A Spatial Assessment of Bushfire Risk in Victoria (GEOM20013 Applications of GIS Sem I 2017) by nancyhyoo

A Spatial Assessment of Bushfire Risk for Victoria

Final Group Project Applications of GIS Semester 1, 2017

Nancy Hwanhee Yoo 832428 Seungwon Jeong 833977 Daeyoo Kang 831684 Stella Radford 694876

Table of Contents

Introduction

Method

Results

Discussion

Response Plan & Recommendations

Conclusion

References

Appendices

Introduction Bushfires have long stood as Victoria’s most severe threat, claiming lives and causing far reaching structural damage most summers. It is well established that the simultaneous increase of Victoria’s population and weather conducive to fire (Middelmann 2007) furthers the need to continue the advancement of fire management strategies. Geographical information systems (GIS) offer the potential to produce detailed maps that take into account multiple factors that contribute to the level of risk of a fire in a certain area. This information can be used by emergency services and others in the fire management process to minimize the impacts of a fire in the most productive way. The delegation of resources, personnel and strategy design may all rely heavily on such information. Through this report, environmental, infrastructural, industrial and population factors have been considered to contribute to an assessment of fire risk. Many aspects of risk were analysed and used to generate the initial hazard map that would be used to help determine the final risk analysis maps. Foremost, aspect was identified as being a significant influence on bushfire severity, with northwesterly aspects associated with the most intense bushfires and southerly aspects the least (Jacobson 2006). Slope was also considered in the analysis as the Country Fire Authority (CFA) notes that the speed of fire increases uphill as the slope increases, due to the flames being able to reach unburnt fuel in front of the fire more easily <http://www.cfa.vic.gov.au/plan-prepare/how-fire-behaves/>. Previously burnt areas and planned burn areas were also analysed, as the longer a vegetated area goes unburnt for the more fuel it accumulates, which can increase the intensity of a bushfire, and planned burn sites often indicate where the CFA believes there is a dangerous amount of fuel that needs to be reduced <http://www.cfa.vic.gov.au/about/planned-burns/>. The Victorian Bushfires Royal Commission (2009) also found that electricity lines were often the cause of bushfires, often due to the failure of electricity assets, and as such these were also considered in the analysis.

Method Note: All data was projected to VICGRID94 prior to any further manipulation. All raster data was set to a cell size of 100. Hazard map elements Hazard

Distance from fire brigade locations

Distance from roads

Distance from power lines

Weighting value

0.8

0.6

Data

Polygon points of fire brigade locations

Polygon lines of freeways, arterial roads and non-arterial state roads

Polygon points of electrical infrastructure

Source

CFA

VicRoads http://vicroadsopendatavicroadsmaps.opendata. arcgis.com/

Open Street Map

Euclidean Distance tool was used to create a raster layer with straight line distances from the roads, with max distance set to 50000m. Reclassify tool was then used to separate values from 10-100 (with hazard increasing as distance increases), using natural breaks. Raster Calculator used to set null/nodata values to 0.

Select by attribute tool was used to select only electrical lines, and these were exported as their own layer. Euclidean Distance tool was used to create a raster layer with straight line distances from the roads, with max distance set to 10000m. Layer was then clipped to Victoriaâ&#x20AC;&#x2122;s size with Clip tool. Reclassify tool was then used to separate values from 10-100 (with hazard decreasing as distance increases), using natural breaks. Raster Calculator used to set null/nodata values to 0.

www.cfa.vic.gov.au/abo ut/locations/brigade_loc ations.kml

Method

Euclidean Distance tool was used to create a raster layer with straight line distances from the points, with max distance set to 50000m. Reclassify tool was then used to separate values from 10-100 (with hazard increasing as distance increases), using natural breaks. Raster Calculator used to set null/nodata values to 0 (so data was not deleted during final raster calculation).

Table 1.1: Hazard element analysis methods

http://www.openstreetma p.org/

Hazard

Temperature

Previously burnt areas

Planned burn areas

Weighting value

0.7

0.6

Data

Text file containing mean maximum temperature for January

Polygons of previously burnt areas since approximately 1903

Polygons of planned burn areas from 2009-2012

Source

Bureau of Meteorology http://www.bom.gov.au/j sp/ncc/climate_average s/temperature/index.jsp

Land Channel

http://services.land.vic. gov.au/landchannel/

http://services.land.vic.gov. au/landchannel/

The ASCII to Raster Conversion tool was used to make the temperature text file compatible. Victoria was then clipped from the image of Australia using the select by attributes tool. Maximum temperature was reclassified into 10 classes. The highest temperature interval was assigned a value of 100, while the lowest was assigned 10.

