Estimating live fuel moisture content from remotely sensed reflectance

Transcription

1 Remote Sensing of Environment 92 (2004) Estimating live fuel moisture content from remotely sensed reflectance F.M. Danson*, P. Bowyer Telford Institute of Environmental Systems, School of Environment and Life Sciences, University of Salford, Manchester M5 4WT, UK Received 15 July 2003; received in revised form 23 March 2004; accepted 30 March 2004 Abstract Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0 100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process. D 2004 Elsevier Inc. All rights reserved. Keywords: Fuel moisture content; Leaf water content; Water index; Normalised difference water index; Prospect model; LOPEX 1. Introduction Fuel moisture content (FMC) is a key variable in forest fire modelling because it is related to the probability of ignition and to the rate of spread of a fire. Forest fire danger models require input data on the live FMC of the tree leaves or needles and on the dead FMC of the litter on the forest floor (Viegas et al., 1992). Operational fire danger predictions currently rely on the computation of meteorological danger indices derived from regional-scale data on temperature, humidity and wind speed. Because dead FMC is closely related to meteorological conditions it may, along with information on fuel type, be estimated directly from meteorological danger indices. Live FMC, however, is influenced by the interaction of plant physiology with soil moisture conditions and is therefore spatially and temporally * Corresponding author. Tel.: address: f.m.danson@salford.ac.uk (F.M. Danson). more variable than dead FMC (Chuvieco et al., 2002). This variability presents a significant challenge to forest fire managers who require information on both the spatial and temporal variations in vegetation FMC. Forest fire managers routinely collect field data on FMC to assess the danger of fire throughout the fire season, but these measurements are time consuming, costly and may be subject to large sampling errors. Remote sensing may provide an alternative approach to determining forest FMC either at the leaf level or at the canopy level. This paper provides the first systematic examination of the physical bases for the relationships between FMC, leaf water content and remotely sensed vegetation indices (VI). It uses both laboratoryderived spectral reflectance measurements and a leaf reflectance model to test for relationships between leaf water content, FMC and a range of spectral vegetation indices designed to estimate the moisture content of vegetation. It also considers the causes of variations in the observed relationships and assesses the prospects for the routine application of remote sensing for FMC estimation /$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi: /j.rse

2 310 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) FMC is derived from field measurements in which a sample of leaves is removed from the vegetation canopy and its fresh weight and dry weight determined. The fresh weight is normally derived from a bulk sample weighed in the field or sealed in an airtight container and weighed in the laboratory. The dry weight of the sample is determined by oven drying at a temperature of around 80 jc for 24 h. Leaf water content is usually expressed as a percentage of leaf fresh weight with a range between 0% and 100%, but fuel moisture content (FMC, %) is most commonly expressed as a percentage of leaf dry weight (Eq. (1)) and may therefore have a value in excess of 100%. FMC ¼ððFW DWÞ=DWÞ100 ð1þ where FW is leaf fresh weight and DW is leaf dry weight. Two related leaf biophysical properties are the equivalent water thickness, the thickness or weight of water per unit area of leaf, and the specific leaf weight (or its reciprocal, the specific leaf area), the weight of dry matter per unit area of leaf. Equivalent water thickness (Eq. (2)) and specific leaf weight (Eq. (3)) require measurements of leaf area to be made along with measurements of leaf fresh and dry weights: EWT ¼ðFW DWÞ=A SLW ¼ DW=A ð2þ ð3þ where EWT is equivalent water thickness (g cm 2 or cm), SLW is specific leaf weight (g cm 2 ) and A is one-sided leaf area (cm 2 ). Rearranging Eqs. (1) and (2) shows that FMC is related both to the amount of water in a leaf and to the amount of dry matter (Eq. (4)) FMC ¼ðEWT=SLWÞ100 ð4þ Independent field measurements of EWT and SLW would provide more information on the water status of forest vegetation for forest fire managers, but because they require measurement of leaf area, they are rarely collected for forest fire management operations. Ceccato et al. (2001) used data from Gond et al. (1999) and from the Leaf Optical Properties EXperiment (LOPEX) to show that leaves with similar EWT may have different FMC and vice versa. As Eq. (4) shows, this is due to species- or phenology-dependent variations in the density of leaf dry matter. These relationships may be illustrated graphically by plotting SLW isolines against EWT and FMC (Fig. 1). The data points in this figure are from the LOPEX and illustrate the range of FMC, SLW and EWT for leaves of different species. Fig. 1 also shows that FMC and EWT are perfectly correlated when SLW is constant. FMC is considered a key control on fuel flammability, but experimental work by Dimitrakopoulos and Papaioannou (2001) showed that the critical FMC, termed the moisture of extinction, is species-specific. In their data, the moisture of extinction for a range of 24 Mediterranean species varied between 40% and 140%. There was considerable variability in the moisture of extinction for some species, but overall, 75% of the species has a lower limit of 100% or less. In this paper, the estimation of FMC from Fig. 1. Relationship between equivalent water thickness (EWT) and fuel moisture content (FMC), for the LOPEX data set, with specific leaf weight (SLW) isolines plotted from to g/cm 2 at g/cm 2 intervals.

