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Abstract:The goal of the research reported here is to assess the capability of satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer onboard both Terra and Aqua satellites, in order to replicate live fuel moisture content of Southern California chaparral ecosystems. We compared seasonal and interannual characteristics of in-situ live fuel moisture with satellite vegetation indices that were averaged over different radial extents around each live fuel moisture observation site. The highest correlations are found using the Aqua Enhanced Vegetation Index for a radius of 10 km, independently verifying the validity of in-situ live fuel moisture measurements over a large extent around each in-situ site. With this optimally averaged Enhanced Vegetation Index, we developed an empirical model function of live fuel moisture. Trends in the wet-to-dry phase of vegetation are well captured by the empirical model function on interannual time-scales, indicating a promising method to monitor fire danger levels by combining satellite, in-situ, and model results during the transition before active fire seasons. An example map of Enhanced Vegetation Index-derived live fuel moisture for the Colby Fire shows a complex spatial pattern of significant live fuel moisture reduction along an extensive wildland-urban interface, and illustrates a key advantage in using satellites across the large extent of wildland areas in Southern California.

Southern California possesses a Mediterranean climate having semi-arid to arid characteristics and contains shrubland areas at high risk to wildfire. To assess wildfire danger, fire agencies have been monitoring the moisture of vegetation, called live fuel moisture (LFM), using field-based sampling. Unfortunately, spatial and temporal resolution of live fuel moisture data are significantly limited because sampling is labor intensive. Remote sensing satellite data has been used to monitor vegetation moisture content and health of shrublands. Therefore, a potential approach to overcome the limitations of manual measurements of live fuel moisture is to use vegetation indices (VIs) derived from satellite data. The objective of this study is to understand the link between vegetation indices derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both Terra and Aqua satellites and in-situ live fuel moisture data. In this study, five vegetation indices were calculated using 6 bands of MODIS data within the visible and infrared spectrum collectively with the focus on the three best performing: enhanced vegetation index (EVI), normalized difference water index (NDWI), and visible atmospherically resistant index (VARI). Six sites with multi-year live fuel moisture data collection type were each represented with one pixel of MODIS data with a 500m by 500m spatial resolution covering the time period of February 2000 through December 2017 acquired aboard Terra and June 2002 through December 2017 acquired aboard Aqua. Linear regression was then applied to measure the coefficient of determination (R 2 ) between the vegetation indices and live fuel moisture data. The results show a great variance of R 2 between the sites as well as a variance of best performing VI. The two strongest coefficients of determination, R 2 =0.74 and R 2 =0.72, were calculated at one site for enhanced vegetation index vs. live fuel moisture over a 15-year time period of data collected on Aqua and a 17-year time period of data collected on Terra respectively. The relationship was also affected by annual climate conditions including precipitation. Our results indicate that the satellite data reasonably well-represents the live fuel moisture with higher temporal resolutions over a large area. Utilizing the remote sensing data in wildfire danger assessment will support fire agencies by saving resources for collecting ground data and providing better dataset in both time and space. This will also be beneficial for land management and planning, stakeholders and local governments.