Exploiting three dimensional vegetation structure to map wildland extent

Remote Sensing of Environment 123 (2012) 155–162

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Exploiting three dimensional vegetation structure to map wildland extent Glenn J. Newnham a, c, d,⁎, Anders S. Siggins a, Raphaele M. Blanchi b, c, d, Darius S. Culvenor a, Justin E. Leonard b, c, d, John S. Mashford a a

CSIRO Land and Water, Private Bag 10, Clayton South, VIC 3169, Australia CSIRO Ecosystem Sciences, PO Box 56, Highett, VIC 3190, Australia CSIRO Climate Adaptation Flagship, GPO Box 2583 Brisbane QLD 4101, Australia d Bushfire Cooperative Research Centre, 340 Albert Street, Melbourne, VIC 3002, Australia b c

a r t i c l e

i n f o

Article history: Received 19 September 2011 Received in revised form 23 February 2012 Accepted 25 February 2012 Available online 17 April 2012 Keywords: Forest extent Managed vegetation Morphology Urban interface Fire Risk Hazard

a b s t r a c t Wildland and wilderness refer to areas of land which have been subject to little or no modification by human activity. These areas are important due to their role as wildlife habitats, the contributions they make to air and water quality and for human recreation. However, the intermingling of wildland and homes also increases the risk to life and property through wildfires. Management of this risk requires current and detailed knowledge of the spatial extent of wildland. What constitutes wildland vegetation is often difficult to define and may be influenced by both the horizontal continuity and vertical structure. We present a method to map wildland vegetation based on a combination of a vertically stratified cover threshold and spatial morphology. To test its practical application, the method was applied to airborne lidar data collected prior to a major wildfire that occurred in Australia in 2009. Distance between the lidar defined wildland extent and homes impacted by the fire was assessed and compared to previously published data using manual delineation of wildland extent. Results showed that the proportion of homes destroyed at the wildland boundary was greater than reported in previous fires and that there was an exponential decline in the proportion of homes destroyed as a function of distances to wildland. Although the method is objective the extent of wildland depends on the parameters which define thresholds of cover and lateral extent and connectivity. This highlights the need for a clear definition of wildland that can be used to determine extent using objective methods such as those described, whether this is in the context of quantifying wildfire vulnerability or other related applications such as ecological assessment and monitoring. © 2012 Elsevier Inc. All rights reserved.

1. Introduction A designation of wildland indicates a lack of human management of the environment, where natural ecological processes dominate (Fenton, 1996). Wildland typically infers a certain density of vegetation and continuity across the landscape (Stewart et al., 2007). This brings with it an increased risk of fire. The wildland–urban interface (WUI) is an area where homes and built infrastructure meet or intermingle with unmanaged wildland vegetation (Lampin-Maillet et al., 2010; Radeloff et al., 2005). Fires that impact the WUI often result in loss of life and the destruction of homes (Haynes et al., 2010; Mell et al., 2010). This is of great concern to many national and regional governments, who have tasked fire management agencies with developing methods to quantify risk in the WUI a priori as a basis for fuel reduction and other fire preparedness measures.

⁎ Corresponding author at: Private Bag 10, Clayton South, Vic., 3169, Australia. Tel.: + 61 3 9545 2234. E-mail address: [email protected] (G.J. Newnham). 0034-4257/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2012.02.026

Studies have shown that within an area impacted by fire the number of homes destroyed is strongly related to the distance from wildland vegetation. Ahern and Chladil (1999) defined the wildland extent by manually delineating aerial photography. The locations of burnt homes were then recorded using post fire field surveys and manual image interrogation. Chen and McAneney (2004) used a similar approach, employing a combination of high resolution IKONOS-2 and Quickbird satellite imagery and aerial photography to manually delineate the wildland boundary. Their analysis considered the location of both burnt and unburnt homes within the fire affected zone, allowing them to examine the proportion of homes burnt. They found a linear relationship between this proportion and the distance to wildland vegetation in the two fire events examined. Despite these studies, there are difficulties presented by the manual delineation of continuous unmanaged vegetation. In the WUI, vegetation often exists as a patchwork of isolated forest blocks. Homes may also exist in small clearings and forest gaps. This complexity in spatial structure and vagueness in the definition of wildland may lead to significant variations in manual delineation of wildland. Lampin-Maillet et al. (2010) defined WUI typologies in southeastern France as a method of characterising fire risk. They mapped

