Traditional aboriginal plant use on the South coast of New South Wales

Kioloa located on the South of New South Wales has variety of vegetation types, in which Australian National University (ANU) Coastal Campus is situated on southern part of the Sydney Basin. There is a hypothetical problem that the ANU research station at the Kioloa must become selfsupporting, so an increase in income has become a favourable choice of ANU at the Kioloa. Farm operation would be a choice to gain benefits, but agricultural activities can only supply some of the operating costs. Furthermore, the potential returns from forests might be restricted. Consequently, the management committee has decided two options. The first choice is dividing the property into small parts along with careful investment to gain a capital sum and to balance the operating funds. The second choice is to build a conference centre with up to sixty beds in a motel style room. These two options are not easy to achieve because the property has been seemed as a national heritage. This is really a big constraint because the owners on the one hand want to maximise financial revenue, on the other hand they attempt to preserve the value of the property. In order to achieve these goals, an Integrated Geographical Information System was hired to establish a land use planning that reconciles the effects of developments and changing landuse on the environment, in terms hazard risks, ecological impacts and aesthetics. ESRI GIS software (ArcMap, ArcCatalogue, ArcScene) was used with a basic data base comprising a Digital Terrain Model, a digitised geology map and classified Landsat TM Imagery.

EXECUTIVE SUMMARY

Kioloa located on the South of New South Wales has variety of vegetation types, in which Australian National University (ANU) Coastal Campus is situated on southern part of the Sydney Basin There is a hypothetical problem that the ANU research station at the Kioloa must become self-supporting, so an increase in income has become a favourable choice of ANU at the Kioloa Farm operation would be a choice to gain benefits, but agricultural activities can only supply some of the operating costs Furthermore, the potential returns from forests might be restricted Consequently, the management committee has decided two options The first choice is dividing the property into small parts along with careful investment to gain a capital sum and to balance the operating funds The second choice is

to build a conference centre with up to sixty beds in a motel style room These two options are not easy to achieve because the property has been seemed as a national heritage This is really a big constraint because the owners on the one hand want to maximise financial revenue, on the other hand they attempt to preserve the value of the property In order to achieve these goals, an Integrated Geographical Information System was hired to establish a land use planning that reconciles the effects of developments and changing land-use on the environment, in terms hazard risks, ecological impacts and aesthetics ESRI GIS software (ArcMap, ArcCatalogue, ArcScene) was used with a basic data base comprising a Digital Terrain Model, a digitised geology map and classified Landsat TM Imagery

The results of MOLA Model suggested that the property should build the conference centre that is near the track and at the slopes being not very steep (Building Map) Also, forestry ought to be protected along the steep slopes so that these areas can be prevented from erosion and flood, and the owners can benefit the returns from forestry In addition, agriculture would

be generated in areas along the coast lines, which can cover some operating costs at the

property The remaining areas are for conservation in order to minimise the impacts of local habitat

CONSULTANT’S REPORT

1 The unsupervised classification process

Unsupervised classification process is a mean by which pixels in an image are assigned to spectral classes without the user having foreknowledge of the existence or names of those classes (Richard and Jia, 1998) The procedures can be used to determine the number and location of the spectral classes into which the data falls, and to determine the spectral classes of each pixel Unsupervised classification process was done prior to field checking

A group of pixels in multispectral space is clustered, so pixels belonging to a particular cluster are spectrally similar This process has been done after Landsat TM was imaged Unsupervised classification has produced 63 spectral classes (see Unsupervised

Classification Map) which in turn need to be collapsed into ten specific classes After finishing this step, each object on the Kioloa map needs to be made a particular colour so that it is easy to determine the specific objects

2 Map classification in the laboratory:

The digital map of Kioloa includes 63 spatial classes which range from white to black colours It is really difficult for scientists to identify the objects in the field, so it was divided into three separated maps, and each map contains approximately 20 classes which have different colours It is so important for field checking to have contrasting colours for the adjacent classes in the maps because each classified area can be easily distinguished from colours around it

2.1 Field checking process

Before commencing the field checking in Kioloa, three separated maps having colours with sufficient contrasts were printed out for determining the positions of the objects more accurately GPSs were utilised to make waypoints and locate the objects as well

Field checking groups went along the tracks and took into account the changes of the objects’ existence It means whenever vegetation structures altered, waypoints were immediately made Also, each member recorded the remarkable features of the landscapes Each group gained about thirty waypoints during the field trip Subsequently, the

reclassification of 63 spatial classes into ten classes was conducted in the laboratory

2.2 Hypothesis formulation and testing

After recording particularly changing features at each waypoint in Kioloa, 63 spectral classes were reclassified into ten classes, namely

