Application of Spot 5 Satellite Image and GIS for Updating Road Network: Towards Building Landslide Spatial Database
Rapid development in urbanization is usually followed by development in transportation network. As the consequence, latest developed road networks are not found on the existing topographic map. As the topographic map-derived road network is not updated in short period, it is important to short...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item PeerReviewed |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/277798/1/2008%20ganjil-%20paper%20ICBEDC%20road%20network%20extraction%20from%20cameron%20Highlands.pdf https://repository.ugm.ac.id/277798/ |
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| Institution: | Universitas Gadjah Mada |
| Language: | English |
| Summary: | Rapid development in urbanization is usually followed by development in
transportation network. As the consequence, latest developed road networks are not found
on the existing topographic map. As the topographic map-derived road network is not
updated in short period, it is important to shorten the map updating cycles. SPOT 5 satellite
image offers a cost effective way for updating the map compared to a conventional mapping
method. The image, acquired in 2005, is used for updating road network on topographic
map scaled at 1:50000, sheet 74, issued by JUPEM which was derived from aerial
photograph taken in 1981. The road connecting Simpang Pulai cross and Kampung Raja,
Cameron Highlands, is selected due to its considerably rapid development and susceptibility
to landslide. Since most landslide occurrences take place along the road, updating road
map as part of landslide geo-database becomes necessary. SPOT5 image is registered into
Malaysian Coordinate System, RSO, to conform to the existing registered topographic map.
Both image classification and on screen digitization methods are used to extract road
network feature. The latest method is applied to complement to the first one in case of
facing uncertainty in image classification. The quality of extracted road network from image
classification is discussed. The extracted road network is stored into landslide spatial
database. In regard to landslide aspects, features such as barren land, vegetation coverage, are also extracted. DEM derived from topographic map is used to generate slope risk map. GIS analysis is performed to locate high risk areas that prone to landslide based on two criteria. Those areas having high risk slope (200-350) and occupy barren/un-vegetated land are considered as high risk area. From this study, only 0.1% of areas occupy high risk locations. Some of which is located at existing slope failure area at Pos Slim. |
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