saga gis tutorial eng 2

8/12/2019 Saga Gis Tutorial Eng 2

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Remote Sensing for planning and good governance in Eastern Indonesia and Northern Australia is supported by the Commonwealththrough the Australia-Indonesia Institute of the Department of Foreign Affairs and Trade

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Materi Pelatihan SAGA GIS

Tampilan dan Analisis dari Citra Satelit dengan fokus padadaerah Nusa Tenggara Timur

Week 2

2.1

Kupang, 5-9 Maret 2012

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Contents

Field Work ..................................................................................................................................... 3

Collecting Field data................................................................................................................... 3

Using a GPS................................................................................................................................ 4

Importing Field data Into SAGA .................................................................................................. 5

Supervised classification ................................................................................................................ 7

Selecting Training Sites .............................................................................................................. 7

Classification .............................................................................................................................. 9

Accuracy assesment .................................................................................................................... 11

Ground Truth Data ................................................................................................................... 11

Creating an Error Matrix .......................................................................................................... 13

3D image visualization ................................................................................................................. 16

DEM data for landscape charachertisation and analysis ............................................................... 20

Charachterising vegetation ...................................................................................................... 23

Introduction to hydrological modelling ........................................................................................ 26

Creating a watershed map for West Timor. .............................................................................. 26

Creating a soil wetness index map. .......................................................................................... 27

Basic Risk assessment modelling .................................................................................................. 29

Inundadation risk for Kota Kabupaten Kupang ......................................................................... 29

Errosion Risk Assesment Sumba .................................................... Error! Bookmark not defined.

Quntam GIS for map production ................................................................................................. 31

Installing Q-GIS ........................................................................................................................ 31

Preparing a map ...................................................................................................................... 31

References .................................................................................................................................. 36


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Field Work

Remote sensing and GIS and are not always about sitting in front of the computer. Field survey is anintegral part of any mapping and monitoring program.

The aim of this field trip is to provide some field skills that are important for anyone working with

spatial data or the application of spatial data in environmental sciences. Being able to locate yourself

in the real world and on remotely sensed imagery or maps is an integral part of spatial science.

During the field trip we learn to:

1. Locate ourselves on a satellite image by identifying features in the environment;

2. Understand the effects of spatial resolution on the amount of information and detail that

can be seen in an image, and in turn how this affects the degree of certainty to which you

can locate yourself in the field;3. Use a GPS to read co-ordinates and collect waypoints;

4. Record field information for ground points

5. Use a field spectrometer to obtain spectral reflectance measurements

6. Use a sighting tube or densitometer to estimate vegetation cover?Maybe

Data collected in the field is most commonly used to either: inform a image classification,

particularly for the selection of training sites for a supervised classificationor as a reference data

for a post classification accuracy assessment. The most important thing throughout the field trip is

to keep in mind how satellite imagery represents what we are seeing on the ground.

Collecting Field data

The first thing we need to do is decide what data we are going to collect in the field. Point data

collected with a GPS can be attributed with as much information about the location as is useful.

Commonly for a landcover map you will want to collect data that describes the landscape

characteristics that will affect the satellite image spectral response, ie vegetation, soil colour, slope

etc. Once we have decided what data we are going to collect we can create a field data collectionform. This is most easily produced in excel. It is important to include on the form coordinate and

waypoint information that we will get from the GPS.

We may want to develop a standard code for entering data to simplify the process. Ie Padi=p, bakau

=b, tanah kosong = tk etc


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As we drive around we will stop at various sites so you can record information about the

land cover. At each site also try and locate your self on the satellite image map.

At some locationsmark out a ground representation of a Landsat 5 TM 30x30m pixel. Atthese locations:

o What are the different landcover types / features within the pixel? List in order of

dominance, and estimate the percentage coverage of each feature

o Imagine how the pixel would look in an image. In a landcover map based on a

Landsat 5 TM image, what landcover category would you assign to this pixel? What

problems does the heterogeneity present when creating a map from remotely

sensed data?

Using a GPS

1. Turn it on (button at the top near the aerial)

2. Wait for it to initialise. It will say Acquiring Satellites until it has

received a signal from enough satellites to determine a position.

On the screen you will see a diagram of the sky, with the location

of various satellites in the hemisphere. As a signal is received, the

satellite will turn black, and you will see a bar down the bottom,

indicating the strength of the signal received. The more satellites

and the larger the bars, the more accurate the position.

