An improved tree crown delineation method based on live crown ratios from airborne LiDAR data. Issue 3 (3rd May 2016)

• Record Type: Journal Article
• Authors: Fang, Fang; Im, Jungho; Lee, Junghee; Kim, Kyoungmin
• Is Part Of: GIScience & remote sensing. Volume 53:Issue 3(2016)
• Page Start: 402
• Page End: 419
• Publication Date: 2016-05-03
• Subjects: LiDAR -- tree crown delineation -- crown ratios -- watershed segmentation -- region growing; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1158774
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Individual tree crowns are one of the basic forest inventory data, which can be used in various forest-related studies such as biomass and carbon stock estimation. High-resolution remote-sensing data including airborne LiDAR-derived surfaces have been widely used for delineating tree crowns. This study proposes an improved tree crown delineation algorithm that can be effectively applied to a range of forests with a limited number of parameters considering its operational use with airborne LiDAR data. The proposed algorithm integrates morphological operators, Otsu's method, marker-controlled watershed segmentation, and the concept of crown ratios. The proposed algorithm was compared with the region growing method, a widely used tree crown delineation algorithm. The two algorithms were evaluated over 10 plots in rugged terrain located in Kangwon Province in South Korea. Results show that the proposed approach produced much better performance (~87% matched on average) for 10 plots with a range of tree densities than the region growing method (~60% matched on average). The proposed algorithm worked better for sparse plots than dense ones. It also worked well for deciduous plots (plots 1 and 4). On the other hand, the region growing method produced relatively low accuracy with many merged crowns, which requires additional postprocessing such as a resplit step.