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International Journal of Wildland Fire 2013, 22, 121–129 http://dx.doi.org/10.1071/WF11165

Satellite-based comparison of fire intensity and smoke plumes from prescribed fires and wildfires in south-eastern Australia Grant J. Williamson A,E, Owen F. Price B, Sarah B. Henderson C and David M. J. S. Bowman D A

School of Plant Science, University of Tasmania, Private Bag 55, Hobart, Tas. 7001, Australia. School of Biological Sciences, University of Wollongong, Wollongong, NSW 2522, Australia. Email: [email protected] C Environmental Health Services, British Columbia Centre for Disease Control, 655 West 12th Avenue, Vancouver, BC, V5Z 4R4, Canada. Email: [email protected] D School of Plant Science, University of Tasmania, Australia. Email: [email protected] E Corresponding author. Email: [email protected] B

Abstract. Smoke pollution from wildfires can adversely affect human health, and there is uncertainty about the amount of smoke pollution caused by prescribed v. wildfires, a problem demanding a landscape perspective given that air quality monitoring is sparse outside of urban airsheds. The primary objective was to assess differences in fire intensity and smoke plume area between prescribed fires and wildfires around Melbourne and Sydney, Australia. We matched thermal anomaly satellite data to databases of fires in forests surrounding both cities. For each matched fire we determined hotspot count and quantified their intensity using the fire radiative power (FRP) measurement. Smoke plumes were mapped using MODIS true colour images. Wildfires had more extreme fire intensity values than did prescribed burns and the mean size of wildfire plumes was six times greater than of prescribed fire plumes for both cities. Statistical modelling showed that the horizontal area covered by smoke plumes could be predicted by hotspot count and sum of FRP, with differences between cities and fire type. Smoke plumes from both fire types reached both urban areas, and particulate pollution was higher on days affected by smoke plumes. Our results suggested that prescribed fires produced smaller smoke plume areas than did wildfires in two different flammable landscapes. Smoke plume and FRP data, combined with air pollution data from static monitors, can be used to improve smoke management for human health. Additional keywords: biomass smoke pollution, eucalypt forest, fire ecology, fire management, landscape ecology, MODIS. Received 21 November 2011, accepted 14 June 2012, published online 31 August 2012

Introduction Australian eucalypt forests are renowned for episodic conflagrations that destroy lives and property. The reduction of fuel loads by imposition of a regime of frequent low intensity fires set under moderate to low fire danger conditions (prescribed fires) is a widely accepted approach to reducing the intensity of wildfires and protecting lives and property (McArthur 1966; Gill et al. 1987; Fernandes and Botelho 2003). The 2009 Victorian Bushfires Royal Commission Final Report (Teague et al. 2010) found that prescribed burning in that state had been insufficient to reduce wildfire risk, and recommended ‘a longterm, robust prescribed burning program’ be implemented, with experts suggesting a minimum of 5% of public lands need to be burnt each year to markedly reduce the severity and frequency of wildfires. Yet, controversy surrounds the use of prescribed fires given concerns about their negative effects on biodiversity Journal compilation Ó IAWF 2013

(Morrison et al. 1996; Whelan 2002), negative perceptions of fires in the community (Jasper 1999) and resulting human exposure to smoke pollution (Bowman and Johnston 2005). Smoke is a by-product of all landscape fires, with reported adverse effects to health in Australia ranging from symptoms such as eye irritation and headaches (Dennekamp and Abramson 2011), exacerbations of pre-existing respiratory diseases (Johnston et al. 2002, 2007; Bowman and Johnston 2005; Morgan et al. 2010) to premature mortality (Johnston et al. 2011). Guidelines and regulations to control air pollution have become important constraints on the use of prescribed fire in many flammable landscapes worldwide (Koperberg 2001; Johnston et al. 2010). Managers are held responsible for air pollution caused by prescribed fires, yet smoke emissions from wildfires are accepted as being beyond control and, in the case of large fires, simply another facet of the natural disaster. There www.publish.csiro.au/journals/ijwf

