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

Remote Sensing of Environment 101 (2006) 399 – 414 www.elsevier.com/locate/rse

Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems J. Verbesselt ⁎, B. Somers, J. van Aardt, I. Jonckheere, P. Coppin Katholieke Universiteit Leuven, Group of Geomatics Engineering, Department Biosystems, Celestijnenlaan 200E, B-3001 Leuven, Belgium Received 13 September 2005; received in revised form 13 December 2005; accepted 4 January 2006

Abstract 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. © 2006 Elsevier Inc. All rights reserved. Keywords: Biomass; Fire risk assessment; SPOT VEGETATION; Time-series; Vegetation water content

1. Introduction Fires, which have always been present in savanna ecosystems, have an important function in maintaining the structure, species composition, and biological diversity in these ecosystems (Mbow et al., 2004). The fire pressure, due to an increased number of fires ignited by man, on savanna ecosystems has risen in the last decades to a level where it has become a threat to biodiversity and ecosystem stability (Goldammer & Crutzen, 1993). Consequently, the evaluation of fire risk has become a top priority in order to allocate fire fighting equipment and alert fire fighting teams. Evaluation of fire risk, however, is difficult because it is a somewhat nebulous concept compared to the better defined fire behavior prediction, which is used to calculate observable fire characteristics such as rate of spread and flame length (Andrews et al., 2003). A fire risk index therefore is not meant to describe the characteristics of a specific fire (e.g. fire ⁎ Corresponding author. Tel.: +32 16 329750; fax: +32 16 329760. E-mail address: [email protected] (J. Verbesselt). 0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2006.01.005

size), but rather to serve as an indication of fire potential for a management area (Pyne et al., 1996). Fire risk assessment in savannas is not only important for effective fire suppression. The use of fire as a management tool can be implemented in a more rational and effective manner when accurate fuel status information in terms of quantity (load) and quality (moisture, distribution, and size) is available (Mbow et al., 2004). Satellite data have the potential of providing repetitive and non-destructive alternatives to expensive and labour-intensive field measurements of fuel quantity and quality (moisture content) (Maki et al., 2004). Previous research has shown the utility of hyper-temporal satellite data (NOAA-AVHRR) to estimate fire risk at a regional to global scale, given the synoptic coverage (e.g. spatial resolution of approximately 1km2) and repeated temporal sampling (e.g. daily) of satellite observations (Chuvieco et al., 2004b; Leblon et al., 2001; Maki et al., 2004; Maselli et al., 2003). A limited number of studies have focused on the monitoring of fire risk variables with hyper-temporal satellite data in savanna ecosystems (Nielsen & Rasmussen, 2001; Sannier et al., 2002). Fire risk in savanna ecosystems also has been monitored with high

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spatial resolution satellite imagery (LANDSAT) to provide accurate information of vegetation characteristics related to fire risk (e.g. fuel types) (Mbow et al., 2004; Smith et al., 2005). However, the main constraint of using high spatial resolution data for fire risk assessment is the low temporal resolution (e.g. revisit cycle of 16days). Fire activity mainly depends on, besides fire source location, the evolution of the vegetation biomass and water content during a fire season (Maki et al., 2004). Consequently, a high temporal resolution (e.g. daily or 10-daily) provided by hyper-temporal satellite sensors (e.g. NOAA-AVHRR, MODIS, and SPOT VEGETATION) is essential to monitor the inter- and intra-yearly fire risk evolution. The majority of studies using hyper-temporal satellite data considered the vegetation water content (VWC) when estimating fire risk (Chuvieco et al., 2003, 2004a,b; Danson & Bowyer, 2004; Dennison et al., 2005; Lasaponara, 2005). VWC is an important factor when monitoring fire risk and therefore should be monitored frequently in order to assess fire risk (Agee et al., 2002; Maki et al., 2004). The physical definitions of VWC vary from water volume per leaf or ground area (equivalent water thickness) to water mass per mass of vegetation dry or fresh matter (fuel moisture content) (Jackson et al., 2004; Maki et al., 2004). VWC also could be used to infer vegetation water stress and to assess drought conditions to determine fire risk (Jackson et al., 2004; Pyne et al., 1996; Tucker, 1979). Single, greenness, and accumulated vegetation indices have been developed to monitor VWC, drought, or vegetation water stress at canopy scale for fire risk assessment specifically in forest ecosystems (Burgan et al., 1998; Lasaponara, 2005). It therefore is essential to examine how the VWC-related fire risk indices perform in savanna ecosystems. However, the quantity of herbaceous biomass in savanna ecosystems is an important factor influencing fire activity, beside the VWC of vegetation (Mbow et al., 2004). van Wilgen et al. (2000) showed that the quantity of herbaceous biomass at the end of the rain season has a significant influence on fire occurrence in savanna ecosystems, since the herbaceous biomass, i.e. grassy understory vegetation, becomes flammable during the dry season. It consequently is necessary to examine the capacity of hyper-temporal satellite imagery to estimate the amount of herbaceous biomass at the end of the rain season. The satellitebased single date and integrated vegetation index approach have been used in this context to monitor the amount of biomass (Sannier et al., 2002). Some authors have favored the use of an integrated vegetation index approach to estimate vegetation biomass (Diallo et al., 1991; Prince, 1991; Tucker et al., 1985), while others obtained significant relationships between biomass and vegetation indices for a single date (Fraser & Li, 2002; Paruelo et al., 2000; Sannier et al., 2002). The different hyper-temporal satellite-based methods to monitor the herbaceous biomass amount and VWC during the fire season have not been fully assessed, compared, or combined for fire risk assessment purposes in savanna ecosystems. The aim of this paper was to evaluate and improve the capacity of SPOT VEGETATION (SPOT VGT) time-series for fire risk assessment in the savanna ecosystem of the Kruger National Park, South Africa. First, the single date and integrated vegetation index approach to quantify the amount of herbaceous biomass at the end

of the rain season were evaluated using in situ biomass data. Next, in situ fire activity data was used to evaluate the performance of single, greenness, and accumulated remotely sensed fire risk indices related to the VWC. Finally, the combination of the most optimal biomass quantity and VWC-related fire risk indices was validated using in situ fire activity data. 2. Study area and data 2.1. Study area The Kruger National Park (KNP), located between latitudes 23°S and 26°S and longitudes 30°E and 32°E in the low-lying savannas of north-eastern South Africa, was selected for this study. Elevations range from 260 to 839 m above sea level, and mean annual rainfall varies between 350mm in the north and 750mm in the south. The rainfall regime within the annual climatic season can be confined to the summer months (November to April), and over a longer period can be described by extended wet and dry seasons. The KNP comprises mainly tropical grassland with scattered thorny, fine-leafed trees from the families Mimosaceae and Burseraceaes (savanna woodland). An exception is the northern part of the KNP where the Mopane, a broad-leafed tree belonging to the Ceasalpinaceae, almost completely dominates the tree layer (tree savanna) (van Wilgen et al., 2000). 2.2. Remote sensing data and pre-processing The S10 products of SPOT VGT data were acquired over the study area for the period April 1998 to September 2003. The S10 NDVI 10-day period maximum value syntheses provided surface reflectance in the blue (0.43–0.47μm), red (0.61– 0.68μm), near-infrared (NIR, 0.78–0.89μm), and shortwaveinfrared (SWIR, 1.58–1.75μm) spectral regions. Images were atmospherically corrected using the simplified method for atmospheric correction (SMAC) (Rahman & Dedieu, 1994). The geometrically and radiometrically corrected S10 images had a spatial resolution of approximately 1 km2 (Ceccato et al., 2001). The SPOT VGT time-series were pre-processed using three masking procedures to improve data quality (Tansey, 2002; Verbesselt et al., in press). The image quality enhancement procedure included: (1) Data points with a satellite viewing zenith angle (VZA) above 50° were masked out since pixels located at the very edge of the image (VZA N 50.5°) swath are affected by re-sampling methods that yield erroneous spectral values (Latifovic et al., 2003). (2) The aberrant SWIR detectors of the SPOT VGT sensor, flagged by the status mask of the S10 synthesis, were masked out. (3) A data point was classified as cloud-free if the blue reflectance was less than 0.07 (Kempeneers et al., 2000; Stroppiana et al., 2003). The developed threshold approach was applied to identify cloud-free pixels for the study area.

