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All Journal EnviroScienteae JURNAL HUTAN TROPIS Journal of the Community Development in Asia
Asyari, Mufidah
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Agriculture, Biological Sciences & Forestry Humanities Earth & Planetary Sciences Environmental Science

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Journal : EnviroScienteae

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KAJIAN STATUS MUTU KUALITAS AIR SUNGAI DAN KUALITAS KIMIA TANAH PADA PERTAMBANGAN RAKYAT Aryani, Laily; Biyatmoko, Danang; Hadi, Abdul; Asyari, Mufidah
EnviroScienteae Vol 19, No 3 (2023): ENVIROSCIENTEAE VOLUME 19 NOMOR 3, AGUSTUS 2023
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v19i3.17283

Abstract

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.
Co-Authors Abdul Hadi Akbari, M. Denny Aryani, Laily Ayudila A. P., Prasasty Danang Biyatmoko, Danang Dini Sofarini Fonny Rianawati Kamaliah Kamaliah, Kamaliah Melkyanus, Melkyanus Muhammad Helmi Muhammad Syahdan Muhana, Muriyani Yudi Firmanul Arifin Zainal Abidin