Assessment of Active LiDAR Data and Passive Optical Imagery for Double-Layered Mangrove Leaf Area Index Estimation: A Case Study in Mai Po, Hong Kong

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating LAI main canopy, ignoring understory. This study investigated capability multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), LiDAR data overstory (OLe) understory (ULe) estimation multi-layered stand in Mai Po, Hong Kong, China. were employed to stratify Vegetation indices (VIs) metrics generated predictors build regression models against OLe ULe with multiple parametric non-parametric methods. The model fitting results typically better than because dominant contribution remotely sensed signal. A single red-edge VI derived from HSI delivered lowest RMSE 0.12 highest R2adj 0.79 fitting. synergetic use S2 VIs performed best = 0.33, 0.84. benefited high spatial spectral resolution that was found confounded In addition their penetration attributes, could separately describe upper lower which reduced noise other components, thereby improving estimation.