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2.3. In situ data 2.3.1. Herbaceous biomass data In situ herbaceous biomass data from 1998 up to 2003 were used to validate the biomass amount measurements derived from the S10 SPOT VGT data. This dataset was comprised of yearly total (live and dead) herbaceous biomass amount measurements from 533 randomly distributed sites across the KNP (van Wilgen et al., 2000). The biomass measurements (kg/ha), obtained at the end of the rain season (from mid March until the end of April, depending on the rainfall duration), were performed using a disc pasture meter (DPM) (Trollope & Potgieter, 1986). One-hundred biomass samples, spaced 2m from each other, were collected with the DPM at each site (50 × 60m). The average of the 100 disc readings (heights) was used as an estimation of the herbaceous biomass amount (kg/ha) at each site (Trollope et al., 1989). 2.3.2. Fire activity data A comprehensive fire activity database of the KNP was used in this study to evaluate the SPOT VGT capacity for fire risk assessment. The date of each fire, its cause, and position were extracted from the database for the study period from 1998 to 2003. The causes of fires were recorded in several categories such as management fires, lightning fires, arson fires, and fires of unknown origin (van Wilgen et al., 2000). Arson fires were selected for this study. Arson fires are anthropogenic fires lit by tourists, immigrants, or poachers. These fires are influenced strongly by seasonal variation of VWC and are spatially and temporally random in nature as opposed to lightning and management fires (van Wilgen et al., 2000). Lightning fires depend on thunderstorms and occur at the beginning of rain seasons when vegetation starts re-greening (Trollope, 1993). Management fires, dependent on fire managers, are not excessively intensive and destructive in order to protect the biodiversity of the park because the fires are lit when vegetation is not completely cured. The human behaviour can be assumed random during the fire season such that VWC is an important factor determining the occurrence of arson fires (personal communication Govender N., scientific services KNP). Arson fires consequently were selected to enable prediction of sites that have a higher fire risk and prevent this intense and destructive type of fire. Fig. 1 illustrates the occurrence of arson fires during the study period (1998– 2003). Fires that burned for several days were considered as single events in tallying the number of fires. The daily fire series were transformed into 10-daily fire series for further analysis by taking the sum of the number of fires per 10days. This was done in order to match the SPOT VGT 10-day periods. 3. Methodology A sampling strategy was defined to obtain data for the validation of the three objectives of this study: (1) estimation of herbaceous biomass amount at the end of the rain season, (2) selection of the superior fire risk index related to VWC, and (3) combination of the best method from (1) and (2) to optimise fire risk assessment with SPOT VGT data for savanna ecosystems.

Fig. 1. The average number of arson fires per month for the study period between 1998 and 2003. The fire season starts approximately in May and ends in October.

3.1. Sampling strategy Three main aspects needed to be taken into account for selection of an accurate and statistically justified sampling strategy, namely the sample unit, the spatial distribution of the sample units (sample scheme), and the sample size (Congalton, 1991). Three common sample units, namely the pixel, a group of 3 × 3 pixels, or a polygon are frequently proposed in studies using remote sensing data. Congalton (1991) stated that a grouping of pixels, such as a 3 × 3 block, or a polygon should be selected depending on the specific needs of the study, because a pixel cannot be located accurately on the ground and imagery. A square of 20 by 20 pixels was chosen as sample unit in this study because a large number of fires (approximately 100 fires) was required for statistical analysis in order to reduce the influence of exceptional fires (Viegas et al., 1999). The proportion of exceptional fires, i.e. fires that occur when vegetation was still wet or lack of fires when vegetation was dry, increases for a smaller fire data set (Andrews et al., 2003; Camia et al., 1999). These exceptional fires disrupt the relationship between fire activity and the remotely sensed vegetation indices, based on the assumption that the drier the vegetation, the greater the fire risk. Pyne et al. (1996) and Andrews et al. (2003) also stipulated that fire risk indices serve as an indication of fire potential for a large area such that the relationships between vegetation dynamics, fire activity, and remotely sensed fire risk indices were valid for the selected sample unit size. The use of 20 × 20 pixels sample units has specific consequences in terms of the sampling scheme. The variability within a sample unit decreased the precision of the sampling strategy (Tian et al., 2002). This intra-polygon variance could, however, be reduced by applying a stratified sampling approach, whereby the study area was divided into a number of homogeneous strata from which the sample units were selected. The stratified random sampling also had the advantage that the total range of biomass levels in the park were equally sampled, unlike with a simple random sampling where small but important areas are often under-sampled

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(Congalton, 1991). A two-stage stratification methodology was applied in this study. First, the KNP was subdivided into two strata, namely the northern Mopane-dominated savanna and the southern mixed bush willow woodlands, by using the land cover map provided by the KNP scientific services (Mutanga & Skidmore, 2004) (Fig. 2). Next, the park was subdivided into three biomass classes based on the interpolated in situ herbaceous biomass values for the KNP (Fig. 2). The inverse distance method (Weber & Englund, 1994) was used to interpolate the mean biomass values, derived for each of the 533 sites during the period that the biomass data were available. An equal area algorithm was used to define three equally represented biomass classes in the park. Consequently, the study area was divided into 6 strata based on the two-stage stratification. Two sample units were chosen in each stratum which resulted in a sample size of 12 sample units (Fig. 2).

3.2. Herbaceous biomass estimation at the end of the rain season In situ herbaceous biomass data, obtained with the stratified sampling strategy, were used to evaluate satellite-based methods that estimate the amount of herbaceous biomass at the end of the rain season. The amount of herbaceous, and not woody biomass, was estimated since fires in savanna ecosystems burn through the herbaceous layer (van Wilgen et al., 2004). The integrated vegetation index approach and single date approach were evaluated by using a correlation analysis with in situ measurements of herbaceous biomass amount at the end of the rain season. A yearly mean herbaceous biomass value per sample unit was derived from the biomass measurement sites located within each sample unit. The number of sites within each sample unit

Fig. 2. Sampling design of the study area. A two-stage stratification approach was used to divide the study area in 6 strata (2 vegetation type strata separated by (‐‐‐) and three biomass classes) to select the 12 sample units. The legend illustrates the range in herbaceous biomass (kg/ha) per class. South Africa is shown with the borders of the provinces and the study area (black) (top right).

