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Ketut Wikantika

Centre for Remote Sensing, Bandung Institute of Technology (ITB) and Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)

Author-ID : 417867

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Education Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Advanced Applications of Synthetic Aperture Radar (SAR) Remote Sensing for Detecting Pre- and Syn-eruption Signatures at Mount Sinabung, North Sumatra, Indonesia Saepuloh, Asep; Mirelva, Prima Rizky; Wikantika, Ketut
Indonesian Journal on Geoscience Vol 6, No 2 (2019)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.6.2.123-140

DOI:10.17014/ijog.6.2.123-140Mount Sinabung was re-activated at August 28th, 2010 after a long repose interval. The early stage of a phreatic eruption was then followed by magmatic eruptions at September 15th, 2013 for years until now. To understand the ground surface changes accompanying the eruption periods, comprehensive analyses of surface and subsurface data are necessary, especially the condition in pre- and syn-eruption periods. This study is raised to identify ground surface and topographical changes before, intra, and after the eruption periods by analyzing the temporal signature of surface roughness, moisture, and deformation derived from Synthetic Aperture Radar (SAR) data. The time series of SAR backscattering intensity were analyzed prior to and after the early eruption periods to know the lateral ground surface changes including estimated lava dome roughness and surface moisture. Meanwhile, the atmospherically corrected Differential Interferometric SAR (D-InSAR) method was also applied to know the vertical topographical changes prior to the eruptions. The atmospheric correction based on modified Referenced Linear Correlation (mRLC) was applied to each D-InSAR pair to exclude the atmospheric phase delay from the deformation signal. The changes of surface moistures on syn-eruptions were estimated by calculating dielectric constant from SAR polarimetric mode following Dubois model. Twenty-one Phased Array type L-band SAR (PALSAR) data on board Advanced Land Observing Satellite (ALOS) and nine Sentinel-1A SAR data were used in this study with the acquisition date between February 2006 and February 2017. For D-InSAR purposes, the ALOS PALSAR data were paired to generate twenty interferograms. Based on the D-InSAR deformation, three times inflation-deflation periods were observed prior to the early eruption at August 28th 2010. The first and second inflation-deflation periods at the end of 2008 and middle 2009 presented migration of magma batches and dike generations in the deep reservoir. The third inflation-deflation periods in the middle of 2010 served as a precursor signal presenting magma feeding to the shallow reservoir. The summit was inflated about 1.4 cm and followed by the eruptions. The deflation of about 2.3 cm indicated the release pressure and temperature in the shallow reservoir after the early eruption at August 28th, 2010. The last inflation-deflation period was also confirmed by the increase of the lava dome roughness size from 5,121 m2 on July to 6,584 m2 on August. The summit then inflated again about 1.1 cm after the first eruption and followed by unrest periods presented by lava dome growth and destruction at September 15th, 2013. The volcanic products including lava and pyroclastics strongly affected the moisture of surface layer. The volcanic products were observed to reduce the surface moisture within syn-eruption periods. The hot materials are presumed responsible for the evaporation of the surface moisture as well.

Spatial Analysis to Mitigate the Spread of Covid-19 Based on Regional Demographic Characteristics Ghazali, Mochamad Firman; Tridawati, Anggun; Sugandi, Mamad; Anesta, Aqilla Fitdhea; Wikantika, Ketut
Forum Geografi Vol 35, No 1 (2021): July 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v35i1.12325

COVID-19 is currently the hot topic of conversation because of its ability to spread relatively quickly, in line with everyday human activities. It is unknown exactly the dominant environmental factors and their influence on the spread of COVID-19 in the last four months. Its distribution ability is no longer locally but has succeeded in making several countries stop its important activities globally. Non-spatial data such as positive confirmed population data, population-based on age, and Landsat 7 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution until October September 2020. Inverse distance weighted (IDW), Moran's I and Local Indicator Spatial Association (LISA), as well as the ratio of the old population to the population, confirmed positive (+) were used as an approach to determine the characteristics of its distribution. Besides information on residential areas, surface temperature, and surface humidity based on supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) of Landsat 7 satellite imagery is used to enrich the spatial analysis carried out. The study results show a population concentration of COVID-19 towards the city of Bandung, with Moran's I result in not showing a good correlation. Meanwhile, the LISA results show that areas with a large or small number of elderly residents do not always have high positive COVID-19 numbers. The relation between the positive population (+) COVID-19 population and the built-up area (settlement), the surface temperature in the built-up area, surface humidity, and old age population based on the coefficient of determination (R2) is 0.03, 0.28, 0.25, and 0.019. This shows the level of vulnerability of the area is low. So, in the end, a recommendation for handling can be produced by taking into account the demographic characteristics of the area appropriately

GIS Based Analysis of Agroclimate Land Suitability for Banana Plants in Bali Province, Indonesia I Wayan Nuarsa; I Nyoman Dibia; Ketut Wikantika; Deni Suwardhi; I Nyoman Rai
HAYATI Journal of Biosciences Vol. 25 No. 1 (2018): January 2018
Publisher : Bogor Agricultural University, Indonesia

