scholarly journals Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

2018 ◽  
Author(s):  
Kristen Whitney ◽  
Seung Hee Kim ◽  
Shenyue Jia ◽  
Menas Kafatos
Keyword(s):  
Southern California ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Wildfire Risk ◽  
Live Fuel Moisture ◽  
The Relationship

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

2017 ◽  
Vol 26 (5) ◽  
pp. 384
Author(s):  
L. M. Ellsworth ◽  
A. P. Dale ◽  
C. M. Litton ◽  
T. Miura
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Native Grass ◽  
Tropical Ecosystems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

scholarly journals Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model

2021 ◽  
Vol 18 (13) ◽  
pp. 4005-4020
Author(s):  
Wu Ma ◽  
Lu Zhai ◽  
Alexandria Pivovaroff ◽  
Jacquelyn Shuman ◽  
Polly Buotte ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Southern California ◽  
Greenhouse Gas Emission ◽  
Precipitation Change ◽  
Fuel Moisture ◽  
Vegetation Model ◽  
Gas Emission ◽  
Co2 Fertilization ◽  
Live Fuel Moisture

Abstract. Live fuel moisture content (LFMC) plays a critical role in wildfire dynamics, but little is known about responses of LFMC to multivariate climate change, e.g., warming temperature, CO2 fertilization, and altered precipitation patterns, leading to a limited prediction ability of future wildfire risks. Here, we use a hydrodynamic demographic vegetation model to estimate LFMC dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. We parameterize the model based on observed shrub allometry and hydraulic traits and evaluate the model's accuracy through comparisons between observed and simulated LFMC of three plant functional types (PFTs) under current climate conditions. Moreover, we estimate the number of days per year of LFMC below 79 % (which is a critical threshold for wildfire danger rating of southern California chaparral shrubs) from 1960 to 2099 for each PFT and compare the number of days below the threshold for medium and high greenhouse gas emission scenarios (RCP4.5 and 8.5). We find that climate change could lead to more days per year (5.2 %–14.8 % increase) with LFMC below 79 % between the historical (1960–1999) and future (2080–2099) periods, implying an increase in wildfire danger for chaparral shrubs in southern California. Under the high greenhouse gas emission scenario during the dry season, we find that the future LFMC reductions mainly result from a warming temperature, which leads to 9.1 %–18.6 % reduction in LFMC. Lower precipitation in the spring leads to a 6.3 %–8.1 % reduction in LFMC. The combined impacts of warming and precipitation change on fire season length are equal to the additive impacts of warming and precipitation change individually. Our results show that the CO2 fertilization will mitigate fire risk by causing a 3.5 %–4.8 % increase in LFMC. Our results suggest that multivariate climate change could cause a significant net reduction in LFMC and thus exacerbate future wildfire danger in chaparral shrub systems.

Time series of chaparral live fuel moisture maps derived from MODIS satellite data

2006 ◽  
Vol 15 (3) ◽  
pp. 347 ◽  
Author(s):  
Douglas Stow ◽  
Madhura Niphadkar ◽  
John Kaiser
Keyword(s):  
Time Series ◽  
San Diego ◽  
Southern California ◽  
San Diego County ◽  
Spatially Explicit ◽  
Fuel Moisture ◽  
Ecological Disturbance ◽  
Moderate Resolution ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

Wildfires in chaparral shrublands of southern California are a major hazard and important ecological disturbance agent. Fire managers typically monitor fuel moisture of chaparral shrublands to assess the risk of wildfires, using field-based sampling methods for a few small study areas located sparsely throughout southern California. Remote sensing provides the potential for deriving spatially explicit and temporally frequent data on live fuel moisture (LFM) conditions. The objective of this present study was to explore the potential for monitoring LFM with maps derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the National Aeronautics and Space Administration (NASA) Terra Earth-observing satellite. A time series of MODIS surface reflectance data (MOD-09_A1) for San Diego County, California from Fall 2000 through 2003 was used to derive normalized difference indices, which were regressed against LFM data. A high degree of temporal co-variability was found, with three MODIS indices providing similar predictability. Regression relationships were inverted and applied to MODIS images to map LFM interval classes for chaparral areas of San Diego County. The spatial–temporal patterns of LFM maps suggest that, at a minimum, the MODIS can provide spatially explicit information that extends the utility of ground-based measurements of LFM data at a few sites.

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

2019 ◽  
Vol 11 (13) ◽  
pp. 1575 ◽  
Author(s):  
Shenyue Jia ◽  
Seung Hee Kim ◽  
Son V. Nghiem ◽  
Menas Kafatos
Keyword(s):  
Soil Moisture ◽  
Southern California ◽  
Reference Model ◽  
Resistance Index ◽  
Mediterranean Ecosystem ◽  
Fuel Moisture ◽  
L Band ◽  
Live Fuel Moisture ◽  
The Impact ◽  
Band Radiometer

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.

scholarly journals Assessing Climate Change Impacts on Live Fuel Moisture and Wildfire Risk Using a Hydrodynamic Vegetation Model

2020 ◽  
Author(s):  
Wu Ma ◽  
Lu Zhai ◽  
Alexandria Pivovaroff ◽  
Jacquelyn Shuman ◽  
Polly Buotte ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Southern California ◽  
Greenhouse Gas Emission ◽  
Precipitation Change ◽  
Fuel Moisture ◽  
Vegetation Model ◽  
Gas Emission ◽  
Co2 Fertilization ◽  
Live Fuel Moisture

Abstract. Live fuel moisture content (LFMC) plays a critical role in wildfire dynamics, but little is known about responses of LFMC to multivariate climate change, e.g., warming temperature, CO2 fertilization and altered precipitation patterns, leading to a limited prediction ability of future wildfire risks. Here, we use a hydrodynamic vegetation model to estimate LFMC dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. We parameterize the model based on observed shrub allometry and hydraulic traits, and evaluate the model's accuracy through comparisons between simulated and observed LFMC of three plant functional types (PFTs) under current climate conditions. Moreover, we estimate the number of days per year of LFMC below 79 % (which is a critical threshold for wildfire danger rating) from 1950 to 2099 for each PFT, and compare the number of days below the threshold for medium and high greenhouse gas emission scenarios (RCP4.5 and 8.5). We find that climate change could lead to more days per year (5.5–15.2 % increase) with LFMC below 79 % from historical period 1950–1999 to future period 2075–2099, and therefore cause an increase in wildlife danger for chaparral shrubs in southern California. Under the high greenhouse gas emission scenario during the dry season, we find that the future LFMC reductions mainly result from a warming temperature, which leads to 9.5–19.1 % reduction in LFMC. Lower precipitation in the spring leads to a 6.6–8.3 % reduction in LFMC. The combined impacts of warming and precipitation change on fire season length are equal to the additive impacts of warming and precipitation change individually. Our results show that the CO2 fertilization will mitigate fire risk by causing a 3.7–5.1 % increase in LFMC. Our results suggest that multivariate climate change could cause a significant net reduction in LFMC and thus exacerbate future wildfire danger in chaparral shrub systems.

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