Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8674
DC FieldValueLanguage
dc.contributor.authorSchneider, P.-
dc.contributor.authorRoberts, D.A.-
dc.contributor.authorKyriakidis, Phaedon-
dc.date.accessioned2016-07-13T11:48:17Z-
dc.date.available2016-07-13T11:48:17Z-
dc.date.issued2008-03-
dc.identifier.citationRemote Sensing of Environment, 2008, vol. 112, iss. 3, pp. 1151–1167en_US
dc.identifier.issn00344257-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8674-
dc.description.abstractThe Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation Index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate that VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FPI outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRemote Sensing of Environmenten_US
dc.rights© Elsevieren_US
dc.subjectFire Potential Indexen_US
dc.subjectMODISen_US
dc.subjectVARIen_US
dc.subjectWildfire risken_US
dc.subjectWildfire susceptibilityen_US
dc.titleA VARI-based relative greenness from MODIS data for computing the Fire Potential Indexen_US
dc.typeArticleen_US
dc.collaborationUniversity of Californiaen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.rse.2007.07.010en_US
dc.dept.handle123456789/54en
dc.relation.issue3en_US
dc.relation.volume112en_US
cut.common.academicyear2007-2008en_US
dc.identifier.spage1151en_US
dc.identifier.epage1167en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextnone-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4222-8567-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.journal.journalissn0034-4257-
crisitem.journal.publisherElsevier-
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