Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR images in forest fire danger studies

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1 INT. J. REMOTE SENSING, 2003, VOL. 24, NO. 8, Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR images in forest fire danger studies E. CHUVIECO, I. AGUADO, D. COCERO and D. RIAÑO University of Alcalá, Department of Geography, Colegios, Alcalá de Henares, Spain; emilio.chuvieco,inmaculada.aguado,david.cocero,david.riano@uah.es Abstract. An empirical estimation of live fuel moisture content (FMC) was generated from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived variables. This estimation was based on the ratio of Normalized Difference Vegetation Index (NDVI) and surface temperature (T ), and the relative greenness (RGRE). It was s derived for the summer season (from June to September). The estimation was calibrated from field measurements collected in central Spain, and included consideration of both herbaceous and shrub live FMC values. The proposed estimation worked reasonably well for other periods in the same study area and also for other similar study areas in Mediterranean environments. This estimation of FMC from remote sensing could be used as an input to standard fire danger and fire behaviour programs, providing spatially comprehensive data, which is critical for regional planning of fire prevention. 1. Introduction Fire danger rating requires assessment of several factors that are related to both human activity and environmental conditions (weather, topography, fuel properties). The estimation of fire danger conditions can be undertaken on several temporal and spatial scales (Chuvieco and Cocero 1996). Satellite data can provide relevant information on the status and characteristics of fuels, for the estimation of both long-term and short-term trends, as well as for local and regional-global scales. It is evident that the spatial and temporal scales are related, since high spatial resolution typically implies low monitoring frequency. Therefore, sensors providing higher resolution images are used to estimate more permanent fuel properties, such as fuel load and fuel structure, commonly through the classification of fuel types (Wilson et al. 1994, Roberts et al. 1997). On the other hand, short-term trends of fuel water status are estimated with low-resolution satellite data (Desbois et al. 1997, Chuvieco et al. 1999). In forest fire danger literature, the estimation of live fuel moisture content (FMC) is considered to be one of the key variables affecting fire ignition and fire propagation (Van Wagner 1967) and is therefore widely used in fire danger rating systems (Brown This paper was presented at the 3rd International Workshop of the Special Interest Group (SIG) on Forest Fires of the European Association of Remote Sensing Laboratories held in Paris in May International Journal of Remote Sensing ISSN print/issn online 2003 Taylor & Francis Ltd DOI: /

2 1622 E. Chuvieco et al. and Kenneth 1979, Burgan 1988). FMC is currently defined as the percentage of moisture over the dry weight (Desbois et al. 1997, among others): FMC= AW w W d 100 (1) W d B where W is the wet weight and W the dry weight of the same sample. Commonly w d this variable is obtained through field sampling using gravimetric methods (wet samples are oven-dried at 60 or 100 C: Viegas et al. 1992). FMC is common in firerelated studies, since the actual amount of water is the critical parameter in fire behaviour modelling (Brown et al. 1989, Viegas et al. 1990). Most ecological literature does not use FMC to study plant water relations but instead the relative water content (RWC), which is defined as the water proportion over the saturated water content (Slavik 1974). This variable is more informative of saturation water deficit and, therefore, of plant water stress. Both variables, FMC and RWC, have been estimated from remotely sensed data. Most studies have been based on laboratory spectral measurements, which have been more concerned with deriving RWC, most of them being based on leaf measurements (Thomas et al. 1971, Tucker 1980, Jackson and Ezra 1985, Ripple 1986, Hunt et al. 1987, Bowman 1989, Cohen 1991). Actual canopy estimations are less common. Recent analysis using radiative transfer models (Jacquemoud 1990, Jacquemoud et al. 1995) demonstrate that the most related variable to leaf reflectance is neither FMC nor RWC, but rather the equivalent water thickness (EWT), which is defined by the amount of leaf water per unit area (A): EWT= AW w W d (2) A B These studies have shown that EWT can be directly estimated from a ratio of Short Wave Infrared (SWIR) and Near Infrared (NIR) reflectance (Ceccato et al. 2001). Similar results were obtained by other authors using empirical fittings between EWT and reflectance measurements (Hunt 1991, Datt 1999). According to Ceccato et al. (2001), the estimation of FMC can not be reliably achieved from reflectance measurements, since it does not discriminate between variations of water and dry matter content as the leaf dries. This is especially critical when several species are combined, because they will probably have different rates of water and dry matter content changes through time. The interest of using FMC instead of EWT in fire danger estimation relies on two aspects: on the one hand, there are no references to using EWT in fire ignition or fire propagation studies; on the other, EWT is extremely difficult to measure operationally in the field, since it requires the calculation of leaf areas. An alternative way of relating EWT, and consequently reflectance data, and FMC will be the previous estimation of the specific weight (S ) for each species. Specific weight is w defined as the ratio of leaf dry weight (W ) and leaf area (A). Therefore, equation (2) d can be written as: EWT= AW w W d W d BA W d AB =FMC S (3) w If S of each species is assumed stable through time, FMC variations in single w species may be correctly estimated from reflectance data. This explains why good