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ranged between 5 and 16 because the sites were randomly distributed over the KNP. Five mean biomass measurements (one per year) for the study period (1998–2003) were available for each sample unit, which resulted in 60 measurements in total (12 sample units). The mean biomass measurement for each sample unit was correlated with the satellite-based estimate. The satellite-based estimate was derived as the median index value of the pixels within each sample unit. The median was preferred to average values since it is less affected by extreme values, and therefore is less sensitive to potentially undetected data errors during pre-processing of the satellite data. The index with the highest coefficient of determination (R2) was selected and was considered most suitable to estimate herbaceous biomass at the end of the rain season. The different correlation coefficients (r) were transformed into a normalized distribution using a Fischer z-transform to test whether the correlation coefficient of the best method was significantly higher than the correlation coefficients of the other methods (Dennison et al., 2005; Neter et al., 1996): Zf ¼ 0:5ln½ð1 þ rÞ=ð1−rÞ

ð1Þ

where r is the correlation coefficient. The difference between Zf for two indices was calculated as: Zf ð1Þ −Zf ð2Þ Z ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi 1=ðnð1Þ−3Þ þ 1=ðnð2Þ−3Þ

ð2Þ

where n is the number of samples and Zf(1) and Zf(2) are the transformed values of index 1 and 2, respectively. A one-tailed t-test was used to determine whether z was significantly positive. A significant z-value indicated a significantly stronger correlation for the index with the highest correlation coefficient. The remote sensing methods used to estimate the amount of herbaceous biomass at the end of the rain season were the integrated vegetation index approach and the single date approach. 3.2.1. Integrated vegetation index approach The integrated vegetation index approach, derived as the integral of the remote sensing time-series during the rain season (November to April), was used to estimate herbaceous biomass quantity at the end of the rain season. van Wilgen et al. (2000) and Scanlon et al. (2002) illustrated that the mean annual rainfall

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of the previous years had a significant effect on the available herbaceous biomass at the end of the rain season. It has also been shown that a positive linear relationship exists between chlorophyll-related vegetation indices (e.g. NDVI) and rainfall for savanna ecosystems (Santos & Negri, 1997). Consequently, the integrated satellite-based vegetation index approach could account for the effect that rainfall in the preceding year has on the quantity of herbaceous biomass at the end of that rain season. 3.2.2. Single date approach In situ biomass measurements were collected during the 2months at the end of the rain season (i.e. second 10-day period (dekad) of March until the last dekad of April). The mean remote sensing index value for this period was derived to estimate the herbaceous biomass quantity at the end of the rain season in the case of the single date approach. 3.2.3. Vegetation indices related to vegetation biomass Three different vegetation indices, related to vegetation biomass, were used in the single date and integrated vegetation index approach to select the best remote sensing index for estimation of herbaceous biomass quantity at the end of the rain season (Table 1). The NDVI was used in this study for estimation of herbaceous biomass since several studies have shown the sensitivity of NDVI (Table 1) to the photosynthetically active herbaceous biomass for grasslands, rangelands, and savannas (Diallo et al., 1991; Moreau et al., 2003; Sannier et al., 2002; Tucker et al., 1985). Many of the studies performed in grassland and rangeland areas, however, involved the development of spectral indices that take the influence of bare, unsaturated soil backgrounds into account to minimize soil noise (Huete, 1988; Moreau et al., 2003). van Leeuwen et al. (1994) successfully used the SAVI (Table 1) to estimate biomass in shrub savanna ecosystems while Bork et al. (1999) found either non-existent or a minimal improvement relative to NDVI. SAVI therefore was included in this study to determine whether background noise had an influence on the estimation of end-of-rain season herbaceous biomass amount in the study area. The RVI was incorporated in the study based on the results of Mutanga and Skidmore (2004) (Table 1), which showed that RVI performed superior compared to NDVI when estimating pasture biomass of Cenchrus ciliaris grass in the low-lying savannas of the KNP. RVI (Moreau et al., 2003) sometimes is

Table 1 Three selected remotely sensed vegetation indices related to the quantity of vegetation, namely (1) Ratio Vegetation index (RVI), (2) Normalized Difference Vegetation index (NDVI), and (3) Soil Adjusted Vegetation index (SAVI) Formulae q 1 RVI ¼ NIR qRED q −q 2 NDVI ¼ NIR RED qNIR þ qRED qNIR −qRED ð1 þ LÞ 3 SAVI ¼ qNIR þ qRED þ L

Properties

References

The RVI is related to the amount of green vegetation. Typical ranges are approximately 1 for bare soil to more than 20 for dense vegetation. The NDVI increases as the vegetation becomes greener or denser. The typical ranges are approximately 0.1 for bare soils to 0.9 for dense vegetation. SAVI minimizes the effect of the soil noise and is related to the biomass amount in grassland ecosystems. L is a unitless constant which can vary from 0 to 1 depending on the amount of visible soil. A value of 0.5 is as an accepted approximation for L.

Birth & McVey, 1968; Mutanga & Skidmore, 2004 Moreau et al., 2003; Tucker, 1979 Huete, 1988

ρNIR and ρRED are the spectral reflectances of the NIR (0.78–0.89μm) and red (0.61–0.68μm) spectral regions, respectively.

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referred to as the Simple Ratio (SR) (Mutanga & Skidmore, 2004). No other spectral indices were tested given the functional equivalence of most biomass-related spectral vegetation indices to photosynthesis (Myneni et al., 1995). 3.3. Fire risk assessment using VWC-related indices

AIC ¼ LRv2 −2df

The second objective of this study was to select the most optimal fire risk index for the study area. Binary logistic regression successfully has been used in several studies to evaluate the performance of indices by analyzing the relationship between the index and fire events (Andrews et al., 2003). This method does not depend on pre-defined index intervals, nor does it require rescaling of indices for comparison. A logistic regression model was used to define the probability of a fire-dekad (Y), a dekad (10-day period) with one or more fires, as a function of an explanatory variable (X), a fire risk index (Verbesselt et al., in press): logitfY ¼ 1jX g ¼ logitðPÞ ¼ log½P=ð1−PÞ ¼ log½odds that Y ¼ 1 occurs ¼ Xb

assess the discrimination power, while the c-index and the model probability range were used to further examine the strength of association and range of the indices (Andrews et al., 2003; Gobin et al., 2002; Harrell, 2001). The AIC was used in adjusted ‘chisquare’ form:

ð3Þ

where P is the probability that Y = 1 for a given X and logit(P) is the logistic function of P / 1 − P. The regression parameter (β) was estimated using the maximum likelihood method (Harrell, 2001). Fire-dekades can be retained as an indication of the stress put on vegetation by seasonal meteorological dynamics, based on the assumption that non-meteorological factors (e.g. human behaviour) do not change drastically during the period of analysis (Viegas et al., 1999). All available fire activity data selected per sample unit were used in the logistic regression model together with the median fire risk index value of the pixels within each sample unit. The median again was preferred to average values since it is less affected by extreme values. The indices were derived from the S10 SPOT VGT data set (1998– 2003) during the period with the highest fire activity (i.e. fire season: May till October) (Fig. 1) since the evaluation of risk is particularly important in that period. 3.3.1. Exploratory statistics The partial deviance chi-square test was used to verify if the explanatory variable was significant to warrant inclusion in the model (Neter et al., 1996). Physical interpretation of index behaviour became feasible by taking the non-linear behaviour of explanatory variables into account (Verbesselt et al., in press). The explanatory variables therefore were expanded into restricted cubic spline functions (RCS), with a specific number of knots. It was assumed that the most complex relationship could be fitted using a RCS function with five knots (Harrell, 2001). Five knots were used in all the models to facilitate comparison of results, while accounting for similar amounts of non-linearity. Three measures were derived from the logistic regression model to assess the performance of the fire risk indices. The modified Akaike's Information Criterion (AIC) was used to

ð4Þ

where LRχ2 is the model likelihood ratio chi-squared statistic and df is the degree of freedom of the model. The AIC was used to rate the models and penalize for complexity, i.e. the number of parameters used in the model. The higher the AIC, the better the goodness-of-fit and discrimination power of the model (Harrell, 2001). However, the value of AIC only provides information about the relative performance of the model, since the value itself has no other specific meaning. The c-index therefore was selected since it provides interpretable information related to the predictive ability of the logistic regression model and also can be used as a parameter to rank the different fire risk indices. The cindex is identical to a widely used measure of diagnostic discrimination, namely the area under a “receiver operating characteristic” (ROC) curve. A c-index value of 0.5 indicates random predictions, whereas a value of 1 indicates a perfect prediction (Harrell, 2001). Finally, the range of probability values also is an important indication of the effectiveness of an index. A model with a wide range of probability values is preferred to a model with a small range of probability values (Andrews et al., 2003). The comparison technique of Andrews et al. (2003) was used in this study to rank the results of the different binary logistic regression models containing a fire risk index. The three selected measures were ranked, with the lowest rank (i.e. 1) being the measure that performed “best” when estimating fire risk assessment performance of an index. The ranks were summed and an overall rank was assigned to each index to facilitate selection of the best performing index. 3.3.2. Fire risk indices related to VWC Single, greenness, and accumulated vegetation indices have been developed to monitor VWC, drought, or vegetation water stress at canopy scale for fire risk assessment (Lasaponara, 2005). The performance to assess fire risk of single chlorophyllrelated vegetation indices (i.e. NDVI, RVI, SAVI) for savanna ecosystems was evaluated in this study. NDVI have been extensively used in fire risk assessment under the assumption that the chlorophyll content of leaves decreases proportional to the VWC (Chladil & Nunez, 1995; Chuvieco et al., 2003, 2002; Hardy & Burgan, 1999; Leblon et al., 2001; Paltridge & Barber, 1988). This assumption has been confirmed for selected species with a shallow rooting system (e.g. grasslands and understory of forest vegetation) (Chladil & Nunez, 1995; Hardy & Burgan, 1999; Paltridge & Barber, 1988), but cannot be generalized for all ecosystems. Variations in chlorophyll content can be caused by water stress but also by non-VWC-related factors (e.g. phenological status of the plant, nutrient deficiency, etc.) (Ceccato et al., 2001). The performance of the chlorophyllrelated indices (i.e. RVI, NDVI and SAVI) to monitor the

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seasonal fire risk dynamics in savanna ecosystems therefore was assessed in this study (Table 1). NDVI, however, does not exhibit a direct response to changes in VWC (Maki et al., 2004; Verbesselt et al., in press). Indices directly related to VWC, such as the NDWI, a variation of NDVI, use the SWIR spectral region rather than the red region, since the SWIR domain is heavily influenced by water content of the plant tissue (Dennison et al., 2005; Gao, 1996). The fire risk assessment performance of NDWI for savanna vegetation therefore was evaluated (Table 2). Burgan (1996) and Burgan et al. (1998) successfully have used greenness indices to monitor fire risk in the United States. Alonso et al. (1996) found correlation coefficients higher than 0.8 between VWC of shrubland and GRNrel. The greenness indices related to fire risk were analyzed in this study to enable comparison of the greenness indices with the other selected indices related to VWC (Table 2). ARND and AS are two fire risk-related indices based on the NDVI which were specifically designed for and applied to forested areas in the Mediterranean climate zone (Illera et al., 1996; Lopez et al., 1991). These accumulated indices therefore were evaluated based on their ability to successfully assess fire risk in a savanna ecosystem (Table 2). 3.4. Combination of vegetation biomass and VWC-related fire risk

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basis of tolerance, prior to producing a multivariate model. The tolerance was computed as (1 − R2) where a value below 0.4 indicated collinearity (Gobin et al., 2002). Next, the interaction between the two variables was examined with the Wald test statistic to verify if the interaction needed to be taken into account in the logistic regression model (Harrell, 2001). The partial deviance chi-square test was applied to determine if the biomass index significantly improved the binary logistic regression model containing the most optimal fire risk index as explanatory variable (Neter et al., 1996). Finally, the ranking method (see Section 3.3.1) was used to rank the models with the biomass index, the fire risk index, and the combination of biomass and fire risk index as explanatory variables (Andrews et al., 2003). This ranking of models was used to evaluate the performance of the indices and to verify whether the combination of both indices resulted in an improved fire risk assessment approach. 4. Results The results and discussion of this study were divided into three sections, namely (1) estimation of the herbaceous biomass amount at the end of the rain season, (2) fire risk assessment using VWCrelated indices, and (3) combination of herbaceous biomass amount and VWC-related indices to optimise fire risk assessment. 4.1. Herbaceous biomass estimation at the end of the rain season

The index for which the highest correlation was found with the end-of-rain-season herbaceous biomass amount (biomass index) was combined with the most optimal fire risk index. Fire activity data were used as binary response variable (i.e. firedekad and no fire-dekad) in the logistic regression model to confirm whether the combination of biomass and fire risk index improved fire risk assessment. Collinearity was examined on the

Results from the correlation analysis for different methods (single date and integrated approach) and indices (RVI, NDVI, and SAVI) were positively correlated with the herbaceous biomass amount measurements at the end of the rain season. Two and 15 measurements from the 60 available measurements of the single date and integrated approach, respectively, were

Table 2 Selected remotely sensed fire risk indices related to VWC divided into three classes, namely (1) single, (2) greenness, and (3) accumulated decrement vegetation indices Class Formulae 1

2

NDWI ¼

qNIR −qSWIR qNIR þ qSWIR

GRNabs ¼ 100

GRNrel ¼ 100 3

AS ¼

NDVI0 −NDVImin NDVImax

References

NDWI is related to the ratio between the quantity of water in vegetation and leaf area of Ceccato et al., 2001; vegetation and has been proposed as a fire risk index related to the water status. Gao, 1996; Leblon et al., 2001 The indices were proposed to isolate the weather-related component from the temporal Burgan et al., 1998; variability of every pixel. Eidenshink et al., 1990; Kogan, 1990

NDVI0 −NDVImin NDVImax −NDVImin

n X NDVIðti Þ −NDVIðti−1 Þ ti −ti−1 i¼1

ARND ¼

Properties

The temporal evolution of the NDVI can be used to monitor fire risk because a decrease in the Illera et al., 1996; NDVI is related to an increase in water stress and fire risk of herbaceous vegetation. Lopez et al., 1991

n X NDVIðtiþ1 Þ −NDVIðti Þ NDVIðti Þ i¼1

NDWI is the Normalized Difference Water index. ρNIR and ρSWIR are the reflectances of the NIR (0.78–0.89μm) and SWIR (1.58–1.75μm) regions. The GRNabs and GRNrel are the absolute and relative greenness indices. NDVImax and NDVImin are the maximum and the minimum NDVI values for that pixel during the whole study period. NDVIo is the NDVI value for the considered satellite image and pixel. ARND is the accumulated relative NDVI decrement and AS the accumulated slope, where NDVI(ti) is the NDVI value at time ti.

