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

Automated summary

Remote sensing technology is an effective method for LAI estimation, especially for inaccessible areas like mangrove forests. This study explored the potential of Sentinel-2 imagery, airborne hyperspectral imagery, and LiDAR data for estimating the LAI of overstory and understory in a multi-layered mangrove stand. The results showed that the models for overstory estimation performed better than understory estimation. A red-edge VI derived from hyperspectral imagery delivered the highest accuracy for overstory estimation, while the combination of LiDAR metrics and Sentinel-2 VIs performed best for understory estimation. It was found that HSI was less affected by the understory, and LiDAR data provided separate information for upper and lower canopy, reducing noise and improving understory estimation.