1

Name : Nor Khairunnisa Abd LatipName : Nor Khairunnisa Abd LatipCourse : SGS (Remote Sensing)Course : SGS (Remote Sensing)

Supervisor ‘s Name: En Zuraimi b SuleimanSupervisor ‘s Name: En Zuraimi b Suleiman

PADDY CLASSIFICATION IN SIBU, PADDY CLASSIFICATION IN SIBU, SARAWAKSARAWAK

OutlineOutline

2

IntroductionIntroduction

ObjectiveObjective

ScopeScope

MethodologyMethodology

Result and DiscussionResult and Discussion

Conclusion and RecommendationConclusion and Recommendation

TimetableTimetable

IntroductionIntroduction• Land cover refers to the surface cover on the ground,

whether vegetation, urban infrastructure, water, bare soil or other. Land use refers to the purpose the land serves, for examples, recreation, wildlife habitat, or agriculture.

• Satellite remote sensing technology using optical and radar remote sensing techniques have been used successfully in land cover map. Some advantages of these techniques are cost effectiveness, wide coverage, near real time data acquisition and frequent revisit capability.

3

• In Remote Sensing Malaysia, Project Land Cover Map collaboration with Malaysia Department of Agriculture, Department of Land and Survey Sarawak and Malaysia Department of Townplan has been conducted. The study area include Kelantan, Pahang, Sarawak and Selangor state. Two map of land cover map for Sibu and Matang, Sarawak area have been produced.

• In Sarawak, there are two types of paddy, wet paddy and hill paddy. This project are focused to classify the hill paddy area and wet paddy area.

4

5

Figure 1.0 show paddy area in satellite image and figure 1.1 show paddy area on ground.

Figure 1.0 Figure 1.1

ObjectiveObjective• To classify the wet paddy and hill paddy area in

Sibu, Sarawak using Remote Sensing and GIS Technology.

6

ScopeScope

Study Area:• Sibu, Sarawak.• Path row:291/346

7

Data :

8

SPOT- 5 (10m)

Multitemporal -6/1/2010-1/7/2006-8/8/2005

Multispectral - B1 (green :0.50 - 0.59m)- B2 (red :0.61 - 0.68m)- B3 (near infrared :0.78 - 0.89m)- B4 SWIR (short-wave infrared :1.58 – 1.75m)

MethodologyMethodology

9

Data CollectionData Collection

ReprojectReproject

Sibu Landuse Classification in vector format

Sibu Landuse Classification in vector format

OverlayOverlay

Refinement: Manual Editing – change the overestimated paddy area to neighboring class

Refinement: Manual Editing – change the overestimated paddy area to neighboring class

Update Sibu Map base on Planted Paddy Area

Update Sibu Map base on Planted Paddy Area

Sibu Landuse Map Sibu Landuse Map

Accuracy AssesmentAccuracy Assesment

SPOT-5Multi spectral (10 m)

SPOT-5Multi spectral (10 m)

Extracted the planted paddy areaRuleset:

•With mean intensity >= 0.52 and mean intensity <=1.3•Mean SWIR >=120 and mean SWIR <=250

Extracted the planted paddy areaRuleset:

•With mean intensity >= 0.52 and mean intensity <=1.3•Mean SWIR >=120 and mean SWIR <=250

RefinementRefinement

Actual of planted paddy areaActual of planted paddy area

Data Collection :Data Collection :

10

6/1/20106/1/2010 1/7/20061/7/2006

8/8/20058/8/2005

• Multi temporal data was used to assist in process of selection suitable imagery date for the classification. Visual interpretations on image for wet paddy in planted season are different from harvest season. In planted season, wet paddy need a lot of water, while in harvest season, wet paddy are in dry conditions. The selection of image during the planted season are important to classify the paddy area

11

6/1/20106/1/2010 1/7/20061/7/2006 8/8/20058/8/2005

12

The graph above show the reflectance of wet paddy and cleared land from image of 8/8/2005

In this project, the data of 1/7/2006 and 8/8/2005 cannot be used because the reflectance of paddy area in the image are almost same with the cleared land area. This is due to the season of paddy growing. On July and August is harvest season. Image of 7/1/2010 has been used to classify the hill paddy and wet paddy area.

Reproject :

• Reproject the image from WGS 84 to BRSO

13

Classification :

14

• Using rule set technique from eCognition 8.0 software.

• Ruleset that have been used:- With mean intensity >= 0.52 and mean intensity <=1.3- Mean SWIR >=120 and mean SWIR <=250

Mean intensityMean intensity

1.6625 0.03130

With mean intensity >= 0.52 and mean intensity<=1.3

With mean intensity >= 0.52 and mean intensity<=1.3

15

SWIRSWIR

21.62 246.82

Mean SWIR >=120 and mean SWIR <=250

Mean SWIR >=120 and mean SWIR <=250

The soil moisture content controls the SWIR reflectance, with much of the incident energy being absorbed by the moisture. More moisture the soil, more SWIR reflectance being absorb.

Refinement :

• Manual Editing – delete the overestimated paddy area based on paddy point in field and orthophoto images.

