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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Forest and Society
Suardana, A. A. Md. Ananda Putra
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science Social Sciences

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Comparisons of benthic associated fauna assemblages in seagrass meadows across conservation and non-conservation areas in Bali and Lombok, Indonesia Atmaja, Putu Satya Pratama; Laharjana, I Ketut Aditya Krisna; Suardana, A. A. Md. Ananda Putra; Van Keulen, Mike
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 29, No 1 (2024): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.29.1.71-84

Abstract

Benthic ecosystem has been widely considered as an important feature of seagrass associated fauna, and its function as a grazer and linkage between primary producers and higher trophic level is well known. Though the importance of benthic fauna in seagrass ecosystems has been indicated in many studies, its biodiversity in spatial scale has often been poorly studied. This study aimed at examining the assemblages and diversity of benthic associated fauna in conservation areas (CA) and non-conservation areas (NCA) across the seagrass meadows in Bali and Lombok. This study found that the assemblages and diversity of benthic fauna greatly varied between the meadows. A total of 430 individuals associated to benthic fauna from 24 species were identified in Bali and Lombok. Of these, Gastropods were the highest class of taxa recorded in this study, followed by Bivalvia, Echinodermata, Decapoda, and Amphipoda. Permutation multivariate analysis of variance (PERMANOVA) revealed a significantly different benthic fauna diversity between sites. Non-metric Multidimensional Scaling (nMDS) and Bray–Curtis analysis showed a clear distinction of benthic fauna assemblages between CA and NCA, both in Bali and Lombok. These results indicated that from spatial perspective, different characteristics of seagrass meadows may represent different biodiversity of associated fauna. These differences might be driven by different anthropogenic pressure and variation of substrates among the meadows which may affect the composition of the benthic fauna ecosystems. The implication of this study was to providing baseline data on guiding the appropriate approach and strategies for management and conservations of seagrass ecosystems.
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.
Co-Authors Abd. Rahman As-syakur Atmaja, Putu Satya Pratama Aziz, Kholifatul Azura Ulfa, Azura Gathot Winarso, Gathot Laharjana, I Ketut Aditya Krisna Nandika, Muhammad Rizki Nanin Anggraini, Nanin Prasetio, Wiji Ratih Dewanti -Hariyadi Van Keulen, Mike Wijaya, Agung Dwi