Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems

Remote Sensing of Environment 115 (2011) 2626–2639

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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems G. Caccamo a,⁎, L.A. Chisholm a, R.A. Bradstock a, b, M.L. Puotinen a a b

Institute for Conservation Biology & Environmental Management (ICBEM), School of Earth and Environmental Science, University of Wollongong, NSW, 2522, Australia Centre for Environmental Risk Management of Bushfires, University of Wollongong, 2522, NSW, Australia

a r t i c l e

i n f o

Article history: Received 8 December 2010 Received in revised form 28 May 2011 Accepted 29 May 2011 Available online 2 July 2011 Keywords: Drought MODIS SPI Bushfire season Australia Vegetation Fire

a b s t r a c t Drought monitoring is important to analyse the influence of rainfall deficiency patterns on bushfire behaviour. Remote sensing provides tools for spatially explicit monitoring of drought across large areas. The objective of this study was to assess the performance of MODIS-based reflectance spectral indices to monitor drought across forest and woodland vegetation types in the fire prone Sydney Basin Bioregion, NSW, Australia. A time series of eight spectral indices were created from 2000 to 2009 to monitor inter-annual changes in drought and were compared to the Standardized Precipitation Index (SPI), a precipitation deficit/surplus indicator. A pixel-to-weather station paired correlation approach was used to assess the relationship between SPI and the MODIS-based spectral indices at different time scales. Results show that the Normalised Difference Infrared Index—band 6 (NDIIb6) provided the most suitable indicator of drought for the high biomass vegetation types considered. The NDIIb6 had the highest sensitivity to drought intensity and was highly correlated with SPI at all time scales analysed (i.e., 1, 3 and 6-month SPI) suggesting that variations in precipitation patterns have a stronger influence on vegetation water content than vegetation greenness properties. Spatial similarities were also found between patterns of NDIIb6-based drought maps and SPI values distribution. NDIIb6 outperformed the spectral index currently in use for operational drought monitoring systems in the region (Normalised Difference Vegetation Index, NDVI) and its implementation in existing drought-monitoring systems is recommended. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Drought is defined by the reflective remote sensing community as a period of abnormally low rainfall that changes vegetation conditions (Heim, 2002; Maselli et al., 2003). Drought in high biomass ecosystems such as forests and woodlands determines the potential for fires, by making biomass available to burn (Bradstock et al., 2009). Reduced water availability decreases the moisture content of live and dead fuels thereby contributing to an increase in the overall availability and spatial connectivity of fuel that is sufficiently dry to burn (Allen, 2007; Bradstock et al., 2009; Peters et al., 2007, 2004). As a consequence, drought has been shown to correlate with the incidence of large fires in many forested/ woodland regions of the world (e.g., western USA, Westerling et al., 2006; Canada, Flannigan & Harrington, 1988; south-eastern Australia, Bradstock et al., 2009; Gill, 1984). Mapping the dynamic patterns of drought in high biomass ecosystems at medium spatial resolution (i.e., 500 m) provides the basis for: (i) monitoring the state, extent and connectivity of flammable fuels; and (ii) prediction of the potential for propagation of fires. Traditional (i.e., non remote sensing) methods of drought monitoring are based on meteorological indices (MI) derived from weather station data (Ji & Peters, 2003). Although a number of MIs have been proposed

⁎ Corresponding author. Tel.: + 61 2 4298 1953. E-mail address: [email protected] (G. Caccamo). 0034-4257/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2011.05.018

(e.g., Keetch Byram Drought Index (KBDI), Keetch & Byram, 1968; Standardized Precipitation Index (SPI), McKee et al., 1993), the network of weather stations used for calculating indices is often sparse (Ashcroft et al., 2009; McVicar & Jupp, 1999), particularly for remote forested areas, and lacks continuous spatial coverage (Oldford et al., 2006). Consequently, spatial interpolation is often required which introduces some uncertainty particularly over complex terrain (Flannigan et al., 1998) where meteorological variables change quickly as a function of topography (McVicar et al., 2007). By contrast, satellite sensors can be used to directly monitor spatially-explicit patterns of drought-related changes in vegetation condition in areas where weather stations are sparse or non-existent (Ji & Peters, 2003). The most frequently used sensor for drought monitoring is the Advanced Very High Resolution Radiometer (AVHRR). The AVHRR Normalised Difference Vegetation Index (NDVI) (Tucker, 1979) and thermal infrared information have been successfully implemented to assess drought conditions across a variety of regions world-wide (e.g., Bayarjargal et al., 2006; Jain et al., 2009; Ji & Peters, 2003; Kogan, 1995; Liu & Kogan, 1996; McVicar & Bierwirth, 2001; McVicar & Jupp, 1998). Compared to the AVHRR sensor, the Moderate resolution Imaging Spectroradiometer (MODIS) sensor provides a finer radiometric resolution in the water sensitive portion of the electromagnetic spectrum (Shortwave-infrared, SWIR, 1200–2500 nm) which includes three water absorption regions (i.e., at 1200 nm, 1450 nm and 1950 nm) and is potentially more suitable for drought monitoring (Ceccato et al., 2001; Gao, 1996). A range of MODIS-based spectral indices have been proposed