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the extent of vegetation using a supervised classification of 2.5 m resolution pan-sharpened SPOT 5 images. They characterised the spatial continuity of this vegetation using the aggregation index (Turner, 1990). The WUI typology was then determined using the level of aggregation as a measure of the exposure of homes to wildland vegetation. The limitation of this approach is that the true distance from individual homes to wildland is lost and the relationship between vulnerability and wildland proximity becomes less clear. Lowell et al. (2009) present a semi-automated method for assessing the forest extent relative to the location of homes using an unsupervised classification of aerial photographs. As with Lampin-Maillet et al. (2010), they were only able to determine the spatial density of forest after degrading the original high resolution data to 10 m. They suggested that finer resolution mapping of forest extent was required in order to determine the true distance from homes to wildland vegetation. Chen and McAneney (2010) assessed the distance from homes to burnt vegetation for fires at Kinglake and Marysville in Australia using an automated classification of post fire 15 cm resolution aerial photography. Although this provides a resolution appropriate for assessing distance to wildland fuel, the method is limited to post fire analysis of spectrally distinct burnt vegetation and cannot be used as a management tool for pre-fire assessment of wildfire risk. Airborne lidar is another source of high spatial resolution data suitable for fire fuel mapping (Loos & Niemann, 2006; Mutlu et al., 2008), or assessing forest density and forest type (Antonarakis et al., 2008; Hall et al., 2005) and post-fire damage to vegetation (Wang & Glenn, 2009). However, previous studies that have used airborne lidar to characterise wildland fuel have focussed on the vertical structure of vegetation and have not considered the extent and spatial continuity, properties which are important in both the definition of wildland and in assessing the propagation of fire across the landscape. In this paper we present a method that employs airborne lidar data and morphological image processing to determine the extent of continuous unmanaged wildland vegetation. The method uses a simple set of parameters which describe vertical structure and spatial continuity and is applied to airborne lidar data flown over the region of Kinglake in Australia, which was impacted by a major wildfire in February 2009. This fire burned approximately 180,000 ha and resulted in 120 fatalities and the destruction of an estimated 1244 homes (Victorian Bushfires Royal Commission, 2010). The relationship between the distance from homes to wildland vegetation and the proportion of homes destroyed is explored and compared to previous studies where manual delineation of wildland extent was employed (Ahern & Chladil, 1999; Chen & McAneney, 2004). 2. Methods Due to the scale of the Australian fires in 2009, a major government inquiry was conducted to investigate the causes of each fire, the preparedness of the community and the responses of fire management agencies. Fire hazard, as specified by the McArthur (1967) Forest Fire Danger Index (FFDI), which is based on temperature, humidity, wind speed and a fuel dryness index (Keetch & Byram, 1968), was higher than for any of the fires studied by Chen and McAneney (2004) or Ahern and Chladil (1999) (Table 1). As part of this enquiry the location Table 1 The number of homes destroyed and the Forest Fire Danger Index (McArthur, 1967) for the Kinglake fire were both higher than the previous fires at Duffy and Como-Jannali (Chen & McAneney, 2004), and the Otway Ranges and Hobart (Ahern & Chladil, 1999). Fire

