Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment

Chuvieco, Emilio; Riaño, David; Aguado, Inmaculada; Cocero, David

doi:10.1080/01431160110069818

Cited by 235 publications

(176 citation statements)

References 31 publications

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“…In this study, the estimated MCI using ETM+ satellite data and ANN technique was closer to the actual value obtained from in-situ data (MAPE < 12%). This implies that fire danger can be predicted with reasonable accuracy as previous studies by Chuvieco et al [83] and Chuvieco et al [22] showed an error of 23% for both Landsat Thematic Mapper, and AVHRR data in LFMC estimation of woodlands, and grassland/shrubland. However, sensitivity analysis must be performed in future to test the effect of different degree of errors in MCI for predicting fire danger.…”

Section: Discussionsupporting

confidence: 53%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%-12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%-33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations.

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Section: Discussionsupporting

confidence: 53%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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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%

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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“…Generally, canopy water content (CWC) and the mean leaf equivalent water thickness at the canopy level ( ̅̅̅̅̅̅̅ ) are widely used for describing the vegetation water status [6,7]. Since many biogeochemical processes including photosynthesis, evapotranspiration and net primary production are closely related to vegetation CWC and ̅̅̅̅̅̅̅ [8][9][10], therefore, gaining a thorough and better understanding of vegetation CWC and ̅̅̅̅̅̅̅ will play an important role in mapping and monitoring the conditions of terrestrial ecosystems such as environmental stress [11], wildfire potential [12] or soil moisture retrieval [13].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Zhang

Zhou

2015

Remote Sensing

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Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level ( ̅̅̅̅̅̅̅ ) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CIgreen), red edge chlorophyll index (CIred edge), and the red edge normalized ratio (NRred edge) were most sensitive to the variations of CWC and ̅̅̅̅̅̅̅ . Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and ̅̅̅̅̅̅̅ . However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, ̅̅̅̅̅̅̅ and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI).

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Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment

Cited by 235 publications

References 31 publications

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

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

Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

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