Consultancy Report: Expansion of Cowan Field Station

Consultancy Report: Expansion of Cowan Field Station GEOS 9016 Kerwin Ferrer (z3444817) Jayson Bausa (z3429936)

Executive summary This Consultancy report was done for the University of New South Wales to help in the identification of a suitable site for the expansion of the Cowan Field Station. GIS analysis was used to identify a suitable site that has the following considerations: minimal risk of   pollution (erosion), minimal risk from fire hazard, minimal effect on conservation and minimal building cost. Four models were generated for each of the considerations. The erosion model considered how much erosion is expected to happen in each area per annum. The fire model considered the vegetation cover and how much fire intensity is expected for  each area. The conservation model considers the surrounding creeks, mangroves and threatened flora and fauna. Lastly, the building model analysed the suitability of areas depending on their distance from the roads and power supplies. An analysis was done by combining the output of the different models. After the analysis, it was found out that Site 1 (Figure 1), situated at the eastern side of the fire trails is the best site in terms of area at 14800 sq m, it also has a small risk of erosion, moderate fire and solar values and having the best view. The distance from the main road and power supply lines is also around 2 km. Site inspection is suggested to further explore the suitability of Site 1. The other sites, Site 2 and 3 can also be inspected to compare with Site 1.

Figure 1: Proposed Sites for the expansion of the Cowan Field Station

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Table of Contents Executive summary........................................................ .............................................................................. ............................................. ....................................... ................i 1. Introduction......................................................... ............................................................................... ............................................. ............................................. ......................1 1.1 Aim ............................................ ................................................................... ............................................. ............................................. ......................................... .................. 1 1.2 Location ............................................. ................................................................... ............................................ ............................................. .................................. ........... 1 2. Key data sets ............................................ .................................................................. ............................................ ............................................. .................................. ........... 2 2. 1 The Digital Elevation model (ANUDEM)............................................ ................................................................... .......................... ... 2 2. 2 Accuracy of the firetrails ............................................ ................................................................... ............................................. ............................. ....... 2 3. Analysis............................................ Analysis................................................................... ............................................. ............................................. ......................................... ..................4 3.1 Erosion model .............................................................. .................................................................................... ............................................. .............................. ....... 4 3.2 Fire model ................................................................ ...................................................................................... ............................................. .................................. ........... 7 3.3 Conservation model .......................................... ................................................................. ............................................. ........................................ .................. 9 3.4 Building model....................................... model............................................................. ............................................ ............................................. ............................ ..... 13 3.5 Combined model ............................................... ...................................................................... ............................................. ...................................... ................ 17 3.6 Ranking of Sites ..................................... ........................................................... ............................................. ............................................. ........................... ..... 20 4. Recommendations.......................................................... ................................................................................ ............................................. ................................ .........22 References ............................................ ................................................................... ............................................. ............................................. ....................................... ................ 23

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List of Figures Figure 1: Proposed Sites for the expansion of the Cowan Field Station .................................... ....................................i Figure 2: Location Map of Cowan where the new field station will be built. ........................... ........................... 2 Figure 3: Erosion factors that were used in the generation of the Erosion model ..................... 5 Figure 4: Soil Erosion model for Cowan Area ........................................................ .......................................................................... .................. 6 Figure 5: Fire model for Cowan Area ........................................... .................................................................. ............................................. ...................... 8 Figure 6: Fuzzy logic used in i n the conservation model ............................................ .............................................................. .................. 9 Figure 7: High Conservation model .......................................... ................................................................. ............................................. ........................ .. 11 Figure 8: Low Conservation Conservat ion model ..................................................... ............................................................................ .................................... ............. 12 Figure 9: Fuzzy logic used use d in the building model for each factor. .......................................... .......................................... 14 Figure 10: High Cost Building Model generated by combining the four factors .................... 15 Figure 11: Low Cost building model generated by combining the four factors ...................... ...................... 16 Figure 12: Result of combining the four models ........................................................ ..................................................................... ............. 18 Figure 13: Result of combining the models and limiting results ........................................... ............................................. .. 19 Figure 14: Viewshed analysis for each site. Higher visible area ar ea is better b etter ............................... ............................... 21 Figure 15: Proposed site (Site 1) for the expansion of the t he Cowan Field Station ..................... ..................... 21

