###### Abstract  
Accurate and efficient estimation of forest volume or biomass is critical for carbon cycles, forest management, and the timber industry. Individual tree detection and segmentation (ITDS) is the first and key step to ensure the accurate extraction of detailed forest structure parameters from LiDAR (light detection and ranging). However, ITDS is still a challenge to achieve using UAV-LiDAR (LiDAR from Unmanned Aerial Vehicles) in broadleaved forests due to the irregular and overlapped canopies. We developed an efficient and accurate ITDS framework for broadleaved forests based on UAV-LiDAR point clouds. It involves ITD (individual tree detection) from point clouds taken during the leaf-off season, initial ITS (individual tree segmentation) based on the seed points from ITD, and improvement of initial ITS through a refining process. The results indicate that this new proposed strategy efficiently provides accurate results for ITDS. We show the following: (1) point-cloud-based ITD methods, especially the Mean Shift, perform better for seed point selection than CHM-based (Canopy Height Model) ITD methods on the point clouds from leaf-off seasons; (2) seed points significantly improved the accuracy and efficiency of ITS algorithms; (3) the refining process using DBSCAN (density-based spatial clustering of applications with noise) and kNN (k-Nearest Neighbor classifier) classification significantly reduced edge errors in ITS results. Our study developed a novel ITDS strategy for UAV-LiDAR point clouds that demonstrates proficiency in dense deciduous broadleaved forests, and this proposed ITDS framework could be applied to single-phase point clouds instead of the multi-temporal LiDAR data in the future if the point clouds have detailed tree trunk points. U  
20231515 March  
density [12], canopy volume [13], canopy cover [14], diameter at breast height (DBH) [15], stem volume [5], tree species [16], and biomass [17]. UAV-LiDAR (LiDAR taken from unmanned aerial vehicles) improves flexibility and has higher point density and lower cost compared to airborne LiDAR [14; 18; 19; 20], adding information to the quantification of forest structure, volume, and biomass to the individual tree scale [19; 21]. This adds considerable information to conventional forest inventory at the individual tree level. For either airborne LiDAR or UAV-LiDAR, individual tree detection and segmentation (ITDS) is a key step to ensure the accurate estimation of single tree structural attributes, volume, and biomass [22; 23; 24]. Two methodological categories, CHM-based (Canopy Height Model) and point-cloud-based, are commonly applied to UAV-LiDAR data for single tree detection and segmentation [24; 25]. Although both CHM-based and point-cloud-based methods have satisfied accuracy for ITDS from UAV-LiDAR [9; 14; 26], both have shown much lower accuracy in broadleaved forests with irregular canopies, overlapping canopies, and/or multiple stems [26; 27; 28; 29]. The CHM-based method first applies search windows to find canopy maxima and to identify tree locations using CHM data. Then segmentation algorithms (e.g., watershed and region growing methods) are applied to delineate individual tree crowns [23; 30; 31; 32; 33; 34; 35]. CHM-based methods significantly improved the accuracy for ITDS when it was first constructed for airborne LiDAR data [36]. However, high commission or omission errors for individual tree detection (ITD) from CHM brings bias into individual tree segmentation (ITS) for broadleaved forests, especially in forests with complex and overlapping crown structures [19; 37; 38]. To improve the accuracy of CHM-based methods for single tree segmentation and detection, previous studies have applied marker-based watershed algorithms [29; 39]. However, most of these methods require human intervention for marker selection and threshold setting [38], limiting the use of automatic, batch processing.