Judul: Kajian Pemanfaatan Citra Satelit ALOS PALSAR Resolusi Sedang untuk Klasifikasi Penutupan Lahan di Pulau Kalimantan Indonesia
Abstrak: Perubahan penutupan hutan dan lahan yang dinamis di Indonesia memerlukan adanya ketersediaan informasi terbaru yang akurat dan tepat waktu. Oleh karena itu pemanfaatan teknologi penginderaan jauh di Indonesia sangat dibutuhkan, karena mampu memberikan informasi secara lengkap, cepat, dan relatif akurat. Di Indonesia, pemanfaatan data citra satelit optik masih menghadapi beberapa keterbatasan berupa penutupan awan, asap, serta data dengan kualitas kurang baik (striping/banding) yang dapat mengganggu proses identifikasi dan pemantauan penutupan lahan. Kelemahan tersebut dapat diatasi dengan memanfaatkan data citra satelit sensor radar. Sensor radar mampu melakukan observasi baik siang maupun malam tanpa terpengaruh kondisi cuaca, sehingga mampu menghasilkan citra bebas awan. PALSAR merupakan sensor radar pada satelit ALOS milik Jepang yang diluncurkan pada 24 Januari 2004. Kajian pemanfaatan data citra ALOS PALSAR ini merupakan pengembangan dari penelitian sebelumnya. Pada penelitian ini klasifikasi dilakukan dengan metode klasifikasi terbimbing dan klasifikasi tidak terbimbing, dengan penambahan saluran buatan rasio (HH/HV) dan normalized ratio [(HH-HV)/(HH+HV)]. Penelitian ini bertujuan mengukur kemampuan data citra ALOS PALSAR resolusi spasial 50 meter untuk klasifikasi penutupan lahan skala regional di Pulau Kalimantan Indonesia, serta mengidentifikasi metode terbaik yang digunakan dalam klasifikasi. Data utama yang digunakan adalah citra ALOS PALSAR resolusi spasial 50 meter dengan polarisasi HH dan HV tahun 2008. Citra diolah menggunakan perangkat lunak Erdas Imagine 9.1 dan ArcView GIS 3.2 dengan sistem pendukung utama Kappa dan Dendrogram (Jaya’s) 1.2, Image Analysis, Mila Utilities 3.4, dan Spatial Analyst.
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Judul: Evaluasi Akurasi Klasifikasi Penutupan Lahan Menggunakan Citra Alos Palsar Resolusi Rendah Studi Kasus Di Pulau Kalimantan
Abstrak: A wide variety of data and information on forest cover in Indonesia may be due to the variety of source of data, date of acquisition, and methods applied. For a wide area, terrestrial inventory methods are usually costly and time consuming. One alternative that may be used to minimize the cost and time is satellite based remote sensing technology. In the tropical country such Indonesia, cloud, fog, and smoke mainly limit the use of optical remote sensing during identification process and object monitoring on earth surface. Objects under the cloud, fog, and smoke could be identified using using Radar images. ALOS is remote sensing satellite which launched by Japan in 2006. One of its censor is PALSAR (Phased-Array type L-band Synthetic Aperture Radar). PALSAR is an advanced development from SAR which carried by the former satellite JERS-1. This is the microactive wave censor which can observe day and night without weather influence. Through one observation mode that is Scan SAR, this censor can observe earth surface in wide area than the conventional SAR. The objective of this study is to evaluate the ability of low resolution ALOS PALSAR image to classify regional scale land cover in Kalimantan Island. ALOS PALSAR image have 200 x 200 m resolution acquired in 2007. Other supporting data used are Land Cover Map year 2003, Administration Border Map, Forest Area Map, and the Base Thematic Forestry Map. The data were analyzed using GIS 3.2 and Erdas Imagine 9.1. The method are consisted of image preprocessing, image processing, separability accuracy evaluation and spatial analysis The study shows that low resolution ALOS PALSAR image could classify land cover into six classes. There are water body, rice field, shrub/bush, estate crop, and forest. Separability analysis for these classes show that there are 2 unseparable class pairs. These classes were then reclassified into four classes. The new classes are water body, sparse vegetation, medium density vegetation, and high density vegetation. The result of separability analysis shows that the these class separabilities are good (well separated). The accuracy of the classification are 88,21% for Overall Accuracy and 85,26% for Kappa Accuracy. Based on ALOS low resolution images (200 m x 200 m spatial resolution, the acreages of each land cover are 11.459.400 hectares (21,33%) for sparse vegetation, 5.070.008 hectares (9,44%) for medium density vegetation, and 36.806.058 hectares (68,52%) for high density vegetation. While the acreages of each land cover based on Land Cover Map year 2003 are 802.233 hectares (1,51%) for sparse vegetation, 20.841.843 hectares (39,32%) for medium density vegetation, 27.583.553 hectares (52,04%) for high density vegetation and 2.457.825 hectares (4,64%) for smoke. Key words : ALOS PALSAR Image, Land cover, Separability, Radar images
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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