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Pemetaan Indeks Kesehatan Vegetasi dengan Menggunakan Data Penginderaan Jauh dan Sistem Informasi Geografis pada Kondisi El Nino, La Nina dan Normal di Provinsi Bali IDA AYU PUTU JELANTIK PARWATI; I WAYAN NUARSA; R. SUYARTO
Jurnal Agroekoteknologi Tropika (Journal of Tropical Agroecotechnology) Vol.10, No.2, April 2021
Publisher : Program Studi Agroekoteknologi, Fakultas Pertanian, Universitas Udayana

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Mapping of Vegetation Health Index Using Remote Sensing Data and Geographical Information Systems in El Nino, La Nina and Normal Conditions in Bali Province This research is about mapping the vegetation health index using Terra Modis level 3 imagery in Bali Province in El Nino conditions in 2015, La Nina in 2016, and normal in 2017. The purpose of this research is (1) to calculate the vegetation health index in El Nino, La Nina and normal, (2) to map the distribution of drought in El Nino, La Nina, and normal conditions, and (3) to know the effects of El Nino, La Nina, and normal conditions on food crop production in Bali Province. The results showed that the vegetation health index in Bali Province was observed based on the drought class. There were 5 drought classes in 2015, 2016 and 2017. The most widespread extreme and strong drought occurred in the 2015 El Nino phenomenon with an area of ??152, 900 ha, followed by normal conditions in 2017 with an area of ??20,100 ha, and the smallest area was in 2016 with area of ??10,100 ha. Temporally, the level of drought in Bali Province in El Nino, La Nina and Normal conditions occurs in September and peaks in October and November, and decreases in December. Spatially, drought occurs in the southern, western, northern and eastern parts of Bali, meanwhile, in the central part, there is generally no drought. The highest production of food crops in Bali Province is shown by data in 2016, followed by production in 2015, and the lowest in 2017. Statistically, through the paired t-test, the El Nino and La Nina phenomena do not have a significant effect on food crop production compared to normal conditions.
Kajian Potensi Sumberdaya Lahan Untuk Pengembangan Tanaman Hortikultura Di Kecamatan Manggis Kabupaten Karangasem I MADE MEGA; I NYOMAN PUJA; I NYOMAN SUNARTA; I WAYAN NUARSA
Agrotrop : Journal on Agriculture Science Vol 4 No 1 (2014)
Publisher : Fakultas Pertanian Universitas Udayana

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The Study of Land Resource Potentials at Manggis District, Karangasem Regency forDevelopment of Horticulture Crops.The objective of the research was to study the potential ofland resources for growing horticulture crops at Manggis District, Karangasem Regency. Soil surveyand laboratory analysis were used in this research. The parameters of soil characteristics observedwere soil morphology in the fields, and physical and chemical properties. The results showed that thelands of Manggis District classified into ‘suitable enough’ until ‘very suitable’ for growing horticulturecrops. The limited factors were root medium; nutrient retention and terrains
Penggunaan Citra Landsat 8 untuk Estimasi Kadar Khlorofil dan Hasil Tanaman Padi I WAYAN NUARSA
Agrotrop : Journal on Agriculture Science Vol 4 No 1 (2014)
Publisher : Fakultas Pertanian Universitas Udayana

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The Use of Landsat 8 Imagery to Estimate Chlorophyl Content and Yield of Rice Plant.Predicting rice yield before harvest time is important to supporting planners and decision makers topredict the amount of rice that should be imported or exported and to enable governments to put inplace strategic contingency plans for the redistribution of food during times of famine. This study usedLandsat 8 that has better both spectral and radiometric resolution compared with the Landsat 7. Theresult of this study shows that using several Landsat 8 band as vegetation index provided better relationshipwith rice chlorophyll compared usind single band. Normalized Difference Vegetation Index (NDVI)has the best relationship with the chlorophyll content of rice plant compared with other vegetation indexand single band of Landsat 8 with the R2 of 0.936 . The form of relationship is linear with equation of y= 7.1043x + 0.0661, where y and x are chlorophyll content of rice plant and NDVI, respectively.Rice yield can be estimated in rice age approximatelly of 2 months with the equation of y = 30.495x2 -36.884x + 19.334, where y and x are rice yield in ton/ha and NDVI, respectively. The R² of thisestimation is 0.893 with the standard error of 0.372.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.668 KB) | DOI: 10.30536/j.ijreses.2006.v3.a1209

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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.
NUMERICAL CALCULATION FOR THE RESIDUAL TIDAL CURRENT IN BENOA BAY-BALI ISLAND GEDE HENDRAWAN; I WAYAN NUARSA; WAYAN SANDI; - A.F. KOROPITAN; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 2(2005)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.613 KB) | DOI: 10.30536/j.ijreses.2005.v2.a1362

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Princeton Ocean Model (POM) was used to calculate the tidal current and M2-residual current in Benoa Bay using barotropic model (mode 2). The model was forced by tidal elevation, which was given along the open boundary condition using tide data prediction from Hydro-Oceanography Division-Indonesian Navy (DISHIDROS TNI-AL). The computed tidal current and residual current have been compared with both data in Benoa Bay, that are data of the open boundary of Benoa Bay and condition of Benoa Bay after developed a port and reclamation of Serangan Island. The maximum velocity of tidal current for open boundary conditions at flood tide is 0.71 m/sec, whereas at ebb tide is 0.65 m/sec and the maximum velocity after developed a port and reclamation of Serangan Island, at flood tide, is 0.69 m/sec. The simulation of residual current with particular emphasis on predominant constituent of M2 after developed a port and reclamation of Serangan Island shows a strong flow at the western part of Tanjung Benoa and Benoa Harbor and also at bay mouth between Serangan Island and Tanjung Benoa. Maximum velocity of M2-residual current is 0.0585 m/sec by the simulation and showed that the current which was produced forming two eddies in the bay of which one eddy is in the mouth of bay in southern part. The residual current for open boundary condition of bay shows four eddies circulation, one big eddies and the others small. The anticlockwise circulation occurs in the inner part of the bay. Key words: model, simulation, tidal current, residual current
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (247.432 KB) | DOI: 10.30536/j.ijreses.2005.v2.a1353

