Abstract
BackgroundModern remote sensing methods enable the prediction of tree-level forest resource data. However, the benefits of using tree-level data in forest or harvest planning is not clear given a relative paucity of research. In particular, there is a need for tree-level methods that simultaneously account for the spatial distribution of trees and other objectives. In this study, we developed a spatial tree selection method that considers tree-level (relative value increment), neighborhood related (proximity of cut trees) and global objectives (total harvest).MethodsWe partitioned the whole surface area of the stand to trees, with the assumption that a large tree occupies a larger area than a small tree. This was implemented using a power diagram. We also utilized spatially explicit tree-level growth models that accounted for competition by neighboring trees. Optimization was conducted with a variant of cellular automata. The proposed method was tested in stone pine (Pinus pinea L.) stands in Spain where we implemented basic individual tree detection with airborne laser scanning data.ResultsWe showed how to mimic four different spatial distributions of cut trees using alternative weightings of objective variables. The Non-spatial selection did not aim at a particular spatial layout, the Single-tree selection dispersed the trees to be cut, and the Tree group and Clearcut selections clustered harvested trees at different magnitudes.ConclusionsThe proposed method can be used to control the spatial layout of trees while extracting trees that are the most economically mature.
Highlights
Modern remote sensing methods enable the prediction of tree-level forest resource data
Forest inventories employing Airborne Laser Scanning (ALS) data have become common in many countries (Nilsson et al 2017)
Individual tree detection and tree attribute prediction The canopy height model (CHM) of 1 m spatial resolution was interpolated by searching the highest ALS echo at above ground level (AGL) within each cell
Summary
Modern remote sensing methods enable the prediction of tree-level forest resource data. The ALS-based forest inventory methods (Hyyppä et al 2008) are typically categorized into two groups: the area-based approach (ABA) (Means et al 2000; Næsset 2002; Magnussen et al 2013) and individual tree detection (ITD) (Hyyppä et al 2001; Koch et al 2006; Lähivaara et al 2014). Most operational forest inventories employing ALS data have been implemented with the ABA (Maltamo et al 2014). Failures in tree detection and errors in the prediction of tree attributes make ITD more sensitive to bias than ABA at the aggregated (e.g. forest (2020) 7:18 stand) level (Vauhkonen 2010). The advantage of ITD is that it produces a more detailed description of forest: tree level attributes, including tree locations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