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remains uncertainty regarding the relative effects on health of smoke produced by prescribed fires compared with smoke produced by wildfires (Bell and Adams 2009). Indeed, their relative smoke contributions have not been quantified. To a large degree these uncertainties reflect a lack of basic data. For example, most urban airsheds in Australia have made hourly measurements of atmospheric particulate matter (PM) for several decades, providing a temporally resolved record of pollution events, and capturing bushfire smoke when it is transported to these areas (Johnston et al. 2010). However, the primary role of these sensors is to monitor background regional air pollution from industrial and transportation sources. Therefore these systems do not necessarily detect localised smoke pollution from small fires near the urban interface, or large fires occurring far from city centres (Johnston et al. 2011). Given the anticipated expansion of prescribed burning programs, comparison of the smoke generated by prescribed fires and wildfires must involve a landscape-scale perspective and a broad cross-section of fire events. The widespread availability of remote sensing data and geographic information systems (GIS) means that we have the tools necessary to begin addressing these questions in a systematic manner. Fire management agencies are increasingly building GIS databases that record the date and spatial extent of prescribed fires and wildfires. For example, the Australian states of Victoria and New South Wales (NSW) have maintained long-term collections of digital maps outlining the spatial extent of prescribed fires and wildfires and recording their duration. With respect to remote sensing, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites overpass most parts of the globe four times daily, and can thus provide regular snapshots of landscape fire activity (Justice et al. 1998; Giglio et al. 2003). The MODIS true colour images show the extent of smoke plumes at horizontal resolutions up to 250 m. The thermal anomaly (‘hotspot’) data provide information about fire location (with a horizontal resolution of 1 km2 at nadir) and extent. Another attribute of these data is the fire radiative power (FRP) measurement, indicating the energy output by each detected hotspot in megawatts (Ichoku and Kaufman 2005; Wooster et al. 2005). The FRP has been associated with the aerosol emissions from fires (Wooster et al. 2005; Ichoku et al. 2008; Henderson et al. 2010). These data allow us to associate fire history records (prescribed or wild) with mapped smoke plumes and MODISdetected hotspots and, in turn, to statistically relate plume size to fire intensity as estimated by FRP values. Here, we use these data to test four hypotheses relating prescribed fires and wildfires to smoke emissions in the forests surrounding Melbourne and Sydney, Australia: H1) Prescribed fires are smaller than wildfires, and burn with lower intensity. H2) Smoke plumes from prescribed fires cover smaller areas than those from wildfires. H3) Smoke plumes from both prescribed fires and wildfires reach urban airsheds. H4) Mapped smoke plumes visible on satellite imagery can be associated with elevated particulate matter concentrations in urban airsheds.

G. J. Williamson et al.

Methods Melbourne and Sydney are coastal cities in south-eastern Australia, both with populations exceeding 3 million people. They are the capitals of Victoria and NSW. Sydney is surrounded on three sides by extensive wet and dry subtropical eucalypt forests between 20 and 50 km from the city centre, although dry forest is more common. Melbourne has extensive tracts of wet and dry temperate eucalypt forests to the north and east, and more distant tracts of native vegetation scattered throughout the remainder of the state. The wet forests support infrequent (.50-year intervals), high intensity fires (.5000 kW1 m2, McCarthy et al. 1999) whereas the dry forests are burnt more frequently (10 to 30-year intervals) at lower intensities (.1000 kW1 m2, Cheney 1981). Prescribed burning to date also has been generally restricted to dry forests. These cities were chosen for this study because they support large populations, are subject to frequent seasonal prescribed fires and wildfire in their surrounds, and have spatial fire databases spanning the entire study period. Thus, the two cities act as proxies for differences in fire regime, particularly as a comparison between temperate and subtropical eucalypt forest systems. Data sources Digital maps of prescribed fires and wildfires that occurred in Victoria between January 2002 and May 2009 (covering an area of ,300 000 km2) were obtained from a state-wide database (Victorian Department of Sustainability and Environment, unpubl. data). The fire perimeters were digitised from aerial photographs, with a spatial accuracy of 100 m or less. Each fire in the database had a defined start date, but no end date. For the purpose of these analyses we assumed that each fire could burn for a maximum of 14 days. Examination of sequences of fire records and hotspot days showed this was a reasonable cut-off point because those that burned for longer periods were represented by separate polygons in the database. Data preparation for Sydney proceeded slightly differently owing to differences in the database provided by the NSW government. Digital maps of prescribed fires and wildfires that occurred in a ,250-km radius around Sydney between January 2002 and May 2009 (covering an area of ,50 000 km2) were obtained from a state-wide database (NSW Office of Environment and Heritage, unpubl. data). The fire perimeters were drawn onto 1 : 25 000 topographic maps by fire managers and then digitised, with a spatial accuracy of 100 m or less. Each fire in the database had defined start and end dates, although some manual corrections of end dates were required (miscoding of month or year, for example). Australia-wide thermal anomaly (‘hotspot’) data from MODIS were obtained for the period of January 2002 through December 2009 from NASA’s Fire Information for Resource Management System (FIRMS – http://maps.geog.umd.edu/ firms/, accessed 1 June 2012). The MODIS instruments aboard NASA’s Aqua and Terra satellites overpass the entire globe at ,0130, 1030, 1330 and 2230 hours local time. We obtained all hotspot data from the 1030 and 1330 overpasses. All records were imported into the ArcGIS 10 (Environmental Systems Research Institute, Redlands, CA) geographic information system, and hotspots within the Melbourne and Sydney fire areas

