Estimation of mangrove carbon stocks using unmanned aerial vehicle over coastal vegetation

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.

Keywords

• carbon stock
• mangrove
• multispectral
• normalized difference vegetation index (ndvi)
• unmanned aerial vehicle (uav)