Mapping live fuel moisture with MODIS data: A multiple regression approach

Peterson, S.; Roberts, Dar A.; Dennison, Philip E.

doi:10.1016/j.rse.2008.07.012

Cited by 78 publications

(74 citation statements)

References 38 publications

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“…4). This fact also contradicts other studies that predicted FMC from VARI on chaparral (Peterson et al, 2008;Roberts et al, 2006;Stow et al, 2005Stow et al, , 2006. VARI was developed to detect vegetation fraction in homogenous wheat crops (Gitelson et al, 2002b), but neither Gitelson et al (2002a) nor the above studies have tested this spectral index to detect vegetation water content on sites like ours, with strong seasonal changes in species composition and LAI.…”

Section: Discussioncontrasting

confidence: 85%

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: Discussioncontrasting

confidence: 85%

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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“…In Mediterranean-type ecosystems, vegetation water content was shown to be linearly related to a combination of BVI and MVI using either high or coarse spatial resolution images [29][30][31]. In semi-arid ecosystems, Fensholt and Sandholt [14] found a strong relationship between the MVI and soil moisture (Sahelian zone in Africa); Ceccato et al [32] and, more recently, Sow et al [33] reported consistent relationships between field measurements of EWT and various BVI and MVI, allowing for a regional assessment of the seasonal dynamic of ecosystem dryness.…”

Section: Biomass Vegetation Indexesmentioning

confidence: 99%

“…Methods were then proposed to overcome this fact by modifying the linear regression between VI and EWT variables, accounting for the seasonal and interannual vegetation variability [4,30,35], as well as the species mixture and their related biophysical properties [36]. Considering that leaf area index (LAI) is an integrated proxy for canopy density and ecosystem water content capacity according to the ecohydrological equilibrium theory [37], canopy variables represented by LAI are also important parameters affecting the retrieval of water vegetation status from remote sensing vegetation indexes.…”

Section: Biomass Vegetation Indexesmentioning

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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“…Live fuel moisture varies predictably on an intra-annual basis; however, it is highly variable on an interannual basis owing to differences in annual precipitation (Countryman and Dean 1979;Peterson et al 2008). For fire regime simulations, woody and herbaceous LFM values are stochastically simulated, given annual average values and standard deviations, and seasonal trends.…”

Section: Hfirementioning

confidence: 99%

“…For fire regime simulations, woody and herbaceous LFM values are stochastically simulated, given annual average values and standard deviations, and seasonal trends. LFM data are available at 2-week intervals from government agencies for many regions; LFM can also be predicted using satellite data (Peterson et al 2008).…”

Section: Hfirementioning

confidence: 99%

Modelling long-term fire regimes of southern California shrublands

Peterson

Moritz

Morais

et al. 2011

Int. J. Wildland Fire

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Abstract. This paper explores the environmental factors that drive the southern California chaparral fire regime. Specifically, we examined the response of three fire regime metrics (fire size distributions, fire return interval maps, cumulative total area burned) to variations in the number of ignitions, the spatial pattern of ignitions, the number of Santa Ana wind events, and live fuel moisture, using the HFire fire spread model. HFire is computationally efficient and capable of simulating the spatiotemporal progression of individual fires on a landscape and aggregating results for fully resolved individual fires over hundreds or thousands of years to predict long-term fire regimes. A quantitative understanding of the long-term drivers of a fire regime is of use in fire management and policy.

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Mapping live fuel moisture with MODIS data: A multiple regression approach

Cited by 78 publications

References 38 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

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

Modelling long-term fire regimes of southern California shrublands

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