Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density

Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy

“…The reference objects were manually delineated by a skilled photo-interpreter, based on imagery of higher resolution (1 m NAIP data) and richer thematic content (3D LiDAR point clouds) than the 30 m LiDAR raster. While there is a degree of subjectivity, visually interpreted reference data are commonly used for the validation of land cover and land cover disturbance satellite products (e.g., [63,64]), and for the validation of GEOBIA outputs (e.g., [26,27,31,34,54,61]).…”

“…Previous research to delineate structural or age-related forest stand types using object-based techniques and LiDAR data (e.g., [2,10,11,31,34,35]) did not propose methodologies that could be easily applied outside the original study areas, due to the complexity of the workflow (e.g., [10]), to the exploitation of site-specific forest characteristics and to the small extent of the study areas (e.g., [2,11,34,35]), and to the lack of objective evaluation protocols (e.g., [2,11]). The proposed two-stage evaluation strategy is a general objective workflow, that could be easily replicated and adapted to other study sites, to other data sources, and to delineate different features of the landscape.…”

“…An unsupervised evaluation method based on spatial autocorrelation statistics was used for selecting the optimal segmentation for each LiDAR metric, out of the 819 generated with the sets of parameters defined above. The method is an adaptation of the one introduced by Espindola et al [25] and subsequently used for object-based image segmentation evaluation of land cover and stand mapping [26,31,50].…”

On the Objectivity of the Objective Function—Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy

“…Similarly, a recent study of Varo-Martínez et al [23] compared segmentations produced by two different algorithms using GS. The authors normalized I and v for the segmentations of each algorithm separately before comparing the absolute GS values of both methods.…”

“…The approach described above and variations thereof have been used in numerous studies in recent years [16][17][18][19][20][21][22][23][24][25][26][27][28]. Considerable efforts have been made to extend the original approach, e.g., to use multiple bands of input imagery [13,16], to consider optima at multiple scales [20,29,30], or to use GS for class specific optimization [26].…”