Judul: Identifikasi Hutan Lahan Basah Menggunakan Citra ALOS PALSAR di Kalimantan Selatan
Abstrak: Wetland forest ecosystems have significant role in supporting human life, as a sources of water, food, biodiversity concervation, as well as a micro climate controller. Davies et al. (1995) noted that the extent of wetlands in Indonesia is about 38 million hectares. In addition, according to Komite Nasional Pengelolaan Ekosistem Lahan Basah (2004), Indonesia has about 40,5 million hectares of wetlands. While, according to Mulyani and Las (2008), Indonesia has about 40,2 million hectares of wetlands. The records of the extent of wetland in Indonesia is varied significantly due to different methods and data used. Thus, a development of remote sensing technology for monitoring of wetland ecosystems is required. In 2006, the government of Japan had launched ALOS (Advanced Land Observing Satellite) that carry a radar sensor called PALSAR (Phased Array Type L-band Shynyhetic Aperture Radar). ALOS PALSAR imagery is a new imagery and its interpretation methods has not been developed yet. The utilization of radar imagery data for forestry sector is also relatively undeveloped. The examination to evaluate the capability of the imagery is required. Considering the above mentioned matters, a study on the capability of ALOS PALSAR imagery for establishing identification techniques of forest wetland is required. The objective of the study is to evaluate the capability of ALOS PALSAR imagery for identifying wetland forest ecosystems. The research was conducted in Banjar, Barito Kuala, Banjarbaru, Banjarmasin, and Tanah Laut Regency, South Kalimantan Province. Identification of wetland forests was performed using ALOS PALSAR imagery, acquired in June of 2009 with 50 m resolution, with unsupervised classification method, separability analysis by transformed divergence (TD), and accuracy assesment. The hardware used in this study is a computer with Erdas Imagine Ver 9.1 and ArcView GIS Ver 3.2 softwares. This study found that wetland forest ecosystems that could be identified using ALOS PALSAR imagery with 50 m resolution include swamp forest with wet shrubs (bog) and mixed farms and also mangrove forest along with high shrubs. The accuracy evaluation showed that the accuracy for identification techniques of wetland forest are quite good having the overall accuracy and kappa accuracy are 73,49 % and 65,75 %, respectively.
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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