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for monitoring drought in agricultural and semi-arid areas (e.g., Biggs et al., 2010; Gao et al., 2008; Ghulam et al., 2007; Gu et al., 2007, 2008; Qin et al., 2008; Rhee et al., 2010; Wan & Wang, 2004). To date, only few studies have explored the utility of MODIS-based spectral indices to monitor drought in fire prone forested areas (Anderson et al., 2010; Saleska et al., 2007; Wang et al., 2009). These studies did not include a multi-temporal assessment of drought at different time scales. Furthermore, a comprehensive comparative evaluation of the performance of MODIS-derived spectral indices in the visible, near infrared (NIR) and SWIR channels has only been conducted for agricultural areas (Rhee et al., 2010) and it has never been conducted for high biomass ecosystems. Clearly there is a need to further evaluate the applicability of MODISbased data for monitoring drought in forested areas. In Australia, spatially explicit monitoring of drought is crucial because droughts are frequent and have a strong influence on fire activity (Bradstock et al., 2009; Hennessy et al., 2008). A number of drought monitoring systems have been developed for both agricultural (e.g., National Agricultural Monitoring System, Bureau of Rural Science) and natural systems. Finkele et al. (2006) and Raupach et al. (2008) used meteorological and remotely sensed data to monitor drought and terrestrial water balance at 10 km and 5 km, respectively. The coarse spatial resolution of these products significantly limits their applicability for finer scale assessment of drought patterns. AVHRR-derived NDVI temporal composites are operationally used by several governmental authorities to monitor vegetation condition (e.g., grassland curing) supporting the decision-making process for declaring drought affected areas in Australia (Clark et al., 2000; Cridland et al., 1994; McVicar & Jupp, 1998; Paltridge & Barber, 1988). McVicar and Jupp (2002) developed the Normalised Difference Temperature Index (NDTI) combining AVHRR reflective (i.e., Red and NIR channels) and thermal data with meteorological data to monitor water availability. To date, however, the potential offered by the higher radiometric resolution of MODIS in the SWIR portion of the spectrum has not been thoroughly investigated. The frequent occurrence of prolonged droughts and their demonstrated influence on large fires alone justifies evaluating the performance and sensitivity of different MODIS-derived spectral indices to assess drought in higher biomass vegetation types. Our objectives are to: 1) assess the potential of MODIS reflective data to monitor drought in high biomass ecosystems using SPI as precipitation deficit/surplus indicator; 2) compare the performance of a wide set of MODIS-based spectral indices in the visible, NIR and SWIR portions of the spectrum; 3) investigate the relationships between MODIS data and SPI during drought and non-drought years at different time-scales; and 4) assess the accuracy of MODIS-based drought maps. 2. Study area The research focused on the Sydney Basin Bioregion (Fig. 1) which has extensive fire-prone forested areas and recurrent droughts. The Sydney Basin is located in the central east coast of Australia, in New South Wales, and covers an area of ~3.6 million ha (Fig. 1). The Basin is characterised by a temperate climate with warm summers and no annual dry season (Pippen, 2007). Based on records (1961–1990) of the Australian Bureau of Meteorology (BOM), the average annual temperature ranges from 10 °C to 17 °C (monthly minimum, −1.4 °C to 8.1 °C; monthly maximum 22.4 °C to 31.9 °C) and mean annual rainfall varies from 522 to 2395 mm along a west–east precipitation gradient (Pippen, 2007). The landscape presents a rich array of highly flammable vegetation communities dominated by sclerophyll woodlands and forests of Eucalyptus species, covering extended areas around the greater metropolitan area of Sydney (Keith, 2004). Bushfires occur between October and March (the bushfire season), with the highest danger between October and January, before the onset of the rainy weather common in February and March (Bryant, 2008; Castles, 1995; Pippen, 2007). Over three million hectares of bushland has burnt in the past 30 years (Bryant, 2008; Smith, 2005) causing costly losses of natural

