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All Journal HAYATI Journal of Biosciences MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management MEDIA KONSERVASI Indonesian Journal of Geography REKA GEOMATIKA Geoplanning : Journal of Geomatics and Planning IJOG : Indonesian Journal on Geoscience Journal of Degraded and Mining Lands Management Indonesian Journal of Geospatial Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Indonesian Journal of Biotechnology Forum Geografi Indonesian Journal of Science and Technology JURNAL ILMIAH GEOMATIKA Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL GEOGRAFI Geografi dan Pengajarannya Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal on Geoscience Indonesian Journal on Geoscience Bulletin of Geology Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Ketut Wikantika
Centre for Remote Sensing, Bandung Institute of Technology (ITB) and Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : Forum Geografi

 1 
Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest) Jaya, Laode M Golok; Wikantika, Ketut; Sambodo, Katmoko Ari; Susandi, Armi
Forum Geografi Vol 31, No 1 (2017): July 2017
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v31i1.2518

Abstract

This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks.  
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

Abstract

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
Spatial Analysis to Mitigate the Spread of Covid-19 Based on Regional Demographic Characteristics Mochamad Firman Ghazali; Anggun Tridawati; Mamad Sugandi; Aqilla Fitdhea Anesta; Ketut Wikantika
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

Abstract

COVID-19 is currently the hot topic of discussion by scientists because of its ability to quickly spread, in line with everyday human activities. One of the environmental factors related to climatic parameters, such as the air temperature, contributed to the spreading of COVID-19 in the last four months. Its distribution ability is no longer local as it successfully halts the important activities in many countries globally. This study aims to explain the opportunity of geospatial analysis in handling the COVID-19 distribution locally based on the characteristics of demographic data. Various data, including the confirmed positive for COVID-19, age-based population, and Landsat 8 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution per September 2020 in Bandung, Indonesia. An inverse distance weighted (IDW), Moran's I index and local indicator spatial association (LISA), and a proposed ratio of the elderly population against the population with confirmed positive for COVID-19 (CoVE) were used as the approach to determine its distribution characteristics. The information derived from Landsat 8 satellite imagery, such as the residential area, surface temperature, and humidity based on the supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) was used to perform the analysis.  The results showed that the positive population of COVID-19 was concentrated in Bandung city. However, with a Moran's I value of 0.316, not all are grouped into the same category. There are only 8, 2, 5, and 3 districts categorized as HH, HL, LL, and LH. However, the areas with a large or small number of elderlies do not always correlate with the high number of confirmed positives for COVID-19. There are only 3, 1, and 3 districts classified as HH, HL, and LL. They were represented by the values of Moran's I, for about 0.057. The positive relationship between confirmed positive for COVID-19 and the built-up area, surface temperature, humidity, and the elderly population based on the coefficient of determination (R2) were 0.03, 0.28, 0.25, and 0.019, respectively. The study also shows that the vulnerability of those areas is relatively low. The study shows that the vulnerabilities in these areas are relatively low and the recommendation for COVID-19 widespread mitigation has to consider the demographic characteristics precisely in the large scale social restrictions (LSSR).
Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest) Laode M Golok Jaya; Ketut Wikantika; Katmoko Ari Sambodo; Armi Susandi
Forum Geografi Vol 31, No 1 (2017): July 2017
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v31i1.2518

Abstract

This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks.  
Co-Authors Abd. Rasyid Syamsuri Adhi Wibowo Adriana Hiariej, Adriana 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 Handoyo Harsolumakso, Agus Handoyo Agus Sutanto Agus Sutanto Ahmad Luthfi Hadiyanto Akihiko Kondoh Aldyansyah, Muhammad Aminah Kastuari Anesta, Aqilla Fitdhea Anggun Tridawati Aqilla Fitdhea Anesta Armi Susandi Armi Susandi Ary Setijadi Prihatmanto Asep Saepuloh 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 Suwardi Desti Ayunda Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Fahmi, Muhammad Nurul Farah Nafisa Ariadji Fauziah, Afi 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 Nengah Widiadnyana Nengah Widiadnyana Nisrina Sukriandi Nurjanna Joko Trilaksono Prihanggo, Maundri Prila Ayu Dwi Prastiwi Purnama, Yustika 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 Suwardhi, Deni 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