3 Forest fire management new methods and sensors 1623 correlations were found between FMC and satellite derived variables in several ecosystems (Paltridge and Barber 1988, Chladil and Nunez 1995, Alonso et al. 1996, Chuvieco et al. 1999, 2002). FMC for grasslands was more efficiently estimated by remote sensing data than for other fuels, since water variations in grasslands have a more direct effect on chlorophyll content, and are more sensitive to seasonal variations than shrubs or trees. This relation of grassland FMC and chlorophyll content explains the good correlation between FMC and vegetation indices (particularly the Normalized Difference Vegetation Index (NDVI)), especially when spring and summer seasons are considered (Paltridge and Barber 1988, Chladil and Nunez 1995, Hardy and Burgan 1999, Chuvieco et al. 2002). Experiences with shrubs were less successful, with diverse trends regarding the different species analysed. In a pilot work developed in south Spain good correlations were found for Cistus ladanifer and Rosmarinus oycinalis, two very common Mediterranean species, while Erica australis offered poorer trends (Alonso et al. 1996). In this case, a ratio of NDVI and Surface Temperature (T s ) provided the best results. Extended analysis of FMC and satellite data was carried out during the Megafires project in several European Mediterranean countries (Deshayes et al. 1998, Chuvieco et al. 1999). Good results have also been observed in Boreal forest (Leblon 2001). Additional studies have also suggested the use of AVHRR data to monitor fire danger (Leblon 2001). Observed fire occurrence was related to multitemporal trends of AVHRR images acquired before the fire, assuming they contain information on vegetation dryness, an important factor in fire danger (López et al. 1991, Prosper- Laget et al. 1994, 1995, Vidal et al. 1994, Illera et al. 1996, González et al. 1997). These studies proposed different techniques to emphasize the multitemporal decrease in vegetation vigour, mainly by using indices based on the measure of change from previous periods (López et al. 1991, Illera et al. 1996, González et al. 1997). An assessment of their methods was performed by comparing changes in NDVI or T s with fire occurrence, but actual field estimations of FMC were not performed. Finally, some authors have also proposed combining satellite data and meteorological danger indices. The former would furnish information on live fuel conditions, while the latter would provide an estimation of FMC for dead fuels. Theoretical frameworks have recently been proposed (Burgan et al. 1998), but additional research is required to obtain a proper integration of these two sources of information (Burgan and Hartford 1993, Aguado et al. 1998, Burgan et al. 1998). 2. Objectives This paper summarizes the research undertaken within two European funded projects (Control-Fire-Sat and Inflame) to improve current fire danger indices by incorporating satellite data. Current fire danger indices rely on meteorological measurements, which try to consider those atmospheric conditions that are relevant to fire ignition or propagation (mainly wind speed and direction, and vegetation water status). The relationships between meteorological indices and FMC have been demonstrated for dead fuels, but are not evident in live fuels, which have different physiological mechanisms to respond to atmospheric changes. The lack of weather stations in forested areas is another limitation for weather indices in fire danger estimation, since atmospheric measurements can be derived from areas quite far from where the forested land is located. Satellite data might solve both problems, since measurements are taken directly from the fuels (and not from the air, although the atmosphere also interferes), and they cover extensively forested areas.