16

RefinementRefinement

Overlay :

17

+ OverlayOverlaySibu Landuse Map

Actual planted paddy area

Paddy area before refinement

Actual planted paddy area

Refinement :

• change the overestimated paddy area to the other class based on visual interpretation

18

RefinementRefinement

Overestimated paddy area

Grassland

Result and DiscussionResult and Discussion

19

• In this project, classification between hill paddy and wet paddy are failed due to the area of hill paddy in Sibu, Sarawak are too small to map. Hill paddy are planted adjacent to the wet paddy. Classification of wet paddy and hill paddy are combine to paddy class.

Hill PaddyWet Paddy

Different type of paddy setup:

20

Image above show the paddy area in Sibu, Sarawak. Different type of paddy setup is due to infrastructure, technique of paddy planting and irrigation.

21

Land Cover Map of Sibu, SarawakLand Cover Map of Sibu, Sarawak

22

• Accuracy Assesment:

23

Class 1 : AlanClass 2 : Oil PalmClass 3 : RiverClass 4 : River SandClass 5 : RubberClass 6 : Urban and Associated AreasClass 7 : PaddyClass 8 : GrasslandsClass 9 : Cleared LandClass 10 : Mixed Swamp Forest

Based on the table above, total accuracy assessment for paddy area of land cover map are 66.67%.

Conclusion and Conclusion and RecommendationRecommendation• Landcover map can be produced using remote sensing

and GIS technology with high accuracy.• To classify the wet paddy and hill paddy area, it is

important to check the crop calendar first before selecting the appropriate multitemporal data.

• Classification of hill paddy need the optimum of hill paddy area on the site.

24

TimetableTimetable

25

Week

Task

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Project proposal

Data Collection (SPOT-5 & Topographical Map)

Digital Image ClassificationOutput: Map of Paddy Area in Sibu, Sarawak

Field VerificationOutput: Accuracy Assesment

Refine Classification

Project Report

Presentation

26

NAME: SHARIFAH NUR AZWA SYED ISANAME: SHARIFAH NUR AZWA SYED ISAYEAR / COURSE: 3 SGS (BACHELOR OF SCIENCE -REMOTE YEAR / COURSE: 3 SGS (BACHELOR OF SCIENCE -REMOTE

SENSING)SENSING)SUPERVISOR’S NAME : CIK MARDIANA SHAFIEESUPERVISOR’S NAME : CIK MARDIANA SHAFIEE

TASK :ROAD ADMINISTRATION NETWORK TASK :ROAD ADMINISTRATION NETWORK DATABASEDATABASE

MINI PROJECT:BEST ROUTE FOR MINI PROJECT:BEST ROUTE FOR EMERGENCY RESPONSEEMERGENCY RESPONSE

OUTLINEOUTLINE• Introduction• Objective• Scope of training• Data Used• Software Used• Methodology• Result & Analysis• Conclusion

27

28

INTRODUCTIONINTRODUCTION• GIS has two distinct utilization capabilities.

Pertaining to querying and obtaining information Pertaining to in targeted analytical modeling.

• GIS database has also to provide to the different needs of applications.

• Database organization needs to ensure the following: Flexibility in the design to adapt the needs of different

users. A controlled and standardized approach to data input

and updated. A system of validation checks to maintain the integrity

and consistency of the data elements. A level of security for minimizing damage to the data. Minimizing redundancy in data storage.

Data for GIS database can be divided into two components:Spatial Data Spatial data consists of maps. Prepared by field surveys or by the interpretation of remotely sensed

data. The examples for spatial data are soil survey map, geological map,

land use map and etc. Some of these data are available in analog form while some data can

be obtained in digital form.

Non-spatial Data The coordinates of vector geometry or the position of raster cell

represent non-spatial data. In vector data, the additional data contains attributes of the feature. For example, a forest inventory polygon may also have an identifier

value and information about tree species. In raster data the cell value can store attribute information, but it can

also be used as an identifier that can relate to records in another table.

OBJECTIVEOBJECTIVE

• To establish a database of road administration containing relevant attribute information that are required in various mapping-related applications.

SCOPE OF TRAININGSCOPE OF TRAINING

• The study area for my task includes of Cheras and Dengkil – Putrajaya. Both of the regions are in Selangor state.

DATA USEDDATA USED

No. Type of Data Custodian

1 Geographical locations layer named as “BGN” in the IGDP database JUPEM

2 Peta Jalanraya Negeri Director of National Mapping Malaysia (DNMM) 9001 (various scales)

JUPEM

3 Peta DNMM 3001 (Scale 1:50,000) JUPEM

4 L7030 series topographic maps (Scale 1:50,000) JUPEM

5 L905 series (various scales) JUPEM

6 JKR road network dataset JKR

7 SPOT-5 pan-sharpened images ARSM

The data used to generate the road administration layer are listed in table below:

SPOT 5 image satellite which are provided by Remote Sensing Data Services Section

SOFTWARE USEDSOFTWARE USED

• The software and parameter used during the processing for this task is ArcGIS – ArcMap 9.3

METHODOLOGYMETHODOLOGY• Remote Sensing data processing

Processing methodology and flowchartThe road administration database for Peninsular Malaysia was prepared in “per state” basis with initial priority given to states that experienced annual monsoon floods. Feature class for each state was named “xxx_rd” when submitted to the Base Data Administrator for incorporation into the IGDP database.

