Name: Biokoro Prince Onoriode

Student Number: 5186250

EESC204: GIS PROJECT REPORT.

EXECUTIVE SUMMARY

Landslides are natural phenomenon that occurs as a mass movement of land over a wide

range of velocities as a result of failure of rock material that make up its slope and is driven by

force of gravity. The damage caused subsequently can be to a great degree. Consequently, it gets to

be important to explore potential zones which are inclined to landslides. While there are different

techniques for researching landslides, GIS programming offers a more exact, precise, and quicker

strategy for examination and map projections which would help in satisfactorily decreasing or even

avoiding damages caused as a result of landslides. This report quickly explains three different

susceptibility degrees to which landslides could occur at the Royal National Park located in New

South wale (NSW) in Australia. These susceptibility degrees to landslides vulnerability areas are

categorized into susceptible, moderately susceptible, and highly susceptible areas. Different

Approaches has been taken considering other factors such as, water proximity, vegetation and slope

and road data. With the aid of ArcGIS, evaluation is done and maps are produced to show zones

within the National Royal Park that are prone to varying degree of landslides.

Introduction Landslides are natural phenomenon that leads to the formation of slopes and the evolution

of landscapes. “It is the mass movement of rocks, debris and/or earth down slope” (Huabi et al.

2005). It occurs as a result of failure of rock materials that make up its slope and is driven by

force of gravity. ). Landslides are categorized based on the type of material involved and the type of

movement (USGS, 2004). Landslide has been known as one of the biggest hazards due to the factors

that cause it, of which most of them are naturally occurring and includes slope angle, climate, water,

vegetation, slope stability, water content, stratigraphy, geotechnical strength parameters,

hydrogeology, geomorphology etc.

It is evident that the earth’s plates are moving beneath our feet every year (4cm per year), but

sometimes this movement is much larger that it is obvious to seen. These movements could be several

meters even kilometres and could result to huge amount of damage. For instance, on August twentieth

2014, landslides happened in Hiroshima Prefecture, Japan, called '2014 Hiroshima landslides'. In this

landslide no less than 32 have been killed and more than 38 individuals have been missed and it

brought on immense property damage close to a mountain. It is known as deadliest landslides in 42

years (Geological Society of Japan). Australia, likewise, has been confronted by different landslides

in a decade ago. 114 landslides are known to have brought about damage or death from 1842-2011

and no less than 138 individuals have been killed. The greater part of the landslides that brought about

any injuries or death from 2000-2011, has happened as a result of human action (Leiba, 2013).

The latest of these landslides being the Columbia landslides which happened on the nineteenth of

May 2015 and killed 52 individuals, leaving 37 injured (ABC News 2015).

Landslides happen quickly and abruptly. As such landslide prediction becomes of great importance.

GIS programming offers map projections and a more exact, precise, and speedier technique for

examination of past events, and make conceivable future events. This is imperative for foreseeing the

presumable event of landslides as it offers different ways and approaches and give solutions to

minimize landslides impacts.

This report is attempted to get to the landslides inclined circumstance in the Royal National Park,

which is 30 km far from Sydney in New South Wales. Being one of the oldest Parks in the world,

thousands of people visit annually and as such, landslide evaluations and predictions becomes of great

importance.

Figure 1. “The Royal National Parks gotten from http://www.npws.nsw.gov.au/.”

Different studies have utilized GIS-based mapping or GIS tools for investigating the landslides

possibilities, impacts, proposals and strategy for such occurrences. For instance, GIS was utilized for

landslides susceptibility mapping in Dervrek, Zonguldak, Turkey (Yilmaz et al., 2012). In their study,

they make used of GIS to produce a map of landslide susceptibility and compared the outcomes with a

statistical analysis conducted with three distinctive methodologies in seed cell concept. Another

instance of GIS used for landslide susceptibility mapping (Saro Lee, 2004). In this study, a Bayesian

likelihood model, a probability ration and statistical model, and logistic regression to Janghung, Korea

was used and proved and afterward GIS was utilized to delineate the susceptible landslides territories.

