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.

Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk.

Wildfires are a major ecological disturbance in Southern California and often lead to great destruction along the Wildland-Urban Interface. Live fuel moisture has been used as an important indicator of wildfire risk in measurements of vegetation water content. However, the limited field measurements of live fuel moisture in both time and space have affected the accuracy of wildfire risk estimations. Traditional estimation of live fuel moisture using remote sensing data was based on vegetation indices, indirect proxies of vegetation water content and subject to influence from weather conditions. In this study, we investigated the feasibility of estimating live fuel moisture using vegetation indices, Soil Moisture Active Passive L-band soil moisture data and the modeled vegetation water content using a non-linear model based on VIs and the stem factor associated with remote sensing moisture data products. The stem factor describes the peak amount of water residing in stems of plants and varies by land cover. We also compared the outcomes from regression models and recurrent neural network using the same independent variables. We found the modeled vegetation water content outperformed vegetation indices and the L-band soil moisture observations, suggesting a nonlinear relationship between live fuel moisture and the remotely sensed vegetation signatures. We discuss our results which will improve the predictability of live fuel moisture.

In California (CA), the wildland-urban interface (WUI) faces escalating challenges due to surging population and real estate development. This study evaluates communities along CA’s WUI that have witnessed substantial population growth from 2010 to 2021, utilizing demographic data and the 2020 WUI boundaries by the University of Wisconsin-Madison SILVIS Lab. Employing the Mann-Kendall test, we analyze yearly population trends for each census tract along the CA WUI and assess their significance. House ownership, affordability, and wildfire risk are examined as potential drivers of this demographic shift. Our findings indicate that 12.7% of CA’s total population now resides in census tracts with significant population increases over the past decade, labeled as ‘high-growth tracts.’ The Bay Area and Southern California, encompassing 76% of all high-growth tracts in CA, witnessed the most substantial population increase along the WUI. Notably, Riverside County stands out with 29.2% of its residents (approximately 717,000 residents) located in high-growth tracts, exemplifying a significant population surge within CA’s WUI. Our analysis identifies a significant relationship between population increase in the WUI, house ownership, and affordability, where lower-priced homes come at the expense of heightened wildfire risk. However, the impact of house affordability on population growth within the WUI varies by region, playing a more prominent role in explaining population proportions in Southern California’s WUI, while in the universally low-affordability Bay Area, other motivations may drive residents to live within the WUI. Given the rapid growth and insufficient consideration of wildfire risk in the WUI, policymakers must take prompt action, ensuring adequate infrastructure and resources as more individuals relocate to areas with heightened wildfire risk.