4 1624 E. Chuvieco et al. The analysis is based on NOAA-AVHRR data, which is very appropriate for this task since it provides adequate temporal and spectral resolution for these studies, and the data are freely downloadable (our Department has had a High Resolution Picture Transmission (HRPT) receiving system since 1998). The main limitation of AVHRR data is the lack of a SWIR channel until the NOAA-15 was launched (in May 1998, although with diverse problems that have reduced the actual availability of AVHRR data), and the radiometric instability of the sensor. Additionally, as in all optical systems, observations are precluded when cloud coverage is frequent, although this constraint is less important in fire danger applications, since highest fire danger conditions commonly occur on hot and sunny days. The purpose of this research was to obtain an empirical estimation of FMC for live fuels, based on reflectance and temperatures derived from AVHRR images, which might be directly applicable to different Mediterranean areas. The empirical index should be applicable to the summer season (June to September), and valid for both grasslands and shrublands. As mentioned, the FMC of live fuels is a critical component of fire ignition and fire propagation, and therefore a reliable estimation from remote sensing data would improve current fire danger estimation methods, as well as providing a spatially comprehensive view of where and when live fuels suffer higher water shortages. Experimental calibration and assessment from field samples taken in central Spain are presented. 3. Field sampling of FMC Field sampling of FMC is costly to assure spatial significance, and is therefore seldom performed. Although FMC refers to the whole plant, moisture content in live fuels is commonly measured from the leaves, since woody parts of the plant are less sensitive to atmospheric variations. Several measures of FMC have been proposed (Desbois et al. 1997), the most common being the percentage of wet to dry weight, see equation (1). Different spatial sampling techniques have been proposed for FMC measurements, the most typical being transects and quadrants. For this project, field sampling of FMC was undertaken from early April to the end of September between 1996 and 1999, although only data from June to September are presented here since our goal was to derive an index for the summer fire campaign. The study area selected was the Cabañeros National Park, located in central Spain, 200 km south of Madrid (figure 1). Plot sizes were 50 m 50 m, positioned in the central valley of the Park, on very gentle slopes (<5%). The distance between the plots ranged from 5 to 3.7 km. The total distance between the first and the last was about 20 km. A total of five plots were sampled. The first three were covered 100% by herbaceous species, although in the summer the effect of soil background was noticeable during the field sampling. They differed in soil depth and grass height and biomass. The fourth and fifth plots were 95% covered by shrub species (the rest were with soil background or some herbaceous species), which are very common in Mediterranean forested areas: Cistus ladanifer and Erica australis were sampled in the fourth plot, and these two species plus Phillyrea angustifolia and Rosmarinus oycinalis in the fifth. Samples were composed of terminal leaves for shrubs, whereas the whole plant was extracted for herbaceous species. Three samples per species and plot were collected every 8 days between 12 and 16 hours GMT, to coincide with the period of highest fire danger and with the acquisition of NOAA-14 AVHRR images that were used in the project.

5 Forest fire management new methods and sensors km Figure 1. Location of the study area. Crosses identify sampling plots. Each sample was composed of approximately between 100 and 200 g of leaves and small terminal branches (in the case of shrub species) or herbaceous plants (in the case of grassland). The sample was then put in an envelope, sealed and weighed with a field balance (precision ±0.1 g). The envelopes with the samples were then dried in an oven for 48 hours at 60 C, and weighed again on the same balance. The weight of the envelopes was subtracted to compute the FMC in accordance with equation (1). The representative value of FMC for each plot and date was calculated as the average of the three samples collected on each day and plot. In the case of plots 4 and 5, since there was a mixture of several shrub species, an average per species was computed, as well as the average value of all the species collected on that plot (C. ladanifer and E. australis in plot 4, and C. ladanifer, E. australis, P. angustifolia and R. oycinalis in plot 5). Figure 2 shows the evolution of FMC for grassland and one of the shrub species (C. ladanifer) during the 4 years of our study. In the case of grasslands, the contrast between the beginning and the end of the summer is noticeable in all years, but 1996 and 1999 are clearly drier than 1997 and 1998, which shows the higher FMC values of the series (250%). Summer FMC values of grasslands are always below 50%, with occasional rainstorms that cause an increase, which is especially evident in C. ladanifer does not show as much contrast as grasslands throughout the season, and FMC values most frequently range between 120 and 60%. Again 1999 is the driest year, and 1997 and 1998 the wetter, the latter being especially evident in the central months of the summer. Although not shown in the figure, E. australis species showed less contrast in FMC values than C. ladanifer, while R. oycinalis presented an intermediate behaviour. 4. Analysis of NOAA-AVHRR images NOAA-AVHRR images were recorded by the HRPT receiving antennas of Infocarto ( ), Dundee (Spring 1996 to 1998) and the University of Alcalá

