spatial fire history analysis in the gnangara sustainability strategy study area · 2015-09-21 ·...

SPATIAL FIRE HISTORY ANALYSIS IN THE

GNANGARA SUSTAINABILITY STRATEGY

STUDY AREA

Tracy Sonneman and Janine Kuehs

Department of Environment and Conservation

December 2010

Spatial fire history analysis in the Gnangara Sustainability Strategy study area

Report for the Gnangara Sustainability Strategy and the Department of Environment and

Conservation

Tracy Sonneman and Janine Kuehs

Gnangara Sustainability Strategy Taskforce Department of Water 168 St Georges Terrace Perth Western Australia 6000 Telephone +61 8 6364 7600 Facsimile +61 8 6364 7601 www.gnangara.water.wa.gov.au © Government of Western Australia 2010 December 2010 This work is copyright. You may download, display, print and reproduce this material in unaltered form only (retaining this notice) for your personal, non-commercial use or use within your organisation. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. Requests and inquiries concerning reproduction and rights should be addressed to the Department of Conservation and Environment. This document has been commissioned/produced as part of the Gnangara Sustainability Strategy (GSS). The GSS is a State Government initiative which aims to provide a framework for a whole of government approach to address land use and water planning issues associated with the Gnangara groundwater system. For more information go to www.gnangara.water.wa.gov.au Acknowledgments The Department of Environment and Conservation – Gnangara Sustainability Strategy would like to thank the following for their contribution to this publication: Brian Inglis, Mike Cantelo, Leigh Sage and other Swan District personnel. Fire Management Services staff including Claudia Marchhart and Li Shu. Jane Mansergh and Rob Towers from Swan Region and Katherine Zdunic from the Geographic Information Services division.

Government of Western Australia Department of Environment and Conservation

Gnangara Sustainability Strategy

Spatial Fire Analysis 3

Table of Contents

Introduction ........................................................................................................................... 4

Aim and objectives ................................................................................................................ 5

Study Parameters ................................................................................................................... 7

Collation of Data Sources...................................................................................................... 8

Remote Sensing Data ...................................................................................................... 11

Updating fire records ........................................................................................................... 12

Updating Procedures ....................................................................................................... 13

Level of updating achieved.............................................................................................. 14

Time taken for updating and analysis.............................................................................. 15

Fire History Analysis........................................................................................................... 15

Annual burn trends .......................................................................................................... 16

Fuel Age distribution as at 2009/2010............................................................................. 19

Patchiness and Fire intensity ........................................................................................... 22

Fire frequency and interval patterns ................................................................................ 24

Discussion............................................................................................................................ 28

Conclusions ......................................................................................................................... 31

Recommendations ............................................................................................................... 32

References ........................................................................................................................... 33

Appendix 1. Case study - Vegetation and fire patterns ....................................................... 35

Appendix 2. Case study – DEC Nature Reserves................................................................ 38



Introduction

Recommendation 15 of the draft Gnangara Sustainability Strategy (GSS) released by the

State Government in 2009 was “On completion of the fire regime trial (July 2010) the

optimum fire regime that will maximise groundwater recharge, while maintaining the

system’s biodiversity values, be implemented”. Modification of the fire regime by

increased burning on Crown land has been proposed as a cost effective option to enhance

water yield on the Gnangara groundwater system (GGS) (Canci 2005; Yesertener 2007).

However, prior to the application of any increased burn frequency, the biodiversity

consequences of burning needs to be understood and the water yield and biodiversity

balance quantified. A number of projects have been undertaken by the Department of

Environment and Conservation (DEC) and CSIRO to assess the impacts of fire on

biodiversity (Mickle et al. 2010a; Mickle et al. 2009; Mickle et al. 2010b; Sonneman et al.

2010; Valentine 2010; Valentine et al. 2009; Wilson et al. 2010a) and water yields

(Silberstein et al. 2010).

This study aims to determine the fire history and current distribution of fire ages within the

GSS study area using a landscape scale assessment of fire history. The study area is

defined by the Gnangara groundwater system on the Swan Coastal Plain, within south-west

Western Australia (Figure 1). Analysis was conducted on the study area as a whole but

primarily focused on the DEC-managed land as defined by Sonneman and Brown (2008).

Operationally, the Gnangara system forms part of the DEC Swan Coastal District.

It was decided to update the fire history records to increase the accuracy of the fuel age

layer in the DEC Geographic Information System (GIS), particularly as errors were

observed in the corporate fire datasets.



Aim and objectives

The aim for this project was to update the fire history in the DEC corporate fire history

information system and, thus, to provide a more accurate basis for analyses of the fire

history, the current fire regime and fire frequency for the GSS study area (Figure 1).

This report outlines the processes undertaken to update and improve the spatial fire history

data. The project was designed with the following main objectives:

• to locate and collate historic primary data sources that can be used to check and

update DEC corporate fire records;

• to update the fire records back to earliest available primary data sources within

given time constraints; and

• to analyse the resulting updated fire history record in terms of

a. annual burn trends

b. fuel age distribution

c. frequency of fire occurrences and intervals

d. fire intensity and patchiness

The resulting updated and analysed fire history has been used to inform several concurrent

GSS fire projects, including Impacts of fire on biodiversity of the Gnangara groundwater

system (Wilson et al. 2010a) and Guidelines for ecological burning regimes for the

Gnangara groundwater system (Wilson et al. 2010b).



Figure 1. Location of Gnangara Groundwater System or Gnangara Sustainability Strategy study area

showing remnant vegetation (DAFWA 2006). The darker green indicates land managed by DEC. No

fire analysis was performed for the 23,000 hectares of pine plantation (grey).



Study Parameters

The fire project initially aimed to update all records of all fires for the GGS area for every

year between 1971 and 2009, for which data was available. A pilot study based on three

fire seasons was undertaken to assess the availability of data and the time required to

update a full year. The fire seasons initially updated included 2002/03, 2005/06, and

2008/09. Each fire season was checked against all other available records of fires for that

season, and updated to ensure all fires were spatially and temporally correct and had the

correct fire information associated with it. This pilot study determined that the time

required to complete this task in detail exceeded the time available for this project. Time

constraints meant that focus was shifted to the most vital updates for further analysis.

For this reason, it was decided that only fire records occurring outside the pine plantation

(and thus predominantly in remnant vegetation) that were larger than two hectares would

be checked.

