Retrieval of forest fuel moisture content using a coupled radiative transfer model

Quan, Xingwen; He, Bin; Yebra, Marta; Yin, Changming; Liao, Ziqi; Li, Xing

doi:10.1016/j.envsoft.2017.06.006

Cited by 63 publications

(34 citation statements)

References 71 publications

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“…High-quality spatial information on LFMC is needed to explore the effect of LFMC on fire occurrence at a regional scale. In this study, we followed the methodologies by Quan et al [28,36] to retrieve LFMC over southwest China using the MCD43A4 product. There was a statistically significant (p <0.01) agreement between retrieved and measured LFMC at field sites used to evaluate the inverse RTM method.…”

Section: Discussionmentioning

confidence: 99%

“…LFMC dynamics were retrieved and mapped from MCD43A4 based on the Look-Up Table (LUT) algorithm following Quan et al [28] (grassland) and Quan et al [36] (forest). In these studies, the PROSAIL RTM (PROSPECT [48] + SAILH [49,50]) was used for the LFMC retrieval for grassland, and the PROSAIL RTM coupled with PROGeoSAIL RTM (PROSPECT + GeoSAIL [51]) was used for forest.…”

Section: Lfmc Retrieval and Validationmentioning

confidence: 99%

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

Luo

Quan

et al. 2019

Forests

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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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Section: Discussionmentioning

confidence: 99%

Section: Lfmc Retrieval and Validationmentioning

confidence: 99%

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

Luo

Quan

et al. 2019

Forests

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“…Using values for the average wind speed and relative humidity for the data recorded at the three meteorological stations as inputs to the CSIRO GFS model may have led to the observed deviations. On the other hand, fuel moisture content (FMC), including live and deal fuels moisture content, has proven to be a key indicator for fire risk assessment and FSR prediction [72][73][74][75]. However, the CSIRO GFS model only considers the dead FMC, which is derived from meteorological factors [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, the CSIRO GFS model only considers the dead FMC, which is derived from meteorological factors [27]. In recent years, several studies have propagated for the use of remote sensing data to retrieve FMC and indeed, this has achieved reasonable results [75][76][77][78]. Therefore, further work will focus on applying the method to the Himawari-8 data to retrieve near real-time live and dead FMC and using those estimates as input parameters for fire behavior and risk assessments.…”

Section: Discussionmentioning

confidence: 99%

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

Liu

Quan

et al. 2018

Remote Sensing

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Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.

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“…The FMC estimation from optical sensors data is primarily made by empirical (statistical) or physical techniques [16]. The former approach establishes a statistical connection between an objective parameter-obtained in field measurements-and reflectance or vegetation indexes (VIs) [17]. Several VIs have been developed to estimate water content from the regions of the electromagnetic spectrum (e.g., visible, near-infrared, and shortwave infrared) [18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

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confidence: 99%

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

Villacrés

Arevalo-Ramirez

Fuentes

et al. 2019

Sensors

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Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata—commonly found in Mediterranean WUI areas—which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave—associated with the water content—is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

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Retrieval of forest fuel moisture content using a coupled radiative transfer model

Cited by 63 publications

References 71 publications

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

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

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

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