Another example is the utilization of GIS to create landslides hazard maps (Wang and et al., 2010).

With this mapping constructions can be then be built according to the level of hazard and existing

structure can be retrofit as necessary. They utilized new GIS-based by utilizing the contributing

weight model, to evaluate the risk of landslide. Thereafter, map produced was made available to the

public in case there is an earthquake.

The purpose of this study is to group areas of the Royal National Park that are susceptible to landslide

in varying degrees from highly susceptible to moderately susceptible and to susceptible areas.

Thereafter, sections of sealed roads that fall inside the landslides zones are determined and this will

help to define some strategist for rescue if there should be an occurrence of landslides. Likewise,

other factors within the region that could contribute and tigger landslides, such as, vegetations and

water proximity are considered and their effects are investigated and discus.

METHODS

This project has been undertaken to map landslides susceptibility assessments in the Royal National

Park located at southern fringe of metropolitan Sydney, New South Wales, Australia. This

analysis would help to zone the park into highly susceptible, moderately susceptible and to

susceptible areas to landslides, and to estimate the degree and extent, to which it may cover using

ArcGIS. Furthermore, result are analysed and its implications for evacuating visitors at the Park

in a likelihood of landslide is deduced.

For this analysis, ArcMarp has been solely used to manipulate and interrogated the available

data that has been provided by course coordinator. All data provided have been referenced to

the same coordinate system. With the aid of ArcCatalog, data were carefully managed.

The objectives of this task are as follow: Using the appropriate GIS tool, we would map out area of the Royal National Park that are

highly susceptible, moderately susceptible and susceptible to landslides and calculate

percentage area of the park that lies in each zone.

Next, is to identify sections of sealed road in the park that falls within each zone of

susceptibility.

Thirdly, is to map out all susceptible zones that are at proximal to water source and

after that calculate its percentages in connection to total susceptible areas in the park

Fourth, is defining the vegetation areas in each zone, and determining the vegetation

type and abundance of particular vegetation that is existed in highly and moderately

susceptible areas. Determine possible factors that are expected to estimate the direction of a landslide starting at

a given point and determine what data layers and ArcGIS tools would be needed.

In conclusion, discuss results, assumptions and limitations encountered.

GIS Tools

Four fundamental characters were utilized as a part of this report to express input,

process, and output.

Input (vector dataset)

ArcGIS Tools

Out put (raster dataset)

Out put (vector datasets)

Below are ArcGIS tools used and their application to the project:

Reclassify: This tool is used to answer a raster database query by creating a new grid which

includes all information in the form of two values 0, and 1. (0 if the query or criteria was not

met and 1 if the query was met).

Buffer: This tool is used to create polygons around an input features to a specified distance. It

makes a boundary (a limit) around that feature at a measure distance

Select: This tool usually creates a new output layer by extracting features from a feature class

as per request using Structured Query Language (SQL) (vector query) expression.

Raster calculator: is used to perform mathematical calculations on raster dataset. The tool

permits making and executing a Map Algebra expression that will output a raster. Raster

calculator uses Boolean algebra and supports several mathematical operators by assigning

values of the variables are the truth-values true and false.

Polygon to raster: It is a conversion tool that converts polygon features to a raster dataset. The

cell_center approach will be use in assigning values to the cells within features.

Polyline to raster: It is a conversion tool that converts Polyline features to a raster dataset. The

Maximum_length approach would be applied in assigning values to the cells within features.

Raster to polygon: This converts raster dataset to polygon features.

Raster Overlay analysis using Union /intercept tool: This tool combines multiple raster

dataset, then unique value determined to each unique combination of input values.

Zonal statistics: Summarizes values in a raster layer by zones (categories) in another layer and

demonstrates the all results in the form of a table.

The GIS data available include; aspect, Boundary, dem, demlandsea, downhill, slope_d, slope_P,

tracks, Veg and water. Using the decision rule set out for the project, each of this data would be

interrogated to map out the different criteria.

