Rule-Based Postclassification Techniques for Remotely Sensed Vegetation Data

Author: Jeffery S. Nighbert
Organization: Bureau of Land Management

1515 SW 5th Avenue
Portland, OR 97201
USA

Phone: 503-952-6399
Fax: 503-952-6419
jnighber@or.blm.gov

Creation of vegetation themes for GISs using remote sensing techniques and satellite imagery is time and cost-effective, but not without problems. Even in the best circumstances, errors can occur due to statistical similarities of vegetative spectral responses in different lighting situations. These errors can be addressed systematically using a rule-based editing program, if the geographical basis for correction can be properly stated. ARC GRID offers the DoCell programming language, which can be used to construct rule-based reclassification programs.

The Interagency Vegetation Mapping Project (IVMP) is a significant effort between the Forest Service and Bureau of Land Management to use Thematic Mapper satellite imagery to create a forest vegetation database for the range of the spotted owl in Oregon and Washington. This area encompasses almost 43 million acres, and has been stratified into nine physiographic provinces. It will involve processing 18 separate Landsat scenes. The mapping methods will utilize a regression analysis technique, and the final database will include information about the vegetation cover, conifer and broadleaf densities, canopy structure, and overstory size class.

This presentation will discuss how rule-based reclassification programs were defined and written using the ARC GRID DoCell language to correct classification problems in this study. The presentation will not dwell on the source code for these programs but will attempt to demystify the "rule-based postclassification" process by providing several simple graphical examples of the results and impact of using this technique.