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resources, lives and property (Bradstock & Gill, 2001; Shakesby et al., 2007). Drought is a recurrent climatic process in the region (Hennessy et al., 2008). Two significant droughts affected the months from October through January during the 2002/03 and 2006/07 bushfire seasons (Coughlan et al., 2003; Murphy & Timbal, 2008). 3. Data and methodology Spatial and temporal relationships between MODIS spectral indices and drought conditions were analysed for the period 2000–2009. The evaluation focused on the bushfire season when the combined effect of water deficiency, high vapour pressure deficit and air temperature has the maximum impact on vegetation condition. SPI and MODIS data processing are first described separately. 3.1. Meteorological data processing: SPI SPI (McKee et al., 1995, 1993) is a monthly MI widely used in drought monitoring studies (e.g., Jain et al., 2009; Ji & Peters, 2003; Wilhite, 2000). Although SPI presents some limitations (i.e., precipitation effectiveness is not considered), it has advantages over other drought indices (i.e., KBDI) because it is temporally flexible and it requires only precipitation data normally available from the majority of weather stations (Ji & Peters, 2003). SPI is computed by fitting long-term records of monthly precipitation data for a given location (e.g., weather station) and time-step (e.g., 1-month, 6-months), to a gamma probability distribution function, and transforming the gamma distribution to a normal distribution with a mean of zero and standard deviation of one (Lloyd-Hughes & Saunders, 2002; McKee et al., 1993). The index values represent the standardised deviations (i.e., Z-score) of the transformed rainfall totals from the mean and capture the accumulated deficit (SPI b0) or surplus (SPI N0) of precipitation over a specified time scale (Lotsch et al., 2005; Smakhtin & Hughes, 2007). Recently, Kumar et al. (2009) and Lloyd-Hughes and Saunders (2002) proposed an eight-category classification based on specific SPI ranges to facilitate the discrimination between drought and non-drought conditions (Table 1). Monthly precipitation data from 48 weather stations within and around the Sydney Basin, proximally located to fire prone vegetation types (i.e., eucalyptus woodland, eucalyptus forest and heath) with data spanning more than 40 years (i.e., open before 1969) were used to construct a SPI time series (Fig. 1). Our analysis included 1-month, 3-month and 6-month SPI for each weather station and each month (October–March) from 2000 to 2009. We excluded longer time steps (e.g., 12-month SPI) based on preliminary analysis of fire history records (4317 fire occurrences, from 1977 to 2009, acquired from the NSW Rural Fire Service and the Australian Department of Environment and Climate Change) which showed that the total area burnt in the study area is more related to seasonal (3-month SPI) and medium (6-month SPI) time scales (unpublished data). We included 1-month SPI to analyse the remotely sensed response to short periods of water deficiency. The 1-month SPI values were averaged at all stations each month (October–March). January 6-month SPI values at each weather station were reclassified into eight drought severity classes accordingly to the categories in Table 1. Additionally, January 6-month SPI values were averaged at all stations each bushfire season from 2000/01 to 2008/09. 3.2. Satellite data processing: MODIS We used 500-meter resolution 8-day composite surface reflectance MODIS data (MOD09A1, collection 5) from 2000 to 2009. Data anomalies (e.g., cloud, cloud shadow, cirrus, view zenith angle N50.5°) were masked using MODIS quality assurance (QA) metadata. Eight spectral indices known to correlate with vegetation characteristics were calculated (Table 2).

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Fig. 1. Map of the Sydney Basin Bioregion showing the location and vegetation cover type of each weather station.

NDVI, SR, EVI (based on visible and NIR bands) and VARI (based only on visible bands) have been shown to be related to a wide range of vegetation greenness properties including canopy chlorophyll content, leaf area index and green vegetation fraction (e.g., Gitelson et al., 2002; Table 1 Drought classification based on SPI ranges (Lloyd-Hughes & Saunders, 2002). SPI range

Drought category

Drought class

N2 1.5 to 1.99 1.0 to 1.49 0.99 to 0 0 to − 0.99 − 1.0 to − 1.49 − 1.5 to − 1.99 b−2

Extremely wet Severely wet Moderately wet Mildly wet Mild drought Moderate drought Severe drought Extreme drought

ND + 4 ND + 3 ND + 2 ND + 1 D−1 D−2 D−3 D−4

Huete et al., 2006; Ji & Peters, 2003; Lu et al., 2003; Myneni et al., 1995). NDIIb6, Depth1640, NDIIb7 and NDWI use information from the NIR and SWIR portions and are sensitive to vegetation water status (e.g., Chuvieco et al., 2002; Gao, 1996; Hunt & Rock, 1989; Van Niel et al., 2003; Yilmaz et al., 2008). Depth1640 is estimated through continuum removal analysis of Band 6 (1628–1652 nm) relative to Band5 (1230–1250 nm) and Band7 (2105–2155 nm) (Kokaly & Clark, 1999; Van Niel et al., 2003). The continuum-removed reflectance (R′, see Table 2) is determined by dividing the reflectance of bands within the absorption feature (MODIS Band 6 in this study) by the value of a continuum line forming a ‘ceiling’ above the entire absorption feature (MODIS Band 5 to 7 in this study) (Kokaly & Clark, 1999; Van Niel et al., 2003). All those indices are expected to be related to SPI (i.e., drought conditions) as limited water availability affects vegetation conditions (e.g., Gu et al., 2007, 2008; Ji & Peters, 2003; Maselli et al., 2003; Wang et al., 2009).