The need for bananas in Bali far exceeds the production. To obtain optimal production according to their genetic potential, the development of banana cultivation should be preceded by a land suitability evaluation study. This study aims to evaluate the land suitability based on agroecological parameters such as rainfall, altitude, dry month, slope, and considering current land use. The results showed that 257.467 ha or 46.16% of the area of Bali Province has the potential to be planted with bananas. Buleleng Regency has the widest area for the development of banana plants, followed by Karangasem, Tabanan, Jembrana and Bangli. Denpasar town has the smallest suitable area. Based on the observed agroclimate parameters, slope is the most severe limiting factor in banana cultivation, while rainfall, altitude, and dry months are not significant limiting factors. Recommended land use for the development of banana plants is garden, grass, rain-fed rice field, scrub, bare land, and moor.

Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia Rian Nurtyawan; Asep Saepuloh; Agung Budi Harto; Ketut Wikantika; Akihiko Kondoh
HAYATI Journal of Biosciences Vol. 25 No. 3 (2018): July 2018
Publisher : Bogor Agricultural University, Indonesia

Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.

Identification of Banana Plants from Unmanned Aerial Vehicles (UAV) Photos Using Object Based Image Analysis (OBIA) Method (A Case Study in Sayang Village, Jatinangor District, West Java) Agung Budi Harto; Prila Ayu Dwi Prastiwi; Farah Nafisa Ariadji; Deni Suwardhi; Fenny M. Dwivany; I Wayan Nuarsa; Ketut Wikantika
HAYATI Journal of Biosciences Vol. 26 No. 1 (2019): January 2019
Publisher : Bogor Agricultural University, Indonesia

Banana is one of the leading fruit commodities of Indonesia and ranks the sixth position as one of the largest banana producers in the world. There are more than 200 types of banana in Indonesia. The utilization of bananas is influenced by the local culture, where in every 10 horticultural households, 5 of them plant bananas both as garden plants or field plants. This horticultural crop is expectantly being one of the actions to improve economic prosperity especially in rural areas. In maintaining the diversity of the growing bananas in rural areas, a geospatial approach to identify the vegetation is required. Remote sensing technology is one of the solutions to observe and to develop banana plants with one of the methods namely Object Based Image Analysis (OBIA). This method consists of segmentation, classification, and validation. In classification process, the OBIA method distinguishes objects not only based on pixel values but also on the basis of the shape, area, and texture around them. This research has proven that the classification using OBIA method is better than the traditional classification such as maximum likelihood classification method to identify banana plants. OBIA method can quickly identifies the vegetation and non-vegetation, also the regular plants and banana plants.

Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province) Desti Ayunda; Ketut Wikantika; Dandy A. Novresiandi; Agung B. Harto; Riantini Virtriana; Tombayu A. Hidayat
HAYATI Journal of Biosciences Vol. 26 No. 2 (2019): April 2019
Publisher : Bogor Agricultural University, Indonesia

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia) Tri Muji Susantoro; Ketut Wikantika; Agung Budi Harto; Deni Suwardi
HAYATI Journal of Biosciences Vol. 26 No. 3 (2019): July 2019
Publisher : Bogor Agricultural University, Indonesia

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

Soil Moisture Mapping at Paddy Field in Indramayu Residence Using Landsat 8 OLI/TIRS Mochamad Firman Ghazali; Tri Muji Susantoro; Ketut Wikantika; Agung Budi Harto; Rian Nurtyawan
HAYATI Journal of Biosciences Vol. 27 No. 1 (2020): January 2020
Publisher : Bogor Agricultural University, Indonesia

Drought monitoring is important for the paddy planting planning. Remote sensing is one tool can be used for it. Paddy field monitoring based on the soil moisture gives much knowledge related to the water content in the soil. Soil moisture analysis in this study is using Normalized Different Water Index (NDWI), Linear Soil Moisture (LSM), and Tasseled Cap. Soil moisture change could explain based on calculation results of NDWI, Linear Soil Moisture (LSM), and Tasseled Cap Transformation (TCT). Based on the results has explained that the driest year occurs in 2015 and June 2016 has a higher soil moisture. Comparison with the radar shows that the results of soil moisture analysis with Landsat was effective can be used with results relatively close to the radar results.

