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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
NUARSA I WAYAN
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Earth & Planetary Sciences

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VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK TAKAHIRO OSAWA; CHAO FANG ZHAO; NUARSA I WAYAN; I KETUT SWARDIKA; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences Vol. 2 (2005)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2005.v2.a1353

Abstract

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.
DEVELOPMENT OF THE NEW ALGORITHM FOR MANGROVE CLASSIFICATION Nuarsa I Wayan; Sandi Adnyana I Wayan; Yasuhiro Sugimori; Susumu Kanno; Fumihiko Nishio
International Journal of Remote Sensing and Earth Sciences Vol. 2 (2005)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2005.v2.a1358

Abstract

The objective of the study is to develop the algorithm for mangrove classification and density. Regression and correlation analysis was used to perform the equation. CE1 = (0.663*Band 3) + (0.l55 *Band 4) - (l.4*Band 5) + 0.995 And CE2 = 36 * Band 4 + 6*Band 5 + Band 3 were two formula that have been used to classify the mangrove. The object will be classified as mangrove when the value of CE1 is between -31.439 and 0.888, and value of CE2 is greater than or equal to 2000. On the other hand, density of the mangrove was expressed as DE = (2 * Band 4)/(Band 1+Band 3). Mangrove classification result in this study was similar to those of the existing methods. Statistical approach in this study generally gives the higher result tendency than other methods.
SPECTRAL CHARACTERISTIZATION OF RICE FIELD USING MULTITEMPORAL LANDSAT ETM+ DATA NUARSA I WAYAN; SUSUMU KANNO; YASUHIRO SUGIMORI; FUMIHIKO NISHIO
International Journal of Remote Sensing and Earth Sciences Vol. 2 (2005)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2005.v2.a1359

Abstract

The preliminary study using Landsat ETM+ to estimate the rice production in Regency of Tabanan, Bali Province was conducted. The objectives of this study were to know spectral characteristic of rice plant in three importance growth periods of rice, and to develop a model to identify the distribution of rice. Landsat ETM+ in two acquisition dates (March 21st, 2003 and May 24*, 2003) were used in this study. Characteristics of rice were analyzed using radiance value of Landsat ETM+ obtained from converting digital number of Landsat data. Multi-variable linear regression analysis was developed to classify the rice in its growth period. The result showed that the rice plant has different reflectance in seedling-development period, ear differentiation period and maturation period. It isexpressed by the radiance value of Landsat ETM+. However, spectral characteristic of rice in each band of Landsat ETM+ is similar to the green vegetations in general, except in blueband (Bl). Based on statistical analysis, the classification of rice in each its growth period can be classified.
Co-Authors CHAO FANG ZHAO Fumihiko Nishio I Ketut Swardika Sandi Adnyana I Wayan Susumu Kanno Takahiro Osawa YASUHIRO SUGIMORI