Date

Homes burnt

FFDI

Kinglake Duffy Como-Jannali Otway Ranges Hobart

Jan 2009 Jan 2003 Jan 1994 Feb 1983 Feb 1967

1241 206 76 648 370

180 105 50 150 96

and level of damage of all homes within the Kinglake region were recorded. Of the 3656 homes recorded within the fire extent, 34% (1241 homes) were destroyed by the fire. 2.1. Lidar data processing Prior to the Kinglake fire, during the period from November 2007 to January 2008, lidar data were collected over the Kinglake region by a fixed wing aircraft using an Optech ALTM3100EA. The primary purpose of the acquisition was for forest management. The flying height for the acquisition was a nominal 1300 m with an average point density of 1.12 pulses per square metre. The extent of the lidar acquisition relative to the Kinglake fire extent is shown in Fig. 1. Lidar returns were classified by the data provider as either ground or non-ground. Ground returns were used to produce a two metre spatial resolution digital elevation model (DEM) using inverse distance weighting for all returns within each grid cell. All lidar returns (ground and non-ground) were then corrected to height above terrain. Considering the point density of the data (nominally 1.12 laser pulses per metre squared) all corrected returns within a five by five metre square centred on each two metre pixel (approximately 28 returns per pixel) were considered in the calculation of a two metre spatial resolution vertically projected woody vegetation cover fraction C. This was calculated in a similar way to the method described by Lovell et al. (2003) using the equation:
 N zj > 2
 C¼ N zj

ð1Þ

where N (z j > 2) is the number of lidar returns more than 2 m above the DEM within the five by five metre window centred at pixel j and N (z j) is the total number of lidar returns in the same search window. A binary vegetation extent layer was then produced based on a cover threshold of C > 0.2 (see example output in Fig. 2). This method is motivated by the definition of woody vegetation used in Australian Montreal Process reporting (Montreal Process Implementation Group for Australia, 2008), where the minimum height of 2 m and minimum overstorey cover fraction of 20% are specified according to the classification system of Specht (1970). 2.2. Image morphology The rural properties and farmland in the Kinglake region include many small isolated groups of trees and linear vegetation wind breaks. Although any vegetation can have an impact on fire behaviour, it is generally assumed that suppression in these managed areas can be performed safely and effectively by land holders and fire fighters (Gill & Stephens, 2009). For this reason, the distance to wildland, as opposed to any woody vegetation, is an important factor in assessing the risk posed to homes by wildfire. Exclusion of managed vegetation from a map of wildland extent is difficult using standard area and shape based processing methods. This is because there is often spatial continuity between unmanaged and managed vegetation. For example, thin managed corridors of vegetation often follow the boundaries of fields and paddocks, and along roads and tracks, connecting unmanaged wildland to managed vegetation surrounding homes. Morphological image processing (Haralick et al., 1987; Serra, 1982) was applied to the binary map of woody vegetation extent to remove these linear features as specified by: A∘B·B ¼ ðððA⊖BÞ⊕BÞ⊕BÞ⊖B

ð2Þ

where the woody vegetation extent represented by the binary image A is eroded (⊖) and dilated (⊕) by the structuring element B (see Fig. 4 for examples of outputs from this processing). This process tends to isolate

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Fig. 1. The extent of the February 2009 Kinglake fire is shown by the black outline. Lidar based estimates of vegetation cover within the fire affected area are shown as a grey scale image. Homes within the affected area are marked and colour coded to show the level of damage that occurred during the fire.

small vegetation patches which can then be removed using a simple area threshold. The process also fills small gaps within the wildland boundary. This was required in order to be consistent with the definition of wildland extent used in previous studies where small gaps within the forest were not considered.

The shape and orientation of managed vegetation features in the WUI are generally not predictable. In this case a spherical structuring element (B) is a reasonable choice, with a diameter just greater than the minimum cross-section of the objects that need to be excluded. As there is no legislative basis in Australia to define a minimum width of

Fig. 2. One of the four 800 m by 800 m subset regions of Kinglake used to calibrate the morphological image processing parameters; a) true colour aerial photo; b) lidar based woody vegetation extent prior to morphology based classification of wildland and managed vegetation.