It was decided to focus on fires in remnant vegetation and exclude those in pine plantations

in order to contribute to further studies on the impact of fires on biodiversity in remnant

vegetation. In addition, records for prescribed burns were limited and those within pine

plantations were often very hard to confirm with Landsat imagery, one of the few reliable

tools available for detecting and confirming fires. It was found that some prescribed burns

recorded on operational maps were not carried out in a given fire season, if at all. This

decreased the confidence of spatial data containing prescribed burns in pine plantation

areas therefore all pine plantation fires were ignored.

The decision to ignore fires less than two hectares was due to poor accuracy in terms of

both location and area. This decision applied to both wildfires and prescribed burns.

According to the spatial procedures for recording wildfires created by the Fire

Management Services (FMS) branch of DEC, any fire less than two hectares is spatially

displayed as approximately a two hectare shape due to the inherent error in recording the

fire’s location. The majority of these small fires were 0.1 hectare car fires or rubbish heap

fires. Thus, only fires larger than two hectares (excluding those in pine compartments)

were checked for this project against DEC records and Landsat imagery.



Where remnant vegetation patches were within pine plantation fires, it was decided to

excise these areas and include them in the final data. Although pine plantation fires were

ignored, small remnant vegetation patches within the pine plantations that may have been

burnt during prescribed burn or wildfire were included where possible. There will be errors

with these pine remnant vegetation patches as the associated pine burn was not checked

and therefore data was not be verified. Errors could include incorrect overlap of fires,

patches that apparently burn every year and digitising errors. Initial recording errors may

also have occurred on the operational plans. For more technical information on the

methodology used contact the authors.

Fire intensity was also not examined during analyses of fire history for the GGS.

Differences are known to exist in intensity between wildfires and prescribed burns

however, due to time constraints, these have not been analysed here. Fire intensity could be

estimated by type of fire, season of burn and ‘patchiness’.

Collation of Data Sources

A variety of data was collected to update and check the current fire history records, or

Annual Fire Event Dataset (AFED). Individual fire reports, annual fire summary reports

and many operational maps were accessed directly through the DEC Swan Coastal District

office at Wanneroo. These records covered the majority of the most recent decade as well

as the late 1990s (Table 1). Some fire reports (for years older than 1986) were accessed

though the DEC central archive system for historic records storage.

Fire reports are generally a one or two page, hand written document detailing the fire

situation. Operational maps are created annually (and historically by hand) by district fire

personnel to spatially map each wildfire and prescribed burn for a given season. Some

electronic copies of historic operational maps were also available from FMS for the period

1970/71 to 1994/95.

Other data sources accessed though FMS included a shapefile of wildfire ignition points

for 13 years between the 1989/90 and 2002/03 fire seasons. FMS extracts from the online

reporting system included details for each fire attended by district fire personnel. Historical



microfiche records retained by FMS were also accessed for earlier years. These records

contained historic fire reports and annual wildfire summaries from the 1970s, and 1980s.

Detail in the microfiche records were limited as not all fires were recorded, and often fires

were recorded without location or area information. Details occasionally conflicted with

corresponding handwritten notes in individual wildfire reports.

Data from the sources listed above was not available for every year. A greater range of

data was available for more recent fire seasons (1999/00 – 2008/09) than was available for

older seasons. Table 1 shows a matrix summarising the availability of the various data

sources for each fire season. Not all sources were utilised due to time constraints.

Fires managed jointly by DEC and the Fire and Emergency Services Authority (FESA)

were recorded by DEC but often with very limited information. FESA was not contacted

for their fire records.



Table 1. Matrix showing fire seasons from 1970/1971 to 2008/2009 and the data sources available for

the fire history update. WF: wildfire, PB: Prescribed Burn.

Fire Season AFED1 Ops2

Maps

Micro-fiche3 maps

Micro- fiche3

records

Annual 4 WF

Summary

WF 5 Reports

FMS Extract6

WF

Annual4

PB Summary

FMS Extract6

PB

FMS Ignition Points7

2008/2009 + + + + + + + 2007/2008 + + + + + + + 2006/2007 + + + + + + + 2005/2006 + + + + + + + 2004/2005 + + + + + + + 2003/2004 + + + + + + + 2002/2003 + + + + + + + + 2001/2002 + + + + + + + 2000/2001 + + + + + + 1999/2000 + + + + + + 1998/1999 + + + + + 1997/1998 + + + 1996/1997 + + + + 1995/1996 + + + 1994/1995 + + + 1993/1994 + + + + 1992/1993 + + + + 1991/1992 + + + 1990/1991 + + + 1989/1990 + + + 1988/1989 + + 1987/1988 + + 1986/1987 + + + + + 1985/1986 + + + + + 1984/1985 + + + + + 1983/1984 + + + + + 1982/1983 + + + + + 1981/1982 + + + + + 1980/1981 + + + + + 1979/1980 + + 1978/1979 + 1977/1978 + 1976/1977 + 1975/1976 + 1974/1975 + + + 1973/1974 + 1972/1973 + + + 1971/1972 + + + + 1970/1971 + + + +

1. Annual Fire Event Dataset (maintained by FMS, DEC) 2. Operational maps created annually by district fire personnel to map wildfire and prescribed burns 3. Micro-fiche information (stored by FMS, DEC) limited to major fires and brief details about total

area burnt 4. Annual fire summaries list basic details including location and area 5. Reports for each individual fire within a season, containing a variety of information 6. FMS extracts from online support system 7. Each ignition point contains information on fire number, date and cause (if wildfire).



Remote Sensing Data

Remote sensing using Landsat imagery was employed to identify fire scars within the

GGS. This technique was used to enhance and update the spatial fire records and assist

with increasing the locality accuracy. Remote sensing analysis was conducted by Katherine

Zdunic (DEC Corporate Services, GIS) and Dr Li Shu (DEC Fire Management Services).

Katherine Zdunic analysed Landsat MSS (multispectral scanner, 50x50m pixels) images

covering the years 1972, 1977, 1980, 1985, 1988 while Dr Li Shu analysed Landsat TM

(thematic mapper, 30x30m pixels) images from 1989 to 2009, as well as two MSS Landsat

images for 1980 and 1988. The procedures used by Katherine Zdunic and Dr Li Shu by can

be obtained by contacting them at DEC.

Landsat data was used by both methods to create 23 fire scar vector shapefiles. Table 2

indicates which years of Landsat data were analysed for fire scars and includes other scene

dates. In total, forty-three time-slices of Landsat imagery where available for comparing

fire scars against fire history. The Landsat imagery was particularly useful for confirming

and updating fire boundaries. Two images were analysed by both techniques: 1988 and

1979.

Table 2. Table indicating Landsat Images dates, and which scenes were analysed for fire scars by

Katherine Zdunic (KZ) or Li Shu (LS). Date format i s YYYY-MM-DD.

