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All Journal MAJALAH ILMIAH GLOBE The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Muhammad Rizki Nandika
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Earth & Planetary Sciences Education

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PEMETAAN MANGROVE MENGGUNAKAN ALGORITMA MULTIVARIATE RANDOM FOREST: Studi Kasus di Segara Anakan, Cilacap Muhammad Rizki Nandika; A.A. Md. Ananda Putra Suardana; Nanin Anggraini
Majalah Ilmiah Globe Vol. 25 No. 1 (2023): GLOBE VOL 25 NO 1 TAHUN 2023
Publisher : Badan Informasi Geospasial

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

Abstract

Potensi pengembangan dan pemanfaatan Artificial Intelligence (AI) dan Machine Learning (ML) terus meningkat untuk dimanfaatkan dalam pemrosesan data penginderaan jauh pada periode waktu terakhir. Teknologi penginderaan jauh telah terbukti dapat diandalkan untuk mendeteksi sebaran tutupan mangrove. Salah satu metode berbasis ML yang digunakan untuk melakukan deteksi sebaran tutupan mangrove adalah metode Random Forest. Penelitian ini berfokus pada pengujian akurasi klasifikasi Random Forest dalam mengidentifikasi mangrove di Segara Anakan, Cilacap. Seluruh pemrosesan data dan analisis dilakukan menggunakan platform berbasis cloud, Google Earth Engine. Data yang digunakan yaitu citra satelit Sentinel-2A akuisisi tanggal 1 Januari - 31 Desember 2020. Metode klasifikasi menggunakan algoritma RF dengan 12 kombinasi band dan indeks yang berbeda: biru, hijau, merah, red edge, NIR, SWIR-1, SWIR-2, NDVI, MNDWI, SR, GCVI, MMRI. Hasil penelitian menunjukkan bahwa hasil klasifikasi menggunakan 12 parameter mampu mengidentifikasi mangrove dengan nilai akurasi yang tinggi (OA = 0,892; kappa = 0,782). Hasil penelitian ini menunjukkan bahwa MMRI menjadi parameter yang diketahui memiliki kemampuan yang paling baik dalam memisahkan objek mangrove dan non-mangrove, diikuti selanjutnya oleh SWIR-2.
BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI A. A. Md. Ananda Putra Suardana; Nanin Anggraini; Kholifatul Aziz; Muhammad Rizki Nandika; Azura Ulfa; Agung Dwi Wijaya; Abd. Rahman As-syakur; Gathot Winarso; Wiji Prasetio; Ratih Dewanti
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3797

Abstract

Remote sensing technology can be used to find out the potential of mangrove forests information. One of the potentials is to be able to absorb three times more CO2 than other forests. CO2 absorbed during the photosynthesis process, produces organic compounds that are stored in the mangrove forest biomass. Utilization of remote sensing technology is able to detect mangrove forest biomass using the density level of the vegetation index. This study focuses on determining the best AGB model based on the vegetation index and the ability of mangrove forests to absorb CO2. This research was conducted in Benoa Bay, Bali Province, Indonesia. The satellite image used is Sentinel-2. Classification of mangroves and non-mangroves using a multivariate random forest algorithm. Furthermore, the mangrove forest biomass model using a semi-empirical approach, while the estimation of CO2 sequestration using allometric equations. Mean Absolute Error (MAE) is used to evaluate the validation of the model results. The classification results showed that the detected area of Benoa Bay mangrove forest reached 1134 ha (OA: 0.98, kappa: 0.95). The best AGB estimation result is the DVI-based AGB model (MAE: 23,525) with a value range of 0 to 468.38 Mg/ha. DVI-based AGB derivatives are BGB with a value range of 0 to 79.425 Mg/ha, TAB with a value range of 0 to 547.8 Mg/ha, TCS with a value range of 0 to 257.47 Mg/ha, and ACS with a value range of 0 to 944.912 Mg/ha.
VERTICAL LAND MOTION AND INUNDATION PROCESSES BASED ON THE INTEGRATION OF REMOTELY SENSED DATA AND IPCC AR5 SCENARIOS IN COASTAL SEMARANG, INDONESIA Muhammad Rizki Nandika; Setyo Budi Susilo; Vincentius Siregar
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3272

Abstract

Vertical land motion (VLM) is an important indicator in obtaining information about relative sea-level rise (SLR) in the coastal environment, but this remains an area of study poorly investigated in Indonesia. The purpose of this study is to investigate the significance of the influence of VLM and SLR on inundation. We address this issue for Semarang, Central Java, by estimating VLM using the small baseline subset time series interferometry SAR method for 24 Sentinel-1 satellite data for the period March 2017 to May 2019. The interferometric synthetic aperture radar (InSAR) method was used to reveal the phase difference between two SAR images with two repetitions of satellite track at different times. The results of this study indicate that the average land subsidence that occurred in Semarang between March 2017 and May 2019 was from (-121) mm/year to + 24 mm/year. Through a combination of VLM and SLR scenario data obtained from the Intergovernmental Panel on Climate Change (IPCC), it was found that the Semarang coastal zone will continue to shrink due to inundation (forecast at 7% in 2065 and 10% in 2100).
Co-Authors A. A. Md. Ananda Putra Suardana A.A. Md. Ananda Putra Suardana Abd. Rahman As-syakur Agung Dwi Wijaya Azura Ulfa Gathot Winarso Kholifatul Aziz Nanin Anggraini Ratih Dewanti Setyo Budi Susilo Vincentius P Siregar Wiji Prasetio