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linear feature that should be considered managed vegetation, we applied a calibration procedure using four image subsets As ⊂ A with n rows and m columns. These subsets contained representative sample areas we considered managed vegetation and areas we considered unmanaged wildland. A training set Ts was produced by removing elements that were visually assessed as being managed vegetation in As. The error ε(φ,δ) from the following objective function was then minimised by varying the structuring element diameter φ and the minimum area for a contiguous block of vegetation δ: n P m
P

εðφ; δÞ ¼

i¼1 j¼1

aij ðφ; δÞ−t ij

2

nm

 100

ð3Þ

where a(φ,δ) : {1, …, n}× {1, …, m}→ {0,1} is the binary image element corresponding to As∘B(φ,δ)·B(φ,δ) and t : {1, …, n}× {1, …, m} → {0,1} is the binary image element corresponding to Ts. Let φmin and δmin be the value for the structuring element diameter and minimum contiguous area at which the minimum in ε(φ,δ) is achieved. Morphological processing using B(φmin,δmin) can then applied to the full image A to produce a map of wildland extent. For each home, a radial search was used to determine the distance from the home centroid to the edge of the nearest wildland forest pixel. Ahern and Chladil (1999) presented percentiles of houses destroyed as a function of distance from the wildland boundary in fires in the Otway Ranges, to the West of Melbourne in 1983 and in Hobart Tasmania in 1967. Later, Chen and McAneney (2004) provided the same analyses of data for fires in the Canberra suburb of Duffy in 2003 and Como-Jannali near Sydney in 1994. We compare the percentiles of distances for destroyed homes at Kinglake using the lidar based distances to wildland and assess the characteristics of these percentiles against the percentile curves produced for these historical fires. Using distance to wildland for both burnt and unburnt homes, Chen and McAneney (2004) calculated the proportion of homes burnt within 50 m intervals from wildland for the Duffy and ComoJannali fires (e.g. 0 m to 50 m, 50 m to 100 m, etc.). Recently they provided an assessment of the proportion of homes burnt at Kinglake in 2009 at 10 m intervals from any burnt fuel (Chen & McAneney, 2010). However, due to a decreasing number of homes with increasing distance to burnt fuel, proportions were only calculated out to a maximum distance of 50 m. Our approach is to determine the proportion of homes destroyed in a continuous manner by selecting a constant number of homes N(s) closest to a distance s from wildland fuel, such that: P ðd; sÞ ¼

NðsjdÞ NðsÞ

ð4Þ

where the N (s|d) is the number of homes in the sample N (s) that were destroyed. This approach (as shown in Fig. 6) allows a more detailed assessment of the relationship between distance to wildland and the proportion of destroyed homes. It also makes a conservative estimate of the proportion of homes destroyed at very large distances where the sample size using the Chen and McAneney (2004) approach would be small. The value of N (s) has a smoothing effect on the function and can be set to balance high frequency variations while maintaining the low frequency trend in the destroyed proportions curve.

System (NCAS) based on Landsat Thematic Mapper data (Furby, 2002). However, the total area of woody vegetation produced using the lidar data was 62,000 ha, while the Landsat based estimate was 51,000 ha. This was due primarily to the inclusion of small patches of vegetation such as wind breaks and isolated trees within areas of pasture and surrounding rural homes, which are deliberately ignored by NCAS. Due to the lower resolution for Landsat, the NCAS forest boundary is significantly simplified relative to the lidar based estimates, resulting in generally balanced errors of omission and commission at the edges of the NCAS wildland extent. The subset of the Kinglake region shown in Fig. 2a and b illustrates the well defined wildland boundary produced using the lidar data. Isolated individual trees, small clumps of woodland and wind breaks along the boundary of paddocks were all delineated accurately. The cover threshold method also produces artefacts at the edge of homes and other buildings. The two metre window used to define the DEM adapts rapidly to changes in elevation using the inverse distance method, effectively defining rooves of buildings as the DEM surface. However, where the five metre window partially overlaps the edge of buildings, those lidar returns which hit the building roof are misclassified as vegetation. This leads to an apparent outline of vegetation at the edge of each building. This characteristic has also been noted by Goodwin et al. (2009) in their study of urban vegetation. In addition to managed vegetation, these building outlines can be eliminated using the same morphological image processing used to remove isolated managed vegetation as described by Eq. (2). Four calibration images were produced via manual image editing to remove woody vegetation not associated with continuous wildland. The original subsets were then processed according to Eq. (2) using a range of structuring element diameters and area thresholds and suitable parameters selected on the basis of minimising Eq. (3). Graphs of the error as a function of structuring element diameter and minimum area for wildland fuel are shown in Fig. 3. A minimum error over all four subset images was found when the spherical structuring element diameter was set to 22 m (11 pixels) and the minimum area retained as wildland fuel was set to 0.4 ha. It is clear from Fig. 3 that the morphological processing is highly sensitive to the definition of the minimum cross section, which dictates the structuring element diameter, but less sensitive to the minimum area threshold. The best fit wildland extent image for each of the four subsets is shown in Fig. 4, where wildland fuel is shown in white and managed interface fuel is shown in grey. The morphological processing is shown to exclude isolated trees, artefacts surrounding buildings and linear vegetation features along the boundary of fields and along tracks. 3.2. Distance from homes to wildland Of the 1241 homes recorded as destroyed in the Kinglake fire, 923 fell within the extent of the 2007–08 lidar acquisition. A further 361