3 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) remotely sensed data is considered for the global range of FMC found in the field environment, but it also examines a narrower range where FMC is less than 100%, and as suggested by the data of Dimitrakopoulos and Papaioannou (2001), ignition is more likely. 2. Remote sensing of leaf water content Leaf reflectance is controlled by water, pigment and dry matter absorption and by the scattering of photons at the interfaces between hydrated cells and intercellular air spaces (Gates et al., 1965). Absorption by leaf water is important throughout the near infrared (NIR) and short-wave infrared (SWIR) with liquid water absorption peaks of increasing size at 970, 1200, 1450 and 1950 nm related to the combinations and overtones of the fundamental vibrational transitions of the water molecules (Palmer & Williams, 1974). Dry matter absorption coefficients are more difficult to define because they depend on the biochemical composition of the leaf material, but in general, absorption by dry matter increases with wavelength through the short-wave infrared (SWIR; Jacquemoud et al., 1996) with little or no absorption in the near infrared (NIR; Merzlyak et al., 2002). Many authors have shown that for individual leaves or stacks of leaves, as leaf water content increases, reflectance in the NIR and SWIR decreases due to absorption (Aldakheel & Danson, 1997; Carter, 1991; Ceccato et al., 2001; Hunt & Rock, 1989; Knapp & Carter, 1998; Thomas et al., 1971). The sensitivity of reflectance to change in leaf water content is wavelength-dependent however. At wavelengths where absorption is high, radiation may be completely absorbed, but at wavelengths where absorption is low, radiation penetrates deeper into the leaf (Sims & Gamon, 2003). Consequently, the wavelength at which the strongest correlation with leaf water content is found is dependent on the magnitude and range of leaf water content in the sample of leaves studied. Research has also demonstrated strong negative correlations between reflectance in individual wavebands and leaf water content (e.g., Carter, 1991; Ceccato et al., 2001; Datt, 1999), but most efforts to estimate leaf water content from reflectance data have employed spectral vegetation indices that combine data in two or more wavebands. These indices are designed to normalise the effect on reflectance of variation in internal leaf structure, which controls internal scattering. Leaf EWT has been estimated from broad waveband ratios combining NIR and SWIR wavelengths (Gao, 1996; Rock et al., 1986) or using narrow wavebands in the NIR and SWIR in simple ratios or derivatives (Ceccato et al., 2001; Danson et al., 1992; Peñuelas et al., 1997). Researchers have also used stepwise regression and leaf reflectance model inversion to estimate leaf EWT (Baret & Fourty, 1997; Jacquemoud et al., 2000; Newnham & Burt, 2001). Leaf water dominates the spectral signature of leaves in the SWIR, and these studies have consistently observed strong correlations between leaf water content and leaf spectral response. Estimates of EWT with root-mean-square error (RMSE) accuracies of around g cm 2 have been reported (Baret & Fourty, 1997; Jacquemoud et al., 2000). Estimation of FMC from spectral indices is more problematic because it is related to two independent leaf properties, EWT and SLW (Eq. (4)). However, Jacquemoud and Baret (1990) showed that the leaf structure, described by the N parameter in the Prospect model, may be correlated with SLW. It follows that if the amount of scattering in a leaf is related to SLW, spectral indices designed to normalise the effects of variation in scattering may be sensitive to leaf FMC. However, only a few studies have explicitly examined the relationship between spectral reflectance and leaf FMC as opposed to leaf EWT. Ceccato et al. (2001) discuss the problems of FMC estimation from remotely sensed data and suggest that reflectance variations in the SWIR only provide information on EWT and not on FMC. However, Datt (1999) examined the relationships between EWT and FMC (referred to as gravimetric water content) and the reflectance of stacked Eucalyptus leaves and found significant correlations with EWT in the SWIR and with FMC in the NIR. In another study, Dawson et al. (1998) trained a neural network using measured slash pine leaf stack reflectance and estimated FMC with an RMSE accuracy of 1.3% over a small FMC range from 53% to 63%; correlation between the water index (WI) and normalised difference water index (NDWI) and FMC was weaker, although no comparative RMSE estimates were given. Although