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Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

Based on the strict radiative transfer algorithm, a new method is developed to derive the precipitable water vapor (PWV) and aerosol optical depth (AOD) from the ground-based direct sun irradiance measurements. The attenuated direct irradiance from 300 to 1700 nm was measured by a pair of grating spectroradiometers, MS711 and MS712 produced by EKO Instruments, located at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing (39.98 ∘ N, 116.38 ∘ E), from June 2020 to March 2021. Compared with regular sun photometers such as CE-318 and POM, EKO instruments can measure a wider range of continuous spectra, but their field of view (FOV) is also relatively large. In the PWV inversion of this work, a strong water vapor absorption band around 1370 nm is introduced to retrieve PWV in a relatively dry atmosphere. The circumsolar radiation (CSR) of the EKO instruments is corrected to reduce the influence of scattering from a relatively larger FOV on the AOD inversion. The PWV and AOD inversion results obtained by MS711 and MS712 are compared with the synchronous data of the CE-318 sun photometer. The two retrieval results are highly consistent. The correlation coefficient, mean bias, and standard deviation of PWVEKO and PWVCIMEL are 0.999, -0.027 cm (-2.42 %), and 0.054 cm (3.93 %), respectively, and the relative deviations of the differences between the two are slightly larger for drier air (PWV < 0.5 cm) and lower solar elevation angle. The correlation coefficients of AODEKO and AODCIMEL at 380, 440, 500, 675, 870, and 1020 nm are greater than 0.99, and the relative deviations vary between -6.59 % and 4.27 %. [ABSTRACT FROM AUTHOR]