3. Results 3.1. Mapping wildland extent The pattern of woody vegetation mapped using the lidar cover threshold method (Eq. 1) and prior to applying wildland classification (using morphological processing), agreed closely to the map of woody vegetation extent used for the Australian National Carbon Accounting

Fig. 3. Error as defined in Eq. (3) as a function of the structuring element diameter (diameter in pixels, equivalent to radius in metres) for a range of minimum wildland areas.

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homes within this region were recorded as receiving minor damage and 963 undamaged. The location of each of these 2247 homes is shown in Fig. 1. The distance to wildland was computed for each house using a radial search in the map of wildland extent. Summary statistics for each of the three damage classes are shown in Table 2. All 2116 homes that fell within the lidar extent were less than 535 m from the forest edge, reflecting the natural landscapes within which the population of Kinglake live. A Tukey honest significant difference test (Tukey, 1949) was used to determine separability in the distribution of distance to forest for the three damage classes (destroyed, minor damage and undamaged). While the undamaged class was statistically separable from both minor and destroyed classes (p b 0.01), the difference between the distance distributions for minor damage and destroyed was not significant (p = 0.31). Some homes in the minor damage class only received small ember induced external scorching while other homes were in the direct path of the fire and were only saved due to active suppression by owners and fire fighters. Due to the ambiguity of the class, only those homes that were either destroyed or undamaged were considered in terms of their relationship to distance to wildland fuel. Ahern and Chladil (1999) presented the cumulative distribution (percentiles) of the distance to wildland vegetation for two fires; the Otway Ranges to the west of Melbourne in 1983 and Hobart in the southern Australian state of Tasmania in 1967. The locations of burnt

159

homes were derived from post fire surveys and distance from wildland was determined using manual measurements from monochrome aerial photographs. The Otway Ranges fire began inland and travelled south, then south west, impacting a number of small coastal towns. The percentiles of distance for the 649 homes recorded as destroyed in this fire (Fig. 5) indicate that only 15 homes (1.7%) were within 2.5 m of the wildland boundary. The distance to wildland is skewed towards shorter distances with a median of 19 m and a mean of 45 m. The largest recorded distance to wildland for a destroyed house was 430 m. The Hobart fire occurred in the forested hilly terrain on the northern edge of the city in 1967. The destroyed homes tended to be along rural roads and at the wildland interface (Ahern & Chladil, 1999). Distances were generally larger than those in the Otway Ranges fire but were again heavily skewed towards low values, with a median of 39 m and a mean of 61 m. The plot of percentiles in Fig. 5 shows that the tails of the distribution are similar to those of the Otway Ranges fire but for the majority of the distribution there were a greater proportion of homes destroyed at shorter distance from the wildland boundary in the Otway Ranges than in Hobart. Chen and McAneney (2004) studied the percentiles of distance to wildland vegetation for homes destroyed in two Australian fires at Duffy in Canberra and Como-Jannali near Sydney (Fig. 5). In these cases the delineation of wildland extent was performed using manual interpretation of high resolution Quickbird and IKONOS-2 satellite

Fig. 4. Four 800 m by 800 m subset regions of Kinglake used to calibrate the morphological image processing parameters. Black indicates no woody vegetation, grey is managed fuel and white is continuous unmanaged wildland.

