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Volume 16
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10.3390/f16040666
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Article

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

by
Can Vatandaslar
1,2,*,
Pete Bettinger
2,
Krista Merry
2,
Jonathan Stober
3 and
Taeyoon Lee
4
1
Faculty of Forestry, Artvin Coruh University, Artvin 08100, Turkey
2
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
3
U.S. Forest Service, Talladega National Forest, Heflin, AL 36264, USA
4
Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Forests 2025, 16(4), 666; https://doi.org/10.3390/f16040666
Submission received: 25 February 2025 / Revised: 1 April 2025 / Accepted: 9 April 2025 / Published: 11 April 2025
(This article belongs to the Special Issue Modeling of Biomass Estimation and Stand Parameters in Forests)
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Abstract

In 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.
Keywords: GEOBIA; segment mean shift algorithm; landform index; visual interpretation; national agriculture imagery program (NAIP) GEOBIA; segment mean shift algorithm; landform index; visual interpretation; national agriculture imagery program (NAIP)

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MDPI and ACS Style

Vatandaslar, C.; Bettinger, P.; Merry, K.; Stober, J.; Lee, T. 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. Forests 2025, 16, 666. https://doi.org/10.3390/f16040666

AMA Style

Vatandaslar C, Bettinger P, Merry K, Stober J, Lee T. 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. Forests. 2025; 16(4):666. https://doi.org/10.3390/f16040666

Chicago/Turabian Style

Vatandaslar, Can, Pete Bettinger, Krista Merry, Jonathan Stober, and Taeyoon Lee. 2025. "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" Forests 16, no. 4: 666. https://doi.org/10.3390/f16040666

APA Style

Vatandaslar, C., Bettinger, P., Merry, K., Stober, J., & Lee, T. (2025). 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. Forests, 16(4), 666. https://doi.org/10.3390/f16040666

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