not the primary focus of this paper, research has also shown that both EWT and FMC may be estimated at the canopy level using similar data analysis techniques to those applied in leaf-level studies. For example, EWT has been successfully estimated in agricultural crops, forests, mediterranean shrublands and savannah woodlands (Ceccato et al., 2002b; Gao & Goetz, 1995; Jacquemoud et al., 1995; Serrano et al., 2000; Ustin et al., 1998; Zarco- Tejada et al., 2003). FMC has also been estimated for a range of vegetation types and using a range of sensors and data analysis techniques. Paltridge and Barber (1988) showed that there was a strong correlation between a modified normalised difference vegetation index (NDVI) determined from National Oceanographic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) imagery and the FMC of four grassland areas in Australia and similar results were obtained by Chladil and Nunez (1995) in Tasmania. However, Paltridge and Barber (1988) noted that although the NDVI is not directly related to vegetation moisture content, FMC may be correlated with leaf chlorophyll content so that spatial and temporal variation in FMC may be mapped with satellite imagery. Ceccato et al. (2001) reinforced this point and suggested that such relationships should only be used in local areas where the correlations between leaf chlorophyll and leaf water content have been established.

4 312 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) In a modelling study, Dawson et al. (1999) found that the form and strength of the relationships between FMC (described as water concentration) and the water index (WI) of Peñuelas et al. (1997) depended on leaf area index (LAI) and fraction of vegetation cover with stronger correlations at high LAI and high canopy cover. Using the normalised difference vegetation index (NDVI), Hardy and Burgan (1999) found strong correlations with grassland FMC and weaker correlations with trees and shrubland FMC. Similar results were obtained using Landsat Thematic Mapper (TM) and AVHRR data of test sites in Spain (Chuvieco et al., 2002, 2003). Chuvieco et al. (2002) found strong correlations between grassland FMC and a range of vegetation indices calculated from Landsat TM data but weaker correlations with shrubland species. Chuvieco et al. (2003) exploited both the optical and thermal wavebands of the AVHRR sensor and found strong correlations between FMC and the ratio of NDVI to surface temperature. Most recently, Roberts et al. (2003) examined the potential of EO-1 Hyperion data for estimating FMC and found a strong correlation between Hyperion and airborne visible infrared imaging spectrometer (AVI- RIS) estimates of the normalised difference water index (NDWI). 3. Spectral indices for estimating vegetation water content A wide range of vegetation indices has been devised to estimate vegetation water content with most based on ratios, or normalised ratios, of broad wavebands in the NIR and SWIR. In this study, two indices based on broad wavebands and three based on a ratio of two narrow wavelengths were selected to represent the range of wavelength regions used in previous work Moisture stress index The moisture stress index (MSI) was developed by Rock et al. (1986) and further tested by Hunt et al. (1987), Hunt and Rock (1989) and Hunt (1991). The index is a simple ratio between reflectance in the SWIR (sensitive to water content) to that in the NIR (sensitive to changes in leaf internal structure) and can be derived from Landsat TM bands TM5 and TM4 or reflectance at 1600 nm (R 1600 ) and 800 nm (R 800 ; Hunt et al., 1987; Hunt & Rock, 1989). The MSI is formulated as follows: researchers (e.g., Dawson et al., 1999; Serrano et al., 2000). The index is designed to be sensitive to water content but less sensitive to the effects of scattering because it is formulated using wavelengths in the NIR where scattering effects are expected to be similar between wavebands. The NDWI is formulated according to Gao (1996) as: NDWI ¼ ðr 860 R 1240 Þ ð6þ ðr 860 þ R 1240 Þ 3.3. Ratio of TM5 to TM7 The simple ratio proposed by Elvidge and Lyon (1985) combines data from the two SWIR wavebands of the Lansdat TM and assumes that scattering has similar effects in both wavebands Global vegetation moisture index The global vegetation moisture index (GVMI) was developed by Ceccato et al. (2002a) and is similar to the normalised difference infrared index of Hardisky et al. (1983). The GVMI is designed to be sensitive to water content and resistant to atmospheric effects, the NIR reflectance being rectified for atmospheric effects in a particular way by