Prescribed fire and wildfire smoke plumes in Australia


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hotspots, nor could all hotspots be matched to database fires. Unmatched database fires and MODIS hotspots were discarded from subsequent analyses. For each matched database fire, hotspot count (the number of hotspots on the fire line), and sum of FRP (representing the total intensity of the fire line) were calculated from each MODIS snapshot. In subsequent statistical analyses we treated these values for each fire in each snapshot dataset as independent observations. The smoke plume mapping was performed only on days for which hotspots were matched to database fires. Fire plumes could not be seen clearly in true colour images on cloudy days, so not all fire lines matched to the fire history database could be associated with traced plumes. Each traceable plume was matched to a fire in the fire history database and categorised as either prescribed or wildfire.

0

5

10

20

30

40 km

Fig. 1. Example of smoke plume extent drawn on a MODIS Aqua image from a prescribed burn in eastern Victoria on April 20, 2009. Hotspots from other fires on this day but without tracing of smoke plumes are also shown. Red squares indicate fire hotspots as detected by MODIS, blue line indicates boundary of plume associated with mapped fire.

were selected and retained. Each hotspot point was then converted into an ellipse with dimensions equalling the scan and track distances of its underlying pixel. Pixel sizes ranged from 1 to 9.7 km2, with an average of 1.92 km2. True colour images (1-km spatial resolution) were obtained from the MODIS Rapid Response system (http://lance.nasa. gov/imagery/rapid-response/, accessed 1 June 2012) for the purpose of mapping smoke plumes. Both Aqua and Terra daytime images were examined, but Aqua images proved most useful. Visible smoke plumes emitted from the fire line were traced in GIS by a single person, to the extent where plume opacity was still discernable from the dark vegetation background (Fig. 1). The horizontal area of each traced plume was calculated. Each plume was considered to be an independent case in subsequent analyses, so it was possible for a multi-day fire to contribute different plumes on different days. Daily pollution records (the concentration of particulate matter less than 2.5 mm in diameter (PM2.5) for 2002–09 were obtained from monitoring stations in each of the cities. For Sydney the monitor is at Richmond (338370 4.800 S, 1508440 45.600 E, NSW Department of Environment and Climate Change, http://www.environment.nsw.gov.au/AQMS, accessed 1 June 2012) and for Melbourne it is at Footscray (378480 S, 1448530 56.400 E, Victorian Environmental Protection Authority). Matching database fires to MODIS hotspots and smoke plumes For each MODIS snapshot, we attempted to match each hotspot to a fire in the Melbourne or Sydney fire databases. A hotspot was considered ‘matched’ to a database fire when its ellipse was contained within or intersected by the fire perimeter, and when it was detected between the start and end dates in the fire database. Not all fires in the GIS databases could be matched to MODIS

