Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help
 

Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

View PDF (389 KB)

Title: A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees

Author: Joy, Suzanne M.; Reich, R. M.; Reynolds, Richard T.;

Date: 2003

Source: International Journal of Remote Sensing. 24(9): 1835-1852.

Publication Series: Scientific Journal (JRNL)

Description: Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in northern Arizona to a 10-m spatial resolution with field data, using topographical information and Landsat TM imagery as auxiliary variables. Vegetation types were identified by clustering the field variables total basal area and proportion of basal area by species, and then using a decision tree based on auxiliary variables to predict vegetation types. Vegetation types modelled included pinyon-juniper, ponderosa pine, mixed conifer, spruce- and deciduous-dominated mixes, and openings. To independently assess the accuracy of the final vegetation maps using reference data from different sources, we used a post-stratified, multivariate composite estimator. Overall accuracy was 74.5% (Kappa statistic=49.9%). Sources of error included differentiating between mixed conifer and spruce-dominated types and between openings in the forest and deciduous-dominated mixes. Overall, our nonparametric classification method successfully identified dominant vegetation types on the study area at a finer spatial resolution than can typically be achieved using traditional classification techniques.

Keywords: Accipiter gentilis, classification, non-parametric classification, vegetation types, decision trees, Kaibab National Forest

Publication Notes:

  • 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.

XML: View XML

Citation:


Joy, Suzanne M.; Reich, R. M.; Reynolds, Richard T. 2003. A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees. International Journal of Remote Sensing. 24(9): 1835-1852.

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.