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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Kholifatul Aziz
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Earth & Planetary Sciences

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PLATFORM REEF LAGOON DETECTION FROM SENTINEL-2 IN PANGGANG ISLAND AND SEMAKDAUN ISLAND Wikanti Asriningrum; Azura Ulfa; Kholifatul Aziz; Kuncoro T. Setiawan; Dyah Pangastuti
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.a3804

Abstract

Processing of satellite image data for the detection of platform reef lagoons is intended as one of the geo-physical parameters of the reef landform. Panggang Island and Semakdaun Island were chosen to make the detection model because they are ideal for lagoon reef landforms and tapulang court reefs. This model is only valid in the continental shelf area and the back arc and small island tectonic type. Determination of this location is done to improve the accuracy of spectral-based data processing. Platform reefs are one of four classes of reef landforms. Sentinel-2A data with a spatial resolution of 10m, blue, green, red, and near infrared bands were selected to investigate their ability to detect lagoons. Processing of data by calculating the Optimum Index Factor (OIF) to produce a composite image and drawing transect lines to produce pixel values and spectral graphics of the lagoon. The results of data processing in the form of graphs, composite images and pixel values were built to realize a digital lagoon detection model. These results are used for lagoon growth stage analysis for the classification of three reef platform landforms, visually and digitally interpretation. This digital and visual detection system design is useful for monitoring coral reef ecosystems.
ANALYSIS OF THE PENETRATION CAPABILITY OF VISIBLE SPECTRUM WITH AN ATTENUATION COEFFICIENT THROUGH THE APPARENT OPTICAL PROPERTIES APPROACH IN THE DETERMINATION OF A BATHYMETRY ANALYTICAL MODEL Kuncoro Teguh Setiawan; Gathot Winarso; Muhammad Ulin Nuha; Maryani Hartuti; Devica Natalia BR Ginting; Emi Yati; Kholifatul Aziz; Fajar Bahari Kusuma; Wikanti Asriningrum
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3667

Abstract

The attenuation coefficient (Kd) can be extracted by an apparent optical properties(AOP) approach to determine marine shallow-water habitat bathymetry based on an analytical method. Such a method was employed in the Red Sea by Benny and Dawson in 1983 using Landsat MSS imagery. Therefore, we applied the Benny and Dawson algorithm to extract bathymetry in shallow marine waters off Karimunjawa Island, Jepara, Central Java, Indonesia. We used the SPOT 6 satellite, which has four multispectral bands with a spatial resolution of 6 meters. The results show that three bands of SPOT 6 data (the blue, green, and red bands) can produce bathymetric information up to 30.29, 24.63 and 18.58 meters depth respectively. The determinations of the attenuation coefficients of the three bands are 0.08069, 0.09330, and 0.39641. The overall accuracy of absolute bathymetry of the blue, green, and red bands is 61.12%, 65.73%, and 26.25% respectively, and the kappa coefficients are 0.45, 0.52, and 0.13.
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 A. A. Md. Ananda Putra Suardana Abd. Rahman As-syakur Agung Dwi Wijaya Azura Ulfa Devica Natalia BR Ginting Dyah Pangastuti Emi Yati Fajar Bahari Kusuma Gathot Winarso Kuncoro T. Setiawan Kuncoro Teguh Setiawan Maryani Hartuti Muhammad Rizki Nandika Muhammad Ulin Nuha Nanin Anggraini Ratih Dewanti Wiji Prasetio Wikanti Asriningrum