Comparing airborne laser scanning, and image-based point clouds by semi-global matching and enhanced automatic terrain extraction to estimate forest timber volume

Abstract: Information pertaining to forest timber volume is crucial for sustainable forest management.
Remotely-sensed data have been incorporated into operational forest inventories to serve the need
for ever more diverse and detailed forest statistics and to produce spatially explicit data products.
In this study, data derived from airborne laser scanning and image-based point clouds were compared
using three volume estimation methods to aid wall-to-wall mapping of forest timber volume.
Estimates of forest height and tree density metrics derived from remotely-sensed data are used
as explanatory variables, and forest timber volumes based on sample field plots are used as response
variables. When compared to data derived from image-based point clouds, airborne laser scanning
produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE)
of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from
image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and
enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing
parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine.
In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when
compared to more costly, one-off airborne laser scanning surveys. This study demonstrates point
clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters
at a stand and forest compartment level-scale using small area estimation methods while at the same
time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Forests. 2017, 8,6 (2017), 215, DOI 10.3390/f8060215, issn: 1999-4907 ; IN COPYRIGHT http://rightsstatements.org/page/InC/1.0 rs

Klassifikation und Themen

• Klassifikation: Landwirtschaft, Veterinärmedizin
• Schlagwort: Waldbestand ; Luftbild ; Lidar

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2017
• Urheber: Ullah, Sami ; Adler, Petra ; Dees, Matthias ; Datta, Pawan ; Koch, Barbara
• Beteiligte Personen und Organisationen: Universitätsbibliothek Freiburg ; Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg ; Albert-Ludwigs-Universität Freiburg. Fakultät für Umwelt und Natürliche Ressourcen ; Albert-Ludwigs-Universität Freiburg

Weitere Informationen

• DOI: 10.3390/f8060215
• URN: urn:nbn:de:bsz:25-freidok-128058
• Rechteinformation: Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 25.03.2025, 13:41 MEZ