Skip to main content

Estimating Stand Characteristics by Combining Single Tree Pattern Recognition of Digital Video Imagery and a Theoretical Diameter Distribution Model

Buy Article:

$21.50 plus tax (Refund Policy)

This article presents a new method combining pattern recognition of single trees and a theoretical diameter distribution to determine stand characteristics. The applied remote sensing material was digital video imagery. A super-resolution technique was used in order to improve the quality of the video imagery. Tree crowns were identified and crown areas segmented from the super-resolution image. After that, tree diameters were predicted using detected crown areas. However, only large trees (dbh >17 cm) could be recognized from digital video image. Therefore, the theoretical Weibull distribution was predicted to be able to also calculate the number of small trees (dbh <17 cm). The mean characteristics information needed for predicting the parameters of Weibull distribution was obtained from the resulting truncated distribution of large trees. The final estimate of the diameter distribution is a combination of these two parts.

The reliability of prediction of stand characteristics considered, i.e., number of stems, stand basal area, and volume was improved with the use of the theoretical diameter distribution model. However, these results should be considered preliminary, because they are based on a small validation data set. According to these results, especially the accuracy of the estimate of the number of stems was increased considerably. This improvement is important when simulating future stand development in forest management planning software packages. FOR. SCI. 49(1):98–109.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Diameter distribution; Weibull-distribution; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; optical flow; stand characteristics estimation; super-resolution

Document Type: Miscellaneous

Affiliations: 1: Faculty of Forestry, University of Joensuu, P.O. Box 111 Joensuu, Finland, FIN-80101, Phone: +358-13-2513615; Fax: +358-13-2513590 [email protected] 2: Faculty of Agriculture and Forestry, University of Helsinki, P.O. Box 27, FIN-00014 University of Helsinki, Phone:+358-9-19158180 [email protected] 3: Oy Arboreal Ltd., Niskakatu 1, Joensuu, FIN-80100, Phone: +358-50-3507435 [email protected]

Publication date: 2003-02-01

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more