Skip to main content
padlock icon - secure page this page is secure

Spectral and temporal linear mixing model for vegetation classification

Buy Article:

$60.00 + tax (Refund Policy)

The objective of this study is to find a better method for sub-pixel classification of vegetation. The proposed new technique of a linear mixing model (LMM) is the sequential combination of spectral LMM and temporal LMM. Sub-pixel components of 'relative green vegetation' are derived by spectral LMM; sub-pixel components of vegetation types are estimated by subsequent temporal LMM. The proposed method was applied to five temporal Landsat Enhanced Thematic Mapper (ETM) images for the year 2000 for areas south of Lake Baikal, Russia. Dominant vegetation types there are pine, birch/aspen, shrubs and wheat with weedy plants. Ground truth data of vegetation types were prepared by field survey and visual interpretation of Landsat ETM images by experts. Both the comparisons of classification results among the proposed method and conventional LMM methods and the simulation results among them indicate that the proposed spectral and temporal LMM has better accuracy than conventional methods.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: Center for Environmental Remote Sensing (CEReS) Chiba University 1-33 Yayoi-cho, Inage-ku Chiba 263-8522 Japan, Email: [email protected] 2: Pasco Corporation No. 1-2, Higashiyama 1-chome, Meguro-ku Tokyo 153-0043 Japan, Email: [email protected] 3: Mongolian Complex Biological Expedition, Institute for Ecology and Evolution Russian Academy of Sciences Pyatnitskaja str. 47, building 3 109017 Moscow Russia, Email: [email protected]

Publication date: October 1, 2004

More about this publication?
  • 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