3. When enough satellite signals are received, the coordinates of

your location will be shown at the top, as well as an estimate of

positional accuracy. Be patient for this to occur!

4. To record your location (mark a waypoint):

a. Press mark and then enter.


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Importing Field data Into SAGA

The first step is to get your field data into excell. Enter the data you collected in the field into your

excell spread sheet:

Once in excell you need to export it into .txt (tab delimited format)

Open SAGA and and use the:

Modules> Files>Shapes>Import>Import Shapes from xyz

Enter which column contains the easting (x) and northing (y) values.

Display your points using vegetation type for

the display attribute.

First right click on the new point layer and

select Create Lookup table.

Select vegetation as the attribute to colour on:


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Open the lookup table from the settings tab:

Change the colours and descrition as you would like.

Open the a Landsat image for the area . How does

your does your vegataion attributes recorded from

the field match the satellite image?


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Supervised classification

We previously learnt how to conduct an cluster analysisclassification. This classification method is

commonly known as an unsupervised classification. This is because the classification is initially

conducted based on dividing the imagery into spectral classes with no direction as to what those

classes represent on the ground. With a supervised classifcation we use pre-existing knowledge of

the landscape, derived from field visits, vegetation mappping or high resoltuion imagery to define

image classes before the classification is run. A supervised classification can be a more intuitive way

to classify a landscape however the key to this technique is having good knowledge of the landscape

you are mapping. A field survey using GPS data is often a useful way of guiding your supervised

classificatuion. Remember it is also important to choose the correct imagery. For example in NTT

imagery early in the year will show a strong greeness response from the wet season making it

difficult to differentiate some landcover types.

In this exerscie we will conduct a supervised classification of the Bipolo/Kupang bay region using

2010 landsat imagery.

DATA>SAT>LANDSAT>11167_UTM>2010 folder.

Use the RGB overlay or composite tool to create and display a few band combinations (ie 3,4,5 /

1,2,3).

Selecting Training Sites

We now need to define areas of known landcover types. To assist us we will use some of the GPS

data from our field survey and the high resolution ALOS imagery. Load your saved survey data

points.

Load the ALOS AVNIR imagery and display it as as TYPE>RGB in the settings TAB.

DATA>SAT>ALOS> ALOS_AVNIR_050708_123_UTM.sgrd

Display the ALOS image in the same map display with your Landsat Band combinations. This will be

usefull for identifying different land cover types. We now need to choose sample areas for the land

cover types we wish to classify. For this exercise find samples for the following:

OceanLaut (L)

Tidal Mud FlatsRawan (R)

Bare/cleared landTanah Kosong (TK)

MangrovesBakau (B)

Savanna(SA)

ScrubSemak (SM)

Open Forest - Hutan Terbuka (HT)

Closed ForestHutan (H)


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We need to place a polygon around at least one area representing each type of landcover. These

regions are known as training sites.

First create a new polygon layer.

Modules>Shapes>Construction>Create new Shapes Layer

In the poroperties box name the new

layer and make the shape type

Polygon.

Double click the new polygon layer

and add it to your map window.

In the data tab right click on the

polygon layer and selct

Edit>Addshape:

Now use the cursor tool to create a polygon around a land cover type. Left click to creat points and

right click to finish the polygon. If you make a mistake

right click the poly layer:

Edit>Edit Selected Shape - do not save changes. This will

remove the incorrect polygonthen add a new shape.

When you are happy with the training site polygon use:

Edit>Edit Selected Shape andsave the changes.


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Now we need to lablel the training site. With the polygon selected

click the attributes tab ( ) and add a name to thepolygon. Click apply.

Continue adding training sites for all of the land cover

types you wish to classifiy. Save your training site

polygons to your working directry so you can use them

again later if needed.

Classification

Now we can run the supervised classification. Select:

Modules>imagery>classification>supervised classification


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Run the classification and then load the resultant grid into the same map window as the other

imagery.

In the settings tab use the select the look up table and change the display colours. approach.

When you are happy with your classifcation colours save them to your

work folder so you can use them again later.

You can see that with a supervised classification it is not nessesary to reclassifiy or group classes as

with a unsupervised

Compare your classification to the satellite imagery. Run the classification again using a different

combination of bands.