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Table 2 MODIS vegetation indices evaluated in this study. MODIS bands: 1 = 620–670 nm, 2 = 841–876 nm, 3 = 459–479 nm, 4 = 545–565 nm, 5 = 1230–1250 nm, 6 = 1628–1652 nm, 7 = 2105–2155 nm. (a) NDVI = Normalised Difference Vegetation Index; (b) VARI = Visible Atmospherically Resistant Index; (c) SR = Simple Ratio; (d) EVI = Enhanced Vegetation Index; (e) NDIIb6 = Normalised Difference Infrared Index—band 6; (f) NDIIb7 = Normalised Difference Infrared Index—band 7; (g) D1640 = Depth of MODIS band 6; (h) NDWI = Normalized Difference Water Index. *The constant c represents the relative position of band 6 (1640 nm) to band 7 (2130 nm) and band 5 (1240 nm) calculated using the mean wavelength of MODIS bands 5, 6 and 7 (adapted from Van Niel et al., 2003). Vegetation greenness indices

NDVI VARI SR EVI

Vegetation water indices

NDIIb6 NDIIb7 D1640

NDWI

Band2−Band1 Band2 + Band1 Band4−Band1 Band4 + Band1−Band3 Band2 Band1   Band2−Band1 2:5 band2 + 6Band1−7:5Band3 + 1

Tucker (1979)

(a)

Gitelson et al. (2002)

(b)

Tucker (1979)

(c)

Huete et al. (2002)

(d)

Band2−Band6 Band2 + Band6 Band2−Band7 Band2 + Band7

Hunt and Rock (1989)

(e)

Hunt and Rock (1989)

(f)

Band6 ðBand5ð1−cÞ + ðcBand7ÞÞ 1640−1240 ≈0:4494 where c = 2130−1240 Band2−Band5 Band2 + Band5

adapted from Van Niel et al. (2003)

(g)

Gao (1996)

(h)

1−R′ = 1−

A 3 × 3 pixel kernel was centred on selected blocks of continuous vegetation stands in proximity to the 48 BOM weather stations (minimum and maximum distance, 0.8 and 12.1 km respectively; average and standard deviation, 6.2 and 3.5 km respectively). The vegetation stand locations were mapped using ancillary data at finer spatial resolution (14.25 m, Landsat 7 ETM+ mosaic, USGS Earth Resources Observation and Science Center). This kernel size was selected because: (i) it has been previously used to link ground measurements to satellite information (Aguado et al., 2003; Huete et al., 2006; Oldford et al., 2006); (ii) the vegetation patches were sufficiently large to avoid pixel mixing with non-vegetated surrounding areas; and (iii) signals extracted from 3 × 3 windows were less affected by extreme values. Median values of the nine pixels in the kernel were calculated for each 8-day composite of each MODIS spectral index. The median was used because it is less affected by extreme values than the mean (Aguado et al., 2003; Oldford et al., 2006). For each month in the bushfire season, median values were averaged to calculate monthly average (M) values from 2000/01 to 2008/09. A fire history dataset was used to accurately define dates and locations of area burned during the study period. Bushfires occurred mostly across remote areas, affecting a limited number of the selected kernels. Fire affected kernels were excluded from further analyses so as to not erroneously influence the MODIS–SPI relationship analyses. Following the approach of Liu and Negron Juarez (2001), Lotsch et al. (2005) and Saleska et al. (2007), drought-induced anomalies in spectral response were calculated as the departure from the long-term average, standardised by the standard deviation (z-score), to provide a normalised and spatially invariant measure, reducing the influence of spatially varying vegetation type and cover.  Zkxy =

 SIkxy −αkx ; σkx

ð1Þ

where, Zkxy is the z-value for kernel k during time-scale x for year y. SIkxy is the spectral index value for kernel k during time-scale x for year y. αkx is the mean spectral index value for kernel k during time scale x for n years and σkx is the standard deviation of kernel k during time scale x over n years (Saleska et al., 2007). The z-score of each M was calculated for the 2000–2009 time series (n) to produce MZ anomaly values of each kernel during the bushfire seasons. Further, January MZ anomaly values were averaged at all stations each season from 2000 to 2009.

3.3. MODIS and SPI data analyses The analyses were divided into four sections: 1) The averaged 1-month and 6-month SPI values were used to define the severity of the droughts that occurred in the study area from 2000/01 to 2008/09. 2) A multi-temporal analysis of the relationships between MODIS MZ and SPI data was performed using a kernel to weather station paired correlation approach. For each bushfire season, only data from October through January were included in the correlations. February and March observations were excluded because average to above average rainfalls occurred after January during 2002/03 and 2006/07 droughts marking a change towards non-drought conditions (Coughlan et al., 2003; Murphy & Timbal, 2008), and fire danger in the Sydney Basin bioregion is low in February and March due to the onset of rainy weather (Bryant, 2008; Castles, 1995). MODIS MZ and SPI data from all available kernelweather station pairs were pooled. The correlations were analysed at different time-steps using 1-month, 3-month and 6-month SPI values. The correlations between MZ and SPI values were analysed at four different monthly time-lags (Lag + 0, Lag + 1, Lag + 2 and Lag + 3) correlating SPI values of the current month with MZ anomaly values of subsequent b months (b = 0, 1, 2, or 3) to consider possible time-lag between precipitation patterns and vegetation response (Li et al., 2002; Quiring & Ganesh, 2010). 3) The sensitivity of MODIS reflective data to precipitation deficit/ surplus and drought intensity classes was evaluated by analysing the relationship between January MZ and January 6-month SPI values. January 6-month SPI was selected to provide a medium-term estimation of precipitation deficiency/surplus until January and before the onset of wetter months at the end of 2002/03 and 2006/07 droughts. 4) Drought maps were generated for 2001/02, 2002/03 and 2006/07 seasons using January MZ values of the most suitable MODIS spectral index to evaluate their spatial coincidence with the distribution of SPI. For each season, fire affected pixels were excluded from MZ calculation and replaced with data of the closest pre-fire 8-day composites. Areas burned during 2001/02 bushfire season was masked out from 2002/03 drought map.