Data analysis We prepared summary statistics of fire size for prescribed fires and wildfires for all fires in the Melbourne and Sydney GIS databases. We plotted the monthly distribution of fire sizes for both fire types and for each city. For the subset of fires that could be matched to MODIS hotspots, we compared hotspot count and summed FRP for prescribed fires and wildfires around each city using log-log plots and a local regression (LOESS) smoothing function (addressing hypothesis H1). We plotted the distribution of smoke plume area, calculated the average area of smoke plumes and determined the area ratio of wildfire to prescribed fire plumes (addressing hypothesis H2). Generalised linear models (with a Gaussian distribution and log link) were used to quantify the relationship between smoke plume area and city (unmeasured environmental factors), fire type (prescribed or wildfire), summed FRP (total intensity of the fire) and hotspot count (also addressing hypothesis H2). We tested four combinations of predictor terms, and used Akaike’s Information Criteria (Akaike 1974) corrected for small sample size (AICc), and model weighting to compare models. Relative FRP per total burnt area for different fire types and cities was determined by dividing the summed FRP of all hotspots associated with a fire over its lifetime by the area of the fire polygon. Comparisons in FRP per area were made between wildfires and prescribed fires in the two cities using Kolmogorov–Smirnov tests. Smoke plumes were overlaid to produce a map of the number of plumes that reached the Melbourne and Sydney airsheds (addressing hypothesis H3). The PM2.5 concentrations in Melbourne and Sydney on days when smoke plumes intersected these airsheds were statistically compared with the long-term average of PM2.5 concentrations using a t-test on logtransformed data (addressing hypothesis H4). Results Both Melbourne and Sydney showed high concentrations of prescribed burns in autumn and, to a lesser degree, in spring. Wildfires, in contrast, were concentrated in the summer months, extending into autumn in the Melbourne study area (Fig. 2). There was considerable overlap in the area of individual prescribed fires and wildfires, but wildfires burnt the largest areas (Fig. 2, Table 1). There were 90 days found with concurrent

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Prescribed

Wildfire

Melbourne

6

4

2

Area (log10 ha)

8

0 8

Sydney

6

4

2

Sum

Aut

Win

Spr

Sum Sum

J F M A M J J A S O N D

Aut

Win

Spr

Sum

0

J F M A M J J A S O N D

Month Fig. 2. Seasonal distribution and size of prescribed fires and wildfires, derived from the fire history databases for environs around Melbourne and Sydney. Prescribed fires are most common in the autumn and spring months, whereas wildfires reach their peak in summer, extending into autumn in Melbourne.

Table 1. The number of, and area burnt by, prescribed fires and wildfires in the Melbourne and Sydney study regions The percentage and number that could be associated with MODIS hotspot detections, their interquartile range and the difference between the lowest and highest 25% of data points (IQR) are shown. A Wilcoxon rank-sum test was used to test for differences in area burnt of matched and unmatched fires for each region Variable Fire type in database (number) % matched to MODIS hotspots (number) Area burnt by all fires (ha) Area burnt by matched fires (ha) (percentage of total) Median (and IQR) area burned of matched fires (ha) Median (and IQR) area burned of non-matched fires (ha) Significant difference between matched and unmatched fires

Melbourne Prescribed (4876) 19.0 (928) 1 068 938 316 838 (29.6%) 76.5 (261) 20.80 (105.0) P , 0.01

prescribed fires and wildfires in Melbourne and 54 in Sydney, based on the fire polygon records. With the restricted dataset containing only days for which we were able to match hotspots, there were 13 days with concurrent prescribed fires and wildfires for Melbourne, and 1 day for Sydney. Of the 7884 mapped fires in the combined Melbourne and Sydney databases 15 and 19% could be matched to MODIS hotspots. These matched fires were significantly (P , 0.01) larger than the unmatched fires, owing to the lower limit of resolution for hotspot detection (Table 1) and the temporal limits of satellite data availability, as small

Wild (1428) 17.9 (257) 3 562 775 3 345 672 (94.0%) 49.2 (600) 5.19 (22.6) P , 0.01

Sydney Prescribed (626) 15.3 (96) 58 100 26 380 (45.4%) 53.3 (310) 2.91 (15.9) P , 0.01

Wild (954) 16.5 (157) 490 124 471 295 (96.2%) 103.0 (856) 0.85 (5.1) P , 0.01

fires were more likely to be extinguished before hotspots were detected. Although there was consequently a lower limit of fire sizes we were able to include, the analyses did represent fire sizes covering several orders of magnitude. Of the matched fires, a total of 498 (34.6%) could be associated with traced plumes, including a higher proportion of the fires in Sydney (41.5%) than in Melbourne (33%) (Table 2). Wildfires caused more extensive smoke plumes than did prescribed fires for both cities, with the most extensive smoke plumes occurring from wildfires around Melbourne



125

Table 2. Number, mean and summed area of smoke plumes from prescribed fires and wildfires that could be associated with MODIS hotspot detections for the Melbourne and Sydney study regions Melbourne

Number of smoke plumes associated with matched fires (%) Mean actual area (s.d.) of smoke plume (ha) Sum of the areas of smoke plumes (ha)

