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Quantitatively characterizing the non-random spatial distributions of foliage elements including coniferous needles is critical to map the radiation regime and retrieve the biophysical parameters of a given forest canopy from three-dimensional (3-D) perspective. Different experimental setups bring various challenges to the process of retrieving forest canopy clumping index (CI) using terrestrial laser scanning (TLS). In this paper, through developing a voxel-based gap size (VGS) algorithm, we compared the TLS-based forest canopy CIs with the ones obtained using the digital hemispherical photography (DHP)-based and tracing radiation and architecture of canopy (TRAC)-based approaches. Moreover, we investigated the effects of incident directions of solar beams, voxel size, and woody canopy components on the final retrieval accuracy of forest canopy CIs. Our results showed that: (1) TLS-based CIs accounted for 81% ( N  = 30, p  < 0.001) of variations in the DHP-based method. (2) the anisotropic nature of forest canopy CIs suggested that a relatively comprehensive TLS data of a forest canopy was required to investigate the 3-D spatial variations of forest gap size distributions and CIs. (3) The user-defined laser sampling spacing was a reliable reference value to determine the voxel size when using the VGS algorithm. (4) It was recommended to separate woody canopy components when computing the forest canopy CI, especially for forest plots with higher proportions of woody material. (5) The effects of the penumbra on TLS-based forest canopy CIs were much more limited compared with the traditional optical instruments (i.e., DHP or TRAC). This work provides a solid foundation to dramatically improve the retrieval accuracy of leaf area index (LAI) using TLS. [ABSTRACT FROM AUTHOR]

The scan angle information implicitly contained within the 3-D point cloud data (PCD) generated from light detection and ranging strongly affects the retrieval accuracy of the forest canopy structural parameters. Using information generated from overlapping aerial laser scanning (ALS) flight paths with multiple scan angles over a forest canopy can help to remove the occlusion effects between foliage elements, ultimately creating a relative comprehensive PCD. In this paper, we develop a novel physically based scan angle correction algorithm to retrieve the effective leaf area index (LAIe) of a forest canopy using ALS. Furthermore, we investigate the effects of scan angle and the number of ALS overpass lines from adjacent flight paths over a forest canopy on directional gap fraction (DGF) estimates. Our results suggest that ALS-based LAIe estimates capture 71.35% of the variations in LAIe derived from digital hemispherical photography. A forest canopy point cloud created using PCD from multiple overlapping ALS flight paths was sufficient to quantitatively reveal the anisotropy characteristics of DGF variations. These results suggest that scan angle information should not be neglected in retrieving forest canopy structural parameters, especially when using ALS data collected with a wide scan angle (i.e., −30° to 30° in this paper). Finally, this paper provides a solid foundation to characterize the 3-D spatial distribution of a forest radiation regime using ALS-based forest PCD. [ABSTRACT FROM PUBLISHER]

Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28 m. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44%), photosynthetic (95.64%), and non-photosynthetic canopy components (89.60%) of an artificial tree and stems of an old-growth coniferous tree (93.53%), two individual broadleaf trees (98.31% and 97.46%) and a broadleaf forest plot (90.26%). By doing this, we computed the parameter WTA for an individual artificial tree (10.90%), an old-growth coniferous tree (29.97%), two individual broadleaf tree (14.83% and 4.27%) and four natural forest stands ranging from 7.74%–15.57%, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method's performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain. [ABSTRACT FROM AUTHOR]

• A method is proposed to estimate forest 3-D PAR distribution using TLS data. • The incident solar direct and diffuse PAR are treated separately in the method. • The effects of leaf orientations and LAIe on forest PAR are investigated. • When NPD≤20cm, lidar data are able to estimate the vertical PAR distribution. The three-dimensional (3-D) spatiotemporal distribution of forest canopy photosynthetically active radiation (PAR) is a major determinant of within-crown variations of leaf photosynthesis and transpiration. However, it remains challenging to map the 3-D spatiotemporal distribution of PAR within and under the forest canopy due to the limited ability to capture the detailed 3-D structures of forest canopies.This study proposes a simple approach to estimate the 3-D spatiotemporal distribution of forest canopy PAR at a fine spatial resolution based on terrestrial laser scanner (TLS) data by characterizing difference in transfer of incident solar direct PAR and diffuse PAR and by considering the effect of the orientation angles of foliage elements on the 3-D distribution of direct PAR. Estimated PAR was validated using 10-minute PAR measurements from pyranometers at different locations within four forest plots, at which effective leaf area index (LAIe) ranged from 0.83–4.25. It showed that the proposed method captured 85% (N=2831, p<0.001) of the variations in the field pyranometer measurements. Furthermore, the proposed method was also applicable for estimating the vertical distribution of PAR (R2=0.95, N=15, p<0.001). Direct, diffuse, and total PAR exhibited similar vertical distributions changing with LAIe. It was shown that without differentiation of direct and diffuse PAR or ignoring effects of orientation angles of foliage elements may cause errors of about 20% in the estimated 3-D PAR distribution. Furthermore, the results indicated that the proposed method can be used to estimate the vertical distribution of PAR when the average neighbor point distance (NPD) is ≤20 cm. This proposed method will be usable for accurate separation of sunlit and shaded leaves and estimation of vertical distribution of PAR, which will benefit to reduce uncertainties in the estimation of terrestrial carbon fluxes and retrieval of vegetation structural and biochemical parameters. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is a key indicator of vegetation photosynthesis, which provides important insights into how vegetation productivity responds to environmental conditions. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which reflects the xanthophyll cycle process of plants under different radiation conditions, and makes LUE related to plant optical characteristics. In this study, tower-based PRI and eddy covariance (EC) based LUEs were used to explore the ability of PRI to track LUE variations in a subtropical, evergreen mixed forest in South China. The results indicate that there is a stronger relationship between PRI and LUE, corrected by the bidirectional reflectance distribution function (BRDF), where R2 = 0.46 before correction and R2 = 0.60 after correction. Generally, PRI is able to capture diurnal and seasonal changes in LUE. Simultaneously, this study highlights a significant correlation between LUE and PRI, but there is also a large seasonal difference in its correlation. The correlation in winter was significantly stronger than summer. The strongest correlation is found in November (R2 = 0.91) and the weakest is in July (R2 = 0.34). Photosynthetically active radiation (PAR) had a strong influence on the LUE-PRI relationship, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the relationship between LUE and PRI. Terrestrial laser scanning is used to retrieve the vertical structure of forest crown. Our results show that the vegetation canopy structure (i.e., effective leaf area index, LAIe), extracted from terrestrial laser scanning (TLS) point data in subtropical mixed forests, had a weak influence on LUE. Our research suggests that environmental factors and vegetation canopy structures should be considered when using PRI to accurately estimate LUE. [ABSTRACT FROM AUTHOR]