Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk 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 (R2) between the vegetation indices and live fuel moisture data. The results show a great variance of R2 between the sites as well as a variance of best performing VI. The two strongest coefficients of determination, R2=0.74 and R2=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

Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA

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

High Spatial and Temporal Resolution Census Data Reveal Communities at Risk Along the Wildland-Urban Interface (WUI) in California, USA

We tracked census tract level population change along California\u27s wild land-urban interface (WUI) during the past decade (2010-2019), an ecological sensitive region transitioning from developed land to wilderness. Our results from Mann-Kendall analysis, a method employed for monotonic trend detection showed that about one-third (29.1%) of census tracts in California’s WUI have seen a significant population increase from 2010 to 2019, affecting 12.7% population in California. The population increase along WUI is largely driven by the sixteen counties in the San Francisco Bay Area (10) and Southern California (6). We also found that higher proportion of WUI residents in Bay Area and larger number of WUI residents in Southern California. Bay Area counties in general have a higher proportion of population living in WUI tracts with significant population increase than Southern California counties. However, the lower proportion of residents living in WUI in Southern California counties account for a much larger population. Riverside is the county with the highest number of residents living in WUI tracts that have experienced significant population increase during the past decade. These residents also account for a high proportion (29.2%) of total population in Riverside. Preliminary results showed that the increase of population along WUI is driven by the house affordability and house ownership in 16 counties of Bay Area and Southern California. These factors can still explain a significant amount of the spatial pattern if extended to all counties in California

Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies

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 non-linear relationship between live fuel moisture and the remotely sensed vegetation signatures. We discuss our results which will improve the predictability of live fuel moisture

Patterns of Population Displacement During Mega-Fires in California detected using Facebook Disaster Maps

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making

CrisisReady\u27s Novel Framework for Transdisciplinary Translation: Case-Studies in Wildfire and Hurricane Response

Extreme weather events including wildfires and hurricanes are becoming increasingly hazardous due to climate change, and often result in transient or permanent population displacements. Disaster-related disruptions in infrastructure, workforce, wages, and social networks can combine with population displacements to result in interruptions in health care access and prolonged impacts on morbidity and mortality. The data needed to make health systems and emergency management approaches more resilient to these hazards, and more responsive to the needs of affected populations, are sequestered in silos across private corporations and public agencies. In two case studies, we describe how our research team at CrisisReady negotiated access to privately held and novel data sources like anonymized geolocation data from cell-phones, while striking a balance between data security and public health utility. We describe how our analytic tools are embedded into disaster response workflows by co-developing our research questions and outputs with responders and policy-makers. ReadyMapper, an interactive data visualization tool to track population mobility, infrastructure damage, and health system capacity, in near real-time, was deployed during wildfires in California and during the Hurricane Ida response in Louisiana. The Data-Methods-Translational framework we have developed is scalable and relies on sharing science and co-creating products with policy makers and response agencies to ensure real-world applicability. These attributes make the framework particularly useful for formulating evidence-based approaches to protect human health through climate change adaptation

Investigating the Lagged Relationship between Smap Soil Moisture and Live Fuel Moisture in California, USA

Live fuel moisture (LFM), defined as the ratio between water in the fresh biomass out of the dry biomass, is a vital measurement of vegetation water content and flammability. In this study, we investigated the dynamics of in-situ measurement of LFM at all the active sites in California, USA and revealed the difference between evergreen forest and shrub/scrub, the two dominant land cover types in California\u27s fire-prone regions. We found that LFM of evergreen forest responses to soil moisture increase later than shrub/scrub, due to a later occurrence of major precipitation, a lower air temperature, and the different plant physiology. The comparison between SMAP L-band radiometer soil moisture and LFM showed that the lag between the rise in soil moisture and the response from LFM was much longer in evergreen forest. Compared with the evergreen forest, LFM of shrub/scrub was more sensitive to the inter-annual variability of soil moisture due to plant physiology and air temperature

High Spatial and Temporal Resolution Census Data Reveals Communities at Risk Along the Wildland-Urban Interface (WUI) in California, USA

We tracked census tract level population change along California\u27s wild land-urban interface (WUI) during the past decade (2010-2019), an ecological sensitive region transitioning from developed land to wilderness. Our results from Mann-Kendall analysis, a method employed for monotonic trend detection showed that about one-third (29.1%) of census tracts in California’s WUI have seen a significant population increase from 2010 to 2019, affecting 12.7% population in California. The population increase along WUI is largely driven by the sixteen counties in the San Francisco Bay Area (10) and Southern California (6). We also found that higher proportion of WUI residents in Bay Area and larger number of WUI residents in Southern California. Bay Area counties in general have a higher proportion of population living in WUI tracts with significant population increase than Southern California counties. However, the lower proportion of residents living in WUI in Southern California counties account for a much larger population. Riverside is the county with the highest number of residents living in WUI tracts that have experienced significant population increase during the past decade. These residents also account for a high proportion (29.2%) of total population in Riverside. Preliminary results showed that the increase of population along WUI is driven by the house affordability and house ownership in 16 counties of Bay Area and Southern California. These factors can still explain a significant amount of the spatial pattern if extended to all counties in California

Environmental Lecture Series: Dr. Shenyue Jia on How Can Crowdsourced Data Support Evacuations During Wildfires?

In this episode of the CURes Environmental Lecture Series, Dr. Jia presents her findings on how crowdsourced data can make wildfire evacuations easier and safer