Skip to main content

Forest Blowdown Prediction: A Correlation of Remotely Sensed Contributing Factors

Buy Article:

$29.50 plus tax (Refund Policy)

On July 15, 1995, a severe windstorm in northern New York affected thousands of acres of forest. A heterogeneous impact by the high winds was captured by two Landsat Thematic Mapper (TM) images taken prior to and following the event. This article uses landscape position, vegetation cover, and edaphic conditions of the damaged areas in an attempt to quantify and explain the differential impact by the wind on the forest. Using remote sensing and geographic information system (GIS) technologies, significantly correlated variables of six site factors were used to predict damaged areas for a 50-square-mile study site. However, the predicted areas had an overall accuracy of only 60%, with a kappa coefficient of 0.22, indicating that the storm impact was too complex to be reliably predicted using the available site variables.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Remote sensing; blowdown; geographic information system; landscape position; predictive model; wind damage

Document Type: Regular Article

Affiliations: Senior Environmental Geographic Information System Analyst Vanasse Hangen Brustlin, Inc. 101 Walnut Street Watertown MA 02472

Publication date: 2005-03-01

More about this publication?
  • Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Northern Journal of Applied Forestry covers northeastern, midwestern, and boreal forests in the United States and Canada.
  • Membership Information
  • 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