6 1626 FMC 300 E. Chuvieco et al. Grassland /6 24/6 10/7 26/7 11/8 27/8 12/9 28/9 (a) Date FMC Cistus ladanifer 8/6 24/6 10/7 26/7 11/8 27/8 12/9 28/9 (b) Date Figure 2. Variations of FMC from 1996 to (a) Grassland (b) C. ladanifer. ( ). Afternoon AVHRR daily images from the NOAA-14 satellite were acquired from 1 April to 30 September in the four years. Crossing times are commonly between and GMT, in coincidence with the field sampling schedule. The pre-processing chain applied to the images was based on calibrating visible and near infrared bands (channels 1 and 2) to convert the raw digital counts to reflectance at the top of the atmosphere, and conversion of digital counts of thermal infrared bands (channels 4 and 5) to brightness temperatures. Geometrical corrections of the images were undertaken using orbital models, but additional manual registration to improve multitemporal matching was performed when necessary.

7 Forest fire management new methods and sensors 1627 Atmospheric corrections were not applied since for most of the images the observation angle was missing. However, cloud masking was undertaken for daily acquisitions, following Saunders and Kriebel s algorithm (1988), and multitemporal value composites of 8 day periods computed. Satellite variables used to derive the empirical Live FMC index were the following: $ NDVI values, computed from channels 1 and 2 of AVHRR, using the common formula: NDVI= r 2 r 1 (4) r +r 2 1 where r and r were respectively the reflectances in near infrared and red 2 1 wavebands. As mentioned previously, multitemporal trends of NDVI values have been extensively used in fire danger assessment and FMC estimation (Paltridge and Barber 1988, Chladil and Nunez 1995, Chuvieco et al. 1999). $ Relative greenness, which emphasizes the relative variation of NDVI for each specific site, as a comparison of the range of NDVI variation for that area (Kogan 1990): RGRE= A NDVI 0 NDVI min NDVI max NDVI min B (5) where RGRE is the relative percent green, NDVI 0 observed NDVI for a pixel, NDVI max and NDVI min the maximum and the minimum NDVI for that pixel during the whole study period. $ Accumulated decrements of NDVI. Defined by López et al. (1991) as: ARND= d 1 NDVI(id ) NDVI(id ) h+1 h (6) NDVI(id ) h d 1 h where ARND is the cumulated relative NDVI decrement, id is the image on h the date h, and d, d... are the dates of available NDVI images. In several 1 2 studies performed in Eastern Spain, a greater decrease of NDVI has been directly related to increased fire danger (Illera et al. 1996). $ Surface temperature (Ts ). For vegetated areas, surface temperature is not only related to air temperature, but also to evapotranspiration rate. As is well known, when the plant dries, it reduces transpiration by closing stomata causing the surface temperature to increase. Therefore, a negative correlation between FMC and T should be expected. Surface temperature was computed s from brightness temperature using algorithms generated by Caselles and others ( Valor and Caselles 1996, Coll and Caselles 1997). $ Ratio of NDVI and surface temperature (Ts ) (NDVI/T ). Many authors have s demonstrated the negative relation between NDVI and T for vegetated areas, s which should be related to seasonal trends of some species as well as the cooling effect of evapotranspiration of green covers (Nemani et al. 1993, Moran et al. 1994). The integrated analysis of NDVI and T has proven very valuable s in fire danger and live FMC estimation (Prosper-Laget et al. 1995, Vidal and Devaux-Ros 1995, Alonso et al. 1996), and specifically, the ratio NDVI/T was s shown to be related to live FMC variation (Chuvieco et al. 1999). The ratio should show a positive correlation with live FMC, since the greater the ratio, the higher the vegetation vigour.

