Garuda - Garba Rujukan Digital

I Ketut Swardika

Udayana University

Author-ID : 3489505

Earth & Planetary Sciences

Published : 3 Documents Claim Missing Document

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ESTIMATION OF TUNA FISHING GROUND IN LOW LATITUDE REGION USING SEA SURFACE HEIGHT GRADIENT DERIVED FROM SATELLITE ALTIMETRY: APPLICATION TO NORTHEASTERN INDIAN OCEAN Susumu Kanno; Yasuo Furushima; I Wayan Nuarsa; I Ketut Swardika; Atsushi Ono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 3,(2006)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

In order to improve the method for prediction of tuna fishing ground, the modification of the analysis about satellite altimeter data was made as trial. In this study, we focused on the satellite altimeter, TOPEX/POSEIDON series, to improve the method of fishing ground prediction. Fishery data were supplied as hook rate by local fishing information around Indonesia and hearing infromation. The gradient of sea surface height is calculated between the neighbor grid which has the maximum gradient. Result showed that the fishery data with hook rate over 0.8 are grouped in a zone from 1.0E-06 of sea prediction of fishing ground quantitatively, but also reasonable accuracy as shown in the change in the standard deviation. This method can be utilized for the effective fishing plan with the resource protection and the economy in the fishing operation in near future. Keywords: sea surface altimeter, sea surface gradient, remote sensing, fishing ground search, hook rate, fishery resource management.

VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK TAKAHIRO OSAWA; CHAO FANG ZHAO; I WAYAN Nuarsa; I KETUT SWARDIKA; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 2(2005)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space. Keywords : Ocean color, Chlorophyll-a (Chl-a), Vertical structure, Artificial Neural Networks (ANN).

STUDY OF OCEAN PRIMARY PRODUCTIVITY USING OCEAN COLOR DATA AROUND JAPAN TAKAHIRO OSAWA; CHAO FANG ZHAO; I WAYAN Nuarsa; I Ketut Swardika; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 2(2005)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Ocean primary production is an important factor for determining the ocean's role in global carbon cycle. In recent years, much more chlorophyll-a concentration data in the euphotic layer were derived from the satellite ocean color sensors. The primary productivity algorithms have been proposed based on satellite chlorophyll measurements (Piatt, 1988; Morel, 1991) and other environmental parameters such as sea surface temperature or mixed layer depth (Behrenfeld and Falkowski, 1997; Esaias, 1996; Asanuma, 2002). In order to estimate integrated primary productivity in the whole water column, the vertical distribution of chlorophyll concentration below the sea surface should be reconstructed based on satellite data. In this paper, the vertical profile data of chlorophyll-a (Chl-a) measured around Japan Islands from 1974 to 1994 were reanalyzed based on the shifted-Gaussian shape proposed by Piatt et al (1988). Using this statistical model (neural network) and the photosynthesis irradiance parameters from Asanuma (2002), the distribution of primary productivity and its seasonal variation around Japan islands were estimated from SeaWiFS data, and the results were compared with in situ data and the other two models estimated from VGPM and mixed layer depth model. Keywords: ocean color, primary productivity, chlorophyll profile, artificial neural network

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