Select by Attributes in attribute table used to select only areas burnt since 1970, and these were exported as a new layer. Clip tool used to cut down size to Victoria. Polygon to Raster tool used to convert data to raster form (of cell size 100). Reclassify tool was used to give hazard values from 10-100, with natural breaks. Raster Calculator used to set null/nodata values to 0.

Polygon to Raster tool used to create raster layer according to burn type. Reclassify tool then used to classify fuel reduction burns as 100, and all other burn types as 20. Raster Calculator used to set null/nodata values to 0.

Method

Table 1.2: Hazard element analysis methods

Hazard

Slope

Aspect

Tree density

Weighting value

0.8

0.75

0.9

Data

Raster DEM

Polygons of low, medium and high tree density areas

Source

Geoscience Australia http://nedf.ga.gov.au/ge oportal/catalog/main/ho me.page

Geoscience Australia http://nedf.ga.gov.au/geo portal/catalog/main/hom e.page

Land Channel

Slope tool was used to calculate slope as a percentage. Reclassify tool was then used to make hazard values from 10 to 100, using natural breaks.

Aspect tool was used to calculate aspect. Aspect was then reclassified using Reclassify tool into hazard values as follows:

Risk values were given to each layer using the attribute table ; 40 for low, 70 for medium and 100 for high density polygons. The Merge tool was then used to merge these into one layer, and the Polygon to Raster tool was used to convert this into a raster layer. Raster Calculator then used to set null/nodata values to 0

Method

100

http://services.land.vic.g ov.au/landchannel/

Table 1.3:â&#x20AC;&#x2039; â&#x20AC;&#x2039;Hazard element analysis methods The different elements were then added together with their weighting values using the Raster Calculator tool, via the following equation: Total Hazard = (0.9 * "Tree Density") + (0.8 * "Distance from fire brigades") + (0.8 * "Distance from roads") + (0.8 * "Slope") + (0.75 * "Aspect") + (0.7 * "Previous burns") + (0.6 * "Distance from power lines") + (0.6 * "Planned burns") + (0.7 * "Maximum temperature") This was then made into the final hazard map (fig. 1), displaying hazard values for each pixel from 0 to 100.

Elements at risk Population, Infrastructure, Industry and Environment elements and their vulnerabilities were considered for analysis, and risk maps for each were made as described in tables 2.1 to 2.4. Infrastructure risk map vulnerabilities Vulnerability

Places

Over ground power lines

Power stations and generators

Weighting value

0.6

0.9

0.8

Data

Polygon points of places (suburbs, towns, villages)

Polygon points of electrical infrastructure

Source

Open Street Map

http://www.openstreet map.org/

Points were clipped to Victoria using Clip tool. Extract Values to Points tool was then used to apply hazard raster values to each point.

Select by attribute tool was used to select only electrical lines, and these were exported as their own layer. Extract Values to Points tool was then used to apply hazard raster values to each point.

Select by attribute tool was used to select only power stations and generators, and these were exported as their own layer. Extract Values to Points tool was then used to apply hazard raster values to each point.

Method

Table 2.1a Infrastructure vulnerabilities risk analysis

Vulnerability

Railway Lines

Railway Stations

Weighting value

0.4

0.5

Data

Polygon lines of railway network

Polygon points of railway stations

Source

Open Street Map

http://www.openstreet map.org/

Polygon lines were cut down to Victoriaâ&#x20AC;&#x2122;s level using Clip tool. Feature Vertices to Points to tool was then used to convert the line data to points. Extract Values to Points tool was used to apply hazard raster values to each point.

Polygon points were cut down to Victoriaâ&#x20AC;&#x2122;s level using Clip tool. Extract Values to Points tool was used to apply hazard raster values to each point.

Method

Table 2.1b Infrastructure vulnerabilities risk analysis

Industry risk map vulnerabilities Vulnerabi lity

Hospitals

Commercial Forestry

Agricultural Production

Fire Management Installations

Weighing Value

0.9

0.4

0.7

0.6

Data

Points of Victorian Hospital Locations

Polygons of Victorian Land Use 2014/2015

Polygons of Victoria Land Use 2014/2015

Points of Fire Brigade + Fire Installation Locations

Source

Land Channel http://services.land .vic.gov.au/Spatial Datamart/

Land Channel http://services.land.vic .gov.au/SpatialDatam art/

Land Channel http://services.land.vic. gov.au/SpatialDatamar t/

Land Channel http://services.land.vi c.gov.au/SpatialData mart/ CFA www.cfa.vic.gov.au/a bout/locations/brigad e_locations.kml

Method

The Extract Values to Points tool was used to extract the

The most recent Land Use data available was used. Relevant

The Extract Values to Points tool was used to extract the

cell values or risk values from the hazard raster layer to the hospital location points. The weighing value was incorporated.

