Live fuel moisture content: variability, predictability and impact on fire behavior and activity

Live fuel moisture content (LFMC) influences fire activity at landscape scale and fire behavior in laboratory experiments. However, field evidences linking LFMC to fire behavior are very limited despite numerous field experiments. In the present study, we reanalyze a shrubland fire dataset with a special focus on LFMC to explain this counterintuitive outcome. We found that this controversy might result from three reasons. First, the range of experimental LFMC  data was too moist to reveal significant effect with the widespread exponential or power functions. Indeed, LFMC exhibited a strong effect below 100%, but marginal above this threshold, contrary to these functions. Second, we found that the LFMC significance was unlikely when the size of the dataset was smaller than 40. Finally, a complementary analysis suggested that 10 to 15% of random measurement error in variables could lead to an underestimation by 30 % of the LFMC effect. The effect of LFMC in field experiments is thus stronger than previously reported in the range prevailing during the actual French fire season and in accordance with observations at different scales. This highlights the need to improve our understanding of the relationship between LFMC and fire behavior to refine fire danger predictions.

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A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models

Remote Sensing ◽

10.3390/rs12111714 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1714

Author(s):

Mariano García ◽

David Riaño ◽

Marta Yebra ◽

Javier Salas ◽

Adrián Cardil ◽

...

Keyword(s):

Time Series ◽

Moisture Content ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fire Behavior ◽

Burned Area ◽

Fuel Moisture ◽

Individual Site ◽

Live Fuel Moisture ◽

Fuel Moisture Content

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

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SAR-enhanced mapping of live fuel moisture content

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111797 ◽

2020 ◽

Vol 245 ◽

pp. 111797 ◽

Cited By ~ 1

Author(s):

Krishna Rao ◽

A. Park Williams ◽

Jacqueline Fortin Flefil ◽

Alexandra G. Konings

Keyword(s):

Moisture Content ◽

Fuel Moisture ◽

Live Fuel Moisture ◽

Fuel Moisture Content

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Mapping Live Fuel Moisture Content and Flammability for Continental Australia Using Optical Remote Sensing

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517662 ◽

2018 ◽

Author(s):

M Yebra ◽

X Quan ◽

D Riano ◽

P Rozas Larraondo ◽

Albert IJM van Dijk ◽

...

Keyword(s):

Remote Sensing ◽

Moisture Content ◽

Optical Remote Sensing ◽

Fuel Moisture ◽

Live Fuel Moisture ◽

Fuel Moisture Content

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Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region

Remote Sensing of Environment ◽

10.1016/j.rse.2017.11.020 ◽

2018 ◽

Vol 205 ◽

pp. 210-223 ◽

Cited By ~ 24

Author(s):

Lei Fan ◽

J.-P. Wigneron ◽

Qing Xiao ◽

A. Al-Yaari ◽

Jianguang Wen ◽

...

Keyword(s):

Remote Sensing ◽

Moisture Content ◽

Mediterranean Region ◽

Microwave Remote Sensing ◽

Fuel Moisture ◽

Live Fuel Moisture ◽

The Mediterranean ◽

Fuel Moisture Content

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Estimation of Grassland Live Fuel Moisture Content From Ratio of Canopy Water Content and Foliage Dry Biomass

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2015.2437391 ◽

2015 ◽

Vol 12 (9) ◽

pp. 1903-1907 ◽

Cited By ~ 14

Author(s):

Xingwen Quan ◽

Binbin He ◽

Xing Li ◽

Zhi Tang

Keyword(s):

Moisture Content ◽

Water Content ◽

Fuel Moisture ◽

Dry Biomass ◽

Live Fuel Moisture ◽

Fuel Moisture Content ◽

Canopy Water

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Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Forests ◽

10.3390/f10100887 ◽

2019 ◽

Vol 10 (10) ◽

pp. 887 ◽

Cited By ~ 4

Author(s):

Kaiwei Luo ◽

Xingwen Quan ◽

Binbin He ◽

Marta Yebra

Keyword(s):

Moisture Content ◽

Southwest China ◽

Burned Area ◽

Fuel Moisture ◽

Fire Occurrence ◽

Subtropical Zone ◽

Wildfire Occurrence ◽

Live Fuel Moisture ◽

Grassland Fire ◽

Fuel Moisture Content

Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

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