List of Tables Table 1: Table 2: Table 3: Table 4: Table 5:

Core data sets that t hat were used in the analyses ....................................................... .............................................................. ....... 3 Fuzzy membership limits for the Low Conservation model ..................................... 10 Fuzzy membership limits for the High Conservation model ..................................... ..................................... 10 Fuzzy membership values for Building model ............................................. .......................................................... ............. 14 Ranking of the three suitable sites .............................. ..................................................... ............................................. ........................ .. 20

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1. Introduction 1.1 Aim The aim of this report is the identification of a suitable site for the expansion of the Cowan Field Station of the University of New South Wales. The University of New South Wales  paid the Meta GIS Inc. to do a consultancy report on the selection of a new site for the expansion which shall primarily be used for accommodation. Different aspects such as fire, erosion, conservation and building shall be taken into consideration in the selection process. The erosion model will identify sites that are not prone to erosion thereby decreasing the  probability of a release of pollutants from septic tanks that will also be part of the building  project. The fire model will assess the risk of bush fires in the area by taking into consideration the vegetation cover. By this, we will be able to identify a location where fire hazard is at a minimum. The conservation model shall take into consideration the various species of flora and fauna in the area and also the surrounding creek and the mangrove areas, so that each of these species/sites will not be affected by the construction/expansion. The  building model will take into consideration the cost of the project by considering considering sites that are near the road/fire trails and also near the power supply lines. If possible, the site should also have a nice view and at the same time a high amount of solar radiation. The minimum area for the expansion site is 5000 m 2. Geographic Information Systems Systems was used in the analysis of the sites. Four models were created namely erosion model, fire model, conservation model and building model. The software ARCGIS v 10.1 by ESRI was used in the analyses.

1.2 Location The town of Cowan lies approximately 40 kilometres north of Sydney, New South Wales in Australia (Figure 2). It is bounded by Berowra Creek, Muogamarra Reserve, Pacific Highway and towns of Berowra Heights. The Cowan Field Station is a reserve site owned by the University of New South Wales which is usually used for scientifi c research.

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Figure 2: Location Map of Cowan where the new field station will be built.

2. Key data sets Several data sets were used in this project. Before using the data sets, it was made sure that they were projected to GDA 1994 MGA Zone 56 (Table 1).

2. 1 The Digital Elevation model m odel (ANUDEM) The ANUDEM is one of the main rasters that was used in this analysis. The DEM was used in calculating the slope values necessary in all the four models. It has a cell size of 10 m and was derived from the contour, creeks, spot heights and rivers key data sets by using the ANUDEM algorithm.

2. 2 Accuracy of the firetrails The data for the firetrails firet rails was derived from the Cowan2013waypoints which was surveyed on 24 March 2013 by the GEOS students of UNSW. The Cowan2013waypoints file was  processed by removing soil and fuel load survey data and eliminating points that are suspicious of having a high error. To be able to use the Cowan2013waypoints in the analysis of the models a polyline (named firetrails) was traced over the points that would somehow show an estimate of the firetrails. The firetrails file was then compared to the fieldstn_firetrails, which contains GPS data collected from previous years. The nearness of  the firetrails points to the lines in the fieldstn_firetrails was calculated. The root mean square 2

error is then calculated by getting the mean of the squares of the near distance values for each firetrail point and then taking the square root of the answer. The RMSE was calculated as 38 m. Table 1: Core data sets that were used in the analyses

Data

Data type

Geometry

Threatened Flora

Shape file feature class

Point

Threatened Fauna

Shape file feature class

Point

Mangroves

Shape file feature class

Polygon

Creeks

Coverage feature class

Arc (Feature)