Commercial Forestry Areas were exported to a new layer. Vulnerability Weighing Values were assigned to the polygons between 0.3 and 0.5. Using Polygon to Raster Tool, the layer was converted to a raster mask layer. Extract by Mask Tool was used to extract the cells of a hazard layer that correspond to the cells in the forestry raster mask layer. Finally Values were reclassified to Risk Values between 10 to 100.

Agricultural Production Areas were exported to a new layer. Vulnerability Weighing Value of 0.7 were assigned to the polygons. Using Polygon to Raster Tool, the layer was converted to a raster mask layer. Extract by Mask Tool was used to extract the cells of a hazard layer that correspond to the cells in the agriculture raster mask layer. Finally Values were reclassified to Risk Values between 10 to 100.

Commercial Areas Included: - Hardwood Plantations, - Softwood Plantations, - Mixed OR Native Plantations

Agricultural Areas Included: Mixed Grazing Areas

Table 2.2 Industry vulnerabilities risk analysis Population risk map vulnerabilities Vulnerability

Population Density

Weighting value

0.6

Data

CSV File containing information regarding population from 2011 census

Source

Bureau of statistics (census)

cell values or risk values from the hazard raster layer to the fire management infrastructure location points. The weighing value was incorporated. Infrastructure Included: - Water points - Radio Towers - Fire Watch Towers - Work Centers - Airfields - Helipads - Fire Stations - Base Camps - Water Points

Method

Table containing numbers on population of each SA2 was joined to a SA2 layer, and divided by the area to find density. Polygon to Raster tool was used to convert the layer to a raster. Then it was reclassified into 10 classes. Raster calculator was used after that to calculate risk value (0.6*Population density) * Bushfire Hazard Value. Then again used the reclassify tool to simplify it into 10 classes from 10-100.

Table 2.3 Population vulnerabilities risk analysis Environment risk map elements Vulnerability

Environmental conservation

Weighting value

0.4

Data

Polygon of Victorian Land Use

Source

http://services.land.vic.gov.au/SpatialDatam art/

Method

The polygon file was converted to a raster. A reclassification was done to display the pixels in a range from 1-38. The extract by mask tool was then used to combine this layer with the hazard map. The areas of highest risk to the environment were simplified to be those that were closest to natural form. The recognised land-use categories included national parks, relatively natural grazing land, irrigated grazing land, modified agricultural land, forestry, urban development/services and water.

Table 2.4 Environment vulnerabilities risk analysis After each layer was completed for each different element, the vulnerabilities (with the applied hazard values) were displayed on a map in their original format (e.g. hospitals shown as polygon points for Industry) as seen in appendix 1 and 2 (which are for Industry and Infrastructure respectively).

An overall risk map for each element was then made (only for the Industry and Infrastructure elements, as these had more than one vulnerability). This was done for polygon data by creating a new field in the attribute table for each vulnerability, and using the field calculator to calculate the new risk value with the vulnerability weighting value. Vulnerabilities in raster form were altered using the Raster Calculator tool in the same way. The vulnerabilities for each element were then displayed together to create a risk map. These risk maps can be seen in figures 2-5. After this, vulnerabilities in polygon form were converted to raster form using the appropriate tools (Polygon to Raster and Point to Raster) and the raster versions of each vulnerability were added (along with their weighting values) to produce a raster layer of each elementâ&#x20AC;&#x2122;s vulnerabilities, to be used in the calculation of the final risk map (fig. 6). Finally, to ensure that the Raster Calculator properly added the layers together in the final calculation, each raster layer had itâ&#x20AC;&#x2122;s nodata/null values set to 0 using the Raster Calculator. The following equation was then used in the Raster Calculator to determine the final risk: Total Risk = ("Hazard Map") + (0.7 * "Infrastructure Risk") + (0.8 * "Industry Risk") + (1.0 * "Population Risk") + (0.5 * Environment Risk)

Results:

