Estimation of live fuel moisture content from MODIS images for fire risk assessment

Yebra, Marta; Chuvieco, Emilio; Riaño, David

doi:10.1016/j.agrformet.2007.12.005

Cited by 184 publications

(196 citation statements)

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“…Other studies related vegetation water content to spectral indices that do not include SWIR bands. In the case of grass the relationship with bands in the Visible (VIS) and Near Infrared (NIR) spectral region, has shown a close relationship between vegetation biomass, chlorophyll and water content (Chuvieco et al, 2003(Chuvieco et al, , 2004García et al, 2008;Yebra et al, 2008b) as water stress produces changes in the chlorophyll activity and biomass of the plant. Least squares regression models have served to empirically relate observed measurements of vegetation water content to spectral indices.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the performance statistics of the fitting equations were evaluated to select the most accurate empirical models. Finally, these models were compared to three RTM based estimates, proposed in the literature to derive two FMC Yebra et al, 2008b) and the other for CWC (Trombetti et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Their model was calibrated with CWC from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral water absorption bands. Yebra et al (2008b) used also PROSAILH to quantify FMC, and more recently, Jurdao et al (2013) inverted the RTM GEOSAIL (Huemmrich, 2001) combined with PROSAILH to estimate FMC. The estimations were validated with extensive field sampling data in Spain.…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is a powerful alternative data source to provide information on vegetation water content as it fills such temporal and spatial gaps. Monitoring vegetation water content with remote sensing benefits agriculture, to control crop production and prevent stress in plants (Peñuelas et al, 1992;SepulcreCantó et al, 2006) and forestry and to assess fire danger associated with vegetation water conditions (Chuvieco et al, 2003(Chuvieco et al, , 2004García et al, 2008;Yebra et al, 2008b).…”

Section: Introductionmentioning

confidence: 99%

“…FMC has been extensively used to estimate fire risk and fire propagation (García et al, 2008;Yebra et al, 2008b). Equivalent water thickness (EWT) or leaf water content (LWC), defined as the mass of water per leaf area, measures the thickness of the water layer with the same leaf area (Danson et al, 1992) EWT g cm…”

Section: Introductionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones.Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R 2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Cloud-to-ground lightning and near-surface fire weather control wildfire occurrence in Arctic tundra

Loboda

Chen

et al. 2021

Preprint

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Wildfire is common across the pan-Arctic tundra. Tundra fires exert significant impacts on terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrences remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud-toground (CG) lightning probability by far the dominant driver controlling tundra fire occurrence. Warmer and drier near-surface weather was required to support burning, while fuel composition and topography have modest impacts on fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning modeling is essential for fire monitoring, forecasting, and management in the Arctic.

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Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

Wang

2011

Hydrological Processes

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Abstract:In this study, we aim at finding efficient and robust hyperspectral indices for estimating forest leaf water content parameters (equivalent water thickness, EWT and fuel moisture content, FMC), which are useful for the understanding of terrestrial ecosystem functioning and evaluating fire risk. The most efficient hyperspectral indices have been identified (both on the context of index types and wavelength domains) using both a simulated dataset generated from the calibrated leaf reflectance model, PROSPECT, and datasets of field measured reflectance. Results indicate that high precision can be obtained using the type of vegetation index of two wavelength bands based on reflectance derivatives to estimate both parameters, with overall R 2 and RMSE of 0Ð60 ¾ 0Ð75 and 0Ð0009 ¾ 0Ð0012 g cm 2 for EWT, 0Ð63 ¾ 0Ð87 and 0Ð12 ¾ 0Ð20 g g 1 for FMC, respectively. The best indices identified in this study for vegetation water status in temperate deciduous forests were dSR (1510, 1560) for EWT and dSR (2110, 2260) for FMC, with widths of wavebands ( ) be variable up to 50 nm for both dSR indices. Despite the obvious discrepancies found in fit when applying the identified indices to different datasets, the indices identified in this study are applicable to various species (Dataset III), various phenological stages, different sites (Dataset I) and various leaf anatomies (Dataset II), and therefore suitable for an all inclusive wide range of application especially in temperate deciduous forests.

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Estimation of live fuel moisture content from MODIS images for fire risk assessment

Cited by 184 publications

References 40 publications

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Cloud-to-ground lightning and near-surface fire weather control wildfire occurrence in Arctic tundra

Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

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