Pengolahan Citra ALOS PALSAR untuk Identifikasi Mangrove sebagai Data Pendukung Pengelolaan Wilayah Pesisir Suaka Margasatwa Sembilang, Sumatera Selatan
| Main Authors: | Karmani, Faiz, Basith, Abdul |
|---|---|
| Format: | Proceeding PeerReviewed application/pdf |
| Bahasa: | eng |
| Terbitan: |
, 2013
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| Subjects: | |
| Online Access: |
https://repository.ugm.ac.id/277797/1/fullpaper-abdul%20basith%20geodesi%20UGM-Identifikasi%20mangrove%20dari%20citra%20ALOS%20PALSAR%20%20untuk%20mendukung%20pengelolaan%20wilayah%20pesisir.pdf https://repository.ugm.ac.id/277797/ |
Daftar Isi:
- Mangrove forest plays an important role in coastal zones such as stabilizing ecosystem of coastal zone, as green belt minimizing the impact of abrasion and tsunami and so on. Preservation of existence of mangrove requires an appropriate monitoring technology. Remote sensing technology offers ways for monitoring mangrove covers. As a tropical country, cloud cover has been a problem in Indonesia when dealing with monitoring objects using optical remote sensing. ALOS provides PALSAR, an active remote sensing sensor, which enable monitoring mangrove under cloud covers. Mangrove covers at Margasatwa Sembilang, South Sumatera is selected as a case study. A level 1.1 of ALOS PALSAR image is digitally processed by employing various filters including Lee, Gamma, and Frost with different Kernel sizes in order to remove speckles, a typical radar image error. SRTM DEM is used for constructing pseudo color of PALSAR image. Application of these methods is intended to seek an optimal filter for reducing the speckles. Image classification is carried out using maximum likelihood method. A classified image derived from Landsat 7 ETM+ image of the same area is used for comparison. This research shows that high accuracy of identified mangrove cover is achieved by means of a band combination of HH, HV, and SRTM DEM. Gamma filter with 5x5 Kernel size is found to be an optimal filter for identifying mangrove cover. The area of identified mangrove cover is 893,46 km2 with user accuracy of 92.450%.