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Discussion There are a number of limitations in regards to each element’s risk map as well as the final risk map, some of which were due to a suite of issues that were encountered while attempting to make these maps in ArcMap as well as limitations in the data used. In regards to the infrastructure risk map (see fig. 2), there were issues with some of the data used. For instance, the ‘places’ polygon file does not represent actual housing infrastructure, and as such it can only be assumed that the polygon points on the map representing ‘places’ ,such as suburbs and towns, have houses. Furthermore, it is not clear which ‘places’ have more infrastructure, again limiting its usefulness. More detailed data including the individual house addresses of these ‘places’ was available. However, ArcMap failed to process this information as it was too extensive and so the Openstreetmap (OSM) data was used instead. Several railway station points from the OSM dataset were somewhat reductant in the risk analysis as they appeared to have no railway line running through them (see appendix 2.4 and 2.5). This may suggest that the OSM railway station dataset was slightly inaccurate or out of date. Points of consideration for future improvements are to use, where possible, up-to-date infrastructure data and a more consistent and reputable source than OSM. Furthermore, a more accurate method of determining risk to electrical infrastructure may have been to also incorporate the distance from nearby vegetation, so that power lines nearer to vegetation could be classified as being at a higher risk of bushfire damage. Concerning limitations of generating the Map for Bushfire Risk in Victorian Industry Areas (see fig. 3), some of the input data for the commercial forestry areas was incomplete. For example the Type of Wood for some Commercial Forestry locations was UNKNOWN. Consequently, these areas were given the same weighing value as MIXED or NATIVE forestry areas which may have produced an inaccurate risk calculation. Additionally, data could not be found on the exact or estimated commercial value of the individual elements at risk. Consequently, the practicality of the map was reduced in terms to assisting bushfire risk managers and communities to prioritise economically in the recovery stages of disaster management. Regarding the limitations of the software used to create the map, when using ArcMap tools that involved an output raster layer (i.e. Polygon to Raster Tool and Extract by Mask Tool), some of the items from the data were lost during processing. The steps involving these tools had to be repeated multiple times to ensure all the data had been successfully processed and was 18

present in the new output layer. Again, the effectiveness and reliability of these tools seemed to falter with large volumes of data. This could present problems in future GIS risk projections as input data becomes more sophisticated or more data is added. The population map shown in this report (see fig. 4) only includes population density and does not take other factors into account. Data of other potential significant aspects of population risk such as age, disability or illness should be considered for further analysis. Future risk calculation including these factors may generate different results. When creating the map using ArcMap, there were problems and limitations with the tools used. For example, when using the â&#x20AC;&#x153;Polygon to Rasterâ&#x20AC;? Tool, there were times when the results produced were inconsistent. Only after repeating the steps multiple times were accurate and consistent results produced. The state environmental risk map aims to project the greater impact a fire would have on environmental factors such as native plant and animal populations and carbon release. The strong correlation between the areas deemed at highest risk on the overall risk map (see fig. 5) with the areas on the environmental risk map, demonstrate that the land cover is a strong representation of the multi-factor fire hazard for an area. Land-use data was absent for the region of Melbourne, possibly because the data may have been too dense for the original polygon file due to the extensive variation in land-use for metropolitan spaces. To address this in the future, a solution may be to add data from another source for the region, such as a satellite image that can be classified. The most major assumption of this map is that land use is wholly reflective of the value of a region for conservation. To improve this, more data could be added, such as where populations of endangered species breed or crucial wildlife corridors in the state. The values used for the legend may be more easily understood if they were reversed. Water is assigned the highest value and stands out on the map in red, while it is at the least environmental risk. This was due to difficulties in the classification process. It should be noted that several different sources from various years were used to generate the series of maps presented in this report. Subsequently, the mismatched data may have reduced the accuracy of the overall risk calculations. Furthermore, the latest official Australian Bureau of Statistics (ABS) Census data available was from 2011 and is significantly outdated in terms of population numbers. A recurring issue of the ArcMap software was its slow processing times, as well as tools such as the Raster Calculator failing to execute numerous times. Furthermore, the 19

Raster Calculator would delete areas that did not overlap with all layers being added, making it necessary to set pixels with no values to zero so they were not omitted from the output raster.

