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Forest delineation based on airborne LIDAR data
Verfasser / Verfasserin Eysn, Lothar ; Hollaus, Markus ; Schadauer, Klemens ; Pfeifer, Norbert
Erschienen in
Remote sensing, 2012, Jg. 4, H. 3, S. 762-783
Published version
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)forest definition / canopy cover / crown coverage / vegetation mapping / airborne laser scanning / forest classification / land cover / canopy height model
URNurn:nbn:at:at-ubtuw:3-2509 Persistent Identifier (URN)
 Das Werk ist frei verfügbar
Forest delineation based on airborne LIDAR data [8.41 mb]
Zusammenfassung (Englisch)

The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack precise geometrical descriptions for the different criteria. A mandatory criterion in forest definitions is the criterion of crown coverage (CC), which defines the proportion of the forest floor covered by the vertical projection of the tree crowns. For loosely stocked areas, this criterion is especially critical, because the size and shape of the reference area for calculating CC is not clearly defined in most definitions. Thus current forest delineations differ and tend to be non-comparable because of different settings for checking the criterion of CC in the delineation process. This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laser scanning (ALS) data with a clearly defined method for calculating CC. The new approach, the ‘tree triples method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. The approach is applied and analyzed for two study areas in Tyrol, Austria. The selected areas show a loosely stocked forest at the upper timberline and a fragmented forest on the hillside. The fully automatic method presented for delineating forested areas from ALS data shows promising results with an overall accuracy of 96%, and provides a beneficial tool for operational applications.

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