Landsat Image Date

Shape file Author Sensor

2009-10-02 + LS TM

Cont. Landsat Image

Date

Cont. Shape

file

Cont. Author

Cont. Sensor

2009-05-11 + LS TM 2002-02-09 + LS TM 2008-12-18 + LS TM 2002-01-08 TM 2008-10-15 TM 2001-12-23 TM 2008-02-02 + LS TM 2001-10-08 + LS TM 2007-07-09 + LS TM 2001-08-01 TM 2007-03-12 TM 2001-01-05 TM 2007-01-14 + LS TM 2000-10-01 TM 2006-05-12 TM 2000-05-26 TM 2006-02-05 + LS TM 2000-02-20 + LS TM 2005-05-25 TM 1999-10-31 + LS TM 2005-02-09 + LS TM 1999-08-12 TM 2005-02-02 TM 1998-01-05 + LS TM 2005-01-17 TM 1995-02-07 + LS TM 2004-11-14 TM 1993-01-07 + LS TM 2004-02-23 + LS TM 1991-01-18 + LS TM 2003-11-28 TM 1990-02-25 + LS TM 2003-08-08 TM 1988-01-03 + KZ, LS MSS 2003-05-03 TM 1985-01-26 + KZ MSS 2003-01-27 TM 1979-12-01 + KZ, LS MSS 2002-12-10 TM 1976-11-19 + KZ MSS 2002-04-14 TM 1973-12-14 + KZ MSS



As Katherine Zdunic and Dr Li Shu used the same Landsat imagery it was possible to

compare the fire scars captured for the years 1980 and 1988. This comparison was used to

highlight differences in analysis techniques between fire scar remote sensing techniques.

For 1980, the MSS method distinguished 64 potential fire scars that may have occurred

between 1977 and 1980. For the same time period, TM mapping only identified two of

these fires (with 14 smaller areas in close proximity, ranging from about one to seven

hectares). For the 1988 image the MSS method picked up 25 fires while the TM method

only identified two (with two small fires nearby). For both years, however, fire scar

polygons created by the TM method were much more detailed than those by MSS.

Updating fire records

The fire records for Western Australia are maintained by DEC in the form of Annual Fire

Event Datasets (AFEDs). These spatial records contain information on each wildfire and

prescribed burn managed or attended by DEC personnel. Each AFED is available as a

vector shapefile managed through GIS. The FMS branch of DEC update the corporate fuel

age layer annually based on the AFED for each year (DEC 2009). Currently, each AFED

represents a single calendar year made up of two halves of adjacent fire seasons. A copy

of each AFED, clipped to the GGS study area (Figure 1), was used as the basis to check

and update fire history.

To measure if there were significant changes made through this updating process, a simple

comparison was undertaken between the original fuel age calculated using the original fire

history information and the fuel age calculated using the fire history information from the

three updated fire seasons used in the pilot study. Fuel age is examined as years since last

burnt (YSLB). As can be seen from Figure 2, there are some large changes in the fuel age

distribution based on the updating of three seasons. For example, almost an additional

1000 hectares was identified in the category 2 YSLB, while 500 hectares was found not to

have occurred in the category 5 YSLB. This difference is a result of changes to fire

boundaries within the three updated fire seasons that either exposed or covered earlier fire

history. The most extreme changes occur on or immediately adjacent to the years that

were updated however it is evident that the effect of even a small amount of updating

influences the area of every YSLB class. This shows that accuracy is improved by

updating fire history records.



-600

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

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Are

a (h

a)

*

*

*

Figure 2. Effect of update pilot, measured as the difference between the original fuel age and the fuel

age calculated with three updated seasons: 2002/03, 2005/06, and 2008/09 (indicated with *).

Updating Procedures

Following the changes to the parameters of the study, each fire was updated primarily to

increase the spatial accuracy of the fire record and to update or verify information for each

fire record including attributes such as date, fire type, and cause. Spatial corrections were

made predominantly using the Landsat images and remote sensing polygons. If a fire did

not match a remote sensing fire scar, but still showed up in the enhanced Landsat imagery,

the boundaries of fires were edited manually. Figure 3 shows an example of an updated fire

boundary for a prescribed burn that was recorded in the 2005 AFED as an area larger than

what was actually burnt.

A fire’s location was also verified with location descriptions and coordinates provided in

the hard copy fire reports. Temporal updates for fires were accomplished predominantly

through information in fire reports but also by checking Landsat images to see in which

scene the fire scar appeared.

In addition to confirming fires recorded in district records, some fires scars were found

during remote sensing that did not have a corresponding fire report or district record. These

*

*

*



fires were examined visually and a decision made as to whether it was a fire or was more

likely to be clearing or other land changes (e.g. market gardens). A true fire or burn would

appear suddenly and fade quickly to pale green when examining Landsat scenes

consecutively from before a fire to after. If this sequence of scenes confirmed that it was

highly likely to be a fire, then it was added to a corresponding AFED using FMS

procedures and rules to assign default dates and information.

Figure 3. Example of an updated fire boundary. The purple line shows the original boundary of the

fire as recorded in the 2005 AFED. The blue dotted line shows the new boundary of the fire as mapped

to match the underlying Landsat image. Burn area shows up as varying degrees of red depending on

time since fire that the image was captured.

Level of updating achieved

Of the 39 fire seasons from 1970/71 to 2008/09, nine were not updated due to time

constraints and five could not be updated as AFEDs do not exist (1973/1974, 1975/76,

1976/77, 1977/78, and 1978/79). The years not updated were those occurring prior to 1985.

Of the 25 fire seasons updated, all were updated for wildfires and prescribed burns greater

than two hectares occurring outside of pine plantations. Only three fire seasons (2002/03,

2005/06 and 2008/09) were updated completely with all wildfires and prescribed burns,

including those in pine plantations or less than two hectares. Fire seasons that were not

updated were still used in analysis however with decreased confidence in accuracy.



Time taken for updating and analysis

An indication of the time taken to complete all updating and analyses, following the

decision to target certain fires, is provided in Table 3. AFED updating only occurred for

the years 1985 to 2009. Since analysis was to be performed for all the data, ‘cleaning’ was

done for all years (1971 to 2009). This activity meant the removal of any fires less than

two hectares and any fire in pine plantation areas.

Table 3. Indication of time taken in days for each step of fire history updating and analysis.

Activity Approximate time taken.

Data gathering 10 days AFED updating (2009 – 1985) 48 days AFED ‘cleaning’1 (2009 – 1971) 7 days Fire history amalgamation and analysis 10 days

1. ‘cleaning’ of each AFED involved the removal of all retired polygons, all fire less than 2 hectares and all fires in pine plantation.

