Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes

Abstract LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscape...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of forestry Jg. 116; H. 4; S. 336 - 346
Hauptverfasser: Jeronimo, Sean M A, Kane, Van R, Churchill, Derek J, McGaughey, Robert J, Franklin, Jerry F
Format: Journal Article
Sprache:Englisch
Veröffentlicht: US Oxford University Press 29.06.2018
Springer Nature B.V
Schlagworte:
Accuracy
Biomass
California
Coniferous forests
Ecological monitoring
Forest & brush fires
Forest management
forestry
Forests
Landscape
Lidar
Managers
Mixed forests
mountains
Pseudotsuga menziesii
Trees
United States > US
California
individual tree detection
Sierra National Forest
forest landscape management
LiDAR
Yosemite National Park
ISSN:0022-1201, 1938-3746, 1938-3746
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Abstract LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California’s Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as “tree-approximate objects” including one dominant tree and up to a few subordinate tree respects LiDAR’s strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0022-1201
1938-3746
1938-3746
DOI:10.1093/jofore/fvy023