Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniquesThis study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95%), and progress is being made towards identifying the mapped spectral classes.
Document ID
19760045106
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Messmore, J. (Old Dominion Univ. Norfolk, VA, United States)
Copeland, G. E. (Old Dominion Univ. Norfolk, VA, United States)
Levy, G. F. (Old Dominion University Norfolk, Va., United States)
Date Acquired
August 8, 2013
Publication Date
January 1, 1975
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Remote sensing of earth resources. Volume 4