{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:40:41Z","timestamp":1764225641232,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"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>Assessing the development of wildfire scars during a period of consecutive active fires and smoke overcast is a challenge. The study was conducted during nine months when Israel experienced massive pyro-terrorism attacks of more than 1100 fires from the Gaza Strip. The current project strives at developing and using an advanced Earth observation approach for accurate post-fire spatial and temporal assessment shortly after the event ends while eliminating the influence of biomass burning smoke on the ground signal. For fulfilling this goal, the Aerosol-Free Vegetation Index (AFRI), which has a meaningful advantage in penetrating an opaque atmosphere influenced by biomass burning smoke, was used. On top of it, under clear sky conditions, the AFRI closely resembles the widely used Normalized Difference Vegetation Index (NDVI), and it retains the same level of index values under smoke. The relative differenced AFRI (RdAFRI) set of algorithms was implemented at the same procedure commonly used with the Relative differenced Normalized Burn Ratio (RdBRN). The algorithm was applied to 24 Sentinel-2 Level-2A images throughout the study period. While validating with ground observations, the RdAFRI-based algorithms produced an overall accuracy of 90%. Furthermore, the RdAFRI maps were smoother than the equivalent RdNBR, with noise levels two orders of magnitude lower than the latter. Consequently, applying the RdAFRI, it is possible to distinguish among four severity categories. However, due to different cloud cover on the two consecutive dates, an automatic determination of a threshold level was not possible. Therefore, two threshold levels were considered through visual inspection and manually assigned to each imaging date. The novel procedure enables calculating the spatio-temporal dynamics of the fire scars along with the statistics of the burned vegetation species within the study area.<\/jats:p>","DOI":"10.3390\/rs12172753","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T09:24:56Z","timestamp":1598347496000},"page":"2753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Burned Area Mapping Using Multi-Temporal Sentinel-2 Data by Applying the Relative Differenced Aerosol-Free Vegetation Index (RdAFRI)"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5810-6156","authenticated-orcid":false,"given":"Manuel","family":"Salvoldi","sequence":"first","affiliation":[{"name":"The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gil","family":"Siaki","sequence":"additional","affiliation":[{"name":"Jewish National Fund-Keren Kayemet LeIsrael, Southern Region\u2019s Forestry Division, Gilat Center 85105, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Sprintsin","sequence":"additional","affiliation":[{"name":"Jewish National Fund-Keren Kayemet LeIsrael, Land Development Authority, Eshtaol 99775, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-9793","authenticated-orcid":false,"given":"Arnon","family":"Karnieli","sequence":"additional","affiliation":[{"name":"The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125009","DOI":"10.1088\/1748-9326\/11\/12\/125009","article-title":"A transdisciplinary approach to understanding the health effects of wildfire and prescribed fire smoke regimes","volume":"11","author":"Williamson","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.isprsjprs.2019.11.027","article-title":"Investigation of wildfire impacts on land surface phenology from MODIS time series in the western US forests","volume":"159","author":"Wang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1016\/j.rse.2009.03.004","article-title":"Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data","volume":"113","author":"Wulder","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"135","article-title":"A review of forest fire surveillance technologies: Mobile ad-hoc network routing protocols perspective","volume":"31","author":"Sabri","year":"2019","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"222","DOI":"10.4314\/sajg.v7i3.2","article-title":"Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas","volume":"7","author":"Molaudzi","year":"2019","journal-title":"S. Afr. J. Geomat."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Szpakowski, D.M., and Jensen, J.L.R. (2019). A Review of the applications of remote sensing in fire ecology. Remote Sens., 11.","DOI":"10.3390\/rs11222638"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0309133309339563","article-title":"A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters","volume":"33","author":"Joyce","year":"2009","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s11069-004-1796-3","article-title":"Monitoring forest fire danger with remote sensing","volume":"35","author":"LeBlon","year":"2005","journal-title":"Nat. Hazards"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1080\/01431160210144697","article-title":"Comparative analysis of daytime fire detection algorithms using AVHRR data for the 1995 fire season in Canada: Perspective for MODIS","volume":"24","author":"Ichoku","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Exploitation of sentinel-2 time series to map burned areas at the national level: A Case Study on the 2017 Italy wildfires. Remote Sens., 11.","DOI":"10.3390\/rs11060622"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sobrino, J.A., Llorens, R., Fern\u00e1ndez, C., Fern\u00e1ndez-Alonso, J.M., and Vega, J.A. (2019). Relationship between soil burn severity in forest fires measured in situ and through spectral indices of remote detection. Forests, 10.","DOI":"10.3390\/f10050457"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1071\/WF18153","article-title":"Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: The case of Mount Taygetos (southern Greece)","volume":"28","author":"Christopoulou","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9927","DOI":"10.1029\/JD095iD07p09927","article-title":"Remote sensing of biomass burning in the tropics","volume":"95","author":"Kaufman","year":"1990","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11676-015-0162-5","article-title":"Estimation of gases emitted by forest fires based on remote sensing data","volume":"27","author":"Wang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/S0034-4257(01)00190-0","article-title":"AFRI\u2014aerosol free vegetation index","volume":"77","author":"Karnieli","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10980-005-4119-5","article-title":"Applying local measures of spatial heterogeneity to landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes","volume":"21","author":"Chuvieco","year":"2006","journal-title":"Landsc. Ecol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s12524-012-0200-0","article-title":"An agent-based approach for regional forest fire detection using modis data: A preliminary study in Iran","volume":"41","author":"Movaghati","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Filipponi, F., Valentini, E., Xuan, A.N., Guerra, C.A., Wolf, F., Andrzejak, M., and Taramelli, A. (2018). Global modis fraction