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4. Results 4.1. SPI-based drought assessment Droughts of different intensity affected the study area from October to January during the 2001/02, 2002/03 and 2006/07 bushfire seasons (Figs. 2 and 3). From October 2001 to January 2002, SPI values remained negative reaching the minimum in January (SPI = − 0.72) indicating mild drought conditions (Fig. 2). After January 2002, SPI increased to positive values. From October 2002 to January 2003, 1-month SPI values ranged from severe to moderate drought conditions (Fig. 2). Starting from January 2003, SPI values increased towards mildly wet conditions (Fig. 2). From October 2006 to January 2007, SPI values ranged from mild to moderate drought conditions (Fig. 2). Similarly to the 2002/03 bushfire season, SPI increased after January 2007 to mildly wet conditions (Fig. 2). The 2000/01, 2003/04, 2004/05, 2005/06, 2007/08 and 2008/09 bushfire season averaged SPI values remained positive indicating mildly wet conditions in those seasons (Fig. 2). January 6-month SPI indicated a clear separation between drought and non-drought affected years (Fig. 3). SPI values were negative in 2001/02, 2002/03 and 2006/07 seasons and positive in all other years (Fig. 3). The SPI value in the 2002/03 season (SPI = −1.88, severe drought) was lower than in 2001/02 (SPI = − 0.77, mild drought) and in 2006/07 seasons (SPI = −1.27, moderate drought) (Fig. 3).

4.2. MODIS–SPI correlation analyses Fig. 4a–h shows the Pearson's correlation coefficients between MZ values and SPI values at three different time-steps (1-month, 3-month and 6-month SPI) and four time-lags (Lag + 0, Lag + 1, Lag + 2 and Lag + 3 months). The correlations were significant (P b 0.01) at all time-steps and time-lags considered (Fig. 4a–h). The strongest correlations were obtained at the 6-month time-step and the weakest at the 1-month time-step across all indices. The best correlations were found for indices using data from the SWIR portion of the spectrum

(Fig. 4a–h). NDIIb6 provided the highest R values at all time-steps and time-lags considered (Fig. 4e). D1640 provided similar R values to NDIIb6 at the 3- and 6-month SPI time-steps, but lower R values at 1-month SPI time step (Fig. 4g). EVI and NDWI provided the lowest R values at all time-steps and time-lags (Fig. 4d and h). VARI outperformed the indices based only on visible-NIR bands (i.e., NDVI, SR and EVI) (Fig. 4a, b, c and d). For each time-step considered, the time-lag had a limited influence on the correlations between MZ and SPI (Fig. 4 a–h). NDVI, NDIIb6 and SR showed only a minor increment in R (b0.03) for correlations with 6-month SPI at lag + 1 (Fig. 4a, c and e). VARI provided the best R values when no time-lag was considered (Fig. 4b). The correlations with NDIIb7 were slightly more influenced by the time-lag (Fig. 4f), with R values increasing by 0.07 at lag + 1 (6-month time-step) and lag + 2 (1-month and 3-month time-step). EVI showed slight increments in R values (b0.05) at lag + 2 (Fig. 4d). NDWI and D1640 showed similar patterns with R slightly increasing (b0.07) for correlations with 3- and 6-month SPI at Lag + 1, and for correlations with 1-month SPI at Lag + 2 (Fig. 4g and h).

4.3. MODIS sensitivity to drought intensity All spectral indices provided a good discrimination between drought and non-drought affected years (Fig. 5a–h). Three SWIRbased spectral indices (i.e., NDIIb6, NDIIb7 and Depth1640) provided stronger correlations with January 6-month SPI and a better discrimination between drought and non-drought observations than NDVI, VARI, SR, EVI and NDWI (Fig. 5a–h). January MZ NDIIb6 showed the strongest correlation (0.77) (Fig. 5e). D1640 provided a slightly lower R value (0.75) not statistically significantly different at P = 0.05 level from the correlation for NDIIb6 (Fig. 5e and g). The 2002/03 and 2006/07 observations clustered in the negative spectral index/SPI domain, separated from non-drought year observations (Fig. 5a–h). Observations in 2001/02 overlapped drought and non-drought domains, as a result of milder water deficit conditions characterising that season (Fig. 5a–h).

Fig. 2. 1-month SPI values averaged at all stations from 2000/01 to 2008/9 bushfire season. 2000/01, 2003/04, 2004/05, 2005/06, 2007/08 and 2008/09 profiles are averaged into one single season (No drought years). Error bars represent the standard error.

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Fig. 3. January 6-month SPI values averaged at all stations for each bushfire season from 2000/01 to 2008/9. Error bars represent the standard error.

