Document Type : ORIGINAL RESEARCH ARTICLE

Authors

• S.H. Larekeng ^{1, 2, 3}
• M. Nursaputra ^{1, 4, 3}
• M.F. Mappiasse ⁵
• S. Ishak ⁴
• M. Basyuni ^{6, 7}
• E. Sumarga ⁸
• V.B. Arifanti ⁹
• A.A. Aznawi ⁷
• Y.I. Rahmila ⁹
• M. Yulianti ⁹
• R. Rahmania ⁹
• A. Mubaraq ⁷
• S.G. Salmo III ¹⁰
• H. Ali ¹¹
• I. Yenny ⁹

¹ Biodiversity Research Group, Faculty of Forestry, Hasanuddin University, Makassar 90245, Indonesia

² Forestry Engineering Studi Program, Faculty of Forestry, Hasanuddin University, Makassar 90245, Indonesia

³ Forest and Society Research Group, Faculty of Forestry, Hasanuddin University, Makassar 90245, Indonesia

⁴ Forestry Study Program, Faculty of Forestry, Hasanuddin University, Makassar 90245, Indonesia

⁵ Faculty of Agriculture, Animal Husbandry and Forestry, University of Muslim, Maros 90513, Indonesia

⁶ Department of Forestry, Faculty of Forestry, Universitas Sumatera Utara, Medan 20155, Indonesia

⁷ Center of Excellence for Mangrove, Universitas Sumatera Utara, Medan 20155, Indonesia

⁸ School of Life Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia

⁹ Research Center for Ecology and Ethnobiology, National Research and Innovation Agency, Cibinong 16911, Indonesia

¹⁰ Institute of Biology, College of Science, University of the Philippines Diliman, Philippines

¹¹ Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia

Abstract

BACKGROUND AND OBJECTIVES: Mangroves play a crucial role in mitigating climate change by absorbing carbon stocks. However, there is a lack of information on mangrove distribution and their carbon absorption abilities. Therefore, this study aimed to bridge this gap by gathering data on the ability of mangrove forest areas to absorb carbon stocks. Specifically, this study aims to assess the carbon absorption potential of the Lantebung mangrove ecosystem through field surveys, allometric calculations, and unmanned aerial vehicle imagery.
METHODS: The methodology employed in this study consisted of field surveys, allometric calculations, and multispectral aerial imagery processing along the coastal of Makassar City, South Sulawesi, within the Lantebung mangrove ecosystem. Field surveys were conducted to determine the species composition of each mangrove stand and measure their diameter at breast height. The allometric formula was then used to calculate mangrove biomass, which was subsequently converted into carbon stock values. Aerial imagery was processed using the normalized difference vegetation index, followed by a regression analysis between normalized difference vegetation index and carbon stock values to obtain a carbon stock estimation model.
FINDINGS: The results of the analysis of red-green-blue aerial imagery from the multispectral unmanned aerial vehicle has provided valuable insights into the extent of mangrove vegetation cover in the Lantebung mangrove forest area, revealing it to be 14.18 hectares. The normalized difference vegetation index results indicated that mangrove objects fall within a value range of 0.21–1, categorized into three density classes: high-, medium-, and low-density mangroves. The field surveys confirmed the presence of three types of mangroves in Lantebung Makassar, namely Rhizophora apiculata, Rhizophora mucronata, and Avicennia sp. The regression analysis conducted to assess the relationship between the normalized difference vegetation index value and carbon stocks yielded the equation model carbon stock = 474.61, vegetation Index value + 17.238, with a linear regression value of 0.7945. The carbon stock values for low-density class mangrove areas were predicted to range between 17.24 and 288.64 tons carbon per hectare, medium-density mangroves' carbon stocks to be between 126.04 and 391.14 tons carbon per hectare, and high-density mangrove areas' carbon stocks to range from 258.04 to 491.85 tons carbon per hectare.
CONCLUSION: The utilization of drones as a technique for monitoring carbon stocks has offered significant benefits. Drones equipped with multispectral sensors enable the collection of precise and comprehensive data on vegetation and elevation in many ecological systems. The survey and subsequent analysis highlighted the wide variation in the density of mangrove forests in the Lantebung mangrove ecosystem. This study demonstrated a strong correlation between the normalized difference vegetation index extracted using unmanned aerial vehicle and mangrove carbon levels obtained from actual field measurements.

Graphical Abstract

Highlights

• The correlation between mangrove carbon and NDVI, as determined by UAV Multispectral aerial imaging, exhibits a robust association with empirical field data;
• The average mangrove carbon in the Lantebung area is 266.34 tons C/ha, with a total area of 14.18 ha divided the density class is not dense, fairly dense and dense;
• The integration of UAV-derived data with field survey has provided insights into the species composition, biomass distribution, and carbon stocks of mangrove ecosystem;
• The NDVI concept helps elucidate the extent of diversity or variability in carbon values.

Keywords

• Carbon stock
• Mangrove
• Multispectral
• Normalized difference vegetation index (NDVI)
• Unmanned aerial vehicle (UAV)

Main Subjects

• Marine and coastal environmental pollution and management

OPEN ACCESS

©2024 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: 

http://creativecommons.org/licenses/by/4.0/

PUBLISHER NOTE

GJESM Publisher remains neutral concerning jurisdictional claims in published maps and institutional affiliations.

CITATION METRICS & CAPTURES

Google Scholar | Scopus | Web of Science | PlumX Metrics | Altmetrics | Mendeley |

CURRENT PUBLISHER

GJESM Publisher