using blue reflectance (Govaerts et al., 1999; Kaufman & Tanré, 1992). The simulated and measured data used here have no atmospheric effect and the NIR data were used without further correction. In this paper, the modelled and measured reflectances were convolved to the NIR and SWIR bands of the SPOT- VEGETATION sensor, and the GVMI formulated as follows: GVMI ¼ 3.5. Water index ððnir þ 0:1Þ ðswir þ 0:02ÞÞ ððnir þ 0:1ÞþðSWIR þ 0:02ÞÞ ð7þ Developed by Peñuelas et al. (1993, 1997) and further tested by Piñol et al. (1998) and Dawson et al. (1999), the WI employs NIR reflectance in two narrow wavebands including the minor water absorption feature at 970 nm and is formulated here using the equation of Peñuelas et al. (1997): WI ¼ R 900 R 970 ð8þ MSI ¼ R 1600 R 820 ð5þ 4. Objectives 3.2. Normalised difference water index The normalised difference water index (NDWI) was developed by Gao (1996) and has been tested by other Studies of the relationships between remotely sensed reflectance and FMC at leaf and canopy level reviewed in Section 2 show that although FMC is a composite variable dependent on both leaf water content and leaf dry matter

5 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) content, useful empirical relationships with the vegetation indices introduced above are commonly found. This implies that in these studies, either leaf water content or leaf dry matter is relatively constant and that the vegetation index is responding to change in just one variable, or that the vegetation indices normalise the effect of change in dry matter but remain sensitive to change in leaf water content. Previous experimental investigations provide evidence of relationships between spectral indices and plant biophysical variables but do not provide an insight into the underlying mechanisms. In this paper, the key objective was to investigate the relationships between leaf FMC, EWT and a range of vegetation indices which have previously been related to vegetation water content. The approach was to use a leaf reflectance model which allowed the range and statistical distribution of the input biophysical variables, like leaf water and dry matter content, to be controlled. It also allowed the decoupling of leaf biophysical variables which may, unknown to the investigator, be correlated in experimental data. In this way, the work provided a way to explore the physical mechanisms that control the relationships between leaf biophysical properties and vegetation indices and specifically provided the first model-based investigation of the relationships with FMC. A second objective was to use measured leaf biophysical and spectral reflectance data, derived from the LOPEX, to determine whether the conclusions of the modelling work were consistent over a wide range of plant species and leaf optical properties. 5. Methodology Leaf reflectance was simulated with the Prospect model with four input parameters: leaf structure parameter, N, chlorophyll content (C a+b, Ag cm 2 ), equivalent water thickness (EWT, g cm 2 ) and dry matter content or specific leaf weight (SLW, g cm 2 ). Prospect describes a leaf as consisting of N homogeneous layers, with reflectance and transmittance calculated from the absorption coefficient and refractive index at a given wavelength for each layer (Jacquemoud et al., 2000; Jacquemoud & Baret, 1990). In the real world, the statistical distribution of these variables over a range of leaves may follow a Gaussian or some other non- Gaussian probability density function. Therefore, a key feature of the experimental design was that the Prospect model input parameters were defined for two different probability density functions, Gaussian and uniform, and for ranges that represented global variability in leaf biophysical properties (Table 1). A uniform distribution may be used in the absence of any other information about the statistical distribution of a given variable and assumes that all values of the input variable are within a range defined by the maximum and minimum and have an equal probability of selection. If a variable is known to be normally distributed and estimates of the mean and variance are available, then a Gaussian distribution may better describe the data (Baret et Table 1 Prospect model parameter mean and standard deviation for the Gaussian distribution, and minimum and maximum for uniform distribution Model parameter Gaussian distribution Uniform distribution Mean Standard deviation Minimum Maximum Leaf structure (N) Leaf chlorophyll (C a+b Agcm 2 ) Equivalent water thickness (EWT g cm 2 ) Specific leaf weight (g cm 2 ) Fuel moisture content (FMC %) FMC, although not a model parameter, is included for completeness. al., 1995; Danson et al., 2003). In this study, the data ranges were set partly from inspection of the LOPEX leaf biophysical database and also from the unpublished results of field measurements of leaf biophysical properties from a range of species. For each distribution, 335 sets of model parameters were computed by randomly selecting input values from that distribution and the data range for each variable. This approach ensures that there is no co-correlation between the input leaf biophysical properties, in contrast to the situation often found in measured leaf data as discussed later. In this way, 335 leaf spectra over the range nm at 5-nm intervals were simulated to produce a spectral database of the same size as that derived from the LOPEX data. For the vegetation indices that employed broad wavebands, the resulting spectra were convolved with the spectral response functions of the appropriate waveband. For each simulated spectrum, FMC was calculated using Eq. (4). The simulated data were compared with measured spectral and biophysical data from LOPEX. LOPEX involved laboratory measurements of leaf spectral reflectance and transmittance and associated biophysical and biochemical data from approximately 50 different plant species (Hosgood et al., 1994; Jacquemoud et al., 1996). Leaf spectral measurements were made on fresh and dry single leaves, leaf stacks and needles, using a Perkin Elmer Lambda 19 spectrophotometer. In this work, only data collected from fresh single leaves have been used, resulting in 67 different samples, on which five replicate measurements had been made, together with the associated biophysical and biochemical data, giving a sample size of 335. Initial inspection of the LOPEX data showed that leaf EWT and SLW were best described by a lognormal, rather than a Gaussian, distribution, and to facilitate comparisons with the uniform and Gaussian distributed simulations, a third spectral and biophysical data set was simulated using the Prospect model with lognormal input data distribution for EWT, SLW and N. Here, the objective was to produce a

6 314 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) simulated data set that replicated the ranges and distribution of the biophysical data in the LOPEX measurements and to allow comparison of the relationships between FMC, EWT and vegetation indices using modelled data with uniform, Gaussian and lognormal input data distributions. The data analysis involved three stages; first, power functions were fitted to the FMC vegetation index and EWT vegetation index relationships derived from the simulated data for all three input data distributions (uniform, Gaussian, lognormal) and for the measured LOPEX data. A least-squares fit was used to model the power functions and coefficients of determination (r 2 ) between FMC and EWT and all vegetation indices computed. Second, for the strongest correlations, the power functions were used to provide estimates of the predictive power of the relationships measured as the RMSE between modelled and estimated FMC and EWT (Prospect), and measured and estimated FMC and EWT (LOPEX). To compute the estimations, the data sets were divided into two halves with a regression model developed on the first half and testing performed with the second half. In a third stage, a subset of the data was selected with a range of FMC from 0% to 100% to simulate the conditions under which fuel ignition is most likely (Dimitrakopoulos & Papaioannou, 2001). The RMSE between measured and modelled FMC and EWT was recalculated for this subset and compared with the RMSE calculated for the global data ranges. 6. Results The Prospect input data ranges for the Gaussian and uniform distributions were generally larger than those in the LOPEX measured data (Tables 1 and 2). For example, the range of EWT in the Prospect data was g cm 2, and in the LOPEX data, g cm 2,andtherange of SLW, and g cm 2,respectively. The range of FMC was similar in the two data sets ( % and %), but the mean and standard deviation were higher in the LOPEX data than in the simulated data. However, given the log distribution of the LOPEX data for Table 2 Range of variation observed in the LOPEX database (n = 335, except C a+b n = 320) Parameter Mean Standard deviation Minimum Maximum Leaf chlorophyll (C a+b Agcm 2 ) Equivalent water thickness (EWT g cm 2 ) Specific leaf weight (SLW g cm 2 ) Fuel moisture content (FMC %) Table 3 Coefficient of determination (r 2 ) between fuel moisture content (FMC) and equivalent water thickness (EWT) and spectral