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

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 887 ◽  
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.

Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem

2009 ◽  
Vol 18 (4) ◽  
pp. 430 ◽  
Author(s):  
Emilio Chuvieco ◽  
Isabel González ◽  
Felipe Verdú ◽  
Inmaculada Aguado ◽  
Marta Yebra
Keyword(s):  
Logistic Regression ◽  
Moisture Content ◽  
Fire Danger ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Fuel Moisture ◽  
Fire Occurrence ◽  
Large Fires ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

The present paper presents and discusses the relationships between live Fuel Moisture Content (FMC) measurements and fire occurrence (number of fires and burned area) in a Mediterranean area of central Spain. Grasslands and four shrub species (Cistus ladanifer L., Rosmarinus officinalis L., Erica australis L. and Phillyrea angustifolia L.) were sampled in the field from the spring to the summer season over a 9-year period. Higher seasonal FMC variability was found for the herbaceous species than for shrubs, as grasslands have very low values in summertime. Moisture variations of grasslands were found to be good predictors of number of fires and total burned surface, while moisture variation of two shrubs (C. ladanifer L. and R. officinalis L.) was more sensitive to both the total burned area and the occurrence of large fires. All these species showed significant differences between the FMC of high and low occurrence periods. Three different logistic regression models were built for the 202 periods of analysis: one to predict periods with more and less than seven fires, another to predict periods with and without large fires (>500 ha), and the third to predict periods with more and less than 200 ha burned. The results showed accuracy in predicting periods with a high number of fires (94%), and extensive burned area (85%), with less accuracy in estimating periods with large fires (58%). Finally, empirical functions based on logistic regression analysis were successfully related to fire ignition or potential burned area from FMC data. These models should be useful to integrate FMC measurements with other variables of fire danger (ignition causes, for instance), to provide a more comprehensive assessment of fire danger conditions.

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

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.

scholarly journals SAR-enhanced mapping of live fuel moisture content

2020 ◽  
Vol 245 ◽  
pp. 111797 ◽  
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

A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

2013 ◽  
Vol 136 ◽  
pp. 455-468 ◽  
Author(s):  
Marta Yebra ◽  
Philip E. Dennison ◽  
Emilio Chuvieco ◽  
David Riaño ◽  
Philip Zylstra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Fire Danger ◽  
Fuel Moisture ◽  
Danger Assessment ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content
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