International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 6, pages: 2450-2459, DOI:10.18517/ijaseit.8.6.7486

Geometric Accuracy Assessment of Very High-Resolution Optical Data Orthorectified using TerraSAR-X DSM to Support Disaster Management in Indonesia

Inggit Lolita Sari, Sukentyas Estuti Siwi, Randy Prima Brahmantara, Haris Suka Dyatmika, Agus Suprijanto, Kuncoro Adi Pradono


Advanced remote sensing satellite data with detail spatial resolution can be an alternative to aerial photography and outweigh in providing rapid and vast spatial, remote area, and consist of multispectral bands to produce continues information. The various types of very high spatial resolution satellite, benefit in producing information for large-scale mappings, such as updating an urban map and supporting disaster management for mitigation, preparedness, emergency response, and recovery effectively and efficiently. Large-scale mapping information for disaster management, particularly for quick response is essential to map the impacted sites, measure the number of houses and infrastructure damaged and determine the evacuation area. However, in producing large-scale mapping, the information should refer to the geospatial specification standard, such as accurate geometric, detail thematic information and completeness. This study aims to identify the use of Pleiades imagery for supporting large-scale mapping, including for disaster management by assessing the geometry accuracy from a standard product acquired from the ground station and precise orthorectification using different types of DSM, including TerraSAR-X and improvement using ground control points. The results show that the improved accuracy to meet geometric accuracy standard for scale 1:5000 can be achieved using a primary product data which process using an insertion of GCPs and selecting the better DSM, while for the standard ortho product can be achieved using shifting the coordinate position of the image. Assessment of the thematic extraction visually shows that the imagery meets the information for large-scale mapping, but detail attribution requires information from field data.


geometric; pleiades; TerraSAR-X; DSM; disaster management.

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