Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems

This paper evaluated the capacity of SPOT VEGETATION time-series to monitor the vegetation biomass and water content in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. First, the single date and integrated vegetation index approach, which quantify the amount of herbaceous biomass at the end of the rain season, were evaluated using in situ biomass data. It was shown that the integral of the Ratio Vegetation Index (iRVI) during the rain season was the most suitable index to estimate herbaceous biomass (R² = 0.69). Next, the performance of single, greenness, and accumulated remotely sensed fire risk indices, related to vegetation water content, were evaluated using fire activity data. The Accumulated Relative Normalised Difference Vegetation Index Decrement (ARND) performed the best when estimating fire risk (c-index = 0.76). Finally, results confirmed that the assessment of fire risk was improved by combination of both the vegetation biomass (iRVI) and vegetation water content (ARND) related indices (c-index = 0.80). The monitoring of vegetation biomass and water content with SPOT VEGETATION time-series provided a more suitable tool for fire management and suppression compared to satellite-based fire risk assessment methods, only related to vegetation water content.     

Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; (R_0.86 − R_1.24) / (R_0.86 + R_1.24)) and NDII (Normalized Difference Infrared Index; (R_0.86 − R_1.64) / (R_0.86 + R_1.64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (< 100), retrieval errors could lead to normalized spread rate errors of 6.5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC < 175, LFMC retrieval errors could amount to normalized spread rate errors of 0.6 or an equivalent error of 9.3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions.     

Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem

Live fuel moisture content (FMC) is a key factor required to evaluate fire risk and its operative and accurate estimation is essential for allocating pre-fire resources as a part of fire prevention. This paper presents an operative and accurate procedure to estimate FMC though MODIS (moderate resolution imaging spectrometer) data and simulation models. The new aspects of the method are its consideration of several ecological criteria to parameterize the models and consistently avoid simulating unrealistic spectra which might produce indetermination (ill-posed) problems when inverting the model. The methodology was operatively applicable to 12 shrubland plots located in different provinces of the Mediterranean region of Spain and tested with field data collected in those areas. The results showed that the proposed method efficiently tracks changes of FMC with average errors around 15%. However the model under-estimates FMC values higher than 135.68% since those situations were not included in the simulation scheme and the inversion precision is also dependent on an accurate estimation of LAI. These limitations will be overcome in future work mainly by including spectral signatures of vegetation with FMC values higher than 135.68% in the simulations, and by exploring new methods for LAI retrieval. Further efforts will also be devoted to extend this approach to other ecosystems.     

Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating

This paper presents an empirical method for deriving fuel moisture content (FMC) for Mediterranean grasslands and shrub species based on multitemporal analysis of NOAA-AVHRR data. The results are based on 6 years of field measurements of FMC. The empirical function was derived from a 4-year series and includes multitemporal composites of AVHRR's normalized difference vegetation index (NDVI) and surface temperature (ST) values, as well as a function of the day of the year. It was tested using data from 2 other years on the same site as well as other sites with similar species but very distant from each other and with different elevation ranges. The results show that the model provides a consistent estimation of FMC, with high accuracies for all study sites and species considered, with r² values over 0.8 for both grasslands and shrub species. This performance enables the model to be used to derive spatial estimator of FMC, which is a key factor in operational fire danger management in Mediterranean conditions.