January MZ values during non-drought affected years were above the long term mean across all indices considered except for EVI in 2004/05, and NDVI and SR in 2008/09 that showed negative values (Fig. 6a–h). NDIIb6, NDWI, D1640 and VARI showed in 2001/02 lower values than NDVI, SR, NDIIb7 and EVI (Fig. 6a–h). January MZ values in 2002/03 and 2006/07 were negative across all spectral indices and departed substantially from the mean (Fig. 6a–h). NDVI, SR, VARI, NDIIb6, D1640 and NDIIb7 values in 2002/03 were lower than in 2006/07, whilst NDWI and EVI showed lower values in 2006/07 than in 2002/03 (Fig. 6a–h). NDVI showed similar values in 2002/03 and 2006/07 (Fig. 6a). The median values of January MZ for drought classes (D − 4, D − 3, D − 2 and D − 1, Table 1) were lower than the median values for nondrought classes (ND + 1, ND + 2 and ND + 3, Table 1) across all the spectral indices considered (Fig. 7a–h). In the drought domain (D − 4, D − 3, D − 2 and D − 1), median values for all indices (except for NDWI and EVI) decreased constantly as drought conditions became more severe (Fig. 7a–h). NDIIb6 provided the best overall separability between drought (D − 4, D − 3, D − 2 and D − 1) and non-drought (ND + 1, ND + 2 and ND + 3) classes (Fig. 7e). The median values of NDVI, SR, VARI, EVI, NDIIb7, D1640 and NDWI for D − 1 drought class were closer to the long term mean (i.e., zero) than the median value of NDIIb6 (Fig. 7a–h). Further, for NDIIb6, the D − 1 interquartile range displayed less overlap with ND + 1 interquartile range than for the other indices (Fig. 7e). In the drought domain, NDIIb6 interquartile ranges were smaller than for the other indices and overlapped less (Fig. 7e). 4.4. Drought spatial distribution Based on the strong correlations and the optimal discrimination between drought and non-drought classes, NDIIb6 was selected as the best MODIS drought indicator (Fig. 8a–c). Although local discrepancies between SPI and NDIIb6 MZ values were found, the two variables showed similarities in their spatial patterns (Fig. 8a–c). In 2001/02, the NDIIb6 data showed less severe conditions in the north-eastern and southern regions (Fig. 8a). In agreement, SPI values in those regions tend to be higher suggesting less severe rainfall deficiencies were affecting those areas (Fig. 8a). In the north-central areas, the

NDIIb6-based data showed more severe drought conditions and SPI values ranged from 0 to − 1.99, indicating extended mild to severe drought conditions in the area (Fig. 8a). The entire Sydney Basin was affected by drought conditions during 2002/03 bushfire season (Fig. 8b). SPI values in the same season were low throughout the entire region in agreement with NDIIb6 pattern (Fig. 8b). Drought was less severe in 2006/07 (Fig. 8c) affecting mostly the western areas, whilst the north-eastern and eastern areas experienced less severe conditions (Fig. 8c). SPI showed a similar gradient with lower values towards the western areas (Fig. 8c). 5. Discussion The discussion is divided into four sections to address the objectives of this study separately. 5.1. Potential of MODIS for drought monitoring MODIS reflective data can provide useful information for monitoring drought conditions in high biomass ecosystems (Figs. 4a–h, 5a–h, 6a–h). All the spectral indices considered were significantly correlated (P b 0.01) to SPI values at all time-steps and time-lags considered (Fig. 4 a–h). All correlations were positive (Figs. 4a–h and 5a–h), indicating that both greenness and water sensitive indices responded to drought, decreasing under water stress conditions. This is because reduced water availability alters vegetation greenness (e.g., leaf area index) and causes a decline in the leaf moisture content, which are detectable by MODIS (e.g., Gu et al., 2007; Ji & Peters, 2003; Li et al., 2002; Maselli et al., 2003; Pook, 1985; Stone et al., 2005; Wang et al., 2009). 5.2. Comparative performance of MODIS spectral indices Our results clearly show a differential capacity to detect drought in high biomass ecosystem depending on which region of the electromagnetic spectrum (i.e., visible, NIR and SWIR) was used in the indices (Figs. 4a–h, 5a–h and 6a–h). Better results were obtained in the SWIR water absorption region, where three SWIR-based spectral indices (i.e., NDIIb6, NDIIb7 and D1640) demonstrated higher overall performance (Figs. 4a–h, 5a–h and 6a–h). These water-sensitive indices

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Fig. 4. Correlation coefficient values between SPI values and monthly anomaly values (MZ) for (a) NDVI, (b) VARI, (c) SR, (d) EVI, (e) NDIIb6, (f) NDIIb7, (g) D1640 and (h) NDWI. The correlations used October–January observations from all weather stations from 2000/01 to 2008/09 pooled. SPI 1-M = 1-month SPI; SPI 3-M = 3-month SPI; SPI 6-M = 6-month SPI. The best overall R value for each time-step is labelled. All correlations are significat at 0.01 level. n = 1517.

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Fig. 5. Scatter-plots of January monthly anomaly values (MZ) on January 6-month SPI values for (a) NDVI, (b) VARI, (c) SR, (d) EVI, (e) NDIIb6, (f) NDIIb7, (g) D1640 and (h) NDWI. The scatter-plots include observations from all stations from 2000/01 to 2008/09. All correlations are significant at 0.01 level. n = 358.

