Judul: Klasifikasi Penutupan Lahan Hutan Mangrove di Kecamatan Buduran, Kabupaten Sidoarjo, Propinsi Jawa Timur, dengan Citra TerraSAR-X High Resolution
Abstrak: Indonesia actually has used remote sensing imagery in monitoring natural resources especially the optical image since apllied remote sensing technology. Indonesia’s position in the tropics cause the area always cover with clouds in most of the year. This condition affects the ability of an optical image to produce a clear image. Active remote sensing systems (radio detecting and ranging/Radar) has provided an alternative to overcome the limitations of the information than can be taken through an optical image data. TerraSAR-X, an earth observation satellite owned by Germany which was launched on June 15, 2007 at Boikonur Cosmodrome, Kazakhstan, is the latest radar technology for mapping the active wavelength X-band (wavelength 31 mm, frquency 9,6 GHz), which stated able to overcome the cloud cover. Since its launch, satellite TerraSAR-X has been in full operation starting in January 2008. The purpose of this research was to test the ability of high resolution image of TerraSAR-X for land cover classification and identification of constituent species of mangrove forests. Imagery used in this reseach consisted of the main image TerraSAR-X with HH and VV polarization that has made the extraction process to obtain the composition of RGB |HH|-|VV|-|HH-VV|. While the other supporting image are Quickbird image, distribution mangrove map of Sidoarjo regency and Sidoarjo regency administration map. The method used for classification of land cover types to make a class is supervised classification method and unsupervised classification method by reference to the results of the visual image interpretation. Then with the best classification method used for land cover classification process of mangrove forests. By appying the stratified classification method, it is expected to be able to reduce errors during the classification process. This is because the other objects than mangrove forests have been eliminated. The results showed the accuracy of the supervised classification by the method of Support Vector Machine (SVM) in the image of TerraSAR-X is the best method of classification with the overall accuracy of 77,93 % and kappa accuracy by 58,85 %. The value of mangrove forests on the classification accuracy using SVM and applying the two stage classification amounted to 90,74 % and kappa accuracy by 84,30 %. RGB image of the arrangement [HH - VV - (HH-VV)] of the dual polarization HH and VV can produce a 3 class contituent species of mangroves, that is Avicennia alba, Rhizophora mucronata and Xylocarpus molucensis class, Hibiscus tiliaceus class and shrubs and grasses class.
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Judul: Klasifikasi dan Detektsi Perubahan Tutupan Hutan dan Lahan Menggunakan Citra ALOS PALSAR Resolusi 50 Meter di Wilayah Barat Provinsi Jambi.
Abstrak: Currently, knowledge on conditions of forest and land cover as well as its dynamics changes, particularly in the form of deforestation and forest or land degradation in the region is highly important, especially to support sustainable forest management. Detection of forest and land condition could be performed either using terrestrial, remote sensing or a combination between remote sensing and terrestrial approach. In Indonesia, since the 1990s, the mapping and monitoring forest and land cover condition is generally performed using optical satellite remote sensing approach. However, in the tropics, the use of optical images often have serious obstacle due to the presence of clouds and haze in the rainy seasonand the presence of smog and smoke during the dry season. However, since the presence ofradar images, a newperspective is coming, which may give complementary and/or substitution information over optical image. Satellite ALOS (Advanced Land Observation Sattelite) with sensor PALSAR (Phased Array L-band Synthetic Aperture Radar) is a radar image that had been launched in 2006 by the Government of Japan. They have four polarizations, namely HH, HV, VH, and VV. However, the image released to the public is commonly consisted of only HH and HV polarization. The study objective are (1) to identify forest and land cover and (2) to detect patterns of change in forest and land cover in the Western Part of Jambi Province using 50-meter ALOS PALSAR. The results of this research are expected to be used for supporting the preparation of MRV or REDD++ as well as for basic information in regional spatial planning. The method used in this study includes (1) image classification using qualitative classification approach (visual) on the composite image ALOS PALSAR (HH-HV-HH/HV) of 2007 and 2009, (2) image classification using quantitative classification (digital analysis) with the supervised method and (3) post-classification comparison by comparing 2007 and 2009 classified imageries. For ground checking and accuracy analysis, the location of ground observations were determined using systematic sampling with random start with the distance range from 100 meters to 500 meters from the edge of the road. The study results show that there are 13 classes of forest and land cover that can be identified through visual classification of 50-meter ALOS PALSAR composite image, namely water body, dry land forest, swamp forest, dry land farming, mixed farms/crop, rubber plantations, oil palm plantations, bush, shrub swamps, marshes, settlements and bare area. The qualitative classification approach provides Kappa accuracy of 82,38% for 2007 and 83,48% for 2009. With quantitative classification approach, slightly lower accuracy and separability were identified, where the average of inter-class separability value of Transformed Divergence for the image of 2007 and 2009 are 1641.7 and 1611.78. The Kappa accuracy with quantitative approach for 2007 is 53,3% and for 2009 is 58.8%. Based on the pattern distribution analysis of forest and landcover changes occurred between 2007 and 2009, it was found that the pattern of forest and land cover changes within the study site is clustered particularly in Tebo and Sarolangun Regencies. Deforestation of 14,583.5 hectaresis mostly occurred in Tebo Regency, while forest and land degradation of 399.8
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Judul: Analysis of M/G/1 Queue Model with Two Phases of Service and Bernoulli Feedback System
Abstrak: Model antrean M/G/1 dengan sistem feedback Bernoulli adalah model antrean dengan proses kedatangan mengikuti proses Poisson. Pada karya ilmiah ini dibahas sistem antrean M/G/1 dengan dua fase layanan. Setelah layanan pertama selesai, pelanggan dapat memilih untuk meninggalkan sistem dengan peluang ... dst, The M/G/1 queueing model with the Bernoulli feedback system is a queueing model with the arrival process following the Poisson process. In this manuscript M/G/1 queueing system with two phases of service is discussed. After the first service is completed, the customer can choose to leave the system with probability ... dst
Keyword: queue, Bernoulli, probability generating function, expected value