Save the classification Grid to your working folder.

A usefull way to visually asses the accuracy of your classification is to export some our all of your

classes as a polygon and overlay that on your sateelite imagery.

Shapes>grids>gridvalues>export grid values to polygons


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Accuracy assesment

Accuracy assessment is an important method for determining how much confidence you can have inyour final mapping product. To conduct an accuracy assessment, ground truth or reference point

data are collected for subsequent comparison with the classified data set. Ground truth data are

generally collected in the field using a GPS however other sources of reference data could be high

resolution satellite imagery (Google earth) or air-photos. It is important if using satellite imagery or

air-photos that they are not too old as on ground features may have changed considerable

compared to the imagery you are classifying. It is also important that your accuracy assessment data

be independent from any data you may have used to assist your classification.

Be aware when collecting and using your ground truth data of confounding problems related to

scale and location as explored during the field work. There are inherent problems in every accuracy

assessment process due to compare data from different scales. Therefore although the accuracyassessment process should be taken seriously remember the results are themselves should only act

as a guide to the level of confidence you can have in your results. For more information about

accuracy assessments refer to the paper included on the tutorial DVD: Accuracy assessment and

validation of remotely sensed and other spatial information(Congalton 1991)

Ground Truth Data

In this exercise we will conduct an accuracy assessment using ground truth points I have derived

from the ALOS-PRISM 2.5 meter resolution data. Load this data:

Data>Vector> GROUD_TRUTH_KupangBay.shp

Display the ground truth points with the classified data. Open the table for the ground truth point

data:


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In the point shape file table add a new field.

Give it the name class type

Now enter the corresponding landcover code for all 41 points. The right click

on the point layer in the data tab and save it as a new file. With a name like:

accuracy_points_kupangbay.shp and save it in your vector data folder.

Creating an Error Matrix

Locate the save shape file in windows explorer and open the

associated .dbf file in excel so we can do an analysis of the accuracy:

In excel select the tipe and class tipe fields then insert>pivot

table


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Click ok for the data range you want to

analyze.

Set up your pivottable with TIPE (your

ground truth data) as your column lables and

Class Tipe (you classified data) as your row

lableswith Count of class typefor the Values.

Copy and paste the resulting table so you have the table values out-side the active pivot table

calculation. You should have a table that looks something like this:

Row Labels B H HT L R SA SM TK (blank)

Grand

Total

B 1 1

H 1 1

HT 1 8 1 8 18

L 4 2 6

R 1 2 3

SA 5 3 8

SM 1 1 2

TK 2 2(blank)

Grand Total 2 2 9 4 4 6 9 5 41

We can then calculate the accuracy for the classification of each land cover type and a total accuracy

by dividing the total of each class by the number of times each class is mapped correctly (is the same

in both the ground truth and classified data). To produce a table that looks like this:


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Row Labels B H HT L R SA SM TK

Grand

Total

B 1 1 100

H 1 1 100HT 1 8 1 8 18 44

L 4 2 6 67

R 1 2 3 67

SA 5 3 8 63

SM 1 1 2 50

TK 2 2 100

(blank) 24

Grand Total 2 2 9 4 4 6 9 5 41

50 50 89 100 50 83 11 40 59

This table in known as an Error Matrix. This shows the number of points for each land cover type

where are the same in both the Ground Truth and Classification data highlighted in orange, the total

number of points classed the same highlighted in yellow and the over all accuracy (total number of

points divided by the total number correct) highlighted in red.

The probability of a reference sample being correctly classified in known as an omission error. The

omission error for each class is highlighted in green.

the probability that a sample classified on the map/image actually represents that category on the

ground is known as a commission error and is highlighted in purple.

In this case we can see that our total accuracy is quite poor (59%). Specifically our accuracy in

differentiating open forest and scrub is particularly poor. We will probably want to re-do the

classification possibly with different bands and or training sites to see if we can improve the result.

Another option would be to decide that the imagery is capable of successfully discriminating

between open forest and scrub and combining the two classes. It is often the case that Landsat data

has difficulty in differentiating low cover classes with the main reflectance response coming from

underlining soil or rock as opposed to vegetation properties.


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3D image visualization

Using a digital elevation model (DEM) we can view a landscape in 3 dimensions. This is very useful

for understanding a landscape and landscape processes particularly in the mountainous landscapes

of NTT.

