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All Journal Journal of Marine and Aquatic Sciences Bumi Lestari Ecotrophic, Journal of Environmental Science International Journal of Remote Sensing and Earth Sciences (IJReSES)

I Wayan Nuarsa

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Author-ID : 2947163

Agriculture, Biological Sciences & Forestry Humanities Earth & Planetary Sciences Energy Environmental Science

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Articles

PENGGUNAAN TEKNOLOGI PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS UNTUK MENGHITUNG PERSENTASE RUANG TERBUKA HIJAU DI DAERAH PERMUKIMAN KOTA DENPASAR I Wayan Nuarsa
Bumi Lestari Journal of Environment Vol 13 No 1 (2013)
Publisher : Environmental Research Center (PPLH) of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

AbstrakGreen open space (GOS) is a very important component in the arrangement of urban space,because the GOS has the function of ecological, aesthetic, social, cultural, and economic.Calculating of the GOS can easily be done in area that are enable for such purposes asurban forests, recreational areas of the city, as well as agricultural areas. However, for theland use consisting of non-GOS and GOS such as settlement, calculation of the GOS will bequite difficult. This research was conducted to measure the percentage of the GOS in settlementareas in the Denpasar city using remote sensing and geographic information systemtechnology. The results of this study showed that the percentage of the GOS in the settlementsarea of Denpasar ranged from 2.97% to 30.01%, with an average value of 14, 43%, and astandard deviation of 7.32% or 182.98 m2. The majority (50%) of the percentage of the GOSin the settlements area in the Denpasar city classified as moderate (10– 20%), 32% are low(<10%), and only 18% had a high percentage of the GOS (> 20%). Factors that influenceto the percentage of the GOS in the settlement area of Denpasar is the location of thesattements and the land area per housing unit.

PEMETAAN DAERAH RAWAN KEKERINGAN DI BALI-NUSA TENGGARA DAN HUBUNGANNYA DENGAN ENSO 2) MENGGUNAKAN APLIKASI DATA PENGINDERAAN JAUH I Wayan Nuarsa; I Wayan Sandi Adnyana; Abd. Rahman As-syakur
Bumi Lestari Journal of Environment Vol 15 No 1 (2015)
Publisher : Environmental Research Center (PPLH) of Udayana University

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In this study, the use of SPI (Standardized Precipitation Index) combined with remote sensingdata is performed to map vulnerable drought areas in Bali-Nusa Tenggara regions. Analysisalso carried out to find the relationship between Vulnerable drought areas in Bali-NusaTenggara with El Niño phenomena. Bali-Nusa Tenggara islands are a chain of islands thathas a semi-arid climate type and resulted vulnerable to meteorological drought. Therefore,mapping of vulnerable drought areas in the region necessary to be carried out. The spatialpattern of the annual average value of SPI-6 in Bali Nusa Tenggara areas in 1998-2010indicates the spatial distribution follows the ENSO events. It also indicated in the spatialpattern relationship between ENSO and SPI in Bali-Nusa Tenggara islands. This studyindicates that remote sensing data such as TRMM 3B43 has the capability to be used as adata source to analyze the spatial patterns of vulnerable drought areas, particularly in theBali-Nusa Tenggara Islands. In addition, the TRMM data also possible to be used as a datasource to analyze the vulnerable drought areas in other parts of Indonesia.

AN APPLICATION OF SEGNET FOR DETECTING LANDSLIDE AREAS BY USING FULLY POLARIMETRIC SAR DATA I Made Oka Guna Antara; Norikazu Shimizu; Takahiro Osawa; I Wayan Nuarsa
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 13 No 2 (2019)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

The study location of landslide is in Hokkaido, Japan which occurred due to the Iburi Earthquake 2018. In this study the landslide has been estimated by the fully Polarimetric SAR (Pol-SAR) technique based on ALOS-2 PALSAR-2 data using the Yamaguchi’s decomposition. The Yamaguchi's decomposition is proposed by Yoshio Yamaguchi et.al. The data has been analyzed using the deep learning process with SegNet architecture with color composite. In this research, the performance of SegNet is fast and efficient in memory usage. However, the result is not good, based on the Intersection over Union (IoU) evaluation obtained the lowest value is 0.0515 and the highest value is 0.1483. That is because of difficulty to make training datasets and of a small number of datasets. The greater difference between accuracy and loss graph along with higher epochs represents overfitting. The overfitting can be caused by the limited amount of training data and failure of the network to generalize the feature set over the training images.

ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI Made Arya Bhaskara Putra; I Wayan Nuarsa; I Wayan Sandi Adnyana
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 12 No 1 (2018)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

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.

Pemetaan Perubahan Penggunaan Lahan Wilayah Pesisir di Kecamatan Bulak, Surabaya Tahun 2014 dan 2020 Maria Laurensyelen Wulu Beda Rianghepat; I Wayan Nuarsa; Ida Bagus Mandhara Brasika
Journal of Marine and Aquatic Sciences Vol 8 No 1 (2022)
Publisher : Fakultas Kelautan dan Perikanan Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/jmas.2022.v08.i01.p15

The coastal area is an intersection between mainland and ocean. The tourism potential in the coastal area of Bulak District in Surabaya is expanded. It is shown by the construction of Surabaya Bridge in 2015. This construction will affect land use change. Remote sensing technology is one of the acquisitions to monitor land use change. This research focuses on identifying the land use change in the coastal area in Bulak District, Surabaya, in 2014 and 2020, as well as to determine the accuracy of classification method applied for mapping the land use change in 2020. The application of 2014 acquisitions data was used as the bridge construction plan, while the application of 2020 acquisitions data was used as the premise for the land classification system in the previous year. There are two methods used to classify land use in coastal areas, that is pixel-based classification (maximum likelihood algorithm) and object-based classification (nearest neighbor algorithm). The research shows that there are 6 land use classes in study area: built-up land, rice fields, forests, shrubs, non-built-up land, and ocean. By applying these two methods, the result shows different area changes. The conversion of the highest mainland by applying a pixel-based classification was found in built-up land (+23.03 ha) and rice fields (-24.84 ha), while the area changes by applying object-based classification method were found in built-up land (+32.75 ha) and rice fields (-26.91 ha), respectively. The accuracy by applying the pixel and object-based method is 89% and 92%, respectively, from the percentage indicates good interpretation.

Co-Authors Abd. Rahman As-syakur Chiharu Hongo Fumihiko Nishio I Made Oka Guna Antara I Wayan Sandi Adnyana Ida Bagus Mandhara Brasika Made Arya Bhaskara Putra Maria Laurensyelen Wulu Beda Rianghepat Norikazu Shimizu Takahiro Osawa

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