Judul: Identifikasi Kelembaban Tanah dengan Pemanfaatan Gelombang Mikro Sensor PALSAR Satelit ALOS (Studi Kasus Kabupaten Bekasi dan Sekitarnya)
Abstrak: Soil moisture one of important factor in determining the level of dryness of the land. Therefore, monitoring the characteristics of spatial and temporal distribution of soil moisture is essential. Effective method for monitoring soil moisture on a large scale is by remote sensing. This research using the image of the PALSAR ALOS satellite which is a type of SAR (Synthetic Aperture Radar) that is sensitive to soil moisture content. Detection of soil moisture content was obtained based on the dielectric constant values that derived from values of backscatter of radar image. The purpose of this research is to study the characteristics and identify the soil moisture levels in some areas land cover Bekasi District and it’s surrounding using PALSAR ALOS satellite imagery. There are two empirical models used in soil moisture model prediction, they are the Dubois and top models. Both methods are suitable for open areas with a relatively flat surface. Dubois used to predict the value of dielectric constant and surface roughness, on the other hand the Top models used to predict soil moisture based on the value of the dielectric constant. This research tried to study the six types of land cover of water bodies, paddy fields, fallow paddy fields, open land, the land building, and land vegetation. The results showed that the highest dielectric constant values are found in water bodies with an average value of 7.9 and the lowest is in the fallow paddy field with an average value of 5.2. The highest soil moisture values are found in the land of vegetation with an average value of 25% and the lowest is in the fallow paddy field with a value of 20%.
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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: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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