Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method

Abstract

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL and PROSPECT RTMs were firstly coupled together to model the near realistic scenario of a twolayered forest structure. Secondly, available ecological information was applied to constrain the coupled RTM modeling phases, in order to decrease the probability of generating unrealistic simulations. Thirdly, the coupled RTMs were linked to three machine learning models -random forest, support vector machine and multi-layer perceptron -as well as a traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multi-layer perceptron (R 2 = 0.77, RMSE = 0.13 and rRMSE = 0.43) outperformed the other three methods. Evaluation of applicability of the FFL estimates was conducted in a southwest China forest where two forest fires occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that fire was likely to occur when the FFL accumulated to a critical point (around 27×10 6 kg), highlighting the relevance of remote sensing-derived FFL estimates for understanding potential fire occurrence.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

“…where ρ BLUE is MODIS data band 01, ρ RED is MODIS data band 02, ρ NIR is MODIS data band 03, and ρ SWIR is MODIS data band 07. In the upper model, PROGeoSAIL, the FMC estimate formula is [33,34]…”

Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

“…Topography can play a key role in governing bushfire severity patterns over a landscape [5], [10]- [14]. Many studies have identified that climate and fuel variables significantly influence bushfire severity [10], [15]- [17] but the situation is complex in hilly landscapes. In a hilly region, topography can not only govern fire behaviour but also influence the local micro-climate and fuel distribution of the area [12], [18], [19].…”

Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China

“…Fuel is another critical element for fire ignition, spread, and combustion. The fuel variables, such as land cover or land use, live fuel moisture content (LFMC) (Quan et al 2015;Quan et al 2017) and dead fuel moisture content (DFMC) (Resco de Dios et al 2015), fuel load (FL) (Quan, Li et al 2021), forestry type (FT), and tree species (TS) play important roles in wildfire probability prediction, since they have an important influence on fire behaviors and regimes (Cao et al 2017;Jaafari et al 2017;Luo et al 2019;Carrasco et al 2021;Gale et al 2021;Quan, Xie et al 2021). Among them, the live and dead fuel moisture contents are most commonly considered in wildfire probability modeling at present (Yebra et al 2013;Nolan, Boer et al 2020;Quan, Xie et al 2021) because the large-scale and dynamic monitoring of these variables is available, either from remote sensing data or meteorological data (Yebra et al 2008;Nolan et al 2016;Fan and He 2021;Quan, Yebra et al 2021).…”