Mapping live fuel moisture with MODIS data: A multiple regression approach

Live fuel moisture (LFM) is an important factor for ascertaining fire risk in shrublands located in Mediterranean climate regions. We examined empirical relationships between LFM and numerous vegetation indices calculated from MODIS composite data for two southern California shrub functional types, chaparral (evergreen) and coastal sage scrub (CSS, drought-deciduous). These relationships were assessed during the annual March–September dry down period for both individual sites, and sites pooled by functional type. The visible atmospherically resistant index (VARI) consistently had the strongest relationships for individual site regressions. An independent method of accuracy assessment, cross validation, was used to determine model robustness for pooled site regressions. Regression models were developed with n − 1 datasets and tested on the dataset that was withheld. Additional variables were included in the regression models to account for site-specific and interannual differences in vegetation amount and condition. This allowed a single equation to be used for a given functional type. Multiple linear regression models based on pooled sites had slightly lower adjusted R2 values compared with simple linear regression models for individual sites. The best regression models for chaparral and CSS were inverted, and LFM was mapped across Los Angeles County, California (LAC). The methods used in this research show promise for monitoring LFM in chaparral and may be applicable to other Mediterranean shrubland communities.