River

Shape file feature class

Polygon

Fire trails

Shape file feature class

Line

Contours

Shape file feature class

Line

Spot heights

Shape file feature class

Point

Cowan2013waypoints

Shape file feature class

Point

Infrastructure

Shape file feature class

Line

Vegetation

Shape file feature class

Polygon

k_pred_2013

Raster

d_infinity

Raster

F_surf_2013

Raster

F_Bark_2013

Raster

F_elev_2013

Raster

NDVI

Raster

Study area

Shape file feature class

Polygon

3

3. Analysis Four models were created to be used in the selection of a suitable site for the Cowan field station expansion namely erosion, fire, conservation and building. Fuzzy logic was employed on the models to be able to account for other possible locations that may not necessarily fit our selection criteria but in a certain degree may be allowed. “Fuzzy logic is a type of logic that does not depend on simple true and false, it recognizes other values not normally considered in models.” (Tarunamulia 2008, p. 23). With the use of fuzzy logic we can give  partial memberships to points depending on their similarity to a certain criteria (Hatzinikolaou et al. 2003). 2003) . Membership values range from 0 to 1, where 1 refers to full membership. The use of fuzzy logic in our analyses will help us in giving intermediate values to sites that may not fit our ideal criteria but maybe close to it.

3.1 Erosion model An erosion model was created to identify the erodability of the different areas in Cowan. We will use this model to find suitable places where erosion is minimal, thereby reducing the  possibility of releasing pollutants in the waterways. The formula used for the erosion model was derived from the Universal Soil Loss equation (USLE) by Wischmeier and Smith (Selby 1993). 1993). The soil erosion can be computed by getting the product of the different soil factors such as Rainfall erosivity factor (R), Erodibility factor (K), Slope gradient factor (S), slope length factor (L) and cropping (C) and practise factor (P) (Figure 3). The formula in calculating the soil erosion model is as follows

   ∗  ∗  ∗  ∗  ∗  =

A Rainfall erosivity factor (R) of 3500 was used in the generation of the erosion model. This value was taken as the average of the 3000 to 4000 rain factors near Cowan that can be seen from the Rainfall erosivity map of New South Wales (Figure 2, Rosewell 1 993). The Soil erodibility factor (K) was provided by UNSW which was estimated from the data in Rosewell’s (1993) Table 2. The best method of calculating the K factor accordi ng to Rosewell (1993) is through laboratory testing, however, this is costly and would be time consuming. The slope gradient factor (S) was an improved version of the USLE formula derived by Moore & Burch (1986) after observing inconsistencies produced by the USLE. It is calculated by using the formula:



sin

=

 ∗ 180

. 0896 0896

1.35



The slope length factor (L) is computed by using the formula below, where the flow accumulation was derived from the d-infinity algorithm of Tarboton of  Tarboton (1997)

4

  ∗  =

0.4

22.13

Figure 3: Erosion factors that were use d in the generation of the Erosion model

The cropping factor (C) takes into consideration the effect of vegetation on the resistance of  the soil to erosion by looking at the current land cover, the history on how the soil was used and the physical properties of the soil (Rosewell 1993). 1993). The value for this is usually 5

determined by long term data collection, however, we can estimate this by using Table D3 and D4 of Rosewell (1993). The practise factor (P) considers the effect of soil erosion management practises in the area. A value of 1 was used which accounts for land areas where the cultivation practise is to plant crops along the slope. After calculating all the factors, the product of all of these will then give us our soil erosion model (Figure 4).