Fire History Analysis

A series of analyses were performed using the updated fire data to examine the fire history

patterns. Only fires greater than two hectares and not occurring in pine plantation were

included in the analysis. Pine plantation areas were clipped out of the data before analysis.

Analysis of fire history was performed for all remnant vegetation on the GGS (as at

DAFWA 2006), as well as for DEC-managed land only (as in Sonneman and Brown

2008). DEC-managed land includes regional parks but does not include Whiteman Park,

Kings Park, Bold Park and ‘North Reserves’. This classification is based on categories

created by Sonneman and Brown (2008, data and Metadata exist). Analysis of fire history

for DEC-managed land is not restricted to remnant vegetation. Only 3,302 hectares or less

than 5% of DEC-managed land is not classified as remnant vegetation. These areas are

generally lakes within conservation reserves. Fire seasons prior to 1985/86 were not

updated, however were still included in analysis once fires smaller than two hectares and

within pine plantations were removed. Five fire seasons are missing including 1978/79,

1977/78, 1976/77, 1975/76, and 1973/74.



A number of fire variables were calculated and examined including fuel age (or years since

last burnt), fire frequency or the number of fire occurrences for any given location, and

interval pattern between consecutive fires. Appendix 1 and Appendix 2 provide case study

examples of the use of the fire data to analyse different aspects including fire history in

relation to vegetation groups (Appendix 1) and fire history for smaller management units

such as different conservation reserves (Appendix 2). Some of the analysis and data

informed reports by Wilson et al. (2010a; 2010b).

Annual burn trends

Annual burn trends were analysed to see if a change occurred over time in the total area

burnt by wildfires and prescribed burns each year. Temporal changes in the average size

of wildfires and prescribed burns were also examined. Finally the average number of fires

per year was also examined.

Total annual area burnt trends suggest an overall increase in prescribed burning over the

last 39 years. Examination of the data by decade however, shows that there is a decreasing

trend for prescribed burning (from 1970 to 2000) followed by an increase in the recent

decade (Figure 4 and Figure 5). The area of prescribed burning was maximal in the early

1970s and mid 2000swhilst the minimum area occurred in the early 1990s. There was a

decrease in the amount of prescribed burning following years with a high level of wildfire

activity (e.g. 1986/87).

Between 72% and 91% of the area burnt by wildfires in the 1985/86, 2002/03 and 2008/09

seasons were the result of a single wildfire. For the fire seasons 1985/86 and 2008/09,

wildfires constituted 80% (13,562 hectares) and 67% (5,397 hectares) respectively of the

total area burnt for those seasons. Of that area 85% and 94% respectively was the result of

a single wildfire in each season. Fire seasons in which wildfires burnt a total area greater

than 1000 hectares, had an average of 50% resulting from a single fire. In fire season

1971/72 and 1979/80 the large area burnt by wildfires was caused by six and ten fires

correspondingly, of which 46% and 58% respectively was caused by one fire. The largest

wildfire occurring in the GSS over the last 39 years of records was 11,540 hectares and

occurred in 1985/86.



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Unknown FireWildfirePrescribed BurnAverage Prescribed Burn (10 years)Average Prescribed Burn (39 years)

Figure 4. Annual area burnt by fire type across whole Gnangara system (excluding pine plantation

areas and fire less than two hectares). Dashed line indicates 39 year average annual area of prescribed

burn, while solid lines indicate 10 year average annual prescribed burn area.

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Unknown FireWildfirePrescribed BurnAverage Prescribed Burn (10 years)Average Prescribed Burn (39 years)

Figure 5. Annual area burnt by different fire types on DEC-managed land (excluding pine plantations

and fire less than two hectares). Dashed line indicates 39 year average annual area of prescribed burn,

while solid lines indicate 10 year average annual prescribed burn area.



Analysis of the annual area burnt by fire type (wildfire, prescribed burn or unknown fire

type) across the total GGS over a 39 year period (1970 to 2009) revealed that wildfires

comprised 43% to 47% of total burns for each decade with an average of 46% (Table 4).

On DEC-managed land wildfires constituted between 26% and 43% of total burns with an

average of 37% (Table 4).

Table 4. Average annual hectares burnt in each decade by fire type for the GGS study area as well as

DEC-managed land only.

Total GGS

Prescribed

Burn Wildfire

Fire Type Unknown

Number of Years of data

Avg % Wildfires

2000/01-2008/09 3821 2676 111 9 43%

1990/91-1999/00 1958 2232 514 10 47%

1980/81-1989/90 2235 2334 352 10 47%

1970/71-1979/80 5062 4839 0 5 40%

Average for 39 yrs* 2989 2763 284 34 46%

DEC-managed Land

Prescribed

Burn Wildfire

Fire Type Unknown

Number of Years of data

Avg % Wildfires

2000/01-2008/09 3751 1348 31 9 26% 1990/91-1999/00 1917 1513 356 10 40% 1980/81-1989/90 2065 1654 131 10 43% 1970/71-1979/80 4494 3270 0 5 42%

Average for 39 yrs* 2852 1769 152 34 37%

* Average for available data over the 39 years (n=34)

Examination of temporal changes of the average size of wildfires and prescribed burns

shows that over the last 20 years there is a trend towards larger prescribed burns

particularly in the last 5 years (Figure 6a). The average number of prescribed burns each

year decreased from a 1970s and 1980s high (Figure 6b) but appears to have stabilized in

the last 25 years. The average size of wildfires has also decreased over time (Figure 6c)

however the number of wildfires each year shows a slight increasing trend (Figure 6d).



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Figure 6. Average area (± SE) of (a) prescribed burns and (c) wildfires and average number (± SE) of

(b) prescribed burns and (d) wildfires between 1970/71 to 2008/09 seasons. Data is limited to fires

greater than 10 hectares as those smaller are considered predominantly to be errors in mapping. Years

1973/1974 and 1975/1976 – 1978/1979 are not included due to missing data. n=5 for all groups except

1970-1975 (n=4), 1975-1980 (n=1) and 2005-2009 (n=4).

