Retrieving suppressed trees from model-based height distribution by combining high and low density airborne laser scanning data
The individual tree detection (ITD) suffers from the problem of underestimating forest total attributes,although it offers accurate tree height information.A new method was proposed in this study to improve the estimation of stem volume and stem number,by means of combining the area-based approach (ABA) and the ITD,the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data.First,ABA with low density ALS data and ITD with high density ALS data were applied,respectively,to predict plot-level height distributions.Second,replacement was employed to calibrate ABA-derived height distributions with ITD-derived tree heights.Replacement only occurred in the right tail of the height distribution,which only included taller trees.The plot-level automatic detection of cut points,which distinguish taller trees and shorter trees from the ITD-derived height distributions,was implemented in a two-step procedure.Tree detection probability function was used to determine the final cut points.Different variants of the calibration method were developed to produce the calibrated height distributions.The calibrated height distributions were assessed and compared using the error indices and volume estimates.The results showed that better estimation accuracy and precision were obtained when calibrations were applied.
Qing Xu Zhengyang Hou Matti Maltamo Timo Tokola
University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, Joensuu, University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, Joensuu,
国际会议
北京
英文
26-34
2013-10-09（万方平台首次上网日期，不代表论文的发表时间）