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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
A. A. Md. Ananda Putra Suardana
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Earth & Planetary Sciences

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DETECTION OF WATER-BODY BOUNDARIES FROM SENTINEL-2 IMAGERY FOR FLOODPLAIN LAKES Azura Ulfa; Fajar Bahari Kusuma; A. A. Md. Ananda Putra Suardana; Wikanti Asriningrum; Andi Ibrahim; Lintang Nur Fadlillah
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 2 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3827

Abstract

The impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans.
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.
Co-Authors Abd. Rahman As-syakur Agung Dwi Wijaya Andi Ibrahim Azura Ulfa Fajar Bahari Kusuma Gathot Winarso Kholifatul Aziz Lintang Nur Fadlillah Muhammad Rizki Nandika Nanin Anggraini Ratih Dewanti Wiji Prasetio Wikanti Asriningrum