Fuel Age distribution as at 2009/2010

Fuel age (also known as years since last burn (YSLB) was calculated based on the number

of fire seasons prior to the 2009/2010 season. A major proportion of the GGS remnant

vegetation with YSLB known is in the 1-7 years since last burn range (61%; see Figure 7)

while the area of old fuel age (greater than 21 years) is very low (14%). The fuel age

distribution for DEC-managed land (with a known fuel age) shows a similar distribution

with 67% younger than 7 years and 11 % greater than 21 years. A simple exponential line

has been fitted to the data to highlight the strongly skewed nature of the fuel age

distribution (see Figure 7).

a) b)

c) d)



0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39

Years Since Last Burn

Are

a (H

a)All Remnant Vegetation on the GGS(Total area: 101,212 ha; 22% unknown fire history)

DEC-managed Land only(Total area: 68,520 ha; 9% unknown fire history)

Negative Exponential Trendline (GGS)

Figure 7. Fuel age distribution at 2009 for all remnant vegetation within the GGS (grey bars) and

DEC-managed land only (black bars). Dashed lines shows the simplest negative exponential fit to GGS

data.

The total area of all remnant vegetation within the Gnangara groundwater system recorded

in 2006 (DAFWA 2006) is 101,212 hectares of which 22,572 hectares have an unknown

fire history. The unknown area of land (grey in Figure 8) makes up 22% of the total

remnant vegetation (Figure 8a), and 9% of DEC land (Figure 8b).



a) b)

Figure 8. Fuel Age Distribution as at 2009 for remnant vegetation within a) the whole Gnangara system and b) DEC-managed land only, not including pine

plantation burns. Data range from 1970 to 2009 with 5 years of missing data in the early 1970s.



Spatial Autocorrelation (spatial fuel age distribution)

In Figure 8 it appears that similar fuel ages are clustered - meaning similar fuel ages are

closer together than dissimilar fuel ages. Spatial autocorrelation is a way of measuring this

clustering pattern statistically. Moran's I evaluates whether the pattern expressed is

clustered, dispersed, or random. When the Z score indicates statistical significance: a

Moran's I value near +1.0 indicates clustering while a value nearer -1.0 indicates

dispersion.

The spatial distribution of fuel age for the whole Gnangara system is considered

statistically clustered with a Moran’s I of 0.08 with z = 4. 97 and p = 0.01 indicating that

there is a less than 1 per cent likelihood that this clustering pattern in the result of random

chance. For DEC-managed land only, where YSLB is known, the Moran’s I is 0.09 with z

= 3.08 and p = 0.01 (Figure 9). For both datasets, areas of unknown fire history were not

included.

Figure 9. Graphical representation of Statistical Autocorrelation output for analysis of clustering of

fuel ages on DEC-managed land.

Patchiness and Fire intensity

While no analysis of patchiness was performed, visual interpretation of enhanced Landsat

imagery provides evidence that fires in Banksia woodland are predominantly non-patchy.

Figure 10 shows an example of a non-patchy wildfire and a prescribed burn within the



GGS. Figure 11 shows a comparative example of what patchy burns look like. Where

prescribed burns were patchy, internal patches of older fuel age (i.e. unlikely to have burnt

in fire) within prescribed burns were taken into account during the mapping process and

removed where possible (Figure 11).

Figure 10. Enhanced Landsat imagery examples of a wildfire and a prescribed burn (dashed line)

within Banksia woodland on the Gnangara groundwater system

Prescribed Burn Burn date 2/11/2005 Landsat Date 5/02/2006

Prescribed Burn Burn date: 15/10/2005, Landsat date: 05/02/2006

Figure 11. Enhanced Landsat imagery examples of patchy burns. The dashed outline marking the fire

boundary showing exclusion of internal unburnt patches. Patchiness appears to depend on topography

and the presence of wetlands. Identifying unburnt patches can be influenced by the density of unburnt

overstorey species. No patchy wildfires were observed.

Wildfire Burn date 2/2/2005 Landsat Date 9/2/2005

Prescribed Burn Burn date: 5/10/2003 Landsat date: 28/11/2003



Fire intensity was not examined during analysis of fire history for the GGS. Differences

are known to exist in intensity between wildfires and prescribed burns however, due to

time constraints, these have not been analysed here. Fire intensity could be estimated by

type of fire, season of burn and patchiness. There is some scope for further studies to

examine patterns of intensity with respect to wildfires versus prescribed burns, seasons of

fire and effects of changing climatic conditions such as decreasing rainfall on wildfire and

prescribed burn intensities.

Fire frequency and interval patterns

Fire frequency can be defined as the number of fires occurring within a specific time

period. This data can be assessed by a number of components including the length of the

inter-fire intervals; the variability of the length of the inter-fire intervals; and the sequence

of fire intervals (Cary and Morrison 1995; Morrison et al. 1995). The components are

interrelated: as the number of fires within a specific time period changes so does the

average length of the inter-fire intervals. For this fire history data, fire frequency was

calculated as the number of overlapping fires over the 39 year time period. The pattern of

fire intervals (pattern of time between consecutive fires) was determined using a similar

methodology as that described by Wittkuhn et al. (2006). However, extensive analysis was

not conducted due to time constraints.

For those areas of a known fuel age, the average number of times any area was burnt

during the 39 year period is 2.9 for the remnant vegetation in the GGS area, compared to

3.13 on DEC-managed land. This difference could be explained by more active fire

management and increased fuel reduction burning occurring on DEC-managed land

(and/or the porosity of data from non-DEC-managed lands). For all remnant vegetation,

85% burnt between one and three times (Figure 12). On DEC-managed land, 78% of land

has been burnt one to three times (Figure 12). The percentage of area burnt between 4 and

11 times was 15% and 22% respectively for all remnant vegetation and DEC-managed

land only. For all mapped areas, the largest area of land burnt one to two times while for

DEC-managed land the largest area burnt two to three times (Figure 12).



0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7-11

Number of Fires

Are

a (x

,000

ha)

All remnant vegetation

DEC-managed land

Figure 12. Fire frequency or number of fire occurring in remnant vegetation between 1970/71 and

2008/09 (39 years) graphed as total land area (in hectares) for each frequency class. Note that there is

missing information for five years in the early 1970s that could alter this graph.

Correlation between years since last burn and fire frequency shows a slight trend towards

the patches of younger fuel age being those most frequently burnt (Figure 13). The spatial

distribution of fire frequency illustrates that higher frequencies of fires occur surrounding

the pine plantations (Figure 14). It is generally operational practice for DEC to prescribe

burn more regularly around the pine plantations to protect this important commercial

resource from wildfire (Muller 2010). Most recently burnt sites have the highest number of

fires occurring over the last 39 years.

A brief examination of fire intervals indicated that the average number of years between

consecutive fires is about eight years, with a maximum time between fire equal to 12 years

for areas burnt two or more times.