of green vegetation cover for monitoring abrupt and gradual vegetation changes. Remote Sens., 10.","DOI":"10.3390\/rs10040653"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1177\/030913330002400404","article-title":"Satellite remote sensing of biomass burning with optical and thermal sensors","volume":"24","author":"Fuller","year":"2000","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/01431161.2018.1519284","article-title":"Determining the use of Sentinel-2A MSI for wildfire burning & severity detection","volume":"40","author":"Amos","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"97","article-title":"Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery","volume":"58","author":"Navarro","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","first-page":"137","article-title":"Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems","volume":"80","author":"Quintano","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1016\/j.rse.2011.06.010","article-title":"Evaluating spectral indices for burned area discrimination using MODIS\/ASTER (MASTER) airborne simulator data","volume":"115","author":"Veraverbeke","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1071\/WF08078","article-title":"Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel","volume":"19","author":"Casady","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","unstructured":"Key, C., and Benson, N.C. (2020, August 25). The Normalized Burn Ratio (NBR): A Landsat TM Radiometric Measure of Burn Severity, Available online: http:\/\/nrmsc.usgs.gov\/research\/ndbr.htm."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S0034-4257(02)00071-8","article-title":"Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data","volume":"82","author":"Miller","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escuin","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.3390\/rs6031827","article-title":"A new metric for quantifying burn severity: The relativized burn ratio","volume":"6","author":"Parks","year":"2014","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2019.111497","article-title":"Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty","volume":"237","author":"Furniss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2016.08.023","article-title":"Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States","volume":"186","author":"Meddens","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_33","first-page":"102034","article-title":"A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests","volume":"87","author":"Hislop","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11","DOI":"10.4067\/S0717-92002020000100011","article-title":"Spatial and temporal analyses of burned areas 1998, 2003 and 2015 in Montes Azules biosphere reserve, Chiapas, Mexico","volume":"41","year":"2020","journal-title":"Bosque"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"64","DOI":"10.4996\/fireecology.0301064","article-title":"The relationship of multispectral satellite imagery to immediate fire effects","volume":"3","author":"Hudak","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.infrared.2017.01.017","article-title":"High spatial resolution shortwave infrared imaging technology based on time delay and digital accumulation method","volume":"81","author":"Jia","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/36.297984","article-title":"Detection of forests using mid-IR reflectance: An application for aerosol studies","volume":"32","author":"Kaufman","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.1080\/01431160500177380","article-title":"Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol-free Vegetation Index (AFRI) on MODIS images","volume":"27","author":"Karnieli","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","unstructured":"Hirschberger, P. (2016). Forests Ablaze: Causes and Effects of Global Forest Fires, WWF."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1111\/j.1466-8238.2010.00525.x","article-title":"Seasonality of vegetation fires as modified by human action: Observing the deviation from eco-climatic fire regimes","volume":"19","author":"Oom","year":"2010","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_41","unstructured":"Short, K.C. (2017). Spatial Wildfire Occurrence Data for the United States, 1992\u20132015, Rocky Mountain Research Station. [4th ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.1073\/pnas.1617394114","article-title":"Human-started wildfires expand the fire niche across the United States","volume":"114","author":"Balch","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/10576100600698477","article-title":"Pyro-terrorism\u2014The threat of arson-induced forest fires as a future terrorist weapon of mass destruction","volume":"29","author":"Baird","year":"2006","journal-title":"Stud. Confl. Terror."},{"key":"ref_44","unstructured":"(2020, August 25). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_45","unstructured":"Gatti, A., Naud, C., Castellani, C., and Carriero, F. (2018). Sentinel-2 Products Specification Document, Thales Alenia Space."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_47","unstructured":"Graham, R.T., McCaffrey, S., and Jain, T.B. (2020, August 25). Science Basis for Changing Forest Structure to Modify Wildfire Behavior and Severity USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-120. Available online: https:\/\/digitalcommons.usu.edu\/cgi\/viewcontent.cgi?article=1161&context=barkbeetles."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.foreco.2017.08.043","article-title":"Fire behavior in Pinus halepensis thickets: Effects of thinning and woody debris decomposition in two rainfall scenarios","volume":"404","author":"Espelta","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1023\/B:VEGE.0000029380.04821.99","article-title":"Reproductive traits of Pinus halepensis in the light of fire\u2014a critical review","volume":"171","author":"Goubitz","year":"2004","journal-title":"Plant Ecol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ting, K.M. (2017). Confusion Matrix. Encyclopedia of Machine Learning and Data Mining, Springer Science and Business Media LLC.","DOI":"10.1007\/978-1-4899-7687-1_50"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"20150345","DOI":"10.1098\/rstb.2015.0345","article-title":"Global trends in wildfire and its impacts: Perceptions versus realities in a changing world","volume":"371","author":"Doerr","year":"2016","journal-title":"Philos. Trans. R. Soc. B Boil. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/s41561-019-0306-x","article-title":"Burning questions about ecosystems","volume":"12","author":"Cochrane","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.rse.2012.06.028","article-title":"Synergy of VSWIR (0.4\u20132.5 \u03bcm) and MTIR (3.5\u201312.5 \u03bcm) data for post-fire assessments","volume":"124","author":"Veraverbeke","year":"2012","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:31Z","timestamp":1760177191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,25]]},"references-count":53,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172753"],"URL":"https:\/\/doi.org\/10.3390\/rs12172753","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,8,25]]}}}