Procedures

The table below and flow chart shows the step taken in arriving at our output in this analysis.

Mapping highly susceptible areas to landslides.

Criteria: Slope should be > 35 degree and must be undercut by water (water must be within

50 meter).

Data used: water dataset and slope_d dataset.

Analysis: First, water layer is buffered by 50m, and is then converted to raster layer.50m Buffered

water is then reclassified (1-451 as 1: and < 1 as 0)

Secondly, reclassification is carried out on slope_d. Slope>35degrees is assigned a value of 1and

slope<35 degrees is a value of zero (0). Finally, raster calculator is used to combine the two new

layers formed using the Boolean (AND (*), a multiplication function) to obtain area that are highly

susceptible as High_sucep1; as show in model below (figure 2). Dataset is added to layer to display

the map of highly susceptible area (Figure 12.).

Figure 2, Shows Model that was used to arrive at area that are highly susceptible to landslide.

Mapping moderately susceptible areas to landslides.

Criteria: if grid cell is steep enough to be a cliff but not at risk from undercutting by water erosion

Data used: water_reclass dataset and sloped1_drecl dataset

Analysis: First, I reclassify the raster layer of water_reclass, and assigned a value of (0 and 1); that is

a reverse of value. The created layer from the first stage (50WaterRecl_Rev1) is then combined with

(sloped1_drecl) created from highly susceptible area. Finally, using the Boolean (AND (*), a

multiplication function) to obtain area that are moderately susceptible as (mod_susccp); as show in

model below (figure 3). Dataset is added to layer to display the map of moderately susceptible area

(Figure 11.).

Figure 3, shows Model that was used to arrive at area that are moderately susceptible to landslide.

Mapping susceptible areas to landslides.

Criteria: Slope greater than 10 percent but not steep enough to be considered a cliff.

Data Used: slope_d and slope_p

Analysis: First, the reclassified tool is used to reverse slope_d > 35degree (used previously in highly

susceptible area) to slope_d <35degree slope, and output is Slope_d35_Rev.

Secondly, slope_p which is the dataset to be used for slope >than 10%, is then reclassified and assign

a value of 1 for slope >10% and the other area was assigned a value of 0 and output is

Reclass_slope_p10.

Finally, using raster calculator; the Boolean (AND (*), a multiplication function) we combine both

layer to obtain area that are susceptible as (susceptible); as show in model below (figure 4). Dataset is

added to layer to display the map of susceptible area (Figure 10).

Figure 4, shows Model that was used to arrive at area that are susceptible to landslide.

Percentage area of the Park in each zone that is susceptible to landslide

This is determined with a simple mathematical calculation by multiplying the cell size of each area

and divide by the whole area of national park. Result is dhow in table 2 in result section.

Mapping the sections of sealed roads within the Park that fall within each susceptibility zone.

Aim: To identify seal road that fell on or intercept each susceptible area.

Data Used: track dataset + (combined) with each susceptible area. Track dataset is used because it

contains all roads, tracks, walking paths, trails, highway, sealed major public road, and sealed minor

public road within the park.

Analysis: first, Select tool is used to select ‘highway, sealed major public road, and sealed minor

public road’ from other road in the park. Using ‘Type’ for selecting them and the SQL was express as

'Type = ' Sealed major public road' OR Type = ' Sealed minor public road'. The resulting data is

tracks_SealedRD.shp. Thereafter, tracks_SealedRD.shp was then converted to raster. The new raster

dataset was reclassified as 1-96 as 1 and Nodata as 0 to get sealed road. The output data is

SealedR_Recl (Figure5).

Figure 5, shows Model that was used to arrive at sealed road. Highway, Sealed major public road, and sealed minor

public road were selected and vector dataset tracks_SealedRD was created. New selected dataset was converted to

raster as Sealed_Raster, and then reclassified to sealedR_Recl.