0

1

2

3

4

0 5 10 15 20 25 30 35 40

Year since last burnt

Ave

rage

num

ber

of f

ires

in 3

9 ye

ar p

erio

d

Figure 13. Correlation between average fire frequency and years since last burnt. Years one to six have

been highlighted in black.



a) b)

Figure 14. Graphic representation of number of fires over a 39 year period between 1970/71 and 2008/09 for a) the Gnangara system and b) DEC-managed land

only (neither includes pine plantation areas). Classes 7 to 11 have been grouped together as they represent less than 0.3% of the total area and are likely a result of

errors from spatial procedures. Unknown fire history area makes up 22% of all remnant vegetation and 9% of DEC land.



Discussion

Data was collated from numerous sources however fire reports were found to contain the

most useful information for updating corporate burn data. They contained anecdotal

comments and notes that assisted in determining the location of a fire or its area.

Operational maps were also a useful source as they provided the estimated spatial location

and shape for fires. On occasion, however, it appears that fires, especially prescribed burns,

were marked on the map but were never actually carried out. This inconsistency resulted in

confusion in mapping in the pilot study, especially for prescribed burns in the pine

plantation. It was found that the more information sources available for a given year, the

more opportunity for cross-referencing and therefore the increased possibility of

identifying errors in transcription of location or area. Therefore the years for which there

was a greater variety of information sources had greater confidence in the accuracy of the

updated data.

Procedures used by Katherine Zdunic and Dr Li Shu to extract fire scars from Landsat

images using remote sensing techniques were useful, but had some errors. They increased

the spatial accuracy of some fires. Errors within the remote sensing included the inclusion

of unburnt areas, such as recent clearing or market gardens, as a fire scar. The greatest

benefit of the remote sensing fire scars was the detection of fires not recorded by DEC that

may have been private burning or may have been managed exclusively by FESA. The

Landsat imagery by itself was most useful in confirming fire boundaries with historic

records.

Following the decisions to exclude fires in pine plantations or less than two hectares in

area, the updating progressed at a faster pace; however, updating was still only completed

for AFEDs 1985 to 2009. It is recommended that the verification and checking of fire

history prior to 1985 is completed (for fires in remnant vegetation and greater than two

hectares) using the fire history records and Landsat information. For this purpose, Landsat

images exist with some fire scar mapping, some operational maps exist and there are some

historic fire records in micro fiche form. Where possible the missing years in the 1970s

should be completed.



Confidence levels in the accuracy and completeness of the dataset still has some

limitations. Areas within DEC-managed lands have the highest confidence level while

areas outside DEC-managed lands have a lower confidence due to possibility of missing

fire information from FESA, local government and private managed fires. Years with

more historical information sources also have higher confidence level. Years prior to 1985

have reasonable confidence in the larger burns as these were checked visually very briefly

during cleaning procedures. The missing years in the 1970s have the lowest degree of

confidence.

Examination of annual area burnt trends, by fire type, was performed for the GSS area as

well as for DEC-managed land, both excluding pine plantations. In general, the patterns are

similar between these two datasets.

Muller (2010) has performed some preliminary analysis of fire ignition points and wildfire

cause based on DEC data prior to updating the fire history. Further study on annual trends

could examine patterns of wildfire ignition points.

Our analyses of fire history on the GGS have found that the burn ages are skewed to

younger ages less than seven years. There is also very little area of older fuel ages. It is

recommended that some ground truthing is undertaken to check these areas: to check that it

has not been burnt recently and to check the health of the old vegetation (see Wilson et al.

2010b). Current DEC fire management practice is to keep average fuel age within 8-12

YSLB as anything older may be a hazard to assets as they are more likely to be fast, hot

and intense fires.

The large area of unknown fire history (fuel age and frequency – see Figure 8 and

Figure 14) within the GSS and DEC-managed land highlights the importance to update the

fire records further to try to fill in some of the gaps especially those from 1970 to 1985

which were not checked or updated by this study. The most likely reason for the large

amount of unknown fire history in all remnant vegetation is that it was outside DEC-

managed land at the time of the fire incident.

The unknown areas within DEC-managed land may be from the missing years in the 70s,

from years prior to 1985 which were not updated or may have a fuel age greater than 39



years. Landsat imagery and MMS analysis does suggest that some large fires may not have

been recorded.

Fire intensity is considered an area requiring future work. Different fire intensities could

have effects on the regeneration time or quality of the vegetation as well as providing

different and often difficult or challenging conditions for fire fighting. Dr Li Shu has

preformed some studies using remote sensing and Landsat information to assess the

intensity of fires in the jarrah forests of the Perth Hills area (Dr Li Shu, 2010, pers.

comm.). This type of study, performed for the Banksia woodland could be used to

understand the different intensities of wildfires versus prescribed burns or fires occurring

in autumn versus spring or summer. Other recorded fire variables (for wildfires only) such

as the fire danger index (FDI), rate of spread (ROS, movement of fire in m/hr), and fuel

weight (measured in tonnes per hectare) could also be compared and correlated with

intensity.

Data from this report has informed and supported other reports including Impacts of Fire

on Biodiversity of the Gnangara Groundwater System (Wilson et al. 2010a) and

Guidelines for ecological burning regimes for the Gnangara Groundwater System (Wilson

et al. 2010b).



Conclusions

The accuracy of the fire history has improved considerably with the updating completed

through this project. Although time consuming, the improvements both spatial and

temporal are considered highly important. The use of remote sensing techniques using

Landsat to detect fire scars is considered very useful. Not only to detect previously

unrecorded fires, but also as supportive material to confirm recorded fires and their

boundaries.

Analysis of the fire history revealed that, on average, wildfires contribute 37% of annual

area burnt in DEC-managed land and 46% of annual area burnt in non-pine GGS

vegetation. The fuel age distribution as of 2009 for the whole Gnangara system, has a very

low area of old fuel age (>21years), and 48% has a young fuel age (1-7 years). The

distribution for DEC-managed land shows a similar distribution as 61% of the area burnt is

younger than seven years. The spatial distribution of fuel ages shows there is a higher

proportion of young fuel age surrounding pine plantations. The small areas of old fuel

ages (21-39) years are predominantly limited to locations in the north east (Yeal Nature

Reserve) and in the northwest (Wilbinga Conservation Reserve). Statistical analysis of the

spatial distribution of fuel age shows a high level of clustering, thus large areas of similar

fuel age are highly associated. There is no evidence of a patchy mosaic at the landscape

spatial scale.



Recommendations

This report recommends the following additional work that could be undertaken by DEC:

1. DEC FMS Branch review the methodology of this study and determine the

cost/benefit of updating its Annual Fire Event Datasets records containing the

spatial information on each wildfire and prescribed burn for Western Australia or

specific areas of the State. It appears from this study that there are significant errors

in the AFEDs.