8 1628 E. Chuvieco et al. 5. Generation of an empirical estimation of FMC While several indices for estimating fire danger have been proposed in the literature, very few were actually based on field measurements of live FMC. While working on the physical analysis of relations between live FMC and radiance by spectroradiometry and the adaptation of canopy reflectance models (Jacquemoud et al. 1995), we proposed at this stage to derive an empirical estimation, based on the quantitative relationship of live FMC and AVHRR derived variables. To simplify the operational application of the index, we intentionally did not use any auxiliary information (fuel types, meteorological indices) that frequently is not available for proper spatial or temporal resolution for fire danger management. This estimation of live FMC could be used as either a direct input to fire danger indices or alternatively as a fire danger value per se. In this case, historical analysis of live FMC values related to fire occurrence might be used to establish critical fire danger thresholds. Within this context, Chandler et al. (1983) suggested that the critical live FMC values for fire spread are 100% for coniferous and 75% for Mediterranean shrubs. Schroeder and Buck (1970) indicated a critical value of 30% for grasslands, and Rothermel (1983) 25% for dead fuels. It is fairly evident that live FMC values only reflect one aspect of fire danger. Therefore, additional variables should be taken into account for integrated fire danger indices, both those related to the physical environment (atmospheric and vegetation conditions, topography, etc.), and those to the human component (socio-economic data, land use, etc.). The empirical estimation of the live FMC proposed in this paper was based on a multiple regression analysis. Being aware of the importance of having physical basis for using more global models, this empirical index is based on independent variables that have a physical relationship with the live FMC. The empirical fitting was based on field data from 1996 and 1997, while data from 1998 and 1999 were used for validation. Finally, to test the applicability of this estimation to other study areas, they have been compared with two additional field surveys, carried out in south Spain (province of Cordoba) and south France, by colleagues of the European projects Inflame and Control-Fire-Sat Correlations of FMC and satellite variables The first stage in deriving the index was based on computing Pearson r correlation coefficients for all the species collected. Thirty periods of field measurements for 1996 and 1997 were included in the correlation (table 1). These two years were used later to calculate the empirical fitting with FMC. Table 1. Pearson r values of FMC and various indices derived from NOAA-AVHRR data (study area of Cabañeros, central Spain). Summer data. Shrub Grass+ Pearson r Grassland C. ladanifer R. oycinalis E. australis P. angustifolia (average) shrubs NDVI T s NDVI/T s ARND RGRE

9 Forest fire management new methods and sensors 1629 Correlation indices are coherent with expected trends, with lower values for grasslands than shrublands. Being mostly annual species, grasslands behave as dead material for most of the summer period. They lose their greenness as soon as the FMC drops below 30%, and is not recuperated until the beginning of the new vegetative cycle. When grasslands receive summer rainfalls, the FMC increases but it does not cause new plant growth or increased greenness and therefore is not observed in NDVI trends. T s trends are similar, but in the opposite direction. In this case, the occurrence of rainfalls may modify T s, and therefore a higher correlation than NDVI, although still low, is observed. The ratio of NDVI and T s behaves in an intermediate way. Higher correlations between NOAA variables and FMC were found for shrublands than for grasslands, since they do not reach a critical level of chlorophyll content during the summer period. Among the species, E. australis offered the poorer trends, especially for 1996 data. This tendency has been observed by other colleagues in the South of France. As far as the satellite variables are concerned, the ratio of NDVI and T s performs generally better than the single NDVI and T s, with the exception of E. australis and P. angustifolia. NDVI and RGRE show the same values in table 1, since these correlations have been computed with average values for grassland plots, in one case, and shrub plots in the other. When the two vegetation types (grassland and shrublands) are combined the correlations are much higher, since the variability of FMC, fuel greenness and fuel temperature is also higher. The highest performance was found for RGRE and NDVI, and the lowest for the temporal decrement of NDVI. When including data from the spring season data in these correlations, trend changes are confirmed, although higher correlation values are observed. Since the spring period was not considered for this analysis, those results will not be commented on Empirical estimation of FMC The empirical fitting to estimate FMC was based on a stepwise regression analysis considering the field measured FMC for the summer seasons (June to September) of 1996 and 1997 as the dependent variable. A total number of 60 observations were used: 15 periods per year, and two vegetation types per year, grasslands and shrublands. However, no distinction between the two vegetation types was introduced at this stage, since we intended to derive a single estimation of FMC without auxiliary information on fuel types. The estimation has been named Synthetic Fuel Moisture Content (SFMC) since it takes into account both grasslands and shrublands. The equation finally obtained was as follows: SFMC= NDVI/T RGRE (7) s where SFMC is the Synthetic Fuel Moisture Content (as percentage of fuel dry weight), NDVI/T the ratio of the NDVI and surface temperature, and RGRE the s relative greenness. The satellite variables were scaled as follows: NDVI/T =((NDVI+1)125)/T (8) s s where NDVI maintained the original scale ( 1 to1),andt s was expressed in C. RGRE was defined as in (5). This fitting provided a Pearson coefficient value of ( p<0.001). Figure 3 includes the observed and predicted values of FMC computed by the empirical model. Trends are coherent, and no clear bias is observed, although some noise in

