A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

Yebra, Marta; Dennison, Philip E.; Chuvieco, Emilio; Riaño, David; Zylstra, Philip; Hunt, E. Raymond; Danson, F. Mark; Jurdao, Sara

doi:10.1016/j.rse.2013.05.029

Cited by 302 publications

(269 citation statements)

References 124 publications

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“…It is possible to have the same FMC and EWT for different LAI and hence different CWC and amount of soil background, which will change its reflectance. Yebra et al (2013) demonstrated through PRO-SAILH simulations how a very different CWC for the same EWT based on changes of LAI translates into a large range of NDII values. Our results confirm this theoretical assumption described in Yebra et al (2013).…”

Section: Discussionmentioning

confidence: 99%

“…Yebra et al (2013) demonstrated through PRO-SAILH simulations how a very different CWC for the same EWT based on changes of LAI translates into a large range of NDII values. Our results confirm this theoretical assumption described in Yebra et al (2013). This issue is especially critical over areas like the one analyzed in this work with an herbaceous cover exhibiting large dynamic annual growth.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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“…LAI accounts for the amount of green vegetation that absorbs or scatters solar radiation, FVC determines the partition between soil and vegetation contributions, while FAPAR is a vegetation health indicator related with ecosystems productivity. In addition, canopy water content (CWC) accounts for the amount of water content at canopy level, varies with vegetation water status, and is usually computed as the product of leaf water content (C w ) and LAI [5,6]. These essential variables can be estimated using remote sensing data and are key inputs in a wide range of ecological, meteorological and agricultural applications and models.…”

Section: Introductionmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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“…Global to regional mapping and monitoring of drought is increasingly considered for food security assessment in agricultural lands [1] and for numerous ecosystem processes, such as carbon fluxes, plant productivity [2,3] or fire risk [4]. Various indexes for monitoring drought have been developed on the basis of meteorological datasets (see [5] for a review).…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation indexes, as the result of empirical combinations of reflectance in various wavelengths, had contributed in detecting changes in biomass status as changes in chlorophyll activity (biomass vegetation indexes, BVI) and vegetation water content (moisture vegetation indexes, MVI). Various studies have investigated the combination of BVI and MVI, especially to assess drought conditions as interannual precipitation deficit/surplus [10] or, more specifically, its impact on vegetation water stress (e.g., [11,12]) or fire risk [4]. By combining the near-infrared (NIR) and shortwave infrared (SWIR) reflectances, variations induced by leaf internal structure and leaf dry matter content can be disentangled to improve the accuracy in retrieving the vegetation water content [13].…”

Section: Introductionmentioning

confidence: 99%

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

2015

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Abstract:The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference 1938Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed.

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A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

Cited by 302 publications

References 124 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

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

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