To get sealed road that intercept each susceptibility zone (Figure 6), dataset SealedR_Recl was

combined using raster calculator; the Boolean (AND (*), a multiplication function) with each

susceptibility zone; that is highly susceptible, moderately susceptible, and susceptible datasets and

model Output for intercepted sealed road are High_Suc_Road, mod_Suc_Road, and

Susceptible_Road respectively. These new Dataset are added to layer to display the map and mark

the point of interception with seal road. (Figure 14).

Figure 6, shows Model that was used to arrive at sealed road that fell on each susceptible zone

Percentage of susceptible areas that is located near water

Flooding can trigger a landslide in a susceptible area, so identifying susceptible areas that is located

near water is very important.

Criteria: Near water source is defined as within 80 m.

Data Used: water dataset and dataset of each susceptible

Analysis: water dataset is buffered within 80 meter, (output= water_80mBuffered), water_80mBuffered is

then converted to raster- then reclassify as (1-1897 as 1: NOData as 0) to get flood_water. Finally reclassified

dataset (flood_water) is then combined with each susceptibility zone using raster calculator, Boolean (AND (*),

a multiplication function) to obtain susceptible areas that are located near water as flood_hi_Sucp,

flood_mod_suscp and flood_susceptible as show in model below (figure 7). The results were added to

display to show the map of these areas (Figure15)

Figure 7, shows Model builder that was used to arrive at each susceptible zone located near water.

Defining the vegetation areas in each zone, and determining the average

susceptibility level for each vegetation type

Landslides can have long term impacts on vegetation.

Data Used: Vegetation dataset were used to determine the vegetations types that are in highly,

moderately and susceptible areas.

Analysis: The Veg dataset is converted to raster dataset and thereafter, combined with all three areas

(susceptible, moderately susceptible, and highly susceptible) using the combine tool. Combined

dataset is reclassified, giving Not susceptible as 0, susceptible as 1, moderately susceptible as 2, and

highly susceptible as 3, then this data is converted using raster to polygon tool to create vector map

(Figure8). Then, this output mapped to showed the vegetations that are in these areas (Figure16).

Figure 8. Shows Model for vegetation in highly susceptible, moderately susceptible, and susceptible areas, combined

with raster vegetation dataset.

Next is to determine the vegetation types that are in highly or moderately susceptible areas, and the

abundance of these vegetations in these two areas. The zonal statistic table tool was used to

determine the type of vegetation in highly susceptible and moderately susceptible areas. Figure 9

below shows the model for obtaining new attribute table (table 3 and 4) for highly susceptible and

moderately susceptible areas.

Figure 9. Model created for vegetation types in highly susceptible and moderately susceptible areas. The outputs were

added to display as a table. From these tables the vegetation types, that are located in highly and moderately

susceptible areas would be determined.

Results

The Royal National Park area was mapped to determine susceptible areas to landslides. Output map

would define where in Royal National Park that is susceptible to landslide. Results from analysis of

the area of Park that are susceptible, moderately susceptible and highly susceptible to landslides are as

follow:

Susceptible Areas

The map below shows the susceptible areas of the Royal National Park. Susceptible areas cover

larger portion of the park and are located near water source from east site of the Park, where the

ocean is, close to rivers and streams in the middle of the Park. It is known from literature, that Royal

National Park cut-across rivers and creeks, hence; it can be use to determined the spread of

susceptible area by their location close to the water. .

Figure 10. Shows output Map of susceptible area. Areas are indicated by purple colour. It almost cover the entire

land area at the Royal National Park

Moderately Susceptible: it covers smaller portion of the park when compare to susceptible areas and

are mostly located at the southernmost and north-south area of the park.

Figure 11. Shows output Map of moderately susceptible areas to landslide in Royal National Park.

Areas are indicated by yellow colour.

Highly susceptible area: These area covers very small portion of the park and are mostly

located around the boundary of the Royal National Park to the North-East, East, and South-

East which are known to be closer to the ocean from demlandsea data value, and a few

located to the NNW and centre of the park.