10 1630 E. Chuvieco et al. 120 R 2 = Predicted Observed Figure 3. Observed (FMC) and predicted (SFMC) values (calibration data: 1996 and 1997). both low and high FMC values was found. Table 2 provides a summary of residuals for grassland and shrubland. Maximum absolute residuals refer to the differences between actual and estimated percentages of FMC, without considering the signs, while relative residuals refer to the proportion of absolute residuals versus observed FMC values for the same period. Maximum residuals (81% of FMC) were found for the first period of 1997, which in grassland presented unusually high FMC values for this time of the year. Lower residuals were found for early August, when FMC values are also lower. Most periods presented absolute residuals lower than 20% of FMC. Relative residuals were also generally acceptable. Again higher errors were found for grasslands. The maximum relative residual occurred in early July 1996, when SFMC values were 50% while actual FMC values were 23 times lower. This Table 2. Residuals of the estimation of FMC (data from 1996 and 1997). Relative residual FMC SFMC Absolute residual (absolute residual/ (FMC values) FMC value) Max-both Min-both Mean-both Max-grassland Min-grassland Mean-grassland Max-shrubland Min-shrubland Mean-shrubland

11 Forest fire management new methods and sensors 1631 overestimation was produced by unusually high values of NDVI, perhaps caused by the effect of geometrical displacement of some images or non-corrected angular effects. Fortunately, it is not at all common, since most periods present estimation errors lower than 45% of the actual FMC. 6. Assessment of the SFMC 6.1. Cabañeros National Park data Another field campaign of FMC measurements in the same study area was undertaken in 1998 and 1999 for validating the SFMC index. We did not intend to validate the SFMC against fire occurrence, since fire incidence may also be related to other factors ( human activities, wind conditions, etc.), and therefore critically low values of FMC do not necessarily imply the occurrence of fires. Consequently, it was necessary to validate the empirical SFMC with actual FMC data collected in the field. The same specifications previously described were followed in this validation phase to collect the samples. The processing of AVHRR images for 1998 and 1999 data followed the same steps described earlier, and derived indices were computed using equations (5), (7) and (8). Table 3 includes results of statistical comparison between estimated and observed FMC values for the assessment sample. These values were computed from 60 independent observations of FMC (15 periods per year and two vegetation types: grassland and shrubland). It is important to note that FMC trends between the assessment ( ) and fitting periods ( ) were not very comparable, since there was a clear meteorological difference, with high moisture content values in 1998, and much lower in In fact, the Pearson r coefficient between FMC contents of and is lower than 0.5. In spite of this, the performance of the SFMC is very similar in both the calibration and the assessment sample. Pearson r values were even higher for the independent sample: for ( p<0.001) against for , which may be caused by the higher contrast in FMC values found in The solid correlation of SFMC and FMC implies a good estimation capacity of FMC from NOAA-AVHRR satellite images. Figure 4 presents the adjustment of observed and predicted FMC values for the data. No bias is observed in any range of the FMC measured. However, the unexplained variance remainder is still important, implying that several factors of noise are present. Table 3. Assessment of the SFMC in Cabañeros National Park (data from 1998 and 1999). Relative residual FMC SFMC Absolute residual (absolute residual/ (FMC values) FMC value) Max-both Min-both Mean-both Max-grassland Min-grassland Mean-grassland Max-shrubland Min-shrubland Mean-shrubland

