Skip to main content

Potential Improvements in the Characterization of Forest Canopy Gaps Caused by Windthrow Using Fine Spatial Resolution Multispectral Data: Comparing Hard and Soft Classification Techniques

Buy Article:

$21.50 plus tax (Refund Policy)

Gaps often form in forest canopies due to windthrow and have important management and ecological implications. Remote sensing has considerable potential for the provision of information on gap properties but this has not been fully realized. This is largely due to the use of conventional (hard, one-pixel one-class) image analysis techniques and imagery with a relatively coarse spatial resolution. This article investigates the potential to extract information on gap properties from fine spatial resolution airborne thematic mapper imagery using soft classification techniques that allow image pixels to have multiple and partial class membership. It is shown that a standard hard maximum likelihood classification may be used to derive an accurate map of the land cover of a forested site (95.1%) from which gaps in a canopy of Sitka spruce were accurately identified (94.5%). The maximum likelihood classification was also softened by outputting probabilities of class membership for each pixel. Softening the classification increased the information on gap properties that could be extracted from the data. In particular, the accuracy with which key gap properties, such as gap area, perimeter length and shape, were estimated was higher in the outputs of the softened than hard classification. Thus, while strong correlations between the remotely sensed and ground data estimates of gap area (r ≥ 0.96) and perimeter (r ≥ 0.87), based on a sample of 36 gaps, were derived from all classifications, the accuracy with which gap properties were estimated was generally highest when a soft classification was used. For example, the use of a soft rather than hard classification resulted in the root mean square error in estimating gap area declining from 144.90 to 132.87 m2. Furthermore, the soft classification allowed the sharpness of the gap boundary to be estimated, enabling further gap properties to be inferred. In particular, the soft classification output enabled the direction of the wind event causing the initial damage to be estimated, and it may aid the definition of sites with a future risk of windthrow. FOR. SCI. 49(3):444–454.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Keywords: Gap boundary; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; soft classification

Document Type: Miscellaneous

Affiliations: 1: Department of Geography, University of Southampton, Highfield, Southampton, UK, SO17 1BJ, Fax: +44 23 8059 3295 [email protected] 2: Forestry Section, Plumpton College, Ditchling Road, Plumpton, Near Lewes, East Sussex, UK, BN7 3AE, [email protected] 3: Forestry Commission Research Agency, Northern Research Station, Roslin, Midlothian, UK, EH25 9SY, [email protected]

Publication date: 2003-06-01

More about this publication?
  • 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