- Rainforest

- R/S – Wet Sclerophyll – Ecotone

- Wet Maculata Forest

- Dry Maculata Forest

- Dry Sclerophyll Forest

- Health and woodland

- Good Grassland

- Sparse Grassland

- Bare Ground

- Sea

These classes are based on the specific features recorded of all groups throughout the field trip During the classification process, the data of all groups were referred to in order to identify these classes more precisely It is confusing to distinguish different kinds of forests owing to the diversity of the Kioloa Forest It is somewhat easy to determine the sea, grassland and bare ground classes On the other hand, the remaining classes such as Dry Maculata and Dry Sclerophyll Forest are difficult to classify In addition, almost all of the groups have the same rout in the field checking, so 63 spectral classes cannot be classified entirely As a consequence, guessing was applied to determine these spectral classes

In order to gain relevant results guessing also needs to be based on scientific method, but not to make a wild guess Tasseled Cap is used in vegetation map classification so that particular classes are classified appropriately Its basis lays in an observation of crop trajectories in band 6 versus Band 5, and Band 5 versus Band 4 subspaces (Richard and Jia, 1998) (Vegetation Map)

2.3 An Error Matrix

After fully working out the vegetation map via hypothesis formulation, students need to verify the accuracy of classification by an error matrix (see Table 1) Owing to the

difficulties in determination the forest’s types, the overall accuracy and producer's

accuracy as well as user's accuracy for each class was somewhat low, average 50% for all Therefore, field rechecking is necessary in order to identify the objects more accurately

Table 1: An error matrix of vegetation map in Kioloa

Rain Forest

RF_WetSclerophyll

- Ecotone

Wet Macuta

Dry Macuta

Dry Sclerophyll

Health and Woodland

Good Grass

Sparse Grass Bare Ground Sea

Row total

RF_WetSclerophyll

Health and

Overall accuracy Producer's accuracy for each class User's accuracy for each class

RF_WetSclerophyll – Ecotone = 75% RF_WetSclerophyll – Ecotone =25%

Health and Woodland = 52.4% Health and Woodland = 68.8%

3 Land use potential

In order to consult for ANU regarding where is suitable for conservation, agriculture, forestry and building conference room, it is necessary to build sub models for MOLA There are a total of seven sub models which are going to be described in this report and will be created in order to support for a final land use potential model

3.1 Conservation model

Kioloa has a number of different forest communities such as the southern warm temperate rain forest and south east dry sclerophyll forest (Caton and Hardwick, n.d) Owing to the complex nature in terms of structurally and floristically and the values in timber as well as medicine, Kioloa needs to be considered, and rainforests ought to be allocated as

conservation sites

Because conservation is the first MCE (Multi-Criteria Evaluation), many daughter data sets need to be generated Conservation sites can be created by combining some essential features such as stream buffers, road buffers as well as endangered species and habitat, which are described in Diagram 1

Diagram 1: Components of Conservation MCE

Firstly, stream buffers are crucial for conservation sites because they can ensure water quality and provide wild-life corridors In order to produce a stream buffer map, Digital

Elevation Map (DEM) was used to find the direction of the streams assumed that flow across a surface will always be in the steepest downhill direction, which was done by the FLOWDIRECTION function If there were any sinks which are defined as both two cells flow together and any cells having undefined flow directions, these sinks must be filled to gain a surface which sheds water Then a depressionless DEM was generated flow

direction again until no more sinks were found The next step is to calculate the

accumulated flow from the newest flow direction This function calculated the weight of all cells that flow into each downslope cell in the output grid After that, CON statement is used to create a stream network Based on flow accumulation map, a grid is assumed that

a stream network would have value 1, otherwise could be zero The wild-life corridors along the banks of the stream were produced by applying EXPAND function (Stream Buffer Map)

Secondly, because the purpose of this report is to conserve rainforest communities,

Rainforest and Rainforest Ecotone classes were given value 1, the values of the rest were zero (Unique Vegetation Map) Then the vegetation map including ten classes was

remapped and collapsed into two values, namely 1 and 0 Finally, in order to form road buffers, road datasets need to be converted from vector to raster Then, road value should

be 1 while anything else is 0, and road buffer was created as same as stream buffers (Road buffer map) The three components were combined to produce the conservation map after any NODATA in any grids were corrected to zero values (Conservation Model 1)

However, a fuzzy conservation model can be another alternative for decisions where is the most suitable for conservation The previous conservation model used the Boolean

approach (true of false) whereas the minimum and maximum values as scaling points were applied for buffer areas by using the fuzzy approach Eucdistance is the best option

to obtain continuous values from the stream, road and unique vegetation, but these values need to be inverted by subtracting them from 1 because Eucdistance gives a value

increasing away from the stream, road and unique vegetation as well Moreover, a mask over the sea should be taken into account in the case the sea needs to be cleaned up without changing the on-land values The fuzzy conservation model was generated by combining all the three fuzzy of stream, road and unique vegetation with the equal

weighted linear combination (WLC), which in turn represents the equal importance of all inputs (Fuzzy Conservation Map) The result of the fuzzy conservation model

recommends that regions along the streams and roads are suitable for preservation,

because the stream networks could help ensure water quality as well as the main roads would create convenient transportation

