Global fuel moisture content mapping from MODIS

Quan, Xingwen; Yebra, Marta; Riaño, David; He, Bin; Lai, Gengke; Liu, Xiangzhuo

doi:10.1016/j.jag.2021.102354

Cited by 37 publications

(42 citation statements)

References 80 publications

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“…The revisit time of these satellites is 16 days, and the resolution is 30 m for the reflective bands. Similarly to Quan et al [38], we employed Landsat 5 TM from Feb 2000 to Oct 2011 for high quality pixels, Landsat 7 ETM+ from Feb 2000 to Oct 2011 when Landsat 5 TM had poor quality pixels and also from Nov 2011 to Apr 2013, and Landsat 8 OLI from May 2013 until 2019. The use of Landsat 5 TM instead of Landsat 7 ETM+ was due to data gaps produced in the latter by failure in a sensor component [38].…”

Section: Landsat Datamentioning

confidence: 99%

“…The global RTM-based product developed by Quan et al [38] was used to compare the results of the ML-based approach proposed here. We chose this product because it is the only currently available database that has produced LFMC maps over the whole Mediterranean basin.…”

Section: Radiative Transfer Model (Rtm) Databasementioning

confidence: 99%

“…Thus, we are still lacking a product that provides LFMC estimates for the Mediterranean basin. The only exception is the global LFMC product developed by Quan et al [38], which is based on an RTM, and it is not yet known whether LFMC estimates could be improved through ML approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The present study aims to fill this knowledge gap by developing an RF algorithm to predict LFMC within the Western Mediterranean basin using the information of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS), and comparing the results with the only other method available for this area, the physically-based estimations of Quan et al [38]. We also aim to generalize the model over a wide range of fuel types with a unique formulation by combining a forward feature selection with a spatial crossvalidation and ML techniques.…”

Section: Introductionmentioning

confidence: 99%

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Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

2022

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Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMCRF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMCRF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin.

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Section: Landsat Datamentioning

confidence: 99%

Section: Radiative Transfer Model (Rtm) Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

2022

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“…LFMC is commonly defined as the amount of water per dry mass of fuel [60], which is one of the primary variables affecting the ignition and spread of wildfire [33]. Although LFMC has been widely used in forest fire risk early warning [29,33,61,62], there are few studies that analyze the relationship between LFMC and burn severity. LAI was selected because it is one of the important variables to characterize vegetation canopy structure [63,64].…”

Section: Environmental Variablesmentioning

confidence: 99%

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

et al. 2021

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Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.

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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

Leite,

Amaral,

Neigh

et al. 2024

Remote Sens Ecol Conserv

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Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km2) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world.

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Global fuel moisture content mapping from MODIS

Cited by 37 publications

References 80 publications

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

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