Figure 4: Soil Erosion model for Cowan Area

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3.2 Fire model The fire model was created to be able to identify the risk of fire hazard in the vicinity of  Cowan. From this we will choose a location/ site where risk of fire is minimal. The fire assessment model is an approximation of reality in identifying the risks associated with fires  based on the equations derived by Noble et al. (1980) from the McArthur forest fire danger in the Mark 5 metre. The fire model was produced by taking into consideration the fuel load and rate of fire spread with respect to slope gradient. Bessie & Johnson (1995) notes that weather  conditions together with fuel load are the main factors that drive fire behaviour. The effect of  weather condition is accounted for in formula for the rate of fire spread. The dominant vegetation type for the study area is forests and grasslands. The rate of fire spread on forests can be calculated by using the forest fire fir e danger index derived by Noble et al (1980):



= 1.25

−  ∗∗ 30

+ 0.0234

∗ 

In the formula, F is the fire danger index; D is the drought factor (10); T is the air temperature (37.2°C); H is the relative humidity (15%) and V is the mean wind velocity at a height of 10 m (40 km/hr). Sirakoff (1985) showed that the value for drought factor should not exceed 10  because it it usually results in an an overestimation overestimation of the rate rate of spread. spread. After getting getting the the value of of F, we can now compute for the Rate of forward spread on level ground (R) which has a formula (Noble et al 1980):



= 0.00 0.0012 12

∗∗

The value of R for forest after using this formula is 0.0802W, where W is the fuel weight or  also known as fuel load. For the grassland, the formula for F (Noble et al) is:

 ∗∗−  ∗ = 3.35 .35

This can be simplified as Since

 ∗ = 0.13

=

∗

∗

.0897 11.04+0.0403 40

6.23

, we can get the value of rate of forward spread for grassland as 0.801W.

The value of R calculated above is only applicable to flat surfaces, therefore we will modify the value by using slope data from the Digital Elevation Model using the formula:

  ∗ =

exp (.069

∗ 

)

After getting the R slope slope for both forest and grassland, the fuel weight or fuel load was calculated by getting the sum of the surface fuel component (f_surf_2013), bark component (f_bark_2013) and elevated fuel component (f_elev_2013 layers). Fuel Load, is the quantity of flammable material in a particular area describe by fire management authorities (Australian Emergency Management 2011). 2011) . These components were the result of processing the survey data gathered by students of UNSW last 24 March 2013 and correlated to topographic and satellite data. The data from the survey done by the students and those from topographic and satellite were all based from the Overall Fuel Hazard guide by McCarthy et al. (1999). (1999). If we were to make a more accurate fire model, we must do a comprehensive fuel load survey on the whole area and employ experts in that f ield. After getting the fuel load, we can then use the final formula to get the fire intensity model: 7



= 1860 18600 0

∗∗ ∗ ∗ Rslope

1000

1

3600

10

The constant 18600 is the energy that a eucalypt fire will burn (joules), this value can still be altered by using different values for each vegetation cover, but in this analysis, we will use this default value. The last two constants converts the final answer to m/s and kg/m 2 from km/hr  and t/ha. After using this formula, we will be able to generate our fire model (Figure 5). It must be noted that the fire model that we generated is based from empirical or statistical data and does not take into consideration other physical components that contribute to fire behaviour  (Perry 1998). 1998). This means that the fire equations used here may not be applicable to other   places.

Figure 5: Fire model for Cowan Area

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3.3 Conservation model The conservation model was created to identify sites that should not be built on because of  the presence of endangered animals and plants, creeks and mangroves. Two conservation models were created namely High Conservation and Low Conservation. The High Conservation model sets aside a large area for conservation use while the Low model has a smaller area for conservation. The conservation data were taken from four factors, namely; endangered flora, endangered fauna, creeks and mangroves. Fuzzy logic was employed in determining the areas that need to be conserved so that partial membership can be given to  places that are not too far from the purely conserved areas. All areas to be used strictly for  conservation are given a value of 1. A value of 0 is given to areas where conservation shall not be enforced. Monotonic Linear fuzzy logic was used such that a distance of 0 to Max (m) is given a value of 1-for strict stric t conservation, and distances from Max (m) to Min (m) are given  partial memberships ranging r anging from 1 to 0 (Table 2 and 3). The highest membership values are given to those that are nearest to the Max (m) distance and it decreases as the Min (m) distance is approached. Values for sites that are higher than the Min (m) distance are given a value of 0. The max and min values for the threatened flora, fauna and mangroves were taken as identical while the creeks had lower values.