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excluded from the analysis. This was done since the masking procedure during the pre-processing of the SPOT VGT data resulted in insufficient data (i.e. more than 10% of masked data) in these cases (Table 3), resulting in 58 and 45 sample units, which were acceptable for performing a statistically significant correlation analysis (Crawley, 2004). The R2 values ranged between 0.45 and 0.69 with all p-values b 0.01. The highest R2 was found for the iRVI, derived as the integral of the RVI reflectance values over the previous rain season (Table 3). The correlation coefficient (r) of the iRVI was found to be significantly higher than the correlation coefficients of all ‘single date approach’ indices at a 95% confidence level. The p-values of the one tailed t-tests of the indices ranged from 0.029 to 0.045. However, the correlation coefficient of the iRVI was not significantly higher at the 95% confidence level than the correlation coefficients of the other indices, based on information of the entire rain season (i.e. integrated vegetation index approach). The iRVI index resulted in the highest R2 with the herbaceous biomass amount at the end of the rain season (R2 = 0.69). This index consequently was selected to evaluate whether fire risk assessment could be optimised by combining herbaceous biomass amount and VWC information. Fig. 3 shows the scatter plot and result of the linear regression model of the iRVI and in situ herbaceous biomass measurements. The Wald test statistic showed that non-linearity was not significant at a 95% confidence interval (p b 0.05). 4.2. Fire risk assessment using VWC-related indices The relationship between three categories of fire risk indices related to VWC (single, greenness, and accumulated indices) and fire activity data was evaluated with a ranking method. Several measures (AIC, c-index, and model probability range) derived from the binary logistic regression model were used to rank the performance of NDVI, SAVI, RVI, NDWI, GRNabs, GRNrel, AS, and ARND as fire risk indicators. The partial deviance chi-square test showed that these fire risk indicators all significantly improved the logistic regression model at a 95% confidence level (p b 0.05). Consequently, the measures extracted from the different logistic regression models of the considered indices were used in the ranking method. Table 4 illustrates the results from the ranking method used to evaluate

Fig. 3. Scatter plot and line of best fit of iRVI and the amount of herbaceous biomass at the end of the rain season. (n = 45, Y = 100.29 × X-2132.23, R2 = 0.69, p b 0.01).

the relation between the selected fire risk indices and fire activity data. The overall rankings are shown in the last column of Table 4. The ARND demonstrated the lowest final ranking and therefore had the strongest relationship with fire activity. Fig. 4 illustrates the logistic regression model fit with the ARND as explanatory variable and fire-dekades as binary predictor variable. It is clear that when the ARND was lower than 0, the probability for a fire-dekad started to increase. The fit of the binary logistic regression model was illustrated with the logit proportions of fire-dekades by deciles of ARND. It also can be seen that the confidence interval of the ARND became broader and data density lower as illustrated by the one-dimensional scatter plot when the ARND value rose above 0. The ARND was selected as most optimal fire risk index based on the three measures used in the ranking method. Consequently, the ARND and the best index to estimate the amount of end-ofrain-season herbaceous biomass (iRVI) were used in the third phase of this study to validate the improved fire risk assessment capacity of SPOT VGT.

Table 4 Ranked statistical measures extracted from the binary logistic models of the tested fire risk indices related to VWC Table 3 Results of correlation analysis between end-of-rain-season herbaceous biomass and vegetation indices derived with the single date (i.e. RVI, NDVI, and SAVI) and integrated approach (i.e. iRVI, iNDVI, and iSAVI) where R2 is the coefficient of determination, Zf the Fisher z-transformation score, p the significance of the one tailed t-test, and n number of sampling points Index

R2

ZfiRVI–Zf

p

n

RVI NDVI SAVI iRVI iNDVI iSAVI

0.47 0.45 0.46 0.69 0.62 0.62

0.34 0.38 0.36 / 0.12 0.12

0.045 0.029 0.036 / 0.417 0.417

58 58 58 45 45 45

Index RVI NDVI SAVI NDWI GRNabs GRNrel ARND AS

AIC

c-index

Model probability range

Rank sum

Final rank

11.5 (4) 10.4 (5.5) 10.4 (5.5) 14.9 (3) 6.0 (7) 6.8 (6) 58.7 (1) 46.8 (2)

0.60 (6.5) 0.61 (4) 0.60 (6.5) 0.63 (3) 0.60 (6.5) 0.60 (6.5) 0.76 (1) 0.73 (2)

0.10 (7) 0.10 (7) 0.10 (7) 0.19 (2) 0.10 (7) 0.10 (7) 0.47 (1) 0.13 (3)

17.5 16.5 19 8 20.5 19.5 3 7

5 4 6 3 8 7 1 2

The ranks are shown between parentheses. n = 1215, with 1138 no fire-dekades and 77 fire-dekades for the binary logistic models with the tested fire risk indices and a null deviance of 574.

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Fig. 4. Logistic regression curve (─) for the log odds (i.e. log(P / 1 − P), where P = probability of a fire-dekad) of a fire-dekad to occur versus ARND. The dotted lines (‐‐‐) indicate the upper and lower borders of the 95% confidence interval. The logit proportions of fire activity by deciles of ARND (n = 1215, with 1138 no firedekades and 77 fire-dekades) are shown by the ▴ to illustrate the model fit. Data density is illustrated by the topmost one-dimensional scatter plot.

4.3. Combination of herbaceous biomass and VWC-related fire risk The iRVI and the ARND were identified as the most optimal indices to monitor biomass and VWC, respectively, based on the use of in situ herbaceous biomass measurement and fire activity data. Fire activity data similarly were used as predictor variable in a binary logistic regression model to validate if iRVI and ARND improved the fire risk assessment capacity of SPOT VGT satellite data. Significant collinearity was not found, based on a 1 − R2 value of 0.87. The iRVI and ARND consequently were not correlated and both variables contributed different information to the model. Next, the interaction effects between the iRVI and ARND were examined before determining whether the iRVI and ARND improved the fire risk assessment capacity of SPOT VGT. No significant interaction effect between iRVI and ARND was found with the Wald test statistic at the 95% confidence level (p = 0.32). Interaction effects therefore were not taken into account during the analysis (Harrell, 2001). Finally, the iRVI and ARND were evaluated with the ranking methodology to confirm whether the combination of iRVI and ARND improved the relation to fire activity and to verify which of the indices was most important. Table 5 shows the exploratory

statistics of the ranking method for the three models (iRVI, ARND, and iRVI + ARND). The results of Table 5 demonstrate that the combination of iRVI and ARND was ranked highest and consequently was an improvement over alternative measures of fire risk. It therefore was shown that the iRVI, related to the amount of herbaceous biomass at the end of the rain season, improved the relationship to fire activity data when combined with ARND. This confirms the hypothesis that in addition to VWC, vegetation amount is important for fire risk assessment in savanna ecosystems. Table 5 also illustrates that the ARND is ranked higher than the iRVI and is consequently more closely related to fire activity.