3.2 Agriculture model

In agriculture, soil fertility and slope seem to be essential parts that allow agricultural practice Therefore, sub datasets, namely soil fertility and slope need to be create to generate agriculture model described in Diagram 2

Diagram 2: Agriculture Sub Model

From the geology map that includes seven types of soil, Quaternary Alluvium and Termeil

Essexite are considered as fertile soil which in turn would produce high productivity

Therefore, high values were given to these soil types whilst the others were given low values, which assume that high values are associated with high agricultural suitability Reclass function was used here to set a value for each soil type, and it was divided by 10.0

to scale it from 0 to 1 (Agriculture-Soil Map) Following this step, slope was calculated from DEM On the other hand, slopes go up their numerical value when they get steeper, which in turn is the inverse of their value for agriculture Therefore it needs to be inverted

in order to produce a fuzzy representation of slope value for agriculture (Slope Map) The final stage is combining the two elements to produce agriculture model One important thing is that this model needs to be multiplied with ‘ocean’ to tidy the sea up so that the model could be more valuable (Agriculture Model)

In this model, most soils which are along the coast line seem suitable for agricultural

activities because along the coast line Quaternary Alluvium and Termeil Essexite are

dominant (Geology Map), associated with low values of slope (Slope Map) Obviously, these two types of soil along with flat topography are highly appropriate for cultivation

As a result, these areas which are depicted as green colour are probably the best option for agriculture regarding soil fertility and slopes in Kioloa

On the other hand, agriculture model could be made based on the grazing value of this area (Diagram 3) It depends on what the purpose of project Because grazing possibly cause a decrease in soil physical quality such as soil compaction due to the impact of animals’ hooves, the chief aim of the model is to focus on crop cultivation That is why Diagram 2 was chosen to build the agriculture model

Diagram 3: alternative agriculture model

3.3 Forestry model

The Kioloa region is covered by various types of forest that provide diverse benefits such

as commercial timber and medicinal values, so forestry sub model probably bases on forest value Furthermore, erosion index is really important to estimate places that need to

be protected from climatic factors such as rainfall Based on these two important elements,

a Forestry Model was built (Diagram 4)

Diagram 4: Forestry model

Firstly, the values of the forest given base on timber value It is known that Rainforest, Rainforest Ecotone and Wet Maculata have high valued timber while Wet Sclerophyll and Dry Maculata have lower timber values, and the remaining has no timber value These values are given by reclassification from the Vegetation Map (Forest Value Map)

Secondly, erosion can be seemed as a serious hazard index of the soil, which is comprised

of many physical parameters such as slope gradient, soil structure and vegetation covers

In order to produce daughter datasets for erosion constraint, universal soil loss equation (USLE) is applied, which consists of rainfall erosivity (R) values, soil erosivity (K), land cover types, slope length and slope steepness Particularly, the erosive power of a

rainstorm (R) assumed a constant in a small area as Kioloa is between 3,000 and 4,000 Next, the erodibility of the soil (K) based on SI units was determined (see Figure 1) Some physical parameters such as permeable ability, particle sizes and the percentage of sand, fine sand and silt, as well as organic matter content are taken into account in order to define K

Figure 1 : K nomograph in SI units

Subsequently, P and C values were also determined, which reply on what types of land covers It was assumed that soil covered by many trees is relatively less risk of erosion than soil that is wrapped by thinly scattered trees or bare ground As a consequence, CP values were assigned to reclass in a manner that higher values are more vulnerable to erosion Following this, slope length (L) was calculated from ‘flow direction’ and slope steep (S) were produced from ‘slope’ The combination of theses factors is to make the erosion constraint (Erosion Map) Finally, forestry model is created in the same pathway

of Diagram 4 The forestry map shows that ANU needs to concern areas that are steep and have high timber value These areas are localized on the map

3.4 Building model

The model of building established bases on many criteria, specifically the cost of slope as well as the distance from the roads and tracks, and the value of view and location

Therefore, these daughter datasets had to be fulfilled before generating Building Model (Figure 5)

Figure 5: Building model

First of all, the cost of slope is assumed that an average house footing cost 1,200$/degree,

so slope cost was create from slope raster formed in conservation model Next, the cost of the distance from roads and tracks was estimated by using EUCDISTANCE function, and

it is said that every 1000 meters from roads access building cost 5,000$ Then these two costs were combined to have the real cost for building (Cost Map) Subsequently, from

‘DEM’, Views were created by using ASPECT function (View Map) It is hypothesised that positions which are directed toward the East, Northeast and Southeast have higher values than other directions As a result, the Views were reclassed to have view values Following this stage, EUCDISTANCE was utilised to calculate the distance of each site from the beaches, and then combining ‘view value’ and ‘location value’ to gain the value