We will start by viewing the SRTM (Space Shuttle Topographic Mission) 80 meter data for West

Timor. Open this data from Data>DEM> Timor Barat_80m.sgrd

Display it in a map window. Click the 3D tool at the top of the map window:

This will open as display

properties window like this:

Set the elevation as the Timor

Barat_80m DEM grid.

Set the exaggeration to 4.

This will increase the

elevation heights by 4 making

the topography easier to see.

Leave the resolution initially

at 200. Later you can increase

this resolution value which

will increase the display detail

and use more of your

computersmemory and

processing power.

Click OK to see the resulting

visualisation:


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Hold down the left or right mouse buttons to move the 3D

image around. Use your mouse wheel to zoom in our out.

Click the 3D tool at the top of the map window to change

the visualization settings. Try increasing the resolution

value.

Click the close window x at the top left of the map window

to close the 3D view:

Now open a Landsat image for path 111 row 67 and create

a RGB composite or overlay display. Display this in the

same map view as the Timor Barat DEM, ie:

Now create a 3D visualization

with the Satellite imagery:


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Another way to create a 3D look to your satellite image is to use an aspect layer to add to see some

topography. Use the Slope, Aspect, Curvature module:

Modules>terrain analysis>mophometry> Slope, Aspect, Curvature

This will create new slope and aspect grids.

Open the new Aspect grid into a new

display window.

Now display your RGB satellite image grid in the same map window over the

aspect grid.

In the settings tab set the transparency value for the RGB

satellite image grid to 20%:


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You will now see your satellite image shows

some topographic relief:


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DEM data for landscape charachertisation and analysis

DEM data can be very useful for describing landscapes. As we saw in the last section it is possible to

derive a range of landscape indices from elevation data including slope and aspect. Slope is a

particularly useful output from a DEM as it is important for a wide range of landscape processes and

the way we use land. Letshave a look at the slope model we produced previously using the Slope,

Aspect, Curvature module.

Double click on the slope

model to display it in a new

map window:

By default SAGA calculates slope in Radians. The radianis the standard unit of angular measure,used in many areas of mathematics. One radian is equal to 180/ degrees.In order to express slope

in degrees we need to multiple it by it by 180/ degrees or 57.2958. When display slope SAGA

automatically calculates degrees for us by applying a Z-Factoror multiplication of 57.2958 for the

display values. In order for us to save and use the slope grid with values in degrees we need to applie

this multiplication manually to create a new grid. To do this we use the grid calculator module:

Modules>Grid>Grid Calculus>Grid Calculator

Select slope as our grid

to perform the

calculation on. As we

have only one grid for

this calculation it takes

the value afor use in

the formula.

In the formula enter:

a*57.2958


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Click ok. Display the resultant slope in degrees grid. Right click this new grid layer and save as

Slope_deg_Timor_Barat.

If you open the histogram for this grid we can see the distribution of land on degrees of slope. To

make this clearer it helps to reclassify this grid to a few discrete slope classes. For example lets re-

classify the grid into the following classes:

Class Deg Slope

Flat 0-1

Slight Slope 2-5

Moderate Slope 6-10

Steep 10-30

Very Steep 30-90

Use the reclassify module:

Modules>Grids>Tools>values>Reclassify Grid Values

Create the Lookup table:


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Create a new lookup tablefor the five classes.

Right click>classification> create a new look up table

You can further refine the lookup table colours in the settings tab.

Open the histogram for the reclassified grid to see the

distribution of slope classes.

Click the convert to table tab at the top of the

display window.

View the resulting table:

You can the right click on the table and export it (save as) to open in excel for further analysis.Remember area is shown in m

2. Divide the area value by 10000 to show it in hectares.

Try and do the same thing with elevation, ie re-classify elevation to a few classes ranging from low

land to high mountains.


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Charachterising vegetation

Now letsuse this reclassified slope grid to characterise our vegetation classification of Kupang bay.

Load the supervised vegetation classification completed in the earlier section. Open up its lookup

table from the Setting Tab and load the Lookup table you saved previously.

First we need to resample the reclassified slope grid so it is in the same coordinate space as our

vegetation classification. Use the resample module:

Modules>Grid>construction

>Resampling

You now need to reclassify the slope grid again this time we will give

the new grid the follow values:

You will see the reason for this when we add the two grids together.

