10.1002\/ecs2.1794"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1071\/WF18156","article-title":"Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: A Case study on a large fire in NW Spain","volume":"28","author":"Taboada","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10021-013-9704-x","article-title":"Previous fires moderate burn severity of subsequent wildland fires in two large western US wilderness areas","volume":"17","author":"Parks","year":"2013","journal-title":"Ecosystems"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1139\/cjfr-2016-0185","article-title":"Prior wildfires influence burn severity of subsequent large fires","volume":"46","author":"Prichard","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_40","unstructured":"Kangaroo Island Council (2020, August 03). Kangaroo Island Development Plan, Available online: https:\/\/www.kangarooisland.sa.gov.au\/council\/plans\/development\/plan."},{"key":"ref_41","unstructured":"Hobbs, R.J., and Yates, C.J. (2000). The distribution, status and threats to temperate woodlands in South Australia. Temperate Eucalypt Woodlands in Australia: Biology, Conservation and Restoration, Surrey Beatty & Sons."},{"key":"ref_42","unstructured":"Department of Agriculture, Water and the Environment (2020, November 16). National Vegetation Information System (NVIS), Available online: https:\/\/www.environment.gov.au\/land\/native-vegetation\/national-vegetation-information-system."},{"key":"ref_43","unstructured":"Australian Government: Department of Agriculture, Water and the Environment (2020, February 03). Australian Land Use and Management Classification Version 8 (October 2016), Available online: https:\/\/www.agriculture.gov.au\/abares\/aclump\/land-use\/alum-classification."},{"key":"ref_44","unstructured":"Government of South Australia: Department for Environment and Water (2020, November 13). Conservation Reserve Boundaries, Available online: https:\/\/data.sa.gov.au\/data\/dataset\/conservation-reserve-boundaries."},{"key":"ref_45","unstructured":"Government of South Australia: Department for Environment and Water (2020, April 03). Bushfires and Prescribed Burns History, Available online: https:\/\/data.sa.gov.au\/data\/dataset\/e5434c77-9815-48e6-8ea7-fb35c78f6786."},{"key":"ref_46","unstructured":"Peace, M., and Mills, G. (2012). A Case Study of the 2007 Kangaroo Island Bushfires."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and Expansion of the FMASK algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal composite Landsat TM\/ETM+ images using the medoid (a multi-dimensional median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_52","first-page":"453","article-title":"Forest cover trends from time series Landsat data for the Australian continent","volume":"21","author":"Lehmann","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape assessment: Remote sensing of severity, the normalized burn ratio, FIREMON: Fire Effects Monitoring and Inventory System."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.jclepro.2018.01.050","article-title":"Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm","volume":"178","author":"Yang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_55","unstructured":"NASA Jet Propulsion Laboratory (2020, March 21). U.S. Releases Enhanced Shuttle Land Elevation Data, Available online: https:\/\/www.jpl.nasa.gov\/news\/news.php?release=2014-321."},{"key":"ref_56","unstructured":"Weiss, A.D. (2001, January 9\u201313). Topographic position and landforms analysis. Proceedings of the Presented at ESRI Users Conference, San Diego, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_59","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1890\/08-0879.1","article-title":"Quantify bufo boreas connectivity in Yellowstone national park with landscape genetics","volume":"91","author":"Murphy","year":"2010","journal-title":"Ecology"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tolhurst, K.G., and McCarthy, G. (2016). Effect of prescribed burning on wildfire severity: A landscape-scale case study from the 2003 fires in Victoria. Aust. For., 79.","DOI":"10.1080\/00049158.2015.1127197"},{"key":"ref_62","unstructured":"Government of South Australia: Kangaroo Island Landscape Board (2020, August 03). Bushfire Recovery, Available online: https:\/\/landscape.sa.gov.au\/ki\/land-and-water\/Bushfire_recovery."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.foreco.2016.08.047","article-title":"Mortality and recruitment of fire-tolerant eucalypts as influenced by wildfire severity and recent prescribed fire","volume":"380","author":"Bennett","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/j.1365-2664.2010.01906.x","article-title":"Habitat or fuel? Implications of long-term, post-fire dynamics for the development of key resources for fauna and fire","volume":"48","author":"Haslem","year":"2010","journal-title":"J. Appl. Ecol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5011","DOI":"10.1002\/ece3.873","article-title":"Eucalypts face increasing climate stress","volume":"3","author":"Butt","year":"2013","journal-title":"Ecol. Evol."},{"key":"ref_66","unstructured":"Government of South Australia: Department of Environment and Water (2020, June 24). Find out how South Australia\u2019s Flinders Chase National Park is recovering post-bushfire, Available online: https:\/\/www.environment.sa.gov.au\/goodliving\/posts\/2020\/04\/flinders-chase-plants-regenerating."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"112167","DOI":"10.1016\/j.rse.2020.112167","article-title":"A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection","volume":"252","author":"Ye","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3942\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:40:32Z","timestamp":1760179232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3942"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,2]]},"references-count":67,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233942"],"URL":"https:\/\/doi.org\/10.3390\/rs12233942","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,2]]}}}