Table 5 Ranked statistical measures extracted from the binary logistic models with vegetation amount (iRVI) and VWC (ARND)-related indices Index

AIC

c-index Model probability range Rank sum

iRVI 17.7 (3) 0.65 (3) ARND 44.6 (2) 0.76 (2) ARND + iRVI 48.6 (1) 0.80 (1)

0.00–0.22 (3) 0.01–0.49 (2) 0.00–0.50 (1)

9 6 3

Final rank 3 2 1

The ranks are shown between parentheses. n = 730, with 670 no fire-dekades and 60 fire-dekades in the significant binary logistic models, namely iRVI, ARND, and ARND + iRVI, with a null deviance of 415.

Fig. 5. 3D-logistic regression fit for the log odds (i.e. log(P / 1 − P), where P = probability of a fire-dekad) of a “fire-dekad” predictor versus ARND and iRVI as explanatory variables (n = 730, with 670 no fire-dekades and 60 fire-dekades). The cross-sections of this figure for the ARND and iRVI are shown in Fig. 6 (a) and (b) at iRVI = 62.59 and ARND = − 0.3456, respectively.

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Fig. 6. Cross-sections of the 3D-logistic model fit (Fig. 5) with (a) ARND and (b) iRVI against the log odds for a fire-dekad (log odds = log(P / 1 − P); P = probability of a fire-dekad). The dotted lines (‐‐‐) indicate the upper and lower borders of the 95% confidence interval. The logit proportions of fire activity by deciles of iRVI and ARND are shown by the ▴ to illustrate the model fit. Data densities are illustrated by the one-dimensional scatter plots (n = 730, with 670 no fire-dekades and 60 fire-dekades).

Figs. 5 and 6 illustrate the logistic regression model fit with ARND and iRVI as explanatory variables and the binary fire activity data as predictor variable. The p-values of the partial deviance chi-square test for the model and individual explanatory variables were significant at a 95% confidence level (p b 0.02). Fig. 5 demonstrates the non-linear behaviour of the ARND and the iRVI in relation to the probability for a firedekad. It is clear that both indices had significant, but different behaviors in relation to fire activity. Fig. 6a and b are the crosssections of Fig. 5, with Fig. 6a illustrating that the probability of a fire gradually increased when the ARND became negative. Fig. 6b shows that the probability for a fire-dekad stagnated at the point where the iRVI value reached approximately 50. Both figures also show that the confidence intervals became broader at high ARND and low iRVI values (Fig. 6a and b). 5. Discussion The potential of hyper-temporal remote sensing data (e.g. daily or 10-daily revisit cycle) to assess fire risk in savanna ecosystems has not yet been fully assessed since most of the existing remote sensing-based fire risk indicators only take the VWC into account. However, the amount of herbaceous biomass, in addition to VWC, is an important factor influencing fire activity in savanna ecosystems (van Wilgen et al., 2004).

This study therefore evaluated the potential of SPOT VGT data to monitor vegetation biomass and VWC to improve fire risk assessment in savanna ecosystems of southern Africa. 5.1. Herbaceous biomass estimation at the end of the rain season We evaluated the single date and integrated index approach using the RVI, NDVI, and SAVI to estimate the herbaceous biomass amount at the end of the rain season in the low lying savannas of the KNP. No significant difference was found between the indices evaluated for the integrated index approach (RVI, SAVI, and NDVI). This can be explained by the functional equivalence of the vegetation indices, as a result of their direct relationship to photosynthesis (Myneni et al., 1995). The iRVI, derived as the integral of the RVI reflectance values over the previous rain season (November to April), had the highest correlation with the amount of herbaceous biomass at the end of the rain season (R2 = 0.69) (Table 3). This index showed a significant improvement over the indices derived by the single date approach, which only took the last 2 months of the rain season, i.e. the period of in situ herbaceous biomass measurements, into account. The ‘single date indices’, based on information of the months March and April, provided information about only the green herbaceous biomass (i.e. living vegetation)

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at that moment. These indices, however, did not incorporate information about the total effect of ‘rain season rainfall’ on the herbaceous biomass amount of a savanna ecosystem in the KNP. The iRVI consequently was best related to the total amount of herbaceous biomass (dead and live), because the iRVI accounted for the total effect of ‘rain season rainfall’ by integrating the iRVI values during the entire rain season. Sannier et al. (2002) pointed out that the single date approach is preferable in semi-arid regions with grassland and steppe vegetation. The pattern of vegetation growth in these semi-arid regions is irregular and closely follows rainfall events. An integrated NDVI approach for grassland and steppe vegetation therefore was not suitable because the time-integration would have to be varied at each location to reflect rainfall distribution. Sannier et al. (2002), however, illustrated that for savanna vegetation the influence of the woody tree layer on the canopy reflectance caused an overestimation of biomass amount when using the single date approach. The integrated vegetation index approach used in this study, conversely, was robust against the influence of the woody tree layer and accounted for the interannual influence of rainfall on the herbaceous layer. This robustness can be attributed to savanna ecosystems being water limited and responsive to rainfall (Scanlon et al., 2002). These findings also corroborate the results of van Wilgen et al. (2004) and Scanlon et al. (2002), who demonstrated that the mean annual rainfall of the preceding year was strongly related to the amount of herbaceous biomass (field measurements) or green biomass (as measured by the NDVI), respectively. The mean annual rainfall of the previous 2years provided an improved relation to the amount of herbaceous biomass, since the effect of rainfall on perennial grasses persists for more than a year (van Wilgen et al., 2004). The estimation of herbaceous biomass amount therefore could be improved by calculating the integral of the remote sensing index during the preceding two rain seasons when satellite time-series become longer. This is one of the factors that explain why the iRVI, based on only one preceding rain season, did not accurately estimate the amount of herbaceous biomass at the end of the rain season. Approximately 69% of the variation in biomass data was explained by the iRVI (R2 = 0.69). The regression model accuracy also could be improved by accounting for the influence of herbivory and decay. van Wilgen et al. (2004) and Scholes et al. (1996) indicated that herbaceous biomass is not only a function of rainfall, but that herbivory and decay also affect vegetation biomass quantity. However, the amount of herbaceous biomass at the end of the rain season, and not the biomass change due to herbivory or decay of biomass during the fire season, is the main factor influencing fire activity during the following fire season (van Wilgen et al., 2000). The total biomass at the end of the rain season is an indication of the amount of vegetation that will be cured in the following dry season (Scholes et al., 1996). This relationship between fire activity and end-of-rain-season biomass varied between years due to the severity of fire weather, with the probability of fire activity increasing in those years where severe fire weather coincides with higher fuel loads (van Wilgen et al., 2000). Trollope and Potgieter (1986) have also shown that herbaceous biomass amount needed to reach at least

