Canopy Cover Mapping in Ratai Bay Mangrove Forests using Airborne LiDAR Data

Active system remote sensing technology is increasingly developing in extracting information on the biophysical aspects of mangrove vegetation, such as mapping the percentage of canopy cover. Mapping the percentage of mangrove canopy cover is essential to maintain the stability of coastal ecosystems. This study uses airborne LiDAR data based on the First Return Cover Index (FRCI) to map and analyze the variation and spatial distribution of the canopy cover percentage in the Ratai Bay mangrove forest, Pesawaran, Lampung, Indonesia. This study aims to (1) Analyze the variation and spatial distribution of the percentage of FRCI-based mangrove canopy cover using LiDAR data and (2) Calculate the accuracy level of the mapping results. FRCI is a LiDAR point cloud data rasterization algorithm that calculates pixel value information from canopy cover recorded by airborne LiDAR. The canopy cover value at each pixel and the regression function obtained from field measurements were integrated to build a model to obtain a map of the percentage of mangrove canopy cover. The resulting map identifies that the Ratai Bay mangrove forest is dominated by the dense and evenly distributed canopy cover class with a mean cover value of 89.78, generally found in almost all study areas. This FRCI-based mangrove canopy cover percentage mapping has high mapping accuracy with minimum and maximum accuracy values of 92.31 and 93.09, respectively. The results of this study indicate that the biophysical aspects of mangrove vegetation, especially canopy cover, can be mapped using LiDAR data with the FRCI algorithm. © 2024 SPIE.