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All Journal HAYATI Journal of Biosciences Indonesian Journal of Geography Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Forum Geografi Jurnal Geosaintek Journal of Applied Geospatial Information Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Jurnal Penginderaan Jauh Indonesia SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Jurnal Geosaintek Jurnal Penginderaan Jauh Indonesia Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Geoid - Journal of Geodesy and Geomatics
Jaelani, Lalu Muhamad
Departemen Teknik Geomatika Institut Teknologi Sepuluh Nopember
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Public Health

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Journal : Journal of Applied Geospatial Information

 1 
Multi-Temporal Chlorophyll-A Monitoring in Lake Matano and Towuti Using Landsat 8 OLI Imagery Lalu Muhamad Jaelani; Rossita Yuli Ratnaningsih
Journal of Applied Geospatial Information Vol 3 No 1 (2019): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5.352 KB) | DOI: 10.30871/jagi.v3i1.1065

Abstract

Lake Matano and Towuti are two of 15 lake priorities in Indonesia. For preservation purposes, a routine water quality monitoring from satellite is needed. In this study, 11 scenes of Landsat 8 data were processed to produce chlorophyll-a concentration as an indicator of water quality condition on these two lakes. The result showed that water quality in Lake Matano and Towuti were in low cholorphyll-a condition with chlorophyll-a concentration ranged from 0.000– 2.298 mg/m3, 0.000-2.236 mg/m3, respectively. The accuracy of estimated Chlorophyll-a in these two lakes were affected by an inaccurate of atmospheric corrected data. To improve the accuracy, a more accurate atmospheric correction algorithm for Landsat 8 was still required.
WorldView-2 Satellite Image Classification using U-Net Deep Learning Model Ilyas Ilyas; Lalu Muhamad Jaelani; Muhammad Aldila Syariz; Husnul Hidayat
Journal of Applied Geospatial Information Vol 5 No 2 (2021): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v5i2.3150

Abstract

Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were 49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.
Co-Authors Abdul Rasam, Abdul Rauf Adillah Alfatinah Afifi , Zulfahmi Agnes Rusnalia T. Agung Budi Cahyono Agustina, Heni Ahyudanari, Ervina Aji, Pujo Albertus Sulaiman Albertus Sulaiman, Albertus Alina, Aldea Noor Amalia Putri Rivani Andie Setiyoko Andika Yudha Gutama Andri A. Wibowo Ari Matiur Aries Sulisetyono Arik Yumna Pratiwi Aryasandah H. Dewantoro Aryasandah H. Dewantoro, Aryasandah H. Azmi, Rahajeng Aulia Bachtiar, Jayed Ali Baiq Arasya Wulandari Bangun Muljo Sukojo Bangun Muljo Sukojo, Bangun Muljo Benedict Chao-Hung Lin Denaro, Lino Garda Dewinta Heriza Diah Ardiani Eddy Setyo Koenhardono, Eddy Setyo erika yuniar tyastiti Fahlefi , Rizha Faisal Adam Yudithia Feny Arafah Filsa Bioresita, Filsa Firmansyah Maulana Azhali Fitriana Kartikasari Fitriana Kartikasari Fultriasantri, Indah Gathot Winarso Gathot Winarso Gathot Winarso, Gathot Gilang Amrullah Sayono Gutama, Andika Yudha Hanansyah, Megivareza Putri Handis Muzaky Harliyanti, Novi Ika Hepi Hapsari Handayani Hepi Hapsari Handayani Hepi Hapsari Handayani, Hepi Hapsari Heriza, Dewinta Hidayat, Arfico Rizky Hidayat, Husnul Husnul Hidayat, Husnul Ilyas Ilyas Irma'atus Sholihah Jayed Ali Bachtiar Jayeng Rangga Bhirawa Kartikasari, Fitriana Kristina Putri Lena Sumargana Lena Sumargana Lin, Chao-Hung Lino Garda Denaro Loryena Ayu Karondia Loryena Ayu Karondia, Loryena Ayu M. Aldila Syariz, M. Aldila M. Nur Cahyadi M. Nur Cahyadi, M. Nur Martanti Aji Pangestu Mochamad Indrawan Muhammad Aldila Syariz Muhammad Aldila Syariz Muhammad Hanif Muhammad Rizka Arief Pratama Muhammad Rizka Arief Pratama, Muhammad Rizka Arief Muhammad Taufik Muhammad Wildan Bobsaid Muhsi Muzaky, Handis Nabilah, Salwa Nadzir, Zulfikar Adlan Nia Kurniadin Niken rahayuningtyas Noorlaila Hayati, Noorlaila Norida Maryantika Novi Ika Harliyanti Nur Aina Rizki Rahmadani Nurahida Laili Nurahida Laili Nurgiantoro, Nurgiantoro Oktavianto G. Pamungkas, Adjie Pangestu, Martanti Aji Pratomo, Danar Guruh Pratomo, Danar Guruh Putri, Rizky Annisa Rahmansyah, Ferdian Zaki Ramadhanni, Rizky Fitria Resti Limehuwey Ricko Andrew FG. Rivani, Amalia Putri Rizha Fahlefi Romadina Indah Wardani Rossita Yuli Ratnaningsih Salam Tarigan Salwa Nabilaha Sanjaya, Hartanto Sayono , Gilang Amrullah Sekartadji, Ratih Sukojo , Bangun Muljo Sulaiman, Albertus Sulistyah, Umroh Dian Sulistyah, Umroh Dian Tambunan, Mangapul Parlindungan tyastiti , erika yuniar Wardani , Romadina Indah Wulandari, Baiq Arasya Yennie Marini Yennie Marini Zulfahmi Afifi