Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8674
Title: VARI-based relative greenness from MODIS data for computing the Fire Potential Index
Authors: Schneider, P.
Roberts, D.A.
Kyriakidis, Phaedon
Keywords: Fire Potential Index;MODIS;VARI;Wildfire risk;Wildfire susceptibility
Category: Environmental Engineering
Field: Engineering and Technology
Issue Date: Mar-2008
Publisher: Elsevier Science Limited
Source: Remote Sensing of Environment, 2008, Volume 112, Issue 3, pages 1151–1167
Abstract: The 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.
URI: http://ktisis.cut.ac.cy/handle/10488/8674
ISSN: 0034-4257
DOI: http://dx.doi.org/10.1016/j.rse.2007.07.010
Rights: Copyright © Elsevier B.V.
Type: Article
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