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Fig. 6. January monthly anomaly values (MZ) for (a) NDVI, (b) VARI, (c) SR, (d) EVI, (e) NDIIb6, (f) NDIIb7, (g) D1640 and (h) NDWI averaged at all stations from 2000/01 to 2008/9. Error bars represent the standard error.

outperformed the greenness sensitive indices using the visible-NIR (i.e., NDVI, SR and EVI) and visible (i.e., VARI) regions of the spectrum. These results suggest that variations in short (i.e., 1-month SPI), seasonal (i.e., 3-month SPI) and medium (i.e., 6-month SPI) term precipitation patterns in forested vegetation have a stronger influence on water status

properties (i.e., fuel moisture content) than greenness properties. This has significant implications for fire risk monitoring as it highlights the importance of using water sensitive indices to monitor the spatial distribution and temporal evolution of forest fuel moisture conditions and flammability.

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Fig. 7. Box-plots of drought classes against January monthly anomaly values (MZ) for (a) NDVI, (b) VARI, (c) SR, (d) EVI, (e) NDIIb6, (f) NDIIb7, (g) D1640 and (h) NDWI. The drought classes are described in Table 1. The box indicates lower quartile, median and upper quartile. The median is indicated by the solid black line within each box. The whiskers show the extreme observations.

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Fig. 8. NDIIb6 anomaly maps based on monthly anomaly values (MZ) of January for (a) 2001/02, (b) 2002/03 and (c) 2006/07 droughts. January 6-month SPI values are represented with graduated symbols. Non-vegetated areas are in light gray. Areas affected by fire in 2001/02 and excluded from 2002/03 map are in dark green in (b). The NDIIb6 legend in (a) applies to (b) and (c), and likewise the January 6-month SPI legend in (b) applies to (a) and (c).

NDIIb6 and D1640 outperformed the other spectral indices considered (Figs. 4a–h, 5a–h and 6a–h), showing the enhanced capacity of the region near the water absorption feature at 1450 nm (i.e., MODIS band 6, 1640 nm) to monitor drought conditions in high biomass ecosystems. NDIIb6 and D1640 provided higher correlations with SPI at all time-steps and time-lags (Fig. 4e and g) and a better discrimination between drought and non-drought affected years (Fig. 5e and g). Strong similarities were also found between drought intensity (Fig. 3) with NDIIb6 and D1640 patterns (Fig. 6e and g). NDIIb6 and D1640 markedly highlighted drought years, recording lower values in 2002/03 (severe drought) than in 2006/07 (moderate drought) and 2001/02 (mild drought), Fig. 6e and g. NDVI, EVI and NDIIb7 did not capture the mild drought conditions of 2001/02 season (Fig. 6a, d and f). EVI and NDWI showed an inverted pattern for 2002/03 and 2006/07 droughts, inconsistent with Fig. 3 (Fig. 6d and h). Their values in 2006/07 were lower than in 2002/03 (Fig. 6d and h). A clear match was found between NDIIb6, D1640 and 6-month SPI patterns for non-drought affected years suggesting a good sensitivity of these spectral indices to precipitation trend also during wet years (Figs. 3, 6e and g). Although NDIIb6 and D1640 performed similarly, NDIIb6 was a more robust indicator in this context and its integration into drought monitoring systems is therefore recommended. NDIIb6 provided the highest correlation coefficients with SPI at all time-steps and time lags considered (Fig. 4e). Varying the time-lag of analysis had an extremely limited influence on the correlations of NDIIb6 with SPI at all timesteps considered (Fig. 4e) but influenced more D1640 especially at 1month and 3-month time-teps (Fig. 4g). This finding is important as it suggests that NDIIb6 provides a quicker response to changes in water availability and has value as a near-real time drought monitoring index. Finally, NDIIb6 provided a better separability between SPI-based drought classes (Table 1 and Fig. 7e). The smaller overlap of the interquartile ranges in the drought domain (i.e., D − 1, D − 2, D − 3 and D − 4) suggests that NDIIb6 is more suitable to map different levels of

