Semi-automatic stand delineation based on very-high-resolution orthophotographs and topographic features: A case study from a structurally complex natural forest in the southern USA

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dc.authorid0000-0001-5552-5670
dc.authorid0000-0002-5454-3970
dc.authorid0000-0001-6450-2286
dc.authorid0000-0001-5376-5638
dc.contributor.authorVatandaşlar, Can
dc.contributor.authorBettinger, Pete
dc.contributor.authorMerry, Krista
dc.contributor.authorStober, Jonathan
dc.contributor.authorLee, Taeyoon
dc.date.accessioned2025-06-20T11:01:06Z
dc.date.available2025-06-20T11:01:06Z
dc.date.issued2025
dc.departmentAÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümü
dc.description.abstractIn the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been conducted to automate the process using aerial imagery or airborne light detection and ranging (LiDAR) data as supporting resources. The work described here applies an object-based image analysis (OBIA) process to aerial imagery and to a landform index database. The size and shape of stands in the outcomes of these applications are then adjusted to conform to the desired product of land managers. These products are then intersected as they each contain information of value in the stand delineation process. The intersected database is then adjusted once again to conform to the desired product of land managers. Conformity of the size and shape of the resulting stand boundaries to a reference database drawn subjectively by hand was low to moderate. Specifically, the overall agreement for spatial and thematic (class names) accuracies was 43.0% and 56.8%, respectively. Nevertheless, the process of automating the stand delineation effort remains promising for achieving an efficient and non-subjective characterization of a structurally complex forested environment.
dc.identifier.doi10.3390/f16040666
dc.identifier.issn19994907
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105003684705
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11494/5564
dc.identifier.volume16
dc.identifier.wosWOS:001475080700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorVatandaşlar, Can
dc.institutionauthorid0000-0001-5552-5670
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofForests
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGEOBIA
dc.subjectLandform index
dc.subjectNational agriculture imagery program (NAIP)
dc.subjectSegment mean shift algorithm
dc.subjectVisual interpretation
dc.titleSemi-automatic stand delineation based on very-high-resolution orthophotographs and topographic features: A case study from a structurally complex natural forest in the southern USA
dc.typeArticle

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