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All Journal Forest and Society J SIG (Jurnal Sains Informasi Geografi) Jurnal Geologi Kelautan: Media Hasil Penelitian Geologi Kelautan
Prasetio, Wiji
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Energy Environmental Science Social Sciences

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Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia Suardana, A. A. Md. Ananda Putra; Anggraini, Nanin; Nandika, Muhammad Rizki; Aziz, Kholifatul; As-syakur, Abd. Rahman; Ulfa, Azura; Wijaya, Agung Dwi; Prasetio, Wiji; Winarso, Gathot; Dewanti, Ratih
Forest and Society Vol. 7 No. 1 (2023): APRIL
Publisher : Forestry Faculty, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/fs.v7i1.22062

Abstract

Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The random forest (RF) method was used to classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced AGC per species. Moreover, the model was built with linear regression equations for one variable x, and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests, because it has been proven to be able to increase the accuracy value of previous studies using optical images.
Efektifitas Identifikasi Perubahan Tutupan Mangrove Menggunakan Citra Landsat-8 di Kecamatan Labuhan Maringgai, Lampung Timur Pratama, Annisa Tias; Prasetio, Wiji; Prasetyo, Budhi Agung
J SIG (Jurnal Sains Informasi Geografi) Vol 8, No 1 (2025): Edisi Mei
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/jsig.v8i1.3067

Abstract

Mangrove forests play a crucial role in protecting coastlines, reducing CO2, and serving as natural resources. However, the growth of mangroves continues to decline due to human activities, necessitating studies to monitor mangrove coverage both directly and indirectly. This research aims to assess the effectiveness of identifying changes in mangrove coverage in Labuhan Maringgai District, East Lampung. The data used is Landsat-8 satellite imagery from the years 2019-2023. The method employed involves NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) algorithms followed by random forest classification to determine mangrove and non-mangrove areas. Accuracy testing uses a confusion matrix with overall accuracy values and Kappa coefficient. In addition, the t-test was used to determine the effectiveness between the NDVI and EVI algorithms. The analysis results indicate a change in the area of mangrove coverage with annual increases in Labuhan Maringgai District. Sequentially from 2019 to 2023, using the NDVI algorithm showed values of 19%, 6%, 13%, and 7%, while the EVI algorithm showed values of 25%, 5%, 11%, and 8%. The accuracy test results produced have the highest average effectiveness in the NDVI algorithm with an overall accuracy of 95.4% and a Kappa coefficient of 0.87 compared to the EVI algorithm with an overall accuracy of 91.4% and a Kappa coefficient of 0.82. On the other hand t-test results indicate that utilize of the NDVI algorithm is more effective than EVI, with the correction value for the NDVI algorithm being 0.0077 compared to the correction value for the EVI algorithm being 0.1664.
PENGUKURAN KESEHATAN DAN LUASAN MANGROVE DI KECAMATAN MUARAGEMBONG: PENDEKATAN ANALISIS KERAPATAN TAJUK Prasetio, Wiji; Manihuruk, Alpeus; Al-Abrar, Ghairandi
JURNAL GEOLOGI KELAUTAN Vol 23, No 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan Geologi Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32693/jgk.23.1.2025.919

Abstract

Kecamatan Muaragembong sebagai muara dari Sungai Citarum, mengalami dinamika ekosistem yang signifikan akibat peningkatan intensitas abrasi dan sedimentasi di area pesisir yang berpotensi mempengaruhi kondisi ekosistem sekitarnya, khususnya ekosistem mangrove. Penelitian ini bertujuan untuk mengevaluasi dan memperkirakan tingkat kesehatan, kerusakan, dan luas total ekosistem mangrove di Kecamatan Muaragembong melalui analisis kerapatan tajuk. Data kerapatan tajuk dikumpulkan di lapangan menggunakan metode purposive stratified sampling dengan pendekatan fotografi hemisferik. Hasil klasifikasi menunjukkan luas total mangrove yang berhasil dipetakan seluas 1.073,65 ha, dengan koefisien Kappa sebesar 0,87, menandakan tingkat akurasi yang sangat baik. Evaluasi kesehatan komunitas mangrove mengungkapkan, bahwa 49,66% dari area mangrove berada dalam kondisi baik, 19,80% dalam kondisi normal, dan 30,54% dalam kondisi buruk. Data kerapatan tajuk menunjukkan hubungan sedang dengan koefisien determinasi sebesar 0,4219 (42,19%), yang dipengaruhi oleh faktor eksternal dan resolusi spasial citra yang digunakan.
Co-Authors Abd. Rahman As-syakur Al-Abrar, Ghairandi Aziz, Kholifatul Azura Ulfa, Azura Budhi Agung Prasetyo Gathot Winarso, Gathot Manihuruk, Alpeus Nandika, Muhammad Rizki Nanin Anggraini, Nanin Pratama, Annisa Tias Ratih Dewanti -Hariyadi Suardana, A. A. Md. Ananda Putra Wijaya, Agung Dwi