Estimation of Herbaceous Fuel Moisture Content Using Vegetation Indices and Land Surface Temperature from MODIS Data

Abstract

Abstract:The monitoring of herbaceous fuel moisture content is a crucial activity in order to assess savanna fire risks. Faced with the difficulty of managing wide areas of vegetated surfaces, remote sensing appears an attractive alternative for terrestrial measurements because of its advantages related to temporal resolution and spatial coverage. Earth observation (EO)-based vegetation indices (VIs) and the ratio between Normalized Difference Vegetation Index (NDVI) and surface temperature (ST) were used for EO-based estimates of water content were more consistent with the use of VI as compared to the ratio NDVI/ST. Different VIs based on near-infrared (NIR) and shortwave infrared (SWIR) reflectance were tested and a consistent relationship was found between field measurements of leaf equivalent water thickness (EWT) from all test sites and Normalized Difference Infrared Index (NDII), Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI). Also, strong relationships were found between fuel moisture content (FMC) and VIs for the sites separately; however, they were weaker for the pooled data. The correlations between EWT/FMC and VIs were found to decrease progressively as the woody cover increased. Although these results suggest that NIR and SWIR reflectance can be used for the estimation of herbaceous water content, additional validation from an increased number of study sites is necessary to study the robustness of such indices for a larger variety of savanna vegetation types.

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

“…Based on the Prospect simulation model [38] combined with canopy models, Ceccato et al [20] showed that GVMI is suitable for retrieving vegetation water content when the LAI is equal to or greater than two due to the effect of soil background. Similar conclusions were reported in [33], who showed that the correlations between measured water vegetation content (EWT or FMC) and VI decreased moderately with decreasing aridity represented by increasing tree cover in the Sahel. Over areas with low LAI, there is a general consensus that red/NIR vegetation indexes saturate and, in turn, are less efficient than in foliar-water indexes for canopy water content estimation [21,39,40].…”

“…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. Similar results were reported by [34] in the semi-arid region of Arizona.…”

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

“…ANN modelling estimated RWC and LDMC with a Pearson correlation coefficients of 0.86 and 0.88, respectively, for upscaled MODIS and ETM+ products. The ANN model greatly improved the accuracy of LFMC estimates (R of 0.97 for ETM+ and 0.83 for MODIS data) compared to previous studies such as that of Qi, et al [37], Sow, et al [38], Verbesselt, et al [30], and Dasgupta, et al [31] who yielded R = 0.49, 0.63, 0.75, and 0.54, respectively, with a LR model ( Table 1).…”

“…This is due to the fact that vegetation indices (i.e., Normalized Dry Matter Index, Dry Matter Content Index, Normalized Difference Water Index and Normalized Difference Infrared Index) have a non-linear and polynomial trend relationships with LFMC [80] and ANNs are able to compute any function for estimating MCI because the relationship between input data and output data can be non-linear, whereas the MLR model cannot identify these non-linear relationships. Although empirical models such as MLR are easy to use and can be applied to a large temporal dataset [24,27,37,38], they have a serious limitation of not being able to capture nonlinear relationships in the data. ANNs have been found to produce lower errors of estimation (e.g., fuel moisture content) than linear regression [57,81].…”

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

“…The combination of this reflectivity information from different bands can be used to characterize the vegetation status. Numerous studies have related the FMC with satellite data [16][17][18], especially using indices involving middle infrared bands. Moreover, in [19] a sensitivity analysis has been done in order to analyze the relationship between spectral indices and different measurements of vegetation water content (FMC and EWT, Equivalent Water Thickness) and the conclusion is that generally the spectral indices are more sensitive to EWT than to FMC.…”