Accuracy Limitation: The scale for this feature class is 1:50,000

Edit the spatial layer using the command such Sketch Tool, Merging, Exploded, Split Tool etc.

Input the attribute information: RD_NAME, RD_NO, RDA_CODE, RDO_CODE, RDT_CODE and TRCAT_NAME.

Code Description

RD_ID Unique number for a feature that will link the spatial layer with its corresponding attributes

TRCAT_CODE

Code representing transport category

RDA_CODE

Code refers to the authority who builds and maintains the road

RDO_CODE

Code refers to the full name of the owner of the road

RDT_CODE

Code representing type of road

RD_NO Refers to the numbering system for Malaysian expressways, and federal and state routes

RD_NAME Name of the road

• Attribute data

RESULT AND ANALYSISRESULT AND ANALYSIS Road Administration Network Database in

Cheras, Selangor.

TRCAT_NAME RDA_CODE RDO_CODE RDT_CODE

CARTRACK_4 1 1 1

DUAL_HIGH 2 2 2

FOOTPATH 3 3B042 3

SCAR_1W1B 5071 507 4

SCAR_2W1A 5181 518 -

UNS_1W2B - - -

Summary of Cheras road administration Number of features: 1728Attributes information relevant to Cheras road administration is as follows:

Road Administration Network Database in Dengkil - Putrajaya, Selangor.

Summary of Dengkil-Putrajaya road administration Number of features: 1728Attributes information relevant to Dengkil-Putrajaya road administration is as follows:

TRCAT_NAME RDA_CODE RDO_CODE RDT_CODE

CARTRACK_4 1 1 1

DUAL_HIGH 2 2 2

FOOTPATH 3 3B075 3

INDIFF_3 5021 3B083 4

SCAR_1W1B 5061 3W02 5

SCAR_2W1A 5091 501 -

UNS_1W2B 5111 506 -

- 5181 511 -

- 5201 518 -

- 5261 520 -

- - 523 -

- - 526 -

CONCLUSION

• The road administration network spatial layer arranged by the Infrastructure and Engineering Section was firstly planned to be use in the flood extent mapping.

• In addition, the TRANSPORT_CATEGORY’s name and description were based on JUPEM’s transportation layer’s name and description, other codes and names were established from information gathered from various sources.

• The road administration network is hoping to help those people who are in emergency in future.

MINI PROJECTMINI PROJECT

BEST ROUTE FOR BEST ROUTE FOR EMERGENCY RESPONSEEMERGENCY RESPONSE

INTRODUCTIONINTRODUCTION• Best route can be defined as the optimal route to a

network destination, based on specified criteria. • The best route to take is the one with the lowest

cost, based on specified criteria such as least of time, appropriate speed, types of road and so on.

• In case of emergency, the best route network are important especially to the service providers likes ambulance, police or fire stations.

OBJECTIVESOBJECTIVES

• To find the best route in terms of times, speed and types of road from response center to the incidents.

STUDY AREASTUDY AREA

• The project will cover Dengkil – Putrajaya region in Selangor State, Malaysia.

DATA USEDDATA USED

• The data used to generate the best route layer for this project are:

– SPOT-5 pan-sharpened images with 2.5m resolutions provided by Remote Sensing Data Services Section

– Geographical locations layer named as “BGN” in the IGDP database

SOFTWARE USEDSOFTWARE USED

• The software used for this project is ArcGIS 9.3 – ArcMap.

METHODOLOGYMETHODOLOGY• Before we can use the Network Analyst, we have to make sure that

the network that we used is in network dataset. The network dataset is built from simple features (lines and points) and turns.

• Set up the Network Dataset properties for Sources, Connectivity, Elevation, Turns, Attributes and Directions.

• The network dataset which has been created consist of three layers:

– Road segments as polyline feature (edges)– Junctions as point feature (system generated junctions)– Road Network itself

• After creation of network dataset following spatial features are added to the ArcMap.

– Network dataset (junctions, edges)– Accident location. – All emergency hospital, which is available in Dengkil -

Putrajaya, is added

RESULT AND ANALYSISRESULT AND ANALYSIS

• The main problem for this project is because of the dataset itself. It is because the way we digitize the road network will affected the dataset. As shown in Figure, when I enter the incident location, the network only recognized the route as shown in figure. The other route cannot be recognized because it is not well connected to each other

during the process of digitizing.

CONCLUSIONCONCLUSION• The GIS - Network Analyst are actually can

be used to solve the problems faced by the ambulance or other response center such as police and fire stations when emergency cases happened in order to find the best route to the incidents location.

• From the result, I can conclude that the project is not working well as I did not get the best route to go to the incident location. But, further changes will be made for this project as a continuation for my thesis in final year project.