indices, calculated from the Prospect modelled reflectances, for the Gaussian and uniform distribution of model input parameters Vegetation Prospect input parameter distribution index Gaussian Uniform FMC EWT FMC EWT MSI r NDWI a r WI r TM5/TM7 r GVMI b r The strongest correlation for FMC and EWT is in bold typeface. MSI = moisture stress index; NDWI = normalised difference water index; WI = water index; TM5/TM7 = ratio of thematic mapper bands 5 and 7; GVMI = global vegetation moisture index. Note: All correlations are significant ( P < 0.01), except those between FMC and TM5/TM7. a n = 316, negative values of NDWI were removed to allow a power function to be fitted. b n = 331, negative values of GVMI were removed to allow a power function to be fitted. EWT and SLW, these comparisons are not strictly valid. Correlation between the LOPEX leaf biophysical data showed statistically significant relationships between all variables ( P < 0.01). There was a strong positive correlation between FMC and EWT (r = 0.69), a weak negative correlation between FMC and SLW (r = 0.37) and a weak positive correlation between EWT and SLW (r =0.23) Relationships between FMC, EWT and vegetation indices For the Prospect modelled data, all but one of the correlation coefficients between FMC, EWT and the vegetation indices were statistically significant ( P <0.01; Table 3). The strongest correlations with FMC were with the WI for both probability Table 4 Coefficient of determination (r 2 ) between fuel moisture content (FMC) and equivalent water thickness (EWT) and spectral indices, calculated from modelled (lognormal) and measured LOPEX data Vegetation Index Lognormal LOPEX FMC EWT FMC EWT MSI r NDWI a r WI r TM5/TM7 r GVMI b r The strongest correlation for FMC and EWT is in bold typeface. MSI = moisture stress index; NDWI = normalised difference water index; WI = water index; TM5/TM7 = ratio of thematic mapper bands 5 and 7; GVMI = global vegetation moisture index. Note: All correlations are significant ( P < 0.01). a n = 316, negative values of NDWI were removed to allow a power function to be fitted. b n = 331, negative values of GVMI were removed to allow a power function to be fitted.

7 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) density functions but overall strongest with the Gaussian data distribution (r 2 = 0.88). The strongest correlations with EWT were with the NDWI for both distributions but strongest with the uniform distribution (r 2 = 0.90). The size of the correlation coefficients was generally higher for EWT than for FMC, and overall, the correlations were generally stronger for the MSI, NDWI and GVMI compared to TM5/TM7. These results confirm the previous experimental findings of Peñuelas et al. (1997) and Dawson et al. (1998) who found significant correlations between EWT or FMC and the WI. The experimental work of Datt (1999) showed that there were strong correlations between FMC or EWT and the WI, MSI and NDWI but weaker correlations with the TM5/TM7 ratio. This is also confirmed by the model-based analysis presented here and suggests that the TM5/TM7 ratio is not a suitable index for estimating water content at leaf level. The results for the LOPEX modelled (lognormal) data and LOPEX measured data (Table 4) show a comparable pattern of correlation between the vegetation indices and both FMC and EWT. All the correlation coefficients were statistically significant, but for some indices, the correlation was higher for the modelled data, and for others, for the measured data. The correlations with FMC were weaker than those with EWT, consistent with the results for the Gaussian and uniform input parameter distributions. For FMC, the strongest correlation was again with the WI, for both modelled and measured data. For EWT, the strongest correlation was with the GVMI for the modelled data and MSI for the measured data. The strongest relationships with FMC for the modelled data and LOPEX data were with the WI and are shown in Fig. 2. For the modelled data (Gaussian distribution), the relationship was linear up to a FMC of around 300%, after which it became asymptotic (Fig. 2a). The FMC WI relationship for the LOPEX data showed considerable scatter about the regression model with very few observations below an FMC of 100% or above 400% (Fig. 2b), and again there was evidence that the relationship was asymptotic at values above about 300%. The strongest Fig. 2. (a) Relationship between the water index and fuel moisture content (FMC), Prospect-simulated reflectance, and Gaussian parameter distribution. (b) Relationship between the water index and FMC, LOPEX-measured data.

