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Tasuku Tanaka
CReSOS Unud
Environmental Science

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Journal : Ecotrophic, Journal of Environmental Science

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CHARACTERISTIC OF RAINFALL PATTERN BEFORE FLOOD OCCUR IN INDONESIA BASED ON RAINFALL DATA FROM GSMaP PUTU ARYASTANA; Tasuku Tanaka; M.S. Mahendra
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 7 No 2 (2012)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

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Abstract

Floods are natural disasters that took place almost all over the world including Indonesia. Indonesia is very vulnerable to disasters because has characteristic a huge amount of rainfall throughout the year. Flood disaster is one of the disasters that often occur in Indonesia. GSMaP is one kind product of satellite precipitation has one hour temporal resolution, 0.1x0.1 degree horizontal resolution, world-wide coverage and operated by JAXA. In this study were investigate the characteristic of rainfall pattern before floods occur by processing hourly GSMaP MVK at each location large flood events occur in Indonesia on 2006-2010. The large flood events data collected from Dartmouth Flood Observatory (2006-2010) and Ministry of Public Works of the Republic of Indonesia (2006-2010). Based on the processing result, generally Indonesia has two characteristic of rainfall patterns before floods occur namely: short term rainfall period and long term rainfall period. Based on the compilation and classification of 69 locations large flood events in Indonesia, from three rainfall pattern before flood occur it obtained 42 locations or 60.87% in the category short term rainfall period, 27 locations or 39.13% in the category long term rainfall period.
SEA SURFACE TEMPERATURE RELATED TO THE BIG EYE TUNA EXISTANCE IN INDIAN OCEAN ON 2010 MARTIWI DIAH SETIAWATI; FUSANORI MIURA; Tasuku Tanaka
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 7 No 2 (2012)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

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Abstract

Indian Ocean, particularly on Southern part of ,Java and Bali was identified for some big pelagic fish. Among all of the big pelagic fishes, big eye tuna is the highest commercial value compare with other species There are several research which studied the relationship between the existence of tuna and environmental factor. Sea Smface Temperature (SST) was used as a main oceanographic factor to relate with big eye tuna abundance. SST data \\aS derived from Level 3 AQUA M0DIS data. In my paper, 1 use additional simple statistical method to prove that SST and big eye tuna has a good con-elation. I used polynomial regression and geographic information system method to improve correlation value of lhe data. There arc three critical value and four equation. Based on the data analysis, SST has significant value to big eye tuna abundance in Indian Ocean and has high correlation.
BOTTOM TYPES IDENTIFICATION IN SHALLOW CORAL REEF ECOSYSTEMS USING IMAGERY SATELLITE DATA MASITA DWI MANDINI MANESSA; TASUKU TANAKA; Takahiro Osawa
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 7 No 2 (2012)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

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Abstract

Satellite data provide information about spectral signatures of objects in detail, based on the wide range of spectral wavelengths. Bottom types in a coral reef Ecosystems are diverse and each object has a different spectral signature. The aim of this research is to define bottom types using Multispectral and Hyperspectral imagery satellite data. Six processes were applied to Hyperspectral Images to identified bottom types using modification of Analytical Imaging and Geophysics LLC (AIG) hyperspectral analysis. The multispectral analysis was focused on correcting water column noise by applying the radiative water column algorithm (Lyzenga, 1978, 1981) and the modified image correction algorithm (Lyzenga et al., 2006). The results showed that multispectral image analysis was able to identify a fine complexity of b bottom types classes with 68.57% overall accuracy. In contrast, Hyperion image identified a coarse complexity of bottom types classes with 61.57% overall accuracy. This low result was caused by low spatial resolution which created a mixing pixel around image of thin and narrow shallow coral reef ecosystem. Spatial resolution, atmosphere and water scattering played an important role in bottom types identification.
Co-Authors FUSANORI MIURA M. Sudiana Mahendra MARTIWI DIAH SETIAWATI MASITA DWI MANDINI MANESSA Putu Aryastana Takahiro Osawa