Modules>Grids>Tools>values>Reclassify Grid Values

Rename the new slope grid slope for calc so you dont get it

confused with the other reclassifiesd slope grid

Now we can add the vegetation classification and the slope grids togeather using the grid calculatortool:

Modules>Grid>Grid Calculus>Grid Calculator


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Right click on the resulting classification grid and create a look up table. Set the classification type

to unique values and using the

colour selector set he count to 100

Now open the histogram from the resulting calculation grid.


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We can see how the in the calculation grid the 100s represent the different slope classes and the 1-

8 values represent the vegetation classes. For example the value 107 represents the amount of

open forest (class 7) on flat land (class 100)

Click the convert to table tab at the top of the display window. Export the resulting table in

.dbf format and open in excel for further analysis. For example the distribution of forest on different

slope classes:

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Flat Slight Slope Moderate

Slope

Steep Very Steep


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Introduction to hydrological modelling

Digital elevation data can be used to derive a wide variety of hydrological parameters. In fact SAGA

was initially developed to allow hydrological modellers to develop their own applications as a

consequence SAGA contains a wide variety of sophisticated models. We will look at two of these just

to introduce to the power of DEMs and SAGA for this application. They are:

Creating a watershed map for West Timor.

Creating a wetness index.

If you still have data open from the previous session start a new project so we are starting clear of

other data.

Creating a watershed map for West Timor.

Open the Timor_Barat_80m DEM and display it.

We will use the module Fill Sinks (Wang Liu) (Wang and Liu 2006)) to create our watershed gird:

Modules>Terrain Analysis>pre-processing>Fill Sinks (Wang Liu)

A water shed model is derived from

understanding water flow direction within a

landscape. In order to model flow direction

correctly a DEM is produced that is free of sinks

or depression areas that water will not flow

through. So this modules creates 3 separate

GRIDS:

a no sink DEM

a flow direction gird and

the watershed basin grid

On the resulting watershed basin grid can you see the Noel Mina catchment?

How would you export one catchment area as a polygon for use elsewhere?

How many catchments has the module identified?


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In the setting tab name the resulting Grid as Noel Mina:

To create our wetness index use:Modules>Terrain

Analysis>Hydrology>Topographic

Indicies>Saga Wetness index

Use the in the properties window set

Elevation to our Clipped Noel Mina Grid.

Think about the type of field surveys you might do

to verify this data.

How might this map be used in rural development

planning?

How might we link this data to malaria occurrence

data?


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Basic Risk assessment modelling

Remotely sensed data can be very useful for natural hazard risk assessment modelling. In particular

digital elevation and land cover data can be applied to develop flood inundation modelling

(storm tide and riverine) and landslide/erosion susceptibility. This type of GIS modelling isbeing used extensively in relation to modelling climate change impacts. Climate change

vulnerability models are commonly expressed in terms of the following framework.

Exposure is the probability of an area or asset being negatively effected by a natural event. Exposure

to a hazard is often modelled using GIS tools with a DEM being the ost important base data.

Sensitivity is usually related to the value and level of sensitivity an area or assets has to a natural

event such as flood. For example in terms of imeiedit effect a clinic would be more sensitive than a

factory or a rice field more sensitive than mud flats to sea level rise and storm surge. So it is also

possible to create a sensitivity map using satellite imagery by locating and attributing regions and

assets with levels of sensitivity. By combining exposureand sensitivityis is possible to map Potential

Impact. Combining this with Adaptive capacityresearch it is possible to understand the Venerabilityof various communities to natural hazards and climate change.

In this section we will look at two examples of risk assessment. In the first we will use the DEM to

model the exposure of the new Kota Kabupaten Kupang has to sea level rise, storm surge and

tsunami. The second example will look at erosion risk modelling from Sumba Timor.

Inundation risk for Kota Kabupaten Kupang

Kota Kupang would be regarded as highly sensitive to flooding due to the value of the built

infrastructure. The potential impact from sea level rise from sea-level rise or Tsunami could be very

high. Start a new project and open and display the ALOS imagery. Try and locate the Kota Kabupaten

Kupang. Use any GPS data you collected to help.

Open the Timor Barat DEM and display it over the ALOS imagery.