8 316 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) relationships with EWT were with the NDWI for the modelled data (Gaussian distribution) and with the MSI for the LOPEX data (Fig. 3). The NDWI exhibited a nearlinear trend with the modelled data (Fig. 3a) and the MSI a negative exponential trend with the LOPEX data as previously shown by Ceccato et al. (2001; Fig. 3b) Estimation of FMC and EWT from vegetation indices The strongest relationships for both modelled and measured data (Figs. 2 and 3) were inverted to determine the accuracy with which FMC and EWT may be estimated from the vegetation index selected. For the modelled data, FMC was estimated using the WI and resulted in an RMSE of 63.2% (Fig. 4a), and EWT was estimated using the NDWI with an RMSE of g cm 2 (Fig. 4b). For the LOPEX data, FMC was estimated using the WI with an RMSE of 179.3% (Fig. 5a) and EWT with the MSI with an RMSE of g cm 2 (Fig. 5b). The results for EWT were consistent with those in previous research, but for FMC, the estimation accuracies were very low. However, it was clear that for FMC, the amount of scatter around the 1:1 line increased with the value of FMC and that below 100% the fit was stronger. The RMSE was therefore recalculated for FMC using only values of 100% or less, corresponding to the range over which ignition is most likely as described earlier. In this case, although the sample size was smaller, the RMSE for the modelled data decreased to 14.8% (n = 27) and for the LOPEX data to 51.4% (n = 9). 7. Discussion and conclusions The results of this work confirm previous research that shows the leaf EWT may be accurately estimated using spectral vegetation indices derived from reflectance measurements. The correlations with EWT were consistently Fig. 3. (a) Relationship between the normalised difference water index and equivalent water thickness (EWT), Prospect-simulated reflectance, and Gaussian parameter distribution. (b) Relationship between the moisture stress index (MSI) and EWT, LOPEX-measured data.

9 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) Fig. 4. (a) Estimates of FMC from the simulated data (water index) and (b) estimates of EWT from the simulated data (normalised difference water index). strong and estimates of EWT over the very large range in the simulated data produced an RMSE of g cm 2. With the more restricted water content range in the LOPEX data, the RMSE was lower again ( g cm 2 ). The vegetation indices most strongly correlated with EWT were those that employed data in a NIR waveband where water absorption is weak and a SWIR waveband where water absorption is strong, supporting the theory that ratios, or normalised ratios, of these wavebands suppress the effect of variation in leaf scattering. These levels of accuracy indicate that leaf EWT may be routinely estimated using spectral reflectance measurements on individual leaves either in the laboratory or with a suitable instrument in the field. For FMC, the strongest correlations were with the WI, based on a ratio of two narrow wavebands (900 and 970 nm) in the NIR, consistent with the results of Dawson et al. (1999). The results also indicated stronger correlation between FMC and WI at low FMC values (below 100%) and weaker correlation at higher FMC. Recent research has

10 318 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) Fig. 5. (a) Estimates of FMC from the LOPEX data (water index) and (b) estimates of EWT from the LOPEX data (moisture stress index). suggested that leaf dry matter does not absorb radiation in the nm region (Merzlyak et al., 2002) so that reflectance at 900 nm is primarily controlled by leaf internal structure, which may be correlated with leaf SLW. It is suggested here that when FMC is below 100% and there is proportionally more dry matter in the leaf than water, then the WI is sensitive to variations in FMC because it is affected primarily by variation in leaf SLW, rather than by change in leaf water content. At these low values of FMC, vegetation indices that use longer wavelengths, where water absorption is stronger, may be less effective because the sensitivity to water content is greater than the sensitivity to change in leaf SLW, suppressing the response to FMC change. These issues have been explored further by applying a sensitivity analysis to simulated data from the Prospect model in a related paper (Bowyer & Danson, in press). Comparing across the relationships between FMC and EWT and the vegetation indices, the correlations for the modelled data were generally higher than those for the measured LOPEX data. This may be partly due to the differences in the distributions of the data, although, when

11 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) the LOPEX biophysical data distributions were simulated using a lognormal distribution, the same outcome was observed. Another factor is likely to have been the cocorrelation between the biophysical variables in the LOPEX data. In the simulations, there is, by definition, no correlation between EWT, SLW and FMC. However, in the LOPEX data, these variables were co-correlated, and because FMC is a function of both leaf water content and leaf dry matter content, the correlations with FMC will be weaker. Measurement errors in both the spectral and biophysical data for LOPEX may also have contributed to lower correlations. A further significant issue here is that the Prospect model and LOPEX data are not strictly independent because the absorption coefficients in version 3.01 of the Prospect model used here were partly derived by fitting data from the LOPEX (Jacquemoud et al., 2000). However, because this version of the model has recently been validated on independent data (le Maire et al., 2004) and the differences in the absorption coefficients compared to the previous version are small, the lack of independence is unlikely to affect the overall pattern of the results presented in