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1500kg ha− 1 to propagate fires in the KNP, but did not define other biomass amount thresholds related to higher fire risk. An accurate assessment of the amount of herbaceous biomass therefore was not necessary for fire risk assessment. 5.2. Fire risk assessment using VWC-related indices Fire risk indices related to the VWC derived from the SPOT VGT S10 data were evaluated using in situ fire activity data. A comparison technique, based on the ranking of statistical measures derived from the fitted logistic regression model, with fire data as predictor variable and the considered indices as explanatory variables, was used to select the most optimal index. The considered indices were divided into three categories as explained in Tables 1 and 2, namely single (NDVI, RVI, SAVI, and NDWI), greenness (GRNabs and GRNrel), and accumulated (AS and ARND) vegetation indices. The partial deviance chisquare test demonstrated that all indices had a significant influence on fire activity at a 95% confidence level. However, not all indices seemed to be equally suitable to estimate fire risk. The three categories of indices also were distinguished based on the rank sums of the eight indices in Table 4. ARND and AS, the accumulated vegetation indices, had a rank sum of less than 8 which constituted an improvement over the other indices. This confirmed that both indices, specifically designed to estimate fire risk in forest ecosystems, also successfully estimated fire risk in savanna ecosystems (Illera et al., 1996; Lopez et al., 1991). The NDWI had a rank sum of 8 and performed better as fire risk index than the chlorophyll-related single indices and the greenness indices, which had rank sums bigger than 16.5. The chlorophyll-related single indices and the greenness indices were least suited to estimate fire risk in the KNP. The improved performance of NDWI, compared to the chlorophyll-related single and greenness indices, was attributed to the fact that chlorophyll content is not only influenced by VWC, but also by numerous different parameters (e.g. phenological status of the plant, toxicity, etc.) (Ceccato et al., 2001). Fig. 7a illustrates that NDWI started to decrease earlier than the NDVI during the dry season. NDVI monitored the degradation of chlorophyll pigments and decreasing leaf area index due to increasing water stress, while NDWI directly monitored the decreasing VWC (Ceccato et al., 2001; Chuvieco et al., 2003). The temporal behaviour of NDVI and NDWI consequently indicated that NDWI reacted directly onto changes in VWC and explained why NDWI performed better than NDVI as a fire risk index (Table 4). This finding also corroborated the results of Verbesselt et al. (in press) who illustrated that NDWI had the highest capacity to monitor fire activity in comparison to NDVI and a meteorological drought index (Keetch–Byram drought index) in the same study area. It was demonstrated that ARND performed better at estimating fire risk when compared to the other VWC-related indices, as illustrated by the final ranking in Table 4. It was shown that the ARND was a better fire risk index than the NDWI, while NDWI performed better than NDVI. Lopez et al. (1991) observed that the temporal evolution of the NDVI

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Fig. 7. The temporal relationship of (a) NDVI-NDWI and (b) NDWI-ARND time-series (1998–2003) during the fire season (i.e. May to October) is illustrated for one sampling block in the study area (Fig. 2). The axes of the figures have an equal range to illustrate the temporal variation of the fire risk indices related to vegetation water content.

reflected the evolution of the meteorological variables, especially precipitation and temperature. Increases in NDVI were due to favorable meteorological conditions, with adequate precipitation and temperatures. Strong NDVI decrements were evident when temperatures were high and precipitation was low. The reasons for ARND being related to fire risk consequently were threefold. Firstly, the ARND integrated the effect of climate on vegetation by differencing values in time (i.e. NDVIt+1–NDVIt) (see formulae in Table 2). Secondly, the NDVIt in the denominator of ARND caused small differences in time between NDVI values to become more important towards the end of dry season (high temperatures and no precipitation). This occurred because small temporal NDVI differences were related to a rapidly increasing fire activity in the middle of the dry season (Figs. 1 and 7a). Thirdly, the accumulated effect of climate on vegetation, which influenced the fire activity in the savanna ecosystem, also was monitored with the ARND by summation of the temporal NDVI differences (van Wilgen et al., 2004). Fig. 7b illustrates the temporal behaviour of ARND and NDWI and shows that the temporal variation of the ARND was bigger compared to NDWI. The higher sensitivity of ARND to VWC variation during the dry

season versus the sensitivity of NDWI explained the superior performances of ARND as a fire risk index (Table 4). The ARND derived from NDWI values also was evaluated in a preliminary phase of the research since it was shown that the NDWI was a better fire risk index than NDVI. It was hypothesized that the ARND based on NDWI values, instead of NDVI, could perform better as a fire risk index. However, it was not possible to derive the ARND based on NDWI values since NDWI values became negative during the major part of the dry season and disrupted the accumulative property of ARND (Fig. 7a). Further research is necessary to derive an index based on NDWI using the ARND principles since it was not within the scope of this paper to derive novel fire risk indices related to VWC. Fig. 4 illustrates that the probability of a fire-dekad increased when the ARND became negative and that the fire-probability was near zero when ARND had a value of 0 or higher. These findings corroborated the results of Lopez et al. (1991) who considered only pixels with a negative value of ARND as fire vulnerable and showed that the larger the negative value, the higher the fire risk. Fig. 4 also shows that the confidence intervals increased when the ARND became positive. The

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logistic regression model did not contain many high ARND values, since the analysis of VWC-related indices was performed during the fire season. Consequently, the lack of positive ARND values, as illustrated by the one-dimensional scatter plot, explained the broadness of the confidence intervals. In summary, the ARND was the most optimal fire risk index related to VWC and was selected to form part of the final logistic regression model. The ARND was combined in the final model with the iRVI, which is related to the herbaceous biomass amount at the end of the rain season, to optimise fire risk assessment with hyper-temporal satellite data in savanna ecosystems. 5.3. Combination of vegetation biomass and VWC-related fire risk The ARND and iRVI were selected as most optimal indices to monitor vegetation biomass and VWC in the KNP, based on results from the previous two objectives. The results from the partial deviance chi-square tests demonstrated that the iRVI was a significant variable in the logistic regression model with the ARND at a 95% confidence level (p b 0.02). Table 5 corroborates the results of the partial deviance chi-square test and the performance measures illustrate that the combination of the ARND with iRVI improved the relationship with fire activity in the KNP (higher AIC, c-index, and p-range values). This study therefore demonstrated that the yearly estimate of herbaceous biomass amount at the end of the rain season significantly improved the fire risk assessment capacity of SPOT VGT with the ARND. The c-index of the logistic regression model with the ARND and iRVI as explanatory variables was 0.80, which indicated that the model has utility in predicting fire activity (Harrell, 2001) (Table 5). However, the predictive precision depends on both the temporal and spatial scales, as well as on the social and environmental features of the study area and period (Maselli et al., 2003). The proposed method in this study forms an important spatial and temporal information source on vegetation dynamics, even though it might lack fine-scale accuracy needed for operational fire risk assessment. Such a satellite-based information source therefore should be integrated with other fire risk estimates, namely meteorological fire risk indices or fire-related vegetation characteristics (e.g. fuel types) derived from high spatial resolution satellite data. The small range in probability values of the ‘iRVI + ARND’ model (Table 5) was attributed to the relatively small fire data set used. The influence of exceptional fires, i.e. fires that occur when vegetation was still wet or lack of fires when vegetation was dry, increased for a smaller fire data set and decreased the range in probability values (Andrews et al., 2003). However, data sets of comparable sizes also were used by Viegas et al. (1999) to assess the relative performance of fire risk indices. Logistic regression models could be used operationally in the future when time-series of remote sensing data become longer and can be related to increased data on fire activity. Table 5 also illustrates that the ARND had an improved ability to predict fire activity in the KNP when compared to the iRVI. In other words, the VWC-related fire risk index was more important than the amount of end-of-rain-season herbaceous