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Table 2 Summary of distance (m) to wildland for each of the three levels of damage used to classify homes in after the Kinglake fire (IQR refers to the interquartile range). Damage level

N

Min

Median

IQR

Max

Destroyed Minor damage Undamaged

923 361 963

0 0 0

14.68 18.97 36.96

34.35 33.96 62.74

333.93 352.65 534.42

20

40

60

80

Kinglake N = 100 Kinglake Como-Jannali Duffy

0

Homes Destroyed (%)

100

images. The Como-Jannali fire occurred when a small forest fire impacted the suburbs to the south of Sydney. The fire was primarily slope driven and was contained to riverside bushland. Ember attack on the suburbs of Como West and Jannali led to approximately 101 homes being destroyed. The distance percentiles showed a greater separation between wildland and homes burnt with the smallest distance to wildland for a burnt home being 5 m and the 10th percentile occurring at 10 m. The maximum distance to a destroyed home was approximately 210 m. The Duffy fire occurred when a major wildfire impacted this western suburb in the Australian capital city of Canberra in 2003. All percentiles of the distance distribution shown in Fig. 5 are greater than for the previous fires studied. This can be attributed to an area of grassland and a suburban road that the fire needed to traverse before reaching the suburban zone. As an evacuation of the area was enforced, house to house ignitions played a significant role in the spread of the fire into the suburban area (Blanchi et al., 2006), further increasing the distance from wildland at which homes were impacted. Percentiles for homes destroyed at Kinglake are shown by the black line in Fig. 5. Of the 923 homes destroyed within the extent of the lidar acquisition, 262 (28%) were within or immediately adjacent to wildland vegetation and 383 homes (41%) within 10 m. The maximum distance of a destroyed home from wildland vegetation was 334 m. All percentiles of the distribution occurred at smaller distances than in the fires studied by Ahern and Chladil (1999) and Chen and McAneney (2004). This may be more an indication of the natural setting of homes in the Kinglake area than a characteristic of the fire event. The results are a contrast to those for the Otway Ranges where homes were in small seaside towns and in Hobart, Como-Jannali and Duffy where the majority of destroyed homes were in residential developments at the urban fringe. Using the location of both destroyed and undamaged homes, Chen and McAneney (2004) were able to show the proportion of homes destroyed in 50 m increments from wildland for both the Como-Jannali and Duffy fires (Fig. 6). They found that the relationship between proportion burnt and distance to wildland was close to linear for both fires. Within the first 50 m from the wildland boundary, 60% of