drought (Fig. 7e), and drought affected pixels are least likely to be misclassified to a different drought class when using this index. The sensitivity of NDIIb6 to drought conditions agrees with previous findings (Verbesselt et al., 2006). Tucker (1980) suggested that the 1550–1750 nm spectral region was the best-suited for monitoring plant canopy water status. Wang et al. (2009) found NDIIb6 to be related to drought conditions over forests and woodlands in Georgia (USA). Several authors found NDIIb6 to be a suitable spectral index for vegetation moisture monitoring (Chuvieco et al., 2002; Yilmaz et al., 2008). Datt (1999) found SWIR reflectance to be highly correlated with vegetation moisture status in Eucalyptus species in Australia. 5.3. The influence of time-scale on MODIS–SPI correlations The influence of time-lag on the correlations was limited when compared to the effect of time-step on the relationship between MODIS data and SPI (Fig. 4a–h). Six-month SPI provided the strongest correlation across all indices and all time-lags (Fig. 4a–h). R values calculated at the 3-month time-step were markedly higher than values at the 1-month time-step and close to 6-month time-step values (Fig. 4a–h). This is because changes in vegetation greenness and moisture due to drought take time to develop and are cumulative, and spectral indices response is not sufficiently sensitive to 1-month SPI (Ji & Peters, 2003). Australian vegetation is well adapted to cope with drought (Cohen et al., 1997; Williams & Woinarski, 1997), and short dry periods, such as those represented by 1-month SPI, seemingly have only a minor impact on vegetation properties. The extensive forests and woodlands in the Sydney Basin Bioregion are dominated by Eucalyptus species. Eucalyptus trees can extend their roots deep into the subsoil (Farrington et al., 1996), use water resources from deeper soil layers (Cohen et al., 1997; Laclau et al., 2001) and, in some cases, access groundwater (e.g., absence of root-impeding layers) (Benyon et al., 2006). Both deeper soil layers and groundwater are less sensitive to short term precipitation deficiencies (Dimitrakopoulos & Bemmerzouk, 2003). Thus, the spectral

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response of forested vegetation show stronger correlations with longerterm dry periods (i.e., 3- and 6-months SPI) as these time-steps are more related to moisture fluctuations in the deeper soil layers and groundwater (Lloyd-Hughes & Saunders, 2002). Similar results have been found in other studies (Ji & Peters, 2003; Lotsch et al., 2003, 2005). Lotsch et al. (2003, 2005) evaluated NDVI–SPI co-variability over broadscales, and suggested the use of 6-month SPI to analyse the response of terrestrial ecosystems to drought. Ji and Peters (2003) found NDVI to be more strongly correlated with 3-month SPI and suggested the use of that time scale for monitoring drought patterns in the Great Plains (USA). 5.4. MODIS-based drought maps The good spatial agreement between NDIIb6-based drought assessment and SPI (Fig. 8a–c) indicates the potential of this spectral index for drought monitoring at 500 metre resolution. NDIIb6-based drought maps would provide fire authorities with useful information to monitor the spatial distribution of flammable fuels, to classify the landscape into different levels of drought intensity and to assess fire danger at a finer spatial resolution than MIs. If that information were available to fire authorities in near real-time it would allow them to make informed decision regarding fire-fighting resources allocation and prescribed burning planning. Further, NDIIb6-based drought maps could be used to model the influence of drought spatio-temporal pattern on the connectivity of flammable fuel patches across landscapes and to predict the probability of large fires. 6. Conclusion Analysis of a nine-year history of eight spectral indices demonstrated that reflective MODIS-based spectral indices in the visible, NIR and SWIR channels have the capacity to detect drought conditions in a predominantly forested (i.e., high biomass) region. Although all MODIS spectral indices showed significant (Pb 0.01) relationships with SPI, the analysis highlighted important differences in their performance. Three spectral indices (i.e., NDIIb6, NDIIb7 and D1640) in the SWIR water absorption region of the spectrum clearly provided higher overall agreement with SPI, indicating that changes in vegetation water content are more dynamic than changes in greenness properties in high biomass ecosystems. NDIIb6 was found to be the most suitable index to monitor drought conditions because of high temporal and spatial agreement with drought patterns, having the highest overall correlation with SPI at all time-steps and time-lags evaluated. NDIIb6 was only marginally influenced by timelag showing a more rapid response to precipitation deficiency at 1-month, 3-month and 6-month time-steps, and showed potential to discriminate between SPI-based drought severity classes. NDIIb6 outperformed NDVI, the spectral index often used in the current decision-making process for declaring drought affected areas in south-eastern Australia. NDIIb6 therefore has potential for application as an operational tool for drought and fire danger monitoring at a medium-scale of resolution in forested landscapes, and possibly for other vegetation types with high levels of perennial woody plant cover. The capacity of the MODIS-based spectral indices to detect drought is affected by the time-step over which observations are integrated. Access by roots to water at deep levels in the soil means that the capacity to detect short-term precipitation deficiencies (i.e., 1-month SPI) is limited. Longer dry spells (i.e., 3 and 6-month SPI) are more detectable because they deplete these deeper storages of water. Reflective remote sensing is therefore more suited for monitoring the effects of seasonal and medium term droughts on similar ecosystems. Finally, NDIIb6-based maps of drought showed similarities with the spatial distribution of SPI during drought. Therefore reflective remote sensing has the potential to be used as a spatially explicit method for ongoing monitoring of fire risk across large temporal and spatial scales in forests and other vegetation types with dense perennial plant cover.

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Acknowledgements This research was conducted as part of a Ph.D. at the University of Wollongong (UoW) with a University Postgraduate Award scholarship. This study has been supported by resources and facilities provided by the School of Earth and Environmental Sciences and the Spatial Analyses Laboratories at UoW. The research was supported by a grant from the GeoQuEST Research Centre, UoW. The authors would also like to thank Dr A. R. Huete for his valuable comments and suggestions. The authors are indebted to the three anonymous reviewers for their comments that greatly improved the quality of this work.

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