Figure 6: Fuzzy logic used in the conservation model

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Table 2: Fuzzy membership limits for the Low Conservation model

Preserved Features

Min (m)

Max (m)

Threatened Flora

300

100

Threatened Fauna

300

100

Mangroves

300

100

Creeks

150

50

Table 3: Fuzzy membership limits for the High C onservation model

Preserved Features

Min (m)

Max (m)

Threatened Flora

400

200

Threatened Fauna

400

200

Mangroves

400

200

Creeks

200

100

After applying the fuzzy membership to each of the four factors, four raster layers were  produced. The conservation model can then be generated by combining the four factors by overlaying them and getting the maximum value for each cell. This is done twice to produce the High Conservation model (Figure 7) and the Low conservation model (Figure 8). The High Conservation Model accounts for higher conservation areas which results from a bigger  radius of conservation given to the four factors. These conservation models show us sites where construction is not allowed. It should be noted that the Berowra creek cree k was not included in the conservation model analysis particularly because it is already far away from the fire trails.

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Figure 7: High Conservation model

11

Figure 8: Low Conservation model

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3.4 Building model The building model was created to identify possible sites for the expansion taking into consideration its nearness to the road/fire trails, the slope/steepness and the nearness to power  supply and high voltage power lines. Fuzzy logic was used in taking into consideration the above factors. Ideal cases were given a value of 1, which means it is suitable for building. Unacceptable cases were given a value of 0, which means it is not suitable for building. Two  building models were considered, namely High C ost and Low Cost building models, with the first having higher limits in terms of distance from the road and power supply and the other  has lower distance values (Table 4). The Low Cost model also minimises the effect of the construction to the surrounding flora and fauna by minimising road development. The first criteria for the building model is that it is close enough to the road to be able to lessen costs in terms of transporting materials, limit the effect on the environment (i.e. cutting of trees) and minimise the costs for the development and construction of roads. The roads data was derived from the firetrails data set. The road factors were calculated by using trapezoidal fuzzy logic on the roads data (Figure 9a & 9b) where distances near the roads (0 to 30 m for High Cost and 0 to 20 m for Low Cost) were excluded so that the possibility of   building too close to the road or on the road itself is minimised. Ideal distances from the road were 30 to 300 m for the high cost and 20 to 150 m for the low cost and these were given a value of 1. Distances that were not too close to the road (HC: 20 to 30m, LC: 15 to 20 m) and not too far (HC: 300 to 400 m, LC: 150 to 200 m) were given partial membership or  conditional values depending on their nearness to ideal distances. The slope was considered to identify sites that are relatively flat in order to facilitate in the construction and development of the area by minimising the need to cut and fill and eliminate sites that are on cliffs and ridges. The slope data was derived from the ANUDEM raster by calculating the rate of change from one cell to its surrounding cells. Monotonic fuzzy logic was then used on the slope data (Figure 9c). Slope values of 0 to 10 were considered as  preferable sites and partial membership were given on slopes from 10 to 15. The high voltage powerlines data was extracted from the infrastructure shapefile. Monotonic fuzzy logic was used considering the distance from high voltage powerlines (Figure 9d). Sites that are near the powerlines (less than 200 m) were given a value of 0, for not suitable as Kroll et al. (2010) identified that magnetic fields emitted by powerlines were associated with childhood leukemia. The ideal distance from the powerline is 300 m and above, while partial membership was given for 200 m to 300 m distances (Table 4). The power supply line data was estimated by extracting the Glendale road feature class from the infrastructure shapefile. A monotonic fuzzy logic was used on the power supply data. For  High Cost power supply factor the ideal distance is from 0 m to 2500 m, while the Low cost  power supply s upply has an ideal distance of 0 m to 1500 m (Figure 9e & 9f). Partial membership were given to distances 2500 m to 3000 m for High Cost while the Low Cost has 1500 m to 2000 m range.

