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

Abstract

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.

Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations

“…In Southern Europe, global change has also led to a shift in fire regimes from fuel-limited to drought-driven due to rural abandonment that favors fuel build-up in unmanaged landscapes [10,11]. Current wildfire 3D models and simulation tools are useful for predicting potential fire behavior and severity at the landscape level, but they require detailed and spatially-explicit fuel characteristics, including LFMC, as key input parameters [12][13][14][15]. So, there is an increasing need for reliable and updated spatial and temporal estimations of LFMC to improve fire danger rating systems and the emergency response [16].…”

Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

“…LFMC is much more difficult to measure in terms of meteorological indicators because of the ability of plants to adapt to drought and to consume moisture accumulated in the soil. The previously described remote sensing method is often used to determine LFMC [55][56][57][58].…”

Modelling chamise fuel moisture content across California: a machine learning approach

“…Many efforts to model LFMC have been made in recent years, most of which include the use of remote sensing technologies to measure leaf water content (Chuvieco 2003;Danson and Bowyer 2004;Peterson et al 2008;Qi et al 2012;Yebra et al 2013;Garcı ´a et al 2020;McCandless et al 2020;Rao et al 2020;Michael et al 2021). Specifically, the use of Moderate Resolution Imaging Spectroradiometer (MODIS) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been reported by Serrano et al (2000), Yebra et al (2008) and Myoung et al (2018).…”