Sydney

Prescribed

Wild

Prescribed

Wild

232/928 (25.0%) 2747 (1432) 412 218

161/257 (62.6%) 16 492 (1637) 2 836 754

45/96 (46.9%) 644 (474) 13 945

60/157 (38.2%) 3973 (2069) 181 273

Prescribed

Wildfire 150

Melbourne

100

50

0

150

Sydney

100

50

0 0

1

2

3

4

5

6

0

1

2

3

4

5

6

Plume area (log10 ha) Fig. 3. Histograms of smoke plume area for prescribed fires and wildfires in Melbourne and Sydney. The y-axis represents the number of plumes, and x-axis represents plume area. Smoke plume area extends to larger sizes for wildfires than for prescribed fires, and maximum plume size was greater in environs around Melbourne than in Sydney.

(Fig. 3, Table 2). The ratio of mean absolute plume area (measured by footprint visible on satellite imagery) between wildfire and prescribed fire was 6.0 for Melbourne and 6.2 for Sydney (unlike the models below, this ratio is not adjusted for fire sizes). For each MODIS snapshot, the hotspot count on the fire line showed a strong log-log relationship to the summed FRP of those hotspots, and patterns were consistent between Melbourne and Sydney and between prescribed fires and wildfires (Fig. 4).

Although prescribed fires and wildfires are similarly aligned in this relationship (suggesting they belong to the same statistical population), many wildfires had much higher hotspot counts and summed FRP values than did any prescribed fires. These wildfires were both larger and burned more intensely than prescribed fires. The best generalised linear model of smoke plume area included fire type (prescribed or wildfire), city (unmeasured environmental factors), and an interaction between summed FRP and hotspot count as the predictive variables. This model

126



explained 43.2% of the deviance and received all the statistical support (Wt ¼ 1.00) (Table 3). The model confirmed that wildfire plumes are significantly larger than prescribed fire plumes, with the average plume area for wildfires being 6.17(e1.82) times as large as the average plume area for prescribed fires, a similar ratio as was found for total plume area attributed to each fire type. The interaction term showed that plume area increased with increasing sum of FRP when the fire line hotspot count was small, but decreased with increasing sum of FRP for fires with more hotspots on the fire line (Fig. 5). Analyses on the FRP-per-area-burned over the life of each fire showed that the intensity of wildfires per area was ,2.5 times greater than that of prescribed fires for both Melbourne Prescribed

Wildfire

Melbourne

3 2 1

Hotspot number (log10)

4

0 4

Sydney

3 2 1 0 0

1

2

3

4

5

6 0

1

2

3

4

5

6

Sum FRP (log10) Fig. 4. Scatter plot of daily sum of fire radiative power (FRP) versus hotspot count for prescribed fires and wildfires in Melbourne and Sydney, with local regression (LOESS) smoothing function for all data shown. Prescribed fires and wildfires form a continuum in fire intensity, with overlap between the two fire types, although wildfires have higher intensity and greater extent.

and Sydney, despite the difference in absolute intensity between the cities (Table 4). The airsheds of both Melbourne and Sydney were affected by smoke plumes of prescribed fires and wildfires (Table 5), though prescribed fire plumes represented less than 4% of all plumes in the regions. Of the 683 smoke plumes in Victoria, 2% intersected the Melbourne airshed. Of the 133 smoke plumes from the smaller part of NSW sampled, 15% intersected the Sydney airshed (Fig. 6). The PM2.5 concentrations were significantly (P , 0.01) higher on days when plumes intersected the urban airsheds compared with background concentration days for Melbourne (Table 5). Discussion We have demonstrated new methods to explore the implications of prescribed burning for fire management, which is practiced in many countries with landscapes that burn on a regular basis. The MODIS FRP measurement is known to be associated with aerosol emissions (Ichoku and Kaufman 2005; Wooster et al. 2005), and we have shown how it can be used to evaluate the effects of prescribed fires, in terms of fire intensity and plume extent. Fire plumes can be delineated from MODIS imagery to provide further information about the localised effect of prescribed fires on airsheds. Combining remote sensing data on fires and smoke with air pollution data from static monitors can improve smoke management and our understanding about how fire management regimes affect human health. Fire activity is frequent in the landscapes surrounding Melbourne and Sydney as a consequence of prescribed burning programs in the autumn and episodic wildfires in the summer. The MODIS FRP data suggest that wildfires are more intense around Melbourne than around Sydney. This may reflect the fact that the forests around Melbourne have a higher proportion of wet forest compared with Sydney, where conditions are dryer. A review of 39 studies of maximum forest litter loads in NSW and Victoria suggested that loads are higher in wet forests than in dry forests (mean 30% higher) and slightly higher in Victoria than in NSW (mean 8.5% higher) (P. Watson, pers comm.). Indeed, the Melbourne region includes forests with the highest carbon density in the world when mature (Keith et al. 2009). A comparison of fire weather among stations within 100 km of Sydney (11 stations) and Melbourne (19 stations) suggests that their extreme weather is comparable and is probably not

