标题：Accuracy of Crown Segmentation and Estimation of Selected Trees and Forest Stand Parameters in Order to Resolution of Used DSM and nDSM Models Generated from Dense Small Footprint Lidar Data
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B6b
页码：27-32
出版社：Copernicus Publications
摘要：The purpose of this study is to categorize how different surface model resolutions impact the estimation of trees and forest stands parameters: number of recognized trees, crown area, treetop location and height of single trees. For forest analysis the following models – DSM (Digital Surface Model) and nDSM (normalized Digital Surface Model) were taken into account. For each model resolutions 0.25; 0.5 and 1.0 m were used. The studies were cared out in 1000 ha area of Forest Experimental Station in Gluchow, owned by Warsaw University of Life Sciences – SGGW, central Poland. Research was based on 34 sample plots measured mainly by airborne laser scanning (LIDAR) and by stereo-photogrammetric observation. Forest structure is mixed, with one layer Scotch pine (Pinus silverstris L.) and Common oak (Quercus robur L.) stands, as we as with multilayer, rebuild stands. In this paper discrimination between coniferous and deciduous was not made. Stereo photogrammetric measurements were used as a reference data to comparisons to results from LIDAR data processing. System Falcon II (TopoSys GmbH, Biberach Germany) was used for LIDAR data acquisition. Just first echo (FE) cloud point was used in processing. In the presented study the main findings were that for raster resolution 0.25 and 0.5 m number of detected trees was the largest, about 80 % of the reference value and was no statistician significant difference between these two resolutions. The number of extracted trees based on nDSM was slightly larger comparing to DSM. Results show for all 3 raster resolutions tree height estimation were close to the reference data and did not vary significantly between models