Genetic Relationship between Tongka Langit Bananas (Musa troglodytarum L.) from Galunggung and Maluku, Indonesia, Based on ITS2 Fenny Martha Dwivany; Giasintha Stefani; Agus Sutanto; Husna Nugrahapraja; Ketut Wikantika; Adriana Hiariej; Topik Hidayat; I Nyoman Rai; Nisrina Sukriandi
HAYATI Journal of Biosciences Vol. 27 No. 3 (2020): July 2020
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.27.3.258

Tongka Langit or Fe’i banana (Musa troglodytarum L.) has the T genome and a very high content of beta-carotene. It only grew and spread around the regions of Maluku islands and Papua. However, recently our team found this banana on the foot of mount Galunggung, West Java, so this raised the question about its origin. The objective of this study was to understand the genetic relationship between Tongka Langit from Galunggung and Maluku islands and compared it with other bananas with different genomes. Genetic diversity analysis was done using ITS2 DNA marker and dendrogram analysis showed three groups. From the comparison of the ITS2 sequences, there were no difference (100% identity) between the ITS2 sequence of Tongka Langit originating from Galunggung and Maluku. In conclusion, based on the ITS2 marker, the Tongka Langit were more distantly related to cultivars with A and B genomes, and there was no difference in the ITS2 sequence of Tongka Langit originating from Galunggung and Maluku. To the best of our knowledge, there is no previous report of genetic relationship between Tongka Langit from Galunggung and other regions.

Implementation of SExI–FS (Spatially Explicit Individual-based Forest Simulator) Model using UAV Aerial Photo Data Case Study: Jatinangor ITB Campus Aminah Kastuari; Deni Suwardhi; Himasari Hanan; Ketut Wikantika; Agung Budi Harto; Riantini Virtriana; Shafarina Wahyu Trisyanti
HAYATI Journal of Biosciences Vol. 27 No. 4 (2020): October 2020
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.27.4.314

Landscape architecture affected by interaction between built and natural environment such as vegetation. Nowadays, landscape architects are using 3D city models for simulations, which requires highly dynamic and time-varying attributes. 3D city modelling structure has been standardized by CityGML, although researches that are related to the storing of dynamic data had been conducted for the past years, it has not been supported by any standard until this very moment. In dynamizer, it is added as a data structure into a CityGML structure that is already existed, although the existing structure is a static one. Kolbe’s research on dynamic data using CityGML called dynamizer could use the spatial data in more dynamic way by changing its geometric, thematic, or appearance data, but its purpose is not specific for trees or vegetation. In this paper, a method of simulating the vegetation growth using SeXI-FS will be discussed to show the dynamic changes that happen in vegetation as part of the dynamic changes in landscape architecture. The result of this research will be used to address the importance of information on vegetation by studying its changes in Jatinangor ITB Campus and as initial research to build dynamizer in CityGML for landscape architecture.

Co-Authors Abd. Rasyid Syamsuri Adhi Wibowo Adriana Hiariej, Adriana Afi Fauziah Agung B. Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agus Handoyo Harsolumakso Agus Sutanto Agus Sutanto Ahmad Luthfi Hadiyanto Akihiko Kondoh Aminah Kastuari Anesta, Aqilla Fitdhea Anggun Tridawati Aqilla Fitdhea Anesta Armi Susandi Armi Susandi Ary Setijadi Prihatmanto Asep Saepuloh Asep Yusup Saptari Asep Yusup Saptari, Asep Yusup Asmi M. Napitu Asmi M. Napitu Aswin Rahadian Bambang Widarsono Bobby S. Dipokusumo Dandy A. Novresiandi Darmawan S Darmawan S, Darmawan Dedi Irawadi Deni Suwardhi Deni Suwardhi Deni Suwardhi Deni Suwardi Desti Ayunda Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Fahmi, Muhammad Nurul Farah Nafisa Ariadji Fenny M. Dwivany FENNY MARTHA DWIVANY Ghazali, Mochamad Firman Ghozali, M. Firman Giasintha Stefani Hary Nugroho Herru Lastiadi Setiawan Himasari Hanan Husna Nugrahapraja I Nyoman Dibia I NYOMAN RAI I Wayan Nuarsa Imam A. Sadisun Intan Fatmawati Irland Fardani Ishak H. Ismullah Jaya, La Ode Muhammad Golok Jevon A. Telaumbanua Karlia Meitha Katmoko Ari Sambodo Katmoko Ari Sambodo, Katmoko Ari Laode Muhammad Golok Jaya LILIK BUDIPRASETYO Lissa F. Yayusman Luky Adrianto Lumbantobing, Marlonroi Mamad Sugandi Marlonroi Lumbantobing Mila Olivia Trianaputri Mirelva, Prima Rizky Mochamad Firman Ghazali Mochamad Firman Ghazali Muhammad Aldyansyah Nengah Widiadnyana Nengah Widiadnyana Nisrina Sukriandi Nurjanna Joko Trilaksono Prihanggo, Maundri Prila Ayu Dwi Prastiwi Retno Dammayatri Rian Nurtyawan Riantini Virtriana S. Suliantara Satria Bijaksana Shafarina Wahyu Trisyanti Sigit Nur Pratama Simarmata, nirmawana Soni Darmawan Sony Darmawan, Sony Sugandi, Mamad Sukristiyanti Sukristiyanti Supriadi A Supriadi A, Supriadi Susantoro, Tri Muji Tahjudil Witra Tan, Alex Tohir, Rizki Kurnia Tombayu A. Hidayat Topik Hidayat Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro, Tri Muji Trianaputri, Mila Olivia Tridawati, Anggun Trika Agnestasia Tarigan Yayusman, Lissa Fajri Yudi Setiawan Yustika Purnama

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