this paper. Critically, the comparison of modelled relationships with relationships derived from the LOPEX data also enabled the influence of co-correlation between leaf biophysical variables on the FMC vegetation index relationships to be examined. This issue could be explored further by, for example, introducing correlation between the biophysical variables in the model simulations. Many studies have shown that vegetation water content may be estimated from remotely sensed data, but most of these have examined relationships with EWT at leaf or canopy scale (e.g., Ceccato et al., 2002b; Gao & Goetz, 1995; Jacquemoud et al., 1995; Serrano et al., 2000; Ustin et al., 1998; Zarco-Tejada et al., 2003), yet only a few studies have explicitly examined the relationships with FMC, which is the variable routinely used in forest fire danger assessment (Chuvieco et al., 2002, 2003). Although FMC is a composite variable that is controlled by both leaf water content and leaf dry matter content, this paper has shown that FMC may be estimated at the leaf level from remotely sensed data using vegetation indices. Both modelled and measured data indicate that the WI, based on a ratio of reflectance of 900 to 970 nm, provides the most accurate FMC estimates. The accuracy of FMC estimation varied with input data distribution, vegetation index and the data measurement accuracy. Estimation accuracy using a global range of FMC was very low, but for an ignition-critical range where FMC was 100% or less, estimation accuracy increased to an RMSE of 14.8% and 51% for modelled and measured data, respectively. However, the small sample size for this range of FMC in the LOPEX data means that further experimental data are required to confirm the generality of this result. Critical values of FMC for fire ignition vary with vegetation type but generally occur when FMC is less than 100% (Chuvieco et al., 2003; Dimitrakopoulos & Papaioannou, 2001). When FMC is at or near this critical range, the estimation accuracies of around 15% for the modelled data may be close to that required for operational applications, although no such measurement accuracy requirements have been published. Estimation accuracies of 50%, for the measured data, are likely to be too low when compared with the magnitude of the critical value of FMC of around 100%. Previous research has estimated EWT to similar levels of accuracy to those reported here, but only two previous studies have examined the relationships with FMC at leaf level (Datt, 1999; Dawson et al., 1999). The first examined the simple linear correlation with FMC across the nm region and the second estimated FMC by training a neural network on measured data over a very narrow range of FMC. A more generalized approach is the application of leaf and canopy reflectance model inversions to estimate vegetation biophysical properties with a number of advantages over the use of vegetation indices (Danson et al., 2003; Jacquemoud et al., 2000; Zarco-Tejada et al., 2003). In contrast to the use of vegetation indices, canopy reflectance model inversion can utilise all the spectral wavebands available, can account explicitly for a wide range of variation in the leaf and canopy biophysical properties and can include a priori information in the model inversion schemes (Danson et al., 2003). Given these advantages, it warrants further investigation for FMC estimation. The nature of the physical relationships between FMC, EWT and leaf spectral properties described in this paper clearly highlight the problems associated with remotely sensed estimation of FMC. Forest fire managers have traditionally collected data on FMC because it is easy to measure under field conditions and because it is closely related to ignition probability and influences the rate of propagation if a fire occurs. However, it may be argued that if information on both leaf EWT and leaf SLW were easily available, the additional information could be useful in forest fire modelling. This issue requires further exploration with the forest fire modelling community and with forest fire managers, and further work is necessary to test the accuracy with which these data could be provided. The work reported here has examined FMC and EWT relationships at the leaf level, but for applications using image data, the scaling-up of the relationships to canopy level must be considered. Canopy EWT has been successfully estimated in a number of studies because EWT is simply scaled by LAI of the canopy. In contrast, FMC is not scaled by LAI, and the sensitivity of canopy reflectance to change in FMC will therefore be affected by spatial and temporal variations in LAI. Scaling-up should also consider the mix of vegetation species and vegetation structures within the instantaneous field of view of an imaging instrument. Because the relationships between vegetation indices and FMC are likely to be species-specific, knowledge of the mixture of species and their likely biophysical properties would be useful in estimating spatial variation in

12 320 F.M. Danson, P. Bowyer / Remote Sensing of Environment 92 (2004) FMC from vegetation indices. A number of studies have shown clearly that FMC may be estimated from image data for a range of vegetation canopy types, but this paper suggests that strong correlations between FMC and vegetation indices are likely only to occur in environments where there is a temporal correlation between canopy EWT and leaf FMC. In a related paper, we explore some of these issues by assessing the sensitivity of canopy reflectance to change in EWT and FMC using a modelling approach that couples a leaf and canopy reflectance model (Bowyer and Danson, 2004). This work, together with the research described in this paper, should provide a sound basis for evaluating the potential of remote sensing for FMC estimation in forest fire danger modelling. 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