Response Plan & Recommendations The set of maps produced throughout this project have the potential to assist a range of services providers, business owners and residents throughout the state in mitigating the consequences of bushfires. Therefore, the most fundamental recommendation that has been determined through this study is to ensure ease of public access to high resolution risk and hazard maps relevant to Victoria’s diverse needs and priorities. A significant proportion of Victoria’s landscape is shown to be at high risk of burning by uncontrolled bushfire, summarized in figure 6. The status of infrastructure such as rail and power lines in the local government areas of Alpine, Murrindindi and Greater Bendigo (fig. 2) should be investigated as they appear to be at high risk of damage by bushfire. Following this, appropriate upgrades should be carried out to reduce the risk posed by bushfires. The high amount of infrastructure concentrated in inner metropolitan Melbourne should also be cause for concern due to the high population density in this area (fig. 4), and as such this infrastructure should be checked and upgraded as needed. To limit the impact of undesirable burning on the state’s environmental assets, figure 5 demonstrates that a productive action would be to dedicate efforts toward preventing extreme burning on the edges of environmental reserves. Eastern Victoria is shown to be the largest area at a higher risk environmentally (fig. 6), including areas such as The Alpine National Park and Snowy River country. Coordinated prescribed burning in spring and autumn would offer the most significant disaster mitigation method in decreasing the intensity, size and damage of an unplanned summertime bushfire (Fernandes & Botelho 2003). The total risk map shows that the highest risk values are in the far west near Mildura, the eastern side surrounding the Alpine region and the area in and around Melbourne’s CBD. The growth of outer suburbs housing development driving the extension of Greater Melbourne, particularly in the east, is also highlighted in the risk map. To ensure comprehensive coverage of the associated increasing risk, it will be necessary for the metropolitan fire brigade to extend its stations and services in this area. Additionally, close collaboration between metropolitan and country fire brigades on fire strategy in these outer suburban areas will be pivotal. Community education programs that increase preparedness, confirm warning strategies, and build appropriate awareness should form a central part of fire mitigation strategy (Gilbert 2007). Such 21

programs are highly recommended for new housing estates encroaching on high risk land areas to ensure residents new to the area receive support in fire preparation. For established communities in high risk areas, it would be sensible for community education programs to continue to be held. Fire services should be maintained at the highest standard throughout the state. The spatial disparities projected through this study ideally serve as a guide as to where preventative actions may need to be actively performed, as well as where fire services are likely to be most productively used. As an all inclusive recommendation, areas of the state depicted in the risk map as having a high risk grading, as well as regions such as Greater Melbourne experiencing rapid growth, should be prioritised in receiving investment in fire services.

Conclusion Overall, through compiling multiple factors that contribute to environmental, infrastructural, industrial and human vulnerability of risk from fire, our study has shown that the 227,400 kmÂ˛ of Victoria (Geoscience Australia 2017) can be realistically predicted to display significant differences in reaction to fire. The spatial representations of a range of assets at risk throughout the state should be used as a guide for both government agencies, who allocate services, funding and make decisions on fire control strategies, as well as by the public, whose actions and awareness has the potential to significantly reduce the impact of a bushfire.

References 2009 Victorian Bushfires Royal Commission & Teague, Bernard 2010, 2009 Victorian Bushfires Royal Commission : final report, 2009 Victorian Bushfires Royal Commission, Melbourne, viewed 30 May 2017, <http://www.royalcommission.vic.gov.au/Commission-Reports> Department of Environment, Land, Water and Planning 2017, Strategic Bushfire Management Plan, viewed 2 June 2017, <http://www.delwp.vic.gov.au/__data/assets/pdf_file/0006/318849/DELWP0016F_BMP15_East Central_web_v2.pdf> Department of Environment, Land, Water and Planning 2017, Measuring Bushfire Risk in Victoria, viewed 2 June 2017 <http://www.delwp.vic.gov.au/__data/assets/pdf_file/0009/318879/DELWP0017_BushfireRiskPr ofiles_rebrand_v5.pdf> Fernandes, P, Botelho, H, 2003, ‘A review of prescribed burning effectiveness in fire hazard reduction’, International Journal of Wildland Fire, vol. 12, no. 2, pp. 117–128. Geoscience Australia, National Location Information, viewed 2 June 2017, <http://www.ga.gov.au/scientific-topics/national-location-information/dimensions/area-of-australi a-states-and-territories> Gilbert. J 2007, Community education, awareness and engagement programs for bushfire: an initial assessment of practices across Australia, Bushfire Cooperative Research Centre, viewed 3 June 2017 <http://www.bushfirecrc.com/sites/default/files/managed/resource/communities-report-gilbert-c0 701_0.pdf> Jacobson, C, 2006, ‘The Influence of aspect and slope on the spatial distribution of bushfire’, paper presented to the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, ACT, 21-24 November 2006, viewed 28 May 2017, <http://www.researchonline.mq.edu.au/vital/access/manager/Repository/mq:11964?f0=sm_subj ect%3A%22vegetation%22> Middelmann, M 2007, Natural Hazards in Australia. Identifying Risk Analysis Requirements, Geoscience Australia, viewed 29 May 2017, <http://www.ga.gov.au/metadata-gateway/metadata/record/65444/> Ottmar, R 2013, ‘Wildland fire emissions, carbon, and climate: Modeling fuel Consumption’, Forest Ecology and Management, vol. 317, pp. 41-50. 24

Appendices 1.1

1.2

1.3

2.1

2.2

2.3

2.4

2.5