###### 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 (R\({}^{2}\) of 0.80 for canopy cover and R\({}^{2}\) of 0.76 for LAI). Under different forest types and species, the R\({}^{2}\) 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 R\({}^{2}\) 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. c 2  
consequently LAI was defined as the maximum projected leaf area per unit of ground surface area [10]. Multispectral remote sensing has been widely used to estimate these parameters through statistical methods based on field measurements [11] or physical methods based on a radiative transfer model [12]. However, those approaches cannot obtain direct indications of canopy structure, in both horizontal and vertical dimensions [13]. Therefore, methods which can provide canopy structural information in three-dimensional (3D) space with high precision can improve the estimation of canopy cover and LAI [14; 15; 16]. During the past 20 years, airborne laser scanning (ALS) systems have become primary data collectors for 3D characteristics of canopy and forest structure [13]. Therefore, ALS systems are proven to be efficient tools to estimate forest 3D structure parameters from 3D point clouds [15; 17]. Different methods have been applied to estimate canopy cover and LAI from ALS data. Regression methods use different variable metrics based on ALS data and ground measurements to quantify the complexity of forest canopies [18]. Contact frequency methods are based on the contact frequency, and calculate the probability of a beam penetrating the canopy coming into contact with a vegetative element [19]. The accuracy of contact frequency methods depends on the size of voxels and they are time-consuming during data acquisition and registration processes [20]. Gap fraction methods generate the correlation between LAS data and an area property such as gap fraction and canopy cover, where the return of each pulse is classified as either canopy or gap [21]. Based on the Beer-Lambert law, canopy cover can be further converted into LAI by inverting the gap fraction and log-transformation [22]. Studies have demonstrated that such log-transformed, inverted gap fraction data from ALS are in good agreement with field measurements [16; 23; 24]. With the recent development of small aircraft technology over recent years, aerial photographs from digital cameras have been widely used in forest inventory.