Enhancing the Precision of Mangrove Area Estimation by Incorporating Canopy Gaps in UAV Methodologies

Abstract. One such coastal ecosystem that has various importance is mangrove. They provide biodiversity support, coastal protection, and carbon sequestration. One problem not well researched and known for accurate monitoring of mangrove forests is often hindered by the lack of consideration for canopy gaps, which significantly influence ecosystem dynamics, seedling recruitment, and overall forest health. This study enhances the precision of mangrove area estimation by integrating canopy gap identification using Unmanned Aerial Vehicle (UAV) imagery. After UAV acquisition, it would go through orthomosaic, and the following orthomosaic would be used for segmentation and Object-Based Image Analysis (OBIA). Conducted in the Lantebung & Untia Mangrove Tourism Area, South Sulawesi, Indonesia, the research employed UAV-based high-resolution RGB imagery to classify mangrove species and detect canopy gaps. Field validation and OBIA classification were used to improve accuracy, resulting in a refined methodology for calculating mangrove area while accounting for canopy gaps. The accuracy of the OBIA classification yielded good results in identifying and mapping the distribution of mangrove species. The overall accuracy is 80.65% for three classes: Rhizophora mucronate, Avicenna sp, and Canopy gaps. Findings reveal that canopy gaps, caused by natural and anthropogenic factors, impact mangrove structure and should be considered in monitoring and conservation strategies. The study introduces a novel formula for more accurate mangrove area estimation, demonstrating that traditional methods may overestimate coverage by ignoring gaps. These findings contribute to improved conservation planning and management of mangrove ecosystems, particularly in mixed-species environments where mapping accuracy remains challenging.