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All Journal EnviroScienteae MCSIJ (Marine, Coastal and Small Islands Journal) : Jurnal Tugas Akhir Mahasiswa Ilmu Kelautan
Melkyanus, Melkyanus
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Physics

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PEMETAAN KERAPATAN DAN PENERAPAN METODE DIFERENSIASI OBIA UNTUK DIFERENSIASI JENIS MANGROVE DI KAWASAN TANJUNG PEMANCINGAN, KOTABARU Melkyanus, Melkyanus; Syahdan, Muhammad; Asyari, Mufidah; Sofarini, Dini
EnviroScienteae Vol 20, No 4 (2024): ENVIROSCIENTEAE VOLUME 20 NOMOR 4, NOVEMBER 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v20i4.20470

Abstract

This study aims to identify mangrove species and map the density of mangrove vegetation in the Tanjung Pemancingan area, Kotabaru, using an object-based classification method (OBIA) applied to Sentinel-2 imagery and Unmanned Aerial Vehicle (UAV) data. Mangroves play a crucial role in protecting coastlines from erosion and serving as habitats for various species, making an in-depth understanding of mangrove distribution and types essential for coastal conservation and environmental management. The OBIA method allows for more accurate mapping by considering texture, shape, and more complex spatial patterns compared to traditional pixel-based methods. This study employs the Support Vector Machine (SVM) algorithm in the classification process to enhance the accuracy of mangrove species identification. The analysis utilizes Sentinel-2 satellite imagery with a spatial resolution of 10x10 meters and UAV data for higher resolution. The results show that the NDVI values for mangroves in the study area range from -0.30 to 0.686, which were classified into three canopy density classes: sparse (-0.30 to 0.026), moderate (0.027 to 0.356), and dense (0.357 to 0.686). The OBIA method combined with the SVM algorithm successfully discriminated between seven mangrove species with an overall accuracy (OA) of 72.46%. The identified mangrove species include Avicennia alba, Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Bruguiera gymnorhiza, Rhizophora apiculata, and Sonneratia alba, with Avicennia rumphiana being the most dominant species, covering an area of 13.87 hectares. The mangrove vegetation density was successfully mapped, providing valuable information that can be used in conservation planning, coastal resource management, and ecotourism development in the area. Furthermore, these results have significant implications for further research in mangrove ecosystem monitoring and the application of remote sensing technology in environmental management.
ANALISIS KATEGORI SUBSTRAT DENGAN METODE OBIA MENGGUNAKAN CITRA SPOT 7 PADA GUGUSAN GOSONG KARANG KIMA DI PERAIRAN ANGSANA KABUPATEN TANAH BUMBU Melkyanus, Melkyanus; Syahdan, Muhammad; Hamdani, Hamdani
Marine Coastal and Small Islands Journal - Jurnal ilmiah Ilmu Kelautan Vol 4, No 2 (2020)
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/m.v4i2.11788

Abstract

Terumbu karang merupakan salah satu ekosistem perairan tropis dengan fungsi dan manfaat penting bagi kehidupan. Oleh karena itu, pemetaan sebaran dan luasan terumbu karang sangatlah dibutuhkan dalam pengembangan potensi sumber daya laut dan pesisir. Pada tahun 2015, 2016 dan 2019 hingga awal tahun 2020 terjadi pemutihan karang di Gugusan Karang Kima yang disebabkan oleh anomali suhu permukaan laut.  Selain itu ada peristiwa banjir di kawasan Desa Bunati dan Angsana yang mempengaruhi penurunan  salinitas dan peningkatan sedimentasi. Kedua peristiwa ini dan beberapa faktor lainnya dapat mempengaruhi perubahan kategori substrat pada paparan karang. Perubahan kategori substrat ini yang akan dipetakan dengan koreksi awal citra dan dilanjutkan dengan analisis OBIA.  Dalam analisis tersebut  dilakukan segmentasi dan klasifikasi terhadap training area atau input data lapangan sebagai pemberi ciri atau penanda. Selanjutnya diproses dengan algoritma  SVM (Support Vector Machine) untuk menghasilkan klasifikasi. Analisis ini membantu memperbaiki dinamika data terumbu karang terkait dengan perubahan variasi kategori substrat pada paparan terumbu.
Co-Authors Asyari, Mufidah Dini Sofarini Hamdani Hamdani Muhammad Syahdan