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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).
DEVELOPMENT OF THE NEW ALGORITHM FOR MANGROVE CLASSIFICATION I WAYAN NUARSA; I WAYAN SANDI ADNYANA; YASUHIRO SUGIMORI; SUSUMU KANNO; FUMIHIKO NISHIO
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 2(2005)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (253.121 KB) | DOI: 10.30536/j.ijreses.2005.v2.a1358

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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. Key words: Mangrove, Landsat ETM+, Empirical Model, Image Classification
APPLICATION OF VAN HENGEL AND SPITZER ALGORITHM FOR INFORMATION ON BATHYMETRY EXTRACTION USING LANDSAT DATA Kuncoro Teguh Setiawan; Syifa Wismayati Adawiah; Takahiro OSAWA; I. Wayan Nuarsa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 11, No 1 (2014)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.148 KB) | DOI: 10.30536/j.ijreses.2014.v11.a2603

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Remote sensing technology provides an opportunity for effective and efficient bathymetry mapping, especially in areas which level of depth changes quickly. Bathymetry information is very useful for hydrographic and shipping safety. Landsat medium resolution satellite imagery can be used for the extraction of bathymetry information. This study aims to extract information from the Landsat bathymetry by using Van Hengel and Spitzer rotation algorithm transformation (1991) in the water of Menjangan Island, Bali. This study shows that Van Hengel and Spitzer rotation algorithm transformation (1991) can be used to extract information on the bathymetry of Menjangan Island. Extraction of bathymetric information generated from Landsat TM imagery data in March 19, 1997 had shown the depth interval of (-0.6) m to (-12.3) m and R2 value of 0.671. While Data LANDSAT ETM + dated June 23, 2000 resulted in depth interval of 0 m to (-19.1) m and R2 value of 0.796. Furthermore, data LANDSAT ETM + dated March 12, 2003 resulted in depth interval of 0 m to (-22.5) m and R2 value of 0.931.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (169.966 KB) | DOI: 10.30536/j.ijreses.2005.v2.a1354

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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
RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA I Wayan Nuarsa; Fumihiko Nishio
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 4,(2007)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.139 KB) | DOI: 10.30536/j.ijreses.2007.v4.a1221

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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. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM.
Co-Authors Abd. Rahman As-syakur Agit Setiyoko Agung Budi Harto Alan Frendy Koropitan Anak Agung Gede Sugianthara ANAK AGUNG PLASA PADMAWATI Atsushi Ono CHAO FANG ZHAO Deni Suwardhi Deni Suwardhi Diah Pertami Dian Novianto Farah Nafisa Ariadji Fenny M. Dwivany FERNANDO JOSUA SINAGA FERONIKA FERONIKA FRANSISKA PURBA Fumihiko Nishio FUMIHIKO NISHIO FUMIHIKO NlSHIO Gede Surya Indrawan HARIANJA JUITA I Dewa Nym. Nurweda P., I Gede Hendrawan I GUSTI ALIT GUNADI I Gusti Bagus Sila Dharma I Gusti Ngurah Bagus Sukertha Diputra I Kade Alfian Kusuma Wirayuda I Ketut Sardiana I Ketut Swardika I Made Adhika I MADE ALIT WIRANATHA I Made Gede Sunarya I Made Mega I Made Sukearsana I Made Sukewijaya I Nyoman Dibia I Nyoman Puja I NYOMAN RAI I NYOMAN SUNARTA I W BUDIARSA SUYASA I Wayan Gede Astawa Karang I Wayan Sandi Adnyana IDA AYU PUTU JELANTIK PARWATI IDA BAGUS PUTU BHAYUNAGIRI INDAYATI LANYA JEREMIA KEVIN RONIO HUTAURUK Ketut Wikantika Kuncoro Teguh Setiawan M.Cs S.Kom I Made Agus Wirawan . Made Santiari Mark Johannes Wiggers MAR’IE ABDA’U ZAL Maulana Ilham Fahmy Alam Ngo The An Nguyen Tuyet Lan Ni Kadek Martini NI KOMANG RINI RATNA DEWI Ni Luh Putu Ria Puspitha NI PUTU AYU KRISMAYANI NI WAYAN FEBRIANA UTAMI Prila Ayu Dwi Prastiwi R. Suyarto Rijal Rahmatullah Romaldo Da Costa Ximenes RUNIA CHRISTINA GULTOM Sagung Putri Chandra Astiti Susumu Kanno Susumu Kanno SUSUMU KANNO SUSUMU KANNO Syifa Wismayati Adawiah Takahiro Osawa Takahiro Osawa Takahiro Osawa Takahiro Osawa Takahiro Osawa TAKAHIRO OSAWA WAHID ARDIANSYAH WAYAN SANDI Widiastuti Widiastuti YASUHIRO SUGIMORI YASUHIRO SUGIMORI YASUHIRO SUGIMORI Yasuo Furushima