12 1632 E. Chuvieco et al. FMC R 2 = Predicted Observed Figure 4. Observed (FMC) and predicted (SFMC) values (assessment data: 1998 and 1999). Average absolute residual values (table 3) were a little higher for than for (23% against 20.9%), because of the wider range of FMC values of grassland, that extended the errors for this fuel type (32 versus 26%). The estimation errors of shrub species were very similar in both the and periods. Maximum residuals were found for those periods of very high FMC values (240%), observed in June 1998, which are quite unusual for the summer period. Maximum relative errors for shrub types were very acceptable, always lower than 50% of actual FMC value. Figure 5 shows temporal trends of actual and estimated FMC for grassland and shrubland. The 1998 data fits well, while in 1999 there is a general trend to overestimate FMC of grassland and underestimate FMC of shrubland Additional assessment Within the Control-Fire-Sat and Inflame projects, additional assessment of the SFMC was performed. FMC data from the Cordoba region, in south Spain, and the Valabre region in south France was obtained for the summer periods (June September) of 1999 (Cordoba) and ( Valabre). Field data collection followed the same rules as those previously described. Temporal trends of estimated and observed FMC for the French study area are shown in figure 6. In this field campaign, FMC was measured for two typical Mediterranean shrubs (Rosmarinus oycinalis and Quercus coccifera) in the summer periods of 1996, 1997 and NOAA-AVHRR images were processed for the same periods and SFMC computed as explained before. The Pearson correlation value between the actual FMC and the proposed empirical estimation of SFMC (5) in

13 Forest fire management new methods and sensors FMC FMC Grass SFMC Grass FMC Shrub SFMC Shrub 0 Jun1 Jul1 Jul4 Aug3 Sep2 Jun1 Jul1 Jul4 Aug3 Sep2 Figure 5. Temporal evolution of FMC and SFMC for grassland and shrublands in the Cabañeros study area FMC SFMC 40 0 Jun1 Jul2 Aug3 Jun1 Jul2 Aug3 Jun1 Jul2 Aug3 Figure 6. Temporal trends of actual and estimated FMC for the Valabre study area Rosmarinus oycinalis (data provided by Ceren). this case was ( p=0.001). Considering that the SFMC was calibrated for both grasslands and shrublands and in a different environment, this can be considered as very satisfactory. The temporal trends show a consistent relationship, especially in 1997 and the first weeks of Summer 1998, although a slight tendency to underestimate FMC values can be observed. The highest residuals were observed in the first weeks of June 1996, when abnormally high FMC values were found for R. oycinalis. Assessment data for the Cordoba region were collected for Summer Shrub species were sampled in the field every 8 days between 15 June and 15 September in five plots. FMC field values were afterwards compared with SFMC computed from our empirical equation (5). As in the case of France, in spite of the environmental differences with the Cabañeros study site, the Pearson r value is also very high (r= 0.732, with a p value <0.001). As in the case of Cabañeros and Valabre, a tendency towards underestimation of actual FMC values was observed.

14 1634 E. Chuvieco et al. 7. Discussion Several studies have tested the application of AVHRR images to fire danger estimation, but most frequently this estimation is based on actual fire occurrence, which is not a direct function of fuel dryness. Areas with high-occurrence will most probably be dried, but not all dry areas are likely to be burned, if other factors of fire danger are not present (especially certain human activities). Therefore, when using satellite images in fire danger estimation, the most coherent approach to validate any proposed index is the measurement of the actual FMC, which is the danger variable most closely related to remote sensor observations. Operational difficulties in obtaining field FMC measurements might prevent the effort. However, this measurement provides a physical assessment of danger indices that otherwise would provide uncontrolled factors of noise. In this paper, an example of empirical estimation of FMC from AVHRR data has been presented. Although empirically derived, it is based on good relations between moisture content and radiometric variables (NDVI/T s and RGRE). Additionally, the proposed estimation has worked reasonably well for other periods in the same study area as well as for other study areas with similar Mediterranean conditions and vegetation types. Therefore, although further assessment is desired, it should be considered as a good starting point to derive from satellite data a physical variable closely related to both fire ignition and fire propagation. The use of this empirical estimation of FMC provides a good approximation to the spatial distribution and temporal variation of FMC values. Figure 7 includes two examples of SFMC maps in the Cabañeros National Park study area that show the changes in FMC spatial trends in the study area, with low values in Central Valley (mainly covered by grasslands) for the July image, and intermediate values for shrub species. This capacity to display a spatially distributed view of FMC is one of the main advantages of using remote sensing techniques over other traditional sources of fire danger information, such as meteorological weather stations, which 0 4 8km Figure 7. SFMC maps of the Cabañeros study area: (a) 6 June 1999; (b) 3 July (Sampled land plots are marked as black dots. Plot numbers are included in figure 1).

