Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests

Abstract

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.

Estimation of oil palm’s leaf area index (LAI) using Unmanned Aerial Vehicle (UAV) images

“…Agisof Photoscan and ArcGIS processed drone images to obtain a canopy coverage area for each sample. Canopy cover refers to the extent of tree or shrub coverage observed from an aerial perspective, encompassing the leaves, branches, and trunk [18]. Therefore, the canopy cover in this study was presented in m 2 area units.…”

A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China

“…The improved progressive triangulation filtering algorithm was used to extract ground points for elevation normalization [45]. Single-tree segmentation was conducted based on seed points generated by the canopy height model [46]. Figure 4 demonstrates the differences before and after point cloud data processing.…”