Skip to Main Content
K-nearest neighbor imputation of forest inventory variables in New HampshireAuthor(s): Andrew Lister; Michael Hoppus; Raymond L. Czaplewski
Source: In: Remote sensing for field users; Proceedings, 10th Biennial USDA Forest Service remote sensing applications conference; 2004 April 5-9; Salt Lake City, UT. Bethesda, MD: American Society for Photogrammetry and Remote Sensing: [unpaginated]. [CD-ROM].
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
PDF: Download Publication (552.79 KB)
DescriptionThe k-nearest neighbor (kNN) method was used to map stand volume for a mosaic of 4 Landsat scenes covering the state of New Hampshire. Data for gross cubic foot volume and trees per acre were summarized from USDA Forest Service Forest Inventory and Analysis (FIA) plots and used as training for kNN. Six bands of Landsat satellite imagery and various topographic measures were assessed for their use as variables defining the dimensions of the n-dimensional prediction space. Results were generally poor due to the weak correlations between the independent and dependent variables. We discuss the technique and results, and suggest avenues for future research.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationLister, Andrew; Hoppus, Michael; Czaplewski, Raymond L. 2005. K-nearest neighbor imputation of forest inventory variables in New Hampshire. In: Remote sensing for field users; Proceedings, 10th Biennial USDA Forest Service remote sensing applications conference; 2004 April 5-9; Salt Lake City, UT. Bethesda, MD: American Society for Photogrammetry and Remote Sensing: [unpaginated]. [CD-ROM].
- Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories
- A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes
- Imputed forest structure uncertainty varies across elevational and longitudinal gradients in the western Cascade mountains, Oregon, USA
XML: View XML