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MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA I Wayan Nuarsa; Fumihiko Nishio; Chiharu Hongo
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 7, No 1 (2010): Vol 7,(2010)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure.

ESTIMATION OF TIDAL ENERGY DISSIPATION AND DIAPYCNAL DIFFUSIVITY IN THE INDONESIAN SEAS I Wayan Gede Astawa Karang; Fumihiko Nishio; Takahiro Osawa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 7, No 1 (2010): Vol 7,(2010)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The Indonesian Seas separating the Indian Ocean from the West Pacific Oceanare representative regions of strong tidal mixing in the world oceans. In the present study,we first carry out numerical simulation of the barotropic tidal elevation field in theIndonesian Seas using horizontally two-dimensional primitive equation model. It is foundthat, to reproduce realistic tidal elevations in the Indonesian Seas, the energy lost by theincoming barotropic tides to internal waves within the Indonesian seas should be taken intoaccount. The numerical experiments show that the model predicted tidal elevations in theIndonesian Seas best fit the observed data when we take into account the baroclinic energyconversion in the Indonesian Seas ~86.1 GW for the M2 tidal constituent and ~134.6 GWfor the major four tidal constituents (M2, S2, K1, O1). For this baroclinic energy conversion,the value of Kñ averaged within the eastern area (Halmahera, Seram, Banda and MalukuSeas), the western area (Makassar and Flores Seas), and the southern area (Lombok Straitand Timor Passage) are estimated to be ~23 × 10-4 m2s-1, ~5 × 10-4 m2s-1, and ~10× 10-4m2s-1, respectively. This value is about 1 order of magnitude more than assumed for theIndonesian Seas in previous ocean general circulation models. We offer this study as awarning against using diapycnal diffusivity just as a tuning parameter to reproduce largescalephenomena.

MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA I Wayan Nuarsa; Fumihiko Nishio; Chiharu Hongo
International Journal of Remote Sensing and Earth Sciences Vol. 7 No. 1 (2010)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2010.v7.a1541

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure.

ESTIMATION OF TIDAL ENERGY DISSIPATION AND DIAPYCNAL DIFFUSIVITY IN THE INDONESIAN SEAS I Wayan Gede Astawa Karang; Fumihiko Nishio; Takahiro Osawa
International Journal of Remote Sensing and Earth Sciences Vol. 7 No. 1 (2010)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2010.v7.a1542

The Indonesian Seas separating the Indian Ocean from the West Pacific Oceanare representative regions of strong tidal mixing in the world oceans. In the present study,we first carry out numerical simulation of the barotropic tidal elevation field in theIndonesian Seas using horizontally two-dimensional primitive equation model. It is foundthat, to reproduce realistic tidal elevations in the Indonesian Seas, the energy lost by theincoming barotropic tides to internal waves within the Indonesian seas should be taken intoaccount. The numerical experiments show that the model predicted tidal elevations in theIndonesian Seas best fit the observed data when we take into account the baroclinic energyconversion in the Indonesian Seas ~86.1 GW for the M2 tidal constituent and ~134.6 GWfor the major four tidal constituents (M2, S2, K1, O1). For this baroclinic energy conversion,the value of Kñ averaged within the eastern area (Halmahera, Seram, Banda and MalukuSeas), the western area (Makassar and Flores Seas), and the southern area (Lombok Straitand Timor Passage) are estimated to be ~23 × 10-4 m2s-1, ~5 × 10-4 m2s-1, and ~10× 10-4m2s-1, respectively. This value is about 1 order of magnitude more than assumed for theIndonesian Seas in previous ocean general circulation models. We offer this study as awarning against using diapycnal diffusivity just as a tuning parameter to reproduce largescalephenomena.

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

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

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.

MONITORING OF LAND USE CHANGES USING AERIAL PHOTOGRAPH AND IKONOS IMAGE IN BEDUGUL, BALI I Wayan Sandi Adnyana; Fumihiko Nishio; Josaphat Tetuko Sri Sumantyo; Gede Hendrawan
International Journal of Remote Sensing and Earth Sciences Vol. 3 (2006)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2006.v3.a1206

There was change of expending land use in Bedugul. It is necessary to monito the change of highland of Bali, catchments area of Beratan, Buyan and Tamblingan lakes. In order to control land use change and to anticipate degradation of hydrology function of this area. This study is to monitor the land use change by remote sensing and GIS technique. To evaluate land use and land cover, aerial photograph imagery and Ikonos imagery were used. Over 22 years of observation (1981-2003), there was land use changes in the catchments area of Beratan, Buyan and Tamblingan lakes at Bedugul area. The area of settlement increased by 62.6 ha, dry land vegetable crops and forest decreased by 116.5 ha and 32.5 ha, respectively. The surface area of Buyan Lake was also decreased, due to sedimentation caused by erosion in the vegetables dry land crops. Planning the land use study on erosion and soil-water conservation in this area necessary, in order to control land use change, erosion, and sedimentation in the lakes.

RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA I Wayan Nuarsa; Fumihiko Nishio
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1221

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages.

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