Skip to main content

Combining Remotely Sensed Optical and Radar Data in kNN-Estimation of Forest Variables

Buy Article:

$29.50 plus tax (Refund Policy)

Abstract:



The use of optical and radar data for estimation of forest variables has been investigated and evaluated by employing the k nearest neighbor (kNN) method. The investigation was performed at a test site located in the south of Sweden consisting mainly of Norway spruce and Scots pine forests with standwise stem volume in the range of 0–430 m3 ha-1. The kNN method imputes weighted reference plot variables to areas to be estimated (target areas), facilitating further use of data in forestry planning models. Remotely sensed multispectral optical data from the SPOT-4 XS satellite and radar data from the airborne CARABAS-II VHF SAR sensor were used, separately and combined, to define weights in the kNN algorithm. The weights were inversely proportional to the image feature distance between the reference plot and the target area. The distance metric was defined using regression models based on the image data sources. Positive impact on the accuracies of stem volume and age estimates was found by combining the two image data sources. Stem volume, at stand level, was estimated with a RMSE of 37 m3 ha-1 (22% of the true mean value) using the combination of optical and radar data, compared to 50 m3 ha-1 (30%) for the best single-sensor case in this study. In conclusion, the results indicate that the accuracy of forest variable estimations was substantially improved by using multisensor data. FOR. SCI. 49(3):409–418.

Keywords: Data assessment; environmental management; forest; forest inventory; forest management; forest resources; forestry; forestry research; forestry science; imputation; natural resource management; natural resources; remote sensing

Document Type: Miscellaneous

Affiliations: 1: Ph.D. Department of Forest Resource Management and Geomatics, Section of Forest Resource Analysis, Swedish University of Agricultural Sciences, Umeå, Sweden, SE-901 83, Phone: +46-90-786 59 14; Fax: +46-90-77 81 16 Hampus.Holmstrom@resgeom.slu.se 2: Associate Professor Department of Forest Resource Management and Geomatics, Remote Sensing Laboratory, Swedish University of Agricultural Sciences, Umeå, Sweden, SE-901 83, Phone: +46-90-786 65 54 Johan.Fransson@resgeom.slu.se

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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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
X
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