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All Journal MAJALAH ILMIAH GLOBE Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Syamsul B. Agus
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Computer Science & IT Earth & Planetary Sciences Education

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KLASIFIKASI MULTIKSKALA UNTUK PEMETAAN ZONA GEOMORFOLOGI DAN HABITAT BENTIK MENGGUNAKAN METODE OBIA DI PULAU PARI (MULTISCALE CLASSIFICATION FOR GEOMORPHIC ZONE AND BENTHIC HABITATS MAPPING USING OBIA METHOD IN PARI ISLAND) Ari Anggoro; Vincentius Paulus Siregar; Syamsul B. Agus
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 Desember 2017
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.544 KB) | DOI: 10.30536/j.pjpdcd.1017.v14.a2622

Abstract

This study used multiscale classification and applied object-based image analysis (OBIA) for geomorphic zone and benthic habitats mapping in Pari islands. An optimized segmentation was performed to get optimum classification result. Classification methods for level 1 and 2 used contextual editing classification and for level 3 used support vector machines classifier. The results showed that overall accuracy for level 1 was 97% (reef level), level 2 was 87% (geomorphic zone), and level 3 was 75% (benthic habitats). Accuracy achieved by support vector machines classification was performed only in level 3 and optimum scale value achieved was 50 in compare with other scale values, i.e. 5, 25, 50, 75, 95. OBIA methods can be used as an alternative for geomorphic zone and benthic habitats map. Abstrak Penelitian ini menggunakan klasifikasi multiskala dan penerapan analisis citra berbasis obyek (OBIA) untuk pemetaan zona geomorfologi dan habitat bentik di Pulau Pari. Analisis berbasis obyek dilakukan optimasi pada proses segmentasi untuk mendapatkan hasil klasifikasi optimal. Metode klasifikasi pada level 1 dan 2 menggunakan klasifikasi contextual editing dan pada level 3 menggunakan klasifikasi Support Vector Machines (SVM). Hasil penelitian ini menunjukkan akurasi keseluruhan pada level 1 yaitu 97% (reef level), level 2 yaitu 87% (Geomorphic level), dan level 3 yaitu 75% (benthic habitat level). Klasifikasi SVM hanya diterapkan pada level 3 dan nilai skala optimum sebesar 50 dari percobaan nilai skala yaitu 5, 25, 50, 75, 95. Metode OBIA dapat digunakan sebagai alternatif untuk pemetaan zona geomorfologi dan habitat bentik.
EVALUASI TINGKAT AKURASI KLASIFIKASI HABITAT BENTIK PERAIRAN DANGKAL PADA PERBEDAAN JUMLAH KELAS MENGUNAKAN CITRA SATELIT RESOLUSI TINGGI: STUDI KASUS: PULAU SEBARU BESAR, KEPULAUAN SERIBU Ayub Sugara; Vincentius P. Siregar; Syamsul B. Agus
Majalah Ilmiah Globe Vol. 22 No. 2 (2020): GLOBE VOL 22 NO 2 TAHUN 2020
Publisher : Badan Informasi Geospasial

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

Abstract

Pulau Sebaru Besar merupakan salah satu pulau yang terdapat di bagian utara Kepulauan Seribu yang memliki keanekaragaman habitat perairan laut dangkal. Citra resolusi tinggi diintegrasikan dengan data observasi lapang dapat menjadi alternatif sumber informasi terkait habitat bentik perairan laut dangkal. Penelitian ini bertujuan untuk melakukan evaluasi akurasi hasil klasifikasi habitat bentik perairan dangkal di Pulau Sebaru Besar Kepulauan Seribu menggunakan citra WorldView-2 dengan penerapan 9 dan 7 kelas serta melakukan uji akurasi hasil klasifikasi. Data citra WorldView-2 yang digunakan merupakan salah satu citra resolusi tinggi dengan resolusi spasial 1,84 x 1,84 meter2 yang diakuisisi pada tanggal 7 Mei 2018. Survei lapang habitat bentik perairan dangkal dilakukan pada tanggal 10-12 Mei 2018 dan 09-10 Desember 2018 dengan teknik foto kuadrat yang menghasilkan sampelsampel sebanyak 159 titik. Persentase tutupan habitat setiap foto kuadrat dianalisis dengan perangkat lunak Coral Point Count with Excel extensions (CPCe). Berdasarkan hasil penelitian akurasi klasifikasi pemetaan habitat bentik perairan dangkal untuk 9 dan 7 kelas dihasilkan akurasi sebesar 63,2% dan 67,5% dengan algoritma Maximum Likelihood Classification (MLC). Habitat bentik perairan dangkal dapat dipetakan dengan baik, sehingga bisa menjadi masukan basis data informasi untuk pengelola Taman Nasional Kepulauan Seribu (TNKpS) kaitannya dalam usaha monitoring habitat bentik terkhusus terumbu karang dan upaya konservasi habitat perairan laut dangkal.
FISHING BOAT DISTRIBUTION ESTABLISHED BY COMPARING VMS AND VIIRS DATA AROUND THE ARU ISLANDS IN MALUKU INDONESIA Ruben van Beek; Jonson L. Gaol; Syamsul B. Agus
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3605

Abstract

Marine protected areas (MPAs) and no take zones (NTZs) are essential for the preservation of marine ecosystems. However, these important areas can be severely harmed by illegal fishing. All vessels above 30 gross tons are required to use vessel monitoring systems (VMSs) that enable vessel tracking by sending geographic data to satellites in each specific time period. The Visible Infrared Radiometer Suite (VIIRS) is a sensor on the National Oceanic and Atmospheric Administration (NOAA)-20 satellite that can detect the light-emitting diode (LED) light used by fishing vessels from space during the night time. In this research, VMS and VIIRS fishery data were combined in order to identify fishing vessels that were detected by the VIIRS sensor of the NOAA-20 satellite. The research was focused on an area near the Aru Islands in the Arafura Sea in Indonesia. Data on LED light used by the fishing techniques of purse seine and bouke ami were obtained for the whole of 2018. The data were then processed using R software. An R package called LLFI (LED Light Fisheries Identifier) was created, containing several R-functions that calculate VMS vessel position during satellite overpass time and then combine the VMS and VIIRS data attributes, resulting in a dataset comprising vessels identified from the VIIRS dataset. Out of all the estimated VMS fishing vessel positions during the VIIRS satellite overpass, approximately 51% could be assigned to fishing vessels detected from the VIIRS dataset. For bouke ami, the identification rate was approximately 87%, while that for small purse seine was around 39%. Ultimately, the LLFI package created daily paths for each identified fishing vessel, displaying all its movements during the day of its’identification. These daily paths did not show any activity within MPA or NTZ. The LLFI package was successful in combining VMS and VIIRS data, estimating VMS vessel positions during the VIIRS satellite overpass, identifying a percentage of the vessels, and creating a daily path for each identified vessel.
Co-Authors Ari Anggoro Ayub Sugara Jonson L. Gaol Ruben van Beek Vincentius P Siregar