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Table 4: Fuzzy membership values for Building model

High Cost Factors

Low Cost

Ideal values

Conditional values

Ideal values

Conditional values

Roads (firetrails)

30 m to 300 m

20 to 30 m 300 m to 400 m

20 m to 150 m

15 to 20 m 150 m to 200 m

Slope (in degrees)

0 to 10

10 to 15

0 to 10

10 to 15

High volatage powerlines

> 300 m

300 to 250 m from powerline

> 300 m

300 to 250 m from powerline

Power supply lines

0 m to 2500 m

2500 to 3000 m

0 to 1000 m

1000 to 1500 m

Figure 9: Fuzzy logic used in the building model for each factor.

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After getting the four factors, the building models can then be generated by combining the relevant factors. The High Cost factors for power supply and roads will be combined with the slope factor and powerline factor by getting the minimum value for each cell to generate the High Cost building model (Figure 10). The same will be done to produce the Low Cost  building model but this time the Low Cost factors for road and power supply will be used (Figure 11).

Figure 10: High Cost Building Model g enerated by combining the four factors

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Figure 11: Low Cost building model gener ated by combining the four factors

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3.5 Combined model Combining the different models will give us the suitable sites that fit our selection criteria. In this combination, we are going to use the fire model, erosion model, the Low Cost building model and the High conservation model. In can be noted that the High Cost Building model and the Low Cost building model were generated as backup in case their counterparts were not able to produce suitable sites. The first step is to convert the values of the fire and erosion model into membership values where one (1) equates to places where it is suitable to build and zero (0) is for not suitable. Fuzzy membership was used on both the erosion and fire models. For erosion, the model that was used in the combination is the erosion model with 3500 rain factor. The ideal range for a suitable site is 0 to 20 t/ha/yr while giving partial membership for values of 20 to 50 t/ha/yr. For the fire model, the ideal range is 0 to 2000 kW/m and giving partial membership to values 2000 to 3000 kW/m. The converted fire and erosion models can then be overlayed to the building model by getting the minimum or the smaller value for each cell from the three models. We will call this model as the constrained model. After getting the constrained model, the high conservation model is then subtracted from it which gives us the combined model with values ranging from -1 to 1(Figure 12). Cells with values of 0 to -1 are those that have higher conservation values, therefore these are not suitable for building. Cells that have a value above 0 are those that have higher  constrained model values; therefore this is where we will look up sites which may be suitable for the expansion. We limited the results by getting only sites that have values higher than .8 and having an area greater than 5000 m 2. After eliminating sites that have values less than .8 and having smaller areas, three sites were found to fit our selection criteria (Figure 13).

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Figure 12: Result of combining the four models

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Figure 13: Result of combining the models a nd limiting results

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3.6 Ranking of Sites The three sites that were generated by the combination of the four models were ranked according to several criteria. Ordinal ranking was employed, giving a rank of 1 to the site that is the best for each criteria and 3 to the lowest. The values for erosion, fire, conservation, and  building for each site were extracted from the four models (Table 5). The site with the smallest value for erosion, fire and conservation was given the highest rank while the largest value for building was given the highest rank. The area of each site was also considered, with the bigger areas given higher ranking values. A view shed analysis was also used to identify which site has a nice view (Figure 14). The site with the biggest visible area was given the highest ranking. Area of solar radiation was derived from the surface of the DEM and the value for solar radiation for each site was extracted from it. The site with the highest solar  radiation value was given highest ranking. The last criterion that was used to rank the sites is the distance of each from the freeway. The distance from the freeway is the distance that you need to travel through the firetrails. Table 5: Ranking of the three suitable sites

Values

Ranking

Criteria Site 1

Site 2

Site 3

Site 1

Site 2

Site 3

14800

10500

6500

1

2

3

2.5

4

4

1

2

2

1470

1490

1280

2

3

1

Conservation

0.2

0.2

0.2

1

1

1

Building

0.8

0.95

0.9

3

1

2

617600

618100

606800

2

1

3

70000

45000

55000

1

3

2

2300

2500

900

2

3

1

1.625

2

1.875

Area (m2) Erosion (t/ha/yr) Fire (kW/m)