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biomass as an explanatory variable when predicting fire activity. This can be explained by the rainfall regime of the study area, which is characterized by extended wet and dry periods, during which the rainfall is either higher than the long-term mean or lower than the mean for between 6 and 12 consecutive years (Trollope, 1996). These extended wet and dry periods have marked effects on the occurrence of fires, mainly through their influence on herbaceous biomass (van Wilgen et al., 2004). The study period (1998–2003) fell in a cycle of consecutive wet years, which resulted in a higher amount of herbaceous biomass in the study area. The minimum amount of biomass for a fire to propagate was reached in most of the areas in the park and explained the decreased importance of the amount of herbaceous biomass compared to the VWC. The influence of herbaceous biomass amount, as described by iRVI, therefore could increase when the remote sensing time-series become longer and both wet and dry periods can be studied. This also explains the width of the confidence intervals of the ‘ARND + iRVI’ logistic regression model at low iRVI values, since low iRVI did not occur frequently during the study period (Fig. 6b). Fig. 6 illustrates the probability of a fire-dekad related to the iRVI and confirms that the sufficient accuracy of iRVI for estimating end-of-rain-season biomass (R2 = 0.69) to have a significant influence on the probability of fires occurrence. This figure also demonstrates that for iRVI values larger than approximately 36, which corresponded to a biomass amount of approximately 1500 kg/ha (Fig. 3), the probability of a fire occurrence increased significantly. Fig. 6 therefore corroborates results of Trollope and Potgieter (1986), who showed that, a minimum amount of herbaceous biomass of at least 1500 kg ha− 1 was needed to propagate fires in the KNP. It is furthermore evident that the fire-probability stabilised when iRVI increased above approximately 50 (2800 kg/ha). This confirms that highly accurate herbaceous biomass estimations are not required when estimating fire risk. Herbaceous biomass maps in practice can be created based on the iRVI for fire risk assessment purposes. These maps can be implemented by fire managers to organise the prescribed burning often used to prevent the occurrence of wildfires through reduction of the amount of herbaceous biomass. It is possible to reclassify biomass maps according to a series of thresholds to indicate the levels of fire risk (Sannier et al., 2002). For example, three classes can be defined for the current study area and period: Low fire risk where biomass b 1500kg/ha; 1500kg/ha biomass N medium fire risk b 2800kg/ha biomass; and high fire risk where biomass N 2800kg/ha. 6. Conclusion This research has demonstrated that the combination of a fire risk index related to VWC (ARND) and an end-of-wet-season herbaceous biomass-related index (iRVI) increased the fire risk assessment capacity of the SPOT VGT sensor for the low-lying savannas of the Kruger National Park, South Africa. The iRVI, calculated as the integral of the SPOT VGT RVI reflectance values over the previous wet season, had the highest correlation with the field-measured amount of herbaceous biomass at the end of the rain season (R2 = 0.69). The iRVI also showed

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significant improvement over the ‘single date approach’ indices, which were based only on SPOT VGT satellite data from the period in time when the biomass ground measurements were performed (March–April). The integrated vegetation index approach could be applied to different savanna types, irrespective of tree density, as predictor of the amount of available herbaceous biomass, since the sample units covered the whole of the KNP containing different savanna types (e.g. woodland and tree savanna). Further research, however, is needed to verify this hypothesis for savanna areas in other climatic zones. The predicting power of VWC-related fire risk indices was evaluated with fire activity data using binary logistic regression. The extracted measures from the logistic regression model (AIC, c-index, and model probability range) were used in a ranking methodology to rank the single, greenness, and accumulated vegetation indices according to their performance in estimating fire occurrence probabilities. This comparative study has shown that the ARND, compared to other indices, had a superior performance when estimating fire risk. Both ARND and AS indices, calculated based on the temporal decrement of NDVI values during the fire season, were top performers in estimating fire risk in the low-lying savannas of the KNP. The NDWI performed better as a fire risk index when compared to the chlorophyll-related single (NDVI, SAVI, RVI) and greenness (GRNabs and GRNrel) indices. Further research therefore is needed to develop a novel index based on NDWI using the ARND principles. Binary logistic regression was shown to be a suitable tool for evaluation of the performance of satellite-based fire risk indices and could be used to optimise fire risk assessment for other ecosystems. This research has shown that the combination of the most optimal indices for vegetation biomass (iRVI) and VWC (ARND) monitoring result in an optimised fire risk assessment method for savanna ecosystems. The binary logistic regression model, containing ARND and iRVI as explanatory variables showed utility in predicting the probability of a fire-dekad occurrence in the KNP (c-index = 0.80). However, this spatial and temporal information source on vegetation amount and VWC should be combined with other fire risk estimates (e.g. meteorological fire risk indices) for operational fire risk assessment. Yearly herbaceous biomass maps in practice can be created based on the iRVI for fire management purposes. These maps can be implemented by fire managers to organise the prescribed burning often used to prevent the occurrence of wildfires through reduction of the amount of herbaceous biomass. Such biomass maps, combined with 10-daily SPOT VGT ARND values, could provide fire managers with updated information on the vegetation biomass and VWC during the fire season. The 10daily data (S10 SPOT VGT) could be unsuitable for observation of daily VWC in operational circumstances. The proposed fire risk assessment subsequently could be improved by using daily satellite imagery (e.g. daily SPOT VGT imagery) when high frequency observations are required. The method proposed in this study was derived using reflectances of red and near-infrared regions. It therefore is considered potentially applicable not only to the SPOT VGT sensor, but also to the TERRA/MODIS and AVHRR sensors. The validation strategy also could be used in

the future to verify whether the proposed fire risk assessment method could be improved by using of thermal satellite data (e.g. from ATSR, AVHRR or MODIS sensor). Ongoing research is geared towards development of an operational approach to monitor vegetation biomass and VWC for fire managers in savanna ecosystems based on the results of this study. Acknowledgements This work was performed in the framework of a research project on satellite remote sensing of terrestrial ecosystem dynamics, funded by the Belgian Science Policy Office (GLOVEG-VG/00/01). The authors would like to thank the Scientific Board of Kruger National Park and N. Govender, N. Zimbatis, and S. Mac Fadyen for providing the in situ fire history and biomass data, and for logistic support during this study. The SPOT VGT S10 datasets were generated by the Flemish Institute for Technological development (VITO). We wish to thank S. Lhermitte for in-depth discussions related to the analyses and acknowledge the statistical support of D. Rizopoulos and Professor F. E. Harrell. We are indebted to the editor and referees for their detailed reviews that improved the content of this paper.

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