homes in Duffy and 57% of homes in Como-Jannali were destroyed by the fires. For Como-Jannali, this proportion fell rapidly, with only 28% of homes destroyed between 50 and 100 m from wildland. The proportion of homes destroyed in the Duffy fire showed a more gradual decline, and it was not until the distance increment between 200 and 250 m before the proportion of homes destroyed was less than half the initial level. The proportion of homes destroyed at Kinglake estimated at 50 m increments is shown in Fig. 6. The proportion destroyed fall between those of the Como-Jannali and Duffy fires within the range from 0 to 100 m and could be considered adequately approximated by a linear relationship. However, from 250 to 350 m the proportion of homes destroyed increases before falling to zero in the range from 350 to 400 m. This highlights an issue of sample size that should be considered. In the 0 to 50 m range, the calculation is based on 984 homes. In contrast, the proportion in the range from 250 to 300 m is based on only 15 homes. It is possible that the calculation of proportion at larger distances to the wildland boundary is less stable due to this small sample size, while detail regarding changes in the proportion destroyed close to the wildland boundary is lost. The proportion of homes destroyed was also computed using the alternative approach suggested in Eq. (4). The distance increment in these calculations was set to 1 m and the resulting estimates are shown in Fig. 6. The value of N in Eq. (4) was set to 100 homes, which provided a qualitative balance between reducing noise in the proportion destroyed curve while maintaining the trend shown using lower sample numbers. The initial 100 homes with the smallest distance to wildland all fell within 2 m of the wildland boundary. Of these, 92% were destroyed. The proportion destroyed fell below 50% at a range of 30 m and stabilised at a distance of 254 m at 12%. Due to the skew in the distribution of distances towards low values the final stable proportion is the most sensitive characteristic of the curve to the value set for N. For example, the final value of 12% is based on homes with distance from forest ranging from 182 m to 534 m. Setting N = 50 samples results in a final value of 13% at 303 m and if N = 200, the value stabilises at 19% at 119 m. Clearly the larger the value for N selected, the more conservative the estimate of the apparent threat to homes at these larger distances. The proportion of homes destroyed at Kinglake, calculated at 50 m increments, appear to conform to the values calculated using Eq. (4), particularly within the first 250 m from wildland. However, the line showing continuous proportions with distance does not appear to exhibit a linear relationship as described by Chen and McAneney (2004). Particularly in the initial 75 m, the proportion of homes destroyed appears to show exponential decline with distance from wildland. This same characteristic has been noted recently by Chen

0

100

200

300

400

Distance to Wildland (m) Fig. 5. The cumulative percentage of homes destroyed relative to the lidar based distances to wildland for the Kinglake region, relative to data published by Ahern and Chladil (1999) for fires in the Otway Ranges and Hobart, and by Chen and McAneney (2004) for fires at Duffy and Como-Jannali.

Fig. 6. The proportion of homes burnt in the Kinglake fire as a function of the lidar based distance from wildland and compared to values published by Chen and McAneney (2004) for the Como-Jannali and Duffy fires. Both the discrete method at 50 m increments (green points) and the continuous method proposed in Eq. (4) (grey line) are shown.

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and McAneney (2010) using discrete proportions of homes destroyed at 10 m intervals from burnt fuel.

4. Discussion The distance from homes destroyed by wildfire to unmanaged wildland as a cumulative distribution of percentiles (Fig. 5) has been used to characterise wildfire risk in Australia (Ahern & Chladil, 1999; Chen & McAneney, 2004). This method requires information about only burnt homes within the area impacted by the fire, which minimises resourcing of post fire surveys. However, the lack of some form of normalisation for the spatial density of homes in an area may limit interpretation regarding the impact of the fire and the risk that similar fires might pose to homes. Results suggest that one of the most useful features of these percentile curves may be the maximum distance at which a home was destroyed. Other characteristics of the distribution are influenced by both the pattern of development in the area and the impact of the fire event, which cannot be deconvolved. For example, patterns of development in agricultural communities, in heavily forested land or at the urban fringe have very different initial distributions for the distance from homes to wildland. This initial distribution is likely to have an impact on percentile graphs for destroyed properties after a fire. Analysis of the proportion of homes destroyed as a function of distance to forest, as presented by Chen and McAneney (2004), is a less ambiguous way to analyse the impact of wildland on the potential risk of wildfires. Fig. 6 not only shows the maximum distance at which a significant proportion of homes were impacted by the fire, but also gives a clear indication of the cumulative effect of the factors which influenced wildfire risk given the particular fire condition present on the day. This analysis cannot stand alone as a tool for determining the risk posed by wildfires to homes, but it does provide useful insights into the integrated effect of a number of factors which influence the destruction of homes during a fire. When coupled with information about fire characteristics, building design, and suppression activities, it may provide useful insights for the development of guidelines of vegetation clearing around homes, for prescribed fuel reduction burning and as a basis for more detailed risk models. The linear relationships between the proportion of destroyed homes and the distance to wildland fuel, as described by Chen and McAneney (2004) may be misleading at sparsely populated tails of the distance to wildland distribution. Proportion loss calculated at 50 m increments for the Kinglake fire showed a similar linear pattern to the Como-Jannali and Duffy fires in the first 250 m. However, beyond this range the proportion is based on a maximum of 15 homes. There is a danger that this small sample size may give an impression which understates the risk to homes at these distances. We have proposed a method to compute proportions based on a set sample size of 100 homes. This produces a continuous function with a similar result to the method used by Chen and McAneney (2004) where the number of homes at a given distance is consistent. In the tail of the distribution it has been shown to provide a conservative indication of the proportion of homes destroyed, which may be more useful in modelling wildfire risk. It is clear that the methods proposed for analysis of the relationship between distance to wildland and the proportion of homes destroyed need to be tested under a range of fire conditions, landscapes and development patterns if we are to build an understanding of the interactions between the many factors which influence wildfire risk. Distance to wildland is obviously a crucial parameter. However, fire intensity and direction, the vertical structure and dryness of fuels, building designs and access for suppression are other factors which have an impact on risk. An unambiguous definition of wildland which includes both vertical and spatial structure, and an automated repeatable method to apply this definition in the mapping of wildland are key steps towards building this understanding.