15 Forest fire management new methods and sensors 1635 are spatially restricted. However, it is evident that weather data are also required to estimate other critical danger variables, such as wind speed or rainfall and dead FMC. The SFMC index is based on a synthetic variable, the ratio of NDVI and T s, and the RGRE. The former explains the relation of vegetation vigour to evapotranspiration in temporal trends of FMC, while the latter is related to changes in chlorophyll content as a result of FMC variations. Future work to determine the source of the unexplained variance might improve the estimation capacity of AVHRR data. First, if fuel maps are available, speciesdependent equations might be determined, at least by distinguishing herbaceous and shrub species. This would most probably reduce estimation errors for both vegetation types. In addition, the calculation of the specific weight (S w ) for each major plant species would make it possible to convert from FMC to EWT, and consequently to extend such empirical calculations to other fuel types. The most notable limitations of AVHRR data for FMC estimation are low radiometric stability, poor spatial resolution and lack of SWIR bands. As mentioned earlier, SWIR bands will provide a much more direct estimation of FMC, since they are directly related to plant water absorption. New sensors, such as MODIS or SPOT-Vegetation, or the new generation of AVHRR sensor (starting with NOAA-15) are overcoming these limitations and should be used in the near future for this application. Acknowledgments This research has been funded by the Inflame and Control Fire Sat projects (ENV4-CT and ENV4-CT , respectively) under the Environment and Climate Program of the European Commission (DG-XII). Financial support was also obtained from the Spanish Ministry of Science through project number AGF CE. Assessment data provided by Rafael Navarro (University of Cordoba) and Frederique Giroud (Ceren) are greatly appreciated. Linguistic help from Patrick Vaughan is also acknowledged. Comments from reviewers were very helpful in improving the original version. Authorities of the Cabañeros National Park greatly facilitated the field work. References AGUADO, I., CHUVIECO, E., CAMARASA, A., MARTÍN, M. P., and CAMIA, A., 1998, Estimation of meteorological fire danger indices from multitemporal series of NOAA-AVHRR data. Proceedings III International Conference on Forest Fire Research 14th Conference on Fire and Forest Meteorology, Coimbra, Portugal, November 1998, edited by D. X. Viegas (Coimbra: ADAI), pp ALONSO, M., CAMARASA, A., CHUVIECO, E., COCERO, D., KYUN, I., MARTÍN, M. P., and SALAS, F. J., 1996, Estimating temporal dynamics of fuel moisture content of Mediterraneam species from NOAA-AVHRR data. EARSEL Advances in Remote Sensing, 4, BOWMAN, W. D., 1989, The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sensing of Environment, 30, BROWN, A. A., and KENNETH, D. P., 1979, Fire danger rating. In Forest Fire Control and Use, edited by A. A. Brown and K. P. Davis (New York: McGraw-Hill), pp BROWN, J. K., BOOTH, G. D., and SIMMERMAN, D. G., 1989, Seasonal change in live fuel moisture of understory plants in western US Aspen. Proceedings of the 10th Conference on Fire and Forest Meteorology, Ottawa, Canada (Ottawa: American Society of Meteorology), pp BURGAN, R. E., 1988, 1988 Revisions to the 1978 National Fire-Danger Rating System. Research Paper SE-273, USDA, Forest Service, Ashville, NC.

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