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A STUDY OF TWO REMOTE SENSING METHODS FOR 
EVALUATING LAND COVER IN SOUTH CAROLINA 
G.R. Minick 
Computer Services Division 
and 
D.J. Cowen 
Department of Geography 
University of South Carolina 
Columbia, South Carolina 29208, USA 
ABSTRACT 
General land cover derived from computer analysis and 
mapping of satellite-based multispectral scanner imagery 
and land use and land cover derived from conventional 
aerial photographic interpretation and mapping were 
analyzed for content similarity and consistency for the 
State of South Carolina. The primary objectives were to 
determine the compatibility of results generated by 
differing remote sensing techniques and to assess the 
potential value of having both data sets available in the 
South Carolina Natural Resource Information System (SCNRIS). 
INTRODUCTION 
Since 1973 the Computer Services Division of the University 
of South Carolina has been actively involved in the imple 
mentation and utilization of an integrated geographical 
information system that can be used to support a variety of 
natural resource research projects. Much of the early work 
involved the installation of necessary hardware and soft 
ware components and assemblage of an experienced staff. 
More recently, a major emphasis has been directed toward 
the creation of a state-wide data base. In order to meet 
this objective, two major projects have involved the deri 
vation of a Landsat based generalized land cover classifi 
cation and the digitization of the United States Geological 
Survey's (USGS) Land Use and Land Cover Maps. The completion 
of these projects and the integration of the resultant 
digital files provides a good basis for comparative analysis. 
The purpose of this paper is to present some findings from 
a preliminary analysis of these two data sets. 
LANDSAT CLASSIFICATION 
Positions of more Landsat scenes were required for complete 
coverage of South Carolina (Fig. 1). Winter scenes (No 
vember through February), without cloud cover, which dated 
as closely as possible to 1977, were selected. The data for 
each scene was processed separately utilizing NASA's ELAS 
software (Junkin et al. 1980) on a Data General/Comtal 
image processing system. Spectral classes for each scene