Table 3. Results of generalised linear models for predicting fire plume area using MODIS fire radiative power (SumFRP), MODIS hotspot count (Count), city and fire type Reported values are the number of parameters (K), corrected AIC (AICc), delta-AICc (DAICc), model weight (Wt), log-likelihood (LL) and percentage deviance explained (%DE). The Wt column indicates the relative support of a model compared with the others in the set, based on differences in AICc scores. In this case, the model 1 is the clear winner with ,100% support. In the equation, binary factors are indicated in brackets, for example, if the city is Sydney this parameter has a value of 1, otherwise it is zero. The equation for the best model is log(Area) ¼ 7.6 þ (2.15 105) SumFRP þ (6 103) CountFRP 1.45 City[Sydney] þ 1.82 Type[Wildfire] (7.3 108) SumFRP CountFRP Model

K

AICc

DAICc

Wt

LL

%DE

1. Area , SumFRP Count þ City þ Type 2. Area , SumFRP þ Count þ City þ Type 3. Area , Count þ City þ Type 4. Area , SumFRP þ City þ Type 5. Area , 1 (Null model)

7 6 5 5 2

1598 1631 1634 1703 1822

0.0 33.0 36.8 105.7 224.4

,1.000 ,0.001 ,0.001 ,0.001 ,0.001

791.7 809.2 812.2 846.6 909.0

43.2 38.2 37.3 26.0 0.0



responsible for the difference in intensity (Bureau of Meteorology data). The 1-in-100 day Forest Fire Danger Index (FFDI) for Melbourne is 35.3 and for Sydney is 39.0 where 50 is classed as Very High Fire Danger. The reported difference in fire intensity is reflected by the incidence of destructive wildfires in the two regions: of the eight most deadly fire disasters in Australian history, six were in Victoria and only one in NSW (Ellis et al. 2004). Although wildfires have more hotspots with high FRP values (especially when associated with geographically large fire events), our data show that prescribed fires and wildfires are not two distinct types, but rather form a statistical continuum with respect to size, intensity, and plume area. We found that the variation in both fire intensity (as measured by FRP) and fire size (as estimated by hotspot count) affects the production of smoke from both prescribed fires and wildfires in a non-linear way. Fig. 5 shows that increasing total fire intensity was associated with increasing plume size for fires with low hotspot count. However, for fires with a high hotspot count (and therefore larger size), increasing total fire intensity was negatively

Plume area (ha)

150 000

100 000 High hotspot count (large fire)

50 000

Low hotspot count (small fire)

127

associated with plume size, likely due to many low intensity hotspots on the flanks of the fire front, contributing more to the summed FRP than to the actual emissions. We acknowledge several limitations to this work. We were only able to use a small proportion of the state agency-based fire perimeter data given limitations of the MODIS hotspot and smoke plume datasets (Table 1). The Aqua and Terra satellite overpasses occur four times per day resulting in an absence of MODIS data for many short-lived prescribed or wildfires, and an incomplete record of the variation in fire intensity for most fires. Remote sensing data can only provide snapshots of fire activity smoke plumes, and cannot account for time lags between actual fire activity and resultant smoke plumes. Nonetheless, our best model was quite strong (43% deviance explained), suggesting that these lag effects were overcome by our methods for the types of fires (typically quite large) that were analysed. Indeed, using a small subset of the data from the 1030 Terra overpass only (i.e. a 3-h lag between hotspot detection and plume delineation) did not improve the explanatory power of our models. Cloud cover compromised our visual assessment of smoke plumes. Automatic classification methods have been developed to discriminate smoke from cloud in MODIS imagery (Wan et al. 2011), and we attempted to apply these methods to our imagery, but found manual plume identification more accurate in the Australian context. Seasonal weather patterns affect cloud cover, and given that prescribed fires and wildfires occur in different seasons this is a potential bias in the dataset. Finally, we only measured the horizontal spatial extent of smoke plumes and did not consider their height (i.e. the total volume of smoke). Nonetheless, we validated our smoke plume mapping by demonstrating that urban particulate air pollution was higher compared with background concentrations when smoke plumes were mapped over the urban airsheds. These results provide confidence in the reliability of our smoke plume mapping, although a larger sample size is required to undertake more statistical analyses.