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The method proposed for determining distance to wildland vegetation using airborne lidar and morphological image processing is both efficient and repeatable. The method can only be implemented if suitable lidar data is available but with the many applications of airborne lidar in facilities management, hydrology and vegetation surveys, the frequency and spatial coverage of these surveys are rapidly increasing. Flexibility exists in setting the size of the structuring element for erosion and dilation operations. The approach demonstrated using a subset image for calibration provides one method to set an optimum structuring element diameter based on a subjective understanding of wildland extent. However, this was primarily designed to allow conformity between our results and previous studies that used manual delineation. An ideal approach would be to use a standard definition of the minimum cross-section length and a minimum area for wildland. In our case we have effectively set this minimum cross-section length to 22 m and the area to 0.4 ha. However, in future it may be better to incorporate these specifications in regulations such as those which dictate what should and what should not be considered wildland fuel (e.g. Standards Australia, 2009). The application of the described wildland mapping method to a comparison of the impact of different fire events is only one example of its utility. Physically based models such as the calculation of radiant heat exposure (Cohen & Butler, 1996) and ember density (Ellis, 2003) both require input of distances to unmanaged fuel. An ability to efficiently map the extent and distribution of wildland at the regional scale also has immediate application in environmental reporting, particularly where broad scale techniques such as those used by NCAS need regional scale calibration.

5. Conclusions The distance from homes to wildland is one factor contributing to the risk of destruction of the home in the event of a wildfire. There is a need for efficient and repeatable methods to determine this distance in order to quantify risk a priori and assist in targeting management activities such as fuel reduction burns and the efficient allocation of fire suppression resources. In such cases the implications of misinforming risk assessments through inaccurate estimates of the distance from homes to wildland fuel are significant. We have presented a method to efficiently define wildland extent at high resolution using a combination of airborne lidar and morphological image processing. The method uses physically based parameters describing both the vertical and spatial structure of vegetation which could easily be incorporated into standard definitions of what constitutes wildland from a wildfire management perspective. The method was used to assess the proportion of homes destroyed as a function of distance to wildland during a fire in Kinglake, Australia, in February 2009. A near linear relationship between distance to wildland vegetation and the proportion of homes destroyed was shown to exist over the first 250 m from wildland when the proportion was analysed at discrete 50 m intervals. This is similar to the results shown for other fires by Ahern and Chladil (1999) and Chen and McAneney (2004). However, when analysed using a continuous function of distance, the results showed an exponential drop in the proportion of homes destroyed as a function of the distance to wildland fuel. The results of this study are not sufficient to predict the risk posed by a wildfire to homes in future fire events. However, the method presented provides an objective way to define the wildland extent based on parameters which define the three dimensional structure of the vegetation. The method can be implemented over large areas without the need for local calibration of parameters. Although this requires the availability of lidar data, the continued increase in the number and extent of lidar surveys being carried out, for purposes ranging from urban planning to environmental reporting, is making the mapping of wildland extent based on these data feasible.

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