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successful when the DSP and Landