LIVE FUEL MOISTURE CONTENT AND IGNITION PROBABILITY IN THE IBERIAN PENINSULAR TERRITORY OF SPAIN

This paper presents an operational algorithm to produce Live Fuel Moisture Content (LFMC) at national scale from MODIS data. The algorithm is based on the inversion of Radiative Transfer Models (RTM) that estimate moisture content based on different simulation scenarios. In addition, logistic regression models were calibrated to convert the derived LFMC values into Ignition Probability (IP) maps. The areas under the curve obtained by the Receiver Operating Characteristic (ROC) plot method provided by the models were close to 0.6. Several statistical analyses were performed in order to ascertain whether the variables proposed to be included in the fire danger model were significantly related to forest fires. A non parametric U-Mann-Withney test confirmed significant differences between fire and non-fire pixels (p<0.001). Fire pixels occurred at significantly lower LFMC values than the non-fire pixels

Generation of Species-Specific Look-Up Table for Fuel Moisture Content Assessment

This study involved the generation of a species-specific Look-Up Table (LUT) for the retrieval of Fuel Moisture Content (FMC) in natural areas dominated by Quercus ilex (Holm oak). Parameter combinations observed in drying Q. ilex samples were used as inputs into the linked PROSPECT and SAILH Radiative Transfer Models (RTM) to avoid unrealistic simulated spectra in the LUT. Terra/MODIS reflectance data, extracted over five plots dominated by Q. ilex, were used to carry out the LUT inversion. This inversion was based on the search for the minimum relative root mean square error (RMSE*?) between observed and simulated reflectance found in the LUT. Different inversion options were tested in order to search for the optimal spectral sampling necessary for accurately estimating FMC. The minimum number of solutions required for the calculation of the estimated FMC was also investigated. The retrieval performance was evaluated with FMC values measured at the five study plots. The most accurate FMC estimation was obtained when using the normalized difference infrared index (NDII6%) and selecting the ten best cases as the solution (RMSE =26.28). Finally, a non-oak-specific LUT (generic LUT) was used in the same way to evaluate whether or not the species-specific LUT retrieved FMC more accurately. The results showed that the species-specific LUT provided more accurate FMC estimations than the generic LUT. Only when the number of solutions was higher than 35 was the accuracy of the two LUT similar. Future work will focus on the possibility of generating a LUT adapted to a wider range of species based on data extracted from field measurements and literature. � 2009 IEEE

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 procedur

Estimation of live fuel moisture content from MODIS images for fire risk assessment

This paper presents a method to estimate fuel moisture content (FMC) of Mediterranean vegetation species from satellite images in the context of fire risk assessment. The relationship between satellite images and field collected FMC data was based on tw

Laboratory Measurements of Plant Drying: Implications to Estimate Moisture Content from Radiative Transfer Models in Two Temperate Species

The estimation of live fuel moisture content (LFMC) is necessary for fire danger assessment. Several studies have successfully used satellite imagery to estimate LFMC, both using empirical and simulation approaches (Yebra et al., 2013). The latter are based on Radiative Transfer Models (RTM). They are generally more robust and easier to generalize, but they rely heavily on the proper parameterization. Since some of the input parameters are associated with different physiological processes, a better understanding of how those parameters co-vary is necessary for constraining the simulation scenarios, thus avoiding combinations of parameters that are unlikely to occur (for instance, in temperate ecosystems, it is unlikely to find simultaneously high values of leaf chlorophyll and low values of leaf moisture).To improve parameterization of RTM models for LFMC estimation, we conducted a laboratory experiment to measure trends in leaf and canopy variables of two tree species broadly distributed in Eurosiberian climates: Beech (Fagus sylvatica L.) and pedunculate Oak (Quercus robur L.). Measurements of LFMC, equivalent water thickness (EWT), dry matter content (DMC), chlorophyll (Ca+b), leaf area index (LAI), leaf angle distribution (LIDF), crown height to width ratio (CHW) and plant reflectance were performed. Significant positive correlations were found between LFMC and EWT (Rs &gt;0.5), and negative ones were found between both parameters and Ca+b (Rs &lt;-0.3). LFMC and EWT were positively related to DMC and LAI, with lower correlation coefficients for the latter. The effect of moisture variation in spectral reflectance was also analyzed using two indices: the spectral angle (SA) and the root mean square error (RMSE).The former contributed the most to the estimation of LFMC variations. Spearman correlation coefficients (Rs) between SA and LFMC were 0.656 and 0.554 for F. sylvatica and Q. robur, respectively; while for RMSE and LFMC they were 0.366 and 0.430, respectively

Development of a framework for fire risk assessment using remote sensing and geographic information system technologies

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1 km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions. � 2008 Elsevier B.V. All rights reserved