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In the map display tab select the DEM layer:

Move the cursor over the Kota Kabupaten Kupang area. Note the

z value at the bottom of the display window. What is theelevation or the Storm or Tsunami surge hight that would impact the city?

Lets now make a simple risk map.

In the Settings Tab change the colour display type to Lookup Table:

Open the lookup table:

Create a look up table

with 4 risk exposure

classes:

Click on the legend tab to see the

names of the displayed classes.

What risk exposure class does Kota

Kabupaten Kupang lie?

Dont forget to save this project in your working folder as we

will come back to it later.

Note the vertical onshore height and distance inland or a

storm event is a function of a variety of variables such as tide,

barometric pressure and windspeed/direction. The likelyhood of these various surge hights being reached needs to

modelled taking into account these multiple variables (Ozcelik, Gorokhovich et al. 2010). However

with climate change induced sea-level rise and a possible increase in severe storm events the chance

of extreme storm surge events is rising.


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Quantum GIS for map production

A significant limitation of SAGA-GIS is its map making function. To produce good maps for reports,

presentations or general display it is better to use alternative software. There is a wide range of

open-source software available to do this. In this workshop we will be using Quantum GIS (Q-GIS). Q-

GIS is a versitle an comprehensive GIS package, although primarilry focused on working with vector

data it is able to handle some Raster formats. Download the latest versions of Q-GIS here at

http://www.qgis.org/.Q-GIS is well supported and used by many government and NGOs around the

world.

Installing Q-GIS

In the software folder of the workshop 2 DVD you will find the Q-GIS installation file: QGIS-1.4.0-1-No-GrassSetup.exe

Install Q-GIS. If you have trouble with Q-GIS installing correctly try installing it to a folder other than

the programs folder.

Preparing a map

We will start by making a map from our Kupang Bay storm surge risk assessment. In SAGA open the

inundation risk project you had previously saved.

Select the ALOS Satellite image Grid in the Data Tab and change its name to ALOS Image in the

Settings Tab.

Change the Elevation grid name to Inundation.
http://www.qgis.org/http://www.qgis.org/http://www.qgis.org/


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Check the legend tab for the

elevation layer.

Zoom to the area of the Kupang bay inundation map you would like to print in the map display

window. To export this in a format we can using in Q-GIS use:

Map>Save As Image

Save in your working folder as TIFF format. In the

properties window increase the Pixel with and height.

Click save Georeference world file. Select save legend

and set the zoom to 5 so it produces a high quality

legend image.

Open Q-GIS.

Use the Add Raster Layerbutton to add

the saved TIFF file.

Click the Print Composerbutton:


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In the Print Composerclick the Add Map button

and draw a box in the layout area where you want

the map to display.

Use the properties bar at the right of the print composer window to change attributes of the display.

For example use Item Tab>General Options>Show Frame to add or remove a boarder around the

map.

Now lets add our map legend using the Add image button. Draw a box in

the layout area where you want the legend to appear.

In the options properties of the legend item menu

load the Legend.tiff

Change the location and size of the legend to suit the

map.

Now we will add a scale bar. Draw a box in the layout area where

you want the scale bar to appear.


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Set the Segment Sizeto

the number of meters

between each map

division. Set the Map units

per bar unit to 1000 (ie

1000 meters = 1km).

Now add a title to your map using the label button:


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Use the output buttons to print or export the map.


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R t S i f l i d d i E t I d i d N th A t li i t d b th C lth

References

Bhner, J., R. Kthe, et al., 2001. "Soil regionalisation by means of terrain analysis and process

parameterisation." Soil Classification: 213-222.

Cohen, J. M., K. C. Ernst, et al., 2008. "Topography-derived wetness indices are associated withhousehold-level malaria risk in two communities in the western Kenyan highlands." Malaria

journal 7(1): 40.

Congalton, R. G., 1991. "A review of assessing the accuracy of classifications of remotely sensed

data." Remote sensing of environment 37(1): 35-46.

Ozcelik, C., Y. Gorokhovich, et al., 2010. "Storm surge modelling with geographic information

systems: estimating areas and population affected by cyclone Nargis." International Journal

of Climatology.

Wang, L. and H. Liu, 2006. "An efficient method for identifying and filling surface depressions in

digital elevation models for hydrologic analysis and modelling." International Journal of

Geographical Information Science 20(2): 193-213.

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