2. It is recommended that the verification and checking of fire history for the

Gnangara groundwater system study area prior to 1985 is completed (following the

guidelines outlined in this report) using fire history records and Landsat

information. Where possible the missing years in the 1970s should be located and

checked.

3. Our analyses of fire history on the GGS have found that the burn ages are skewed

to younger ages (<7 years). There is also very little area of older fuel ages and it is

recommended that some ground truthing is undertaken to check the areas mapped

as older fuel ages have not been burnt recently and to check the health of the old

vegetation i.e. senesence (see Wilson et al. 2010b).

4. Fire intensity is considered an area requiring future work. This could employ

remote sensing and Landsat information to assess the intensity of fires such as that

developed by Dr Li Shu and used in the jarrah forests of the Perth Hills area.

Different fire intensities could effect the regeneration time or quality of the

vegetation as well as providing different, and often difficult or challenging

conditions for fire fighting.

5. Further study on annual trends in wildfire ignition points could be undertaken.

Muller (2010) has performed some preliminary analysis of fire ignition points and

wildfire causes based on DEC data prior to this upgrade.



References

Canci M. (2005) Analysis of Fire Effects on Recharge and Growth of Native Vegetation on Gnangara Mound. Infrastructure Planning Branch, Planning and Infrastructure Division, Water Corporation. , Perth, WA.

Cary G. J. & Morrison D. A. (1995) Effects of fire frequency on plant species composition of sandstone communities in the Sydney region: combinations of inter-fire intervals. Australian Journal of Ecology 20, 418-26.

Department of Agriculture and Food Western Australia. (2006) Spatial Data: Swan Coastal Plain Remnant Vegetation mapping (1:20,000). Extent of remnant vegetation mapping based on December 2005/January 2006 ortho-photos for the PMR portion of the GSS. Department of Agriculture and Food of Western Australia, Perth, WA. .

Department of Environment and Conservation. (2009) Spatial data: Fuel age (Year of last prescribed burn or wildfire). Fire Management Services, Department of Environment and Conservation.

Heddle E. M., Loneragan D. W. & Havel J. J. (1980) Vegetation complexes of the Darling System, Western Australia. In: Atlas of natural resources, Darling System, Western Australia pp. 37-72. Department of Conservation and Environment, Perth, Western Australia.

Mattiske Consulting Pty Ltd. (2003) Flora and vegetation studies - Gnangara Mound. Stages 1, 2 and 3. Part A - Report. Report prepared for Water and Rivers Commission and Water Corporation, Perth.

Mickle D. A., Swinburn M. L. & Kuehs J. M. (2010a) Time to flowering examined across a fire chronosequence. Unpublished report prepared for the Department of Environment and Conservation and the Gnangara Sustainability Strategy, Perth.

Mickle D. A., Valentine L. E. & Kuehs J. M. (2009) Patterns of floristic diversity in the Gnangara Sustainability Strategy study area. Unpublished report prepared for the Department of Environment and Conservation and the Gnangara Sustainability Strategy, Perth, Australia.

Mickle D. A., Valentine L. E., Kuehs J. M. & Swinburn M. L. (2010b) Post-fire juvenile period of plants in Banksia woodland on the northern Swan Coastal Plain. Unpublished report prepared for the Gnangara Sustainability Strategy and the Department of Environment and Conservation Perth.

Morrison D. A., Carey G. J., Pengelly S. M., Ross D. G., Mullins B. J., Thomas C. R. & Anderson T. S. (1995) Effects of fire frequency on plant species composition of sandstone communities in the Sydney region: Inter-fire interval and time-since-fire. Australian Journal of Ecology 20, 239-47.

Muller C. (2010) Fire management operations on the GSS study area. Gnangara Sustainability Strategy - Department of Environment and Conservation, Perth.



Silberstein R., Byrne J., Bastow T. & Dawes W. (2010) Recharge and Fire in Native Banksia Woodland on Gnangara Mound. Report to the Department of Environment and Conservation. CSIRO, Perth, WA.

Sonneman T. & Brown P. (2008) Conservation Reserves, DEC-Managed Land and Bush Forever Sites in the Gnangara Sustainability Strategy Study Area. Unpublished Report for the Department of Environment and Conservation.

Sonneman T., Valentine L., Wilson B., Swinburn M. & Kuehs J. M. (in prep) Post Fire Recolonisation of Fauna on the Gnangara Groundwater System. Department of Environment and Conservation, Perth.

Valentine L. (2010) Food availability for Carnabys black cockatoo in relationship to fire regimes on the GGS. Department of Environment and Conservation, Perth.

Valentine L. E., Wilson B. A., Reaveley A., Huang N., Johnson B. & Brown P. H. (2009) Patterns of ground-dwelling vertebrate biodiversity in the Gnangara Sustainability Strategy study area. Unpublished report prepared by Department of Environment and Conservation for the Gnangara Sustainability Strategy, Perth.

Wilson B. A., Bleby K., Valentine L. E., Swinburn M. L. & Kuehs J. M. (2010a) Impacts of Fire on Biodiversity of the Gnangara Groundwater System. Department of Environment and Conservation, Perth.

Wilson B. A., Kuehs J. M. & Valentine L. (2010b) Guidelines for ecological burning regimes for the Gnangara Groundwater System Department of Environment and Conservation, Perth.

Wittkuhn R. S., Hamilton T. & McCaw L. (2006) Fire Interval Sequences to Aid in Site Selection for Biodiversity Studies: Mapping the Fire Regime. In: Bushfire Conference: Life In A Fire-Prone Environment: Translating Science Into Practice (6-9 June 2006), Brisbane.

Yesertener C. (2007) Assessment of the declining groundwater levels in the Gnangara groundwater mound, Western Australia. In: Hydrogeological Record Series HG14. Department of Water, Perth WA.



Appendix 1. Case study - Vegetation and fire patterns

Analysis of fuel age and fire frequency patterns for different vegetation complexes was

undertaken (see Wilson et al. 2010b) for the purpose of highlighting the usefulness of

having accurate data in the AFEDs for landscape-scale assessment and management.