Solar (kW/m2) View (m2) Distance from freeway (m)

Mean

After evaluating each with the use of several criteria, it was found out that Site 1, which is situated at the east part of the fire trails, is the best site for the expansion of the Cowan Field Station (Figure 15). Even though it was lowest ranked in terms of building, it was the site with the biggest area, smallest erosion value, and the one with the best view (in terms of  area).

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Figure 14: Viewshed analysis for each site. Higher visible area is better

Figure 15: Proposed site (Site 1) for the expansion of the Cowan Field Station

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4. Recommendations After using the four models in our analysis, it was found out that Site 1 (Figure 15) was the most preferable site for the Expansion of the Cowan Field Station. In terms of cost, it has a building model value of .8 which is the lowest among the three considered sites, however it is the biggest in terms of area, has the least erosion value, a moderate fire and solar value and it has the best view. The distance of Site 1 from the main road and the power  supply is still acceptable. Further site inspection can also be done to confirm the suitability of  Site 1 and the other sites can also be inspected to compare them with Site 1.

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References Australian Emergency Management 2011. National 2011.  National Bushfire Fuel Classification System [Online]. Accessed 20 May 2013, http://www.em.gov.au/Fundinginitiatives/NationalEmergencyManagementProjects/N ationalEmergencyManagementProjects20102011/Pages/NationalBushfireFuelClassifi cationSystem.aspx.. cationSystem.aspx Bessie, W & Johnson, E 1995. 'The relative importance of fuels and weather on fire behavior  in subalpine forests'. Ecology, forests'. Ecology, vol. 76 , pp 747-762. Hatzinikolaou, E, Hatzichristos, T, Siolas, A & Mantzourani, E 2003. 'Predicting archaeological site locations using GIS and fuzzy logic'. The digital heritage of  archaeology, vol., pp 169-177. Kroll, M, Swanson, J, Vincent, T & Draper, G 2010. 'Childhood cancer and magnetic fields from high-voltage power lines in England and Wales: a case –  case – control control study'. British study'. British  journal of cancer, vol. 103 , pp 1122-1127. 1122-1127. McCarthy, GJ, Tolhurst, KG & Chatto, K 1999. Overall Fuel Hazard Guide, 3rd edition, edition , Victorian Department of Natural Resources and Environment, Fire Management Research Report 47. Moore, ID & Burch, GJ 1986. 'Physical basis of the length-slope factor in the Universal Soil Loss Equation'. Soil Science Society of America Journal, vol. 50, pp 1294-1298. 1294-1298.  Noble, IR, Gill, AM & Bary, GAV GAV 1980. 'McArthur's fire danger danger meters expressed as equations'. Australian equations'. Australian Journal of Ecology, vol. 5, pp 201-203. Perry, G 1998. 'Current approaches to modelling the spread of wildland fire: a review'.  Progress in Physical Geography, vol. 22, pp 222-245. Rosewell, CJ 1993. SOILOSS: A program to assist in the selection of management practices to reduce erosion, erosion, Soil Conservation Service of New South Wales, Department of  Conservation and Land Management. Selby, MJ 1993. Hillslope 1993.  Hillslope materials and processes, 2nd edition., Oxford, Oxford University Press. Sirakoff, C 1985. 'A correction to the equations describing the McArthur forest fire danger  meter'. Australian meter'. Australian journal of ecology, vol. 10, pp 481-481. Tarboton, DG 1997. 'A new method for the determination of flow directions and upslope areas in grid digital elevation models'. Water resources research, vol. 33 , pp 309-319. Tarunamulia. 2008. Application of fuzzy logic, GIS and remote sensing to the assessment of  environmental factors for extensive brackishwater aquaculture in Indonesia. UNSW.

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