0 0

10 000 20 000 30 000 40 000 50 000 60 000 70 000

Sum FRP Fig. 5. Modelled interaction effect of summed fire radiative power (FRP) and hotspot count on smoke plume area. The ‘high hotspot count’ (large fires) was calculated for 700 hotspots per fire, and the low hotspot count (small fires) was calculated for 100 hotspots per fire. Plume area increases with total fire intensity for small fires, but decreases with total fire intensity for fires with higher hotspot counts (large fires), a relationship probably driven by the presence of low intensity hotspots behind the fire front.

Table 5. Mean and interquartile range (IQR) of particulate matter ,2.5 lm in diameter (PM2.5) concentration in the Melbourne and Sydney airsheds for days when prescribed or wildfire plumes were identified over the urban areas Background levels, representing PM2.5 from non-smoke sources such as dust and industry, based on the average of all daily measurements between January 2002 and May 2009 are shown. A t-test was used to compare logtransformed PM2.5 concentration data for days when smoke plumes were in the airsheds Mean and interquartile range PM2.5 (mg m3) (number of days)

Table 4. Fire radiative power (FRP) per area comparisons between wildfires and prescribed fires, including wildfire : prescribed ratio and Kolmogorov]Smirnov (KS) test Median Median W : Ratio prescribed wildfire FRP per area FRP per area Melbourne Sydney

1.160 0.334

2.960 0.862

2.55 2.58

KS test

Background Prescribed plumes

Wildfire plumes D ¼ 0.306, P , 0.0001 D ¼ 0.306, P ¼ 0.0002

Melbourne

Sydney

5.86 (2922) IQR ¼ 4.05 14.66 (4) P ¼ 0.41 IQR ¼ 19.38 27.95 (12) P , 0.01 IQR ¼ 17.60

9.77 (2922) IQR ¼ 5.2 11.60 (8) P ¼ 0.09 IQR ¼ 2.02 11.60 (12) P ¼ 0.10 IQR ¼ 5.03

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Number of plumes 0 1–2 3–5 6–7 8–10 11–12 13–15 16–18 19–22 23–26

250 km

250 km

Fig. 6. Maps of cumulative smoke plume coverage from prescribed fires and wildfires for Sydney and Melbourne from January 2002 until May 2009. Melbourne plumes were mapped for fires across the entire state of Victoria, whereas Sydney plumes were mapped from fires in the Sydney and Blue Mountains area.

Our finding that prescribed burning produces smaller smoke plumes is important because of plans to increase the amount of prescribed burning in Victoria, and recent reports (Price and Bradstock 2011) suggesting that prescribed burning is required in three hectares of forest to prevent one hectare of wildfire in the Sydney area. We report that, on average, plumes from wildfires are six times larger than plumes from prescribed fires. As such, our results suggest that the increased area burnt by prescribed fires could be partially offset by reduced smoke from those fires. If conducted under existing smoke management best-practice (such as lofting smoke away from settled areas) there should be no increase in the long-term public exposure to biomass smoke from prescribed than from wildfires. Nonetheless, our results also show that prescribed burning does occasionally pollute urban airsheds (Fig. 6). An important consideration is that wildfires cause acute exposures to very high concentrations of particulates whereas prescribed fires cause more regular exposure to lower concentrations of particulate pollution. The relative risks of these contrasting exposures remain uncertain, but our results suggest that prescribed burning (1) produces smaller smoke plumes, (2) pollutes urban airsheds less frequently and hence (3) may result in lower population exposures to smoke-related pollution. If so, the relative harms of wildfires are greater than the risks of prescribed burning to communities in affected airsheds. Given the limitations of this study we suggest that improved monitoring systems are required to better understand the smoke produced by wildfires and prescribed fires.

Acknowledgements The study was funded by an Australian Research Council linkage grant (LP0882048), and O. F. Price was supported by the Rural Fire Service of NSW. The fire mapping data were provided by Victoria Department of Environment and Sustainability and NSW Office of Environment and Heritage.

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