Heddle et al (1980) vegetation complexes were grouped for analysis as shown in Table 1

to reflect vegetation complexes and their patterns in relation to underlying landform

structures. Only the Bassendean Vegetation Complex, Cottesloe/Karrakatta Vegetation

Complex and Quindalup Vegetation Complex were analysed and examined by fuel age

(years since last burnt). Mattiske Consulting Pty Ltd (2003) vegetation categories were

grouped based on the dominant vegetation in each vegetation type. These are shown in

Table 2

Table 1. Heddle Vegetation complexes and the groupings used by GSS to analyse fire history

GSS Grouping Vegetation Complex

Heddle et al (1980) vegetation complexes

Bassendean Bassendean Complex-Central And\South; Bassendean Complex-Central And\South-Transition Vegetation\Complex; Bassendean Complex-North; Bassendean Complex-North-\Transition Vegetation Complex; Caladenia Complex

Cottesloe/Karrakatta Cottesloe Complex-Central And\South; Cottesloe Complex-North; Karrakatta Complex-Central And\South; Karrakatta Complex-North; Karrakatta Complex-North-\Transition Vegetation Complex

Quindalup Quindalup Complex Others (not grouped or analysed)

Beermullah Complex; Coonambidgee Complex; Guildford Complex; Herdsman Complex; Moore River; Pinjar Complex; Southern River Complex; Swan Complex; Vasse Complex; Yanga Complex

Table 2. Mattiske Vegetation Codes and the groupings used by GSS to analyse fire history

GSS Grouping Vegetation Category

Mattiske Vegetation Code (Mattiske Consulting Pty Ltd 2003)

Acacia Shrubland K5, Q1, Banksia B3, C1, D1, E1, F1, G1, G2, H1, H2, H4, I1,

Casuarina K8,

Heath/Shrubland A1, B1, B4,

Marri IJ1, J1, J2, JK1,

Mel/EucRudis K1, K10, K11, K2, K3, K4, K6, K9,

Sedgeland K7,

Tuart B2,



0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Are

a (h

a)

Bassendean Complex(Total area: 36,805ha)

0

1000

2000

3000

4000

5000

6000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Are

a (h

a)

Cottesloe/Karrakatta Complex(Total area: 25,168ha)

0

200

400

600

800

1000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Years since Last Burnt

Are

a (h

a)

Quindalup Complex(Total area: 2,085ha)

Figure 1. Fuel age distribution for vegetation complexes (Heddle, 1980) in DEC-managed land. ‘U’

represents area of unknown fire history.

Figure 2. Fire frequency for each Heddle vegetation complexes for a) all remnant vegetation and b)

DEC-managed land only.

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7-11

Number of Fires

Are

a (x

'000

Ha)

Bassendean Vegetation Complexes

Cottesloe Vegetation Complexes

Quindalup Vegetation Complexes

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7-11

Number of Fires

Are

a (x

'000

Ha)

Bassendean Vegetation Complexes

Cottesloe Vegetation Complexes

Quindalup Vegetation Complexes

a) b)



Figure 3. Vegetation complexes based on Mattiske Consulting Pty Ltd (2003). ‘U’ represents area of

unknown fire history.

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7-11

Number of Fires

Are

a (x

'000

Ha)

Banksia

Marri

Melaleuca/Eucalyptus rudis

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7-11

Number of Fires

Are

a (x

'000

Ha)

Banksia

Marri

Melaleuca/Eucalyptus rudis

Figure 4. Fire frequency for Mattiske vegetation categories a) for the GGS and b) DEC-managed land

only.

0

1,000

2,000

3,000

4,000

5,000

6,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Are

a (H

a)

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Are

a (H

a)

0

100

200

300

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 35 37 38 39 U

Years Since Last Burnt

Are

a (H

a)

Banksia (Total area: 39,507ha)

Marri (Total area: 12,129ha)

Melaleuca/Eucalyptus rudis (Total area: 2,675ha)



Appendix 2. Case study – DEC Nature Reserves

Three conservation reserves were examined in more detail as case studies (Figure 1):

• Yeal Nature Reserve (11,243 hectares)

• Yanchep National Park (2,876 hectares)

• Wilbinga Conservation Park (2,772 hectares)

These three reserves all have very different management histories as well as

landform/vegetation. They provide useful case studies for different aspects of the fire

history for the Gnangara system.

Yeal Nature Reserve is the largest single DEC-managed reserve in the Gnangara system

and is located in the northeast of the GSS (on top of the groundwater connection or

‘window’ which links the aquifers). Yanchep National Park has the highest number of

visitors of the three reserves, is central in the system and close to major residential and

rural developments. Wilbinga Conservation Park is a newly vested reserve with the

Western Australian Conservation Commission and represents one of the only large

reserves on the Swan Coastal Plain that includes coastal dune landforms.

Wilbinga Conservation Park was burnt predominantly by wildfires whilst Yeal Nature

Reserve has a larger portion burnt by prescribed burns than wildfire. Despite this, the

proportion of area burnt by wildfires in Yeal Nature Reserve is very similar to the

proportion burnt in Wilbinga Conservation Park by wildfires. The annual average area

burnt by prescribed burns in Yanchep National Park is less than that for Wilbinga

Conservation Park, however the latter has only recently been added to DEC-managed land.

Even though it was managed by DEC for fire preparedness, its tenure of Unallocated

Crown Land means that it was not managed as extensively as the other reserves. This is

evident in the higher incidence of wildfire than prescribed burns than the other reserves.

This is likely to have contributed to the higher incidence of wildfires than prescribed burns

for this reserve over the last 39 years, as DEC did not commonly conduct prescribed burns.

It also means, however, that some fires may not have been recorded as they may not have

been attended by DEC and its predecessors.



Figure 1. Location of the three conservation reserves used as case studies for fire history analyses.

Burn type comparison

0

50

100

150

200

250

300

350

Wilbinga Yanchep Yeal

DEC Reserve

Ave

rage

ann

ual a

rea

(Ha)

Unknow n fire type

Prescribed Burn

Wildfire

Figure 2. Average annual area burnt by different fire types, for each case study reserve.



Fuel age distribution

a)

0

250

500

750

1,000

1,250

1,500

1,750

2,000

2,250

2,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 U

YSLB

Are

a (H

a)

Wilbinga Conservation Park

Yanchep National Park

Yeal Nature Reserve

b)

Figure 3. Fuel Age distribution for Wilbinga Conservation Park, Yanchep National Park and Yeal

Nature Reserve: a) graphical representation, b) spatial representation.



Frequency

a)

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

1 2 3 4 5 6 7

Number of fires over 39 years

Are

a (H

a)Wilbinga Conservation Park

Yanchep National Park

Yeal Nature Reserve

b)

Figure 4. Number of fires in 39 year period for Wilbinga Conservation Park, Yanchep National Park

and Yeal Nature Reserve: a) graphical representation, b) spatial representation.

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