Figure 12. Shows Map of Highly susceptible area to landslide in Royal National Park. Areas are indicated with Red dots

Map below shows all area of the Park that is Susceptible to landslides. It gives a quick overview of

the percentage, the degree of each susceptibility zone in relation to the entire area of the park. It also

compares the ratio or percentage of each susceptibility area with one another, that is, ratio of

susceptible: moderately susceptible: highly susceptible

Figure 13. Shows Map of all susceptible area to landslide in Royal National Park.

The table below further shows the total area covered in the park for each susceptible area.

Susceptibility Area Percentage (%)

Susceptible 57.29

Moderately susceptible 0.43

Highly susceptible 0.13

Table 2 Shows the percentage of each Susceptable area with respect to total area

The section of sealed roads within the park that falls in each susceptibility zone.

Seal road sections are identified and mapped to contain highway, sealed major and minor public roads

Figure 14. Sealed roads, (Highway, major and minor sealed public roads) that run through the Park.

Identifying point of interception of sealed road with each susceptibility area

The map below shows point of intersects of sealed roads with each susceptibility area. This map is

vital as it would give a guide to area that should be avoided during evacuation in a case of a landslide

occurring at the park. With this map, a better evacuation plan can be deduced, to avoid susceptible

zones to landslides. Locations Number 1, 2, and 3, in figure .15 is a possible evacuation point as those

area neither intercept sealed road nor susceptible to landslides.

Figure 15 Shows the extracted sealed roads and their point of intercept with each susceptibility area. location number

1, 2, and 3 suggested as possible point of evacation in case of a landslide event.

Percentage of susceptible areas that is located near water

Flooding can trigger landslide to occur, so it is important to identify and map susceptibility areas in

each zones that are near to water source (within 80 meter is considered as near a water). In addition,

the percentages of all susceptible areas were calculated as shown in Table 3.

Figure 16. Show all susceptible areas prone to flooding. Area was buffered within 80 meter to water source.

Combine percentage of all susceptible area located near water.

Count Value

All Susceptible areas located

within 80 meter to water source

74561

Total susceptible area 141675

Combined Percentage (%) 52.63

Table 3. Shows combined percentage of all susceptible areas located near a water source

Defining the vegetation areas in each zone, and determining the average

susceptibility level for each vegetation type

Landslides can have long term impacts on vegetation. In the vegetation dataset of the Royal

National Park, 27 types of vegetation are found in the land area of the park. The areas in the park

where vegetations are in none susceptible area, susceptible, moderately susceptible and highly

susceptible area was identified (Figure 17). Also, three types of vegetation were found to be on

average moderately and highly susceptible as show in table 4.

Figure 17. Shows degree of each vegetation type in Royal National Park, regarded as Not susceptible, suscpetible,

moderatly suscpetible ,and highly suscepltibe.

Table 4 shows the table used for percentage calculation. The highlighted cells are the top 3 vegetation, determine by

vegetation types with the largest area.

DISCUSSION

Results shows that that susceptible areas are mostly located near water source and located

away from the ocean. The moderately susceptible areas are located mostly towards the south, with

some scattered inland. The highly susceptible areas are located mostly around the boundary between

land and ocean with a few scattered inland. It can also be observed that there was a major reduction in

area covered from susceptible to highly susceptible areas. This is confirmed from table 2 as

susceptible areas cover most areas of the park. As a result of close proximity of the susceptible areas

to a water source and its high coverage, we can reason that the susceptible areas were affected by the

presence of the water source. Two assumptions can be concluded from this; first, if the susceptible

areas were as a result of past impacts of the water source then it would imply that there would be a

further increase in moderate to highly susceptible zones. Secondly, if this impact is always happening,

then it could imply that in addition to future increment in moderately and highly susceptible areas,

there would likely be higher danger of landslide event happening. Thus, regardless of the fact there is

a higher susceptible area, this will change later on. This point is further reinforced by the fact that

there is a fair amount of scattering of both moderately and highly susceptible areas around the

northern and western parts of the map where larger body of water source is located.

Sealed road affected by each susceptibility zone are less and as such planning for evacuation

route should be simple. The sealed roads are blocked in three places, one is to the Northwest, the other

is to the Southeast and the last one is on the south-west (Figure 15). This give enough escape routes as

numbered out (1, 2, and 3) in figure 15 as possible point of evacuation.

From table 4, it could be deduced that highly susceptible layer are covered by forest. The

presence of these forest trees could reduce the impact of flooding in this area; as trees will break down

the force and speed of water.

To determine and understand the trajectory of a landslide starting at a given point in RNP, more

data would be needed to further carry out this analysis. In addition to the data used for this

analysis, and the outcome of the analysis, we will need data on history of previous landslides

around and within the areas, Geological data, bedrock data, slope orientation, hydrogeological data

and slope steepness. The combination of these data and when used to map the Royal National Park

would help to develop landslide Hazard maps. With the availability of this data, a GIS analysis can be

conducted. Bedrocks influence the stability of an area to some extent, this will eventually trigger

landslide. So bedrock data should contain rocks type of the area. This could be check from attribute

table, then selected and reclassified to show bedrocks that can influence landslides. Slope steepness

also trigger landslide, so areas that a steep-cliff should be determine using buffer tool, and then

reclassify to show point of steepness and know its trajectory in case of land movement. Hydrogeology

plays a key role in the stability of a slope. Its influence can be determined by indirect measurement to

know its contribution to triggering a landslide. Hydrogeology data and other water-bodies can be

buffered using the buffer tool and then reclassified and finally use raster calculator to determine point

of interest. The result can be combined to determine the trajectory of a landslide.

There were errors in the data. So having a more accurate data, would yield accurate results.

For instant, in sealed road analysis, moderately and highly susceptible areas fall on the same point

with the susceptible area. Also, vegetation data did not accurately conform to raster calculator during

the analysis. There were also areas that fell outside the boundary in most of the map. All these error

should be put to check to have more precise result.

Recommendations The result of this project gives a general overview of the susceptibility nature of the Royal national

park in case of landslide occurring. Detail work would be needed to determine the full extent to which

the RNP is susceptible to landslide. This will require more filed work, more data, and further

statistical modeling and analysis.

References ABC News, 2015, Dozens dead after Colombia landslide, viewed 27 October 2015,

<http://www.abc.net.au/news/2015-05-19/dozens-dead-after-colombia-landslide/6479462>.

Gangjun, L, Gonghui, W, Huabin, W, Weiya, X, 2005, ‘GIS-based landslide hazard assessment: an

overview’, Progress in Physical Geography, vol.29, no. 4, pp. 548-567.

Jianping, Q, Meng, W, Siming, HE, 2010, ‘GIS-based Earthquake-Triggered Landslide Hazard

Zoning Using Contributing Weight Model’, Science Press and Institute of Mountain Hazards and

Environment, vol. 7, pp. 339-352.

Lee, S, 2004, ‘Application of Likelihood Ratio and Logistic Regression Models to Landslide

Susceptibility Mapping Using GIS’, Environmental Management, vol. 34,no. 2, pp. 223-232.

Leiba, M, 2013, ‘Impact of landslides in Australia to December 2011’, Australian Journal of

Emergency Management, vol. 28, no.1, pp. 29-34.

U.S. Geological Survey, 2004, Landslide Types and Processes, USGS, p. Fact sheet 3072.

Wang, M, Qiao, J & He, S 2010, 'GIS-based earthquake-triggered landslide hazard zoning using

contributing weight model', in , J. Mt. Sci., vol. 7, no. 4, pp. 339-352

Su¨zen, ML, Topal, T Yilmaz, C, 2012, ‘GIS-based landslide susceptibility mapping using bivariate

statistical analysis in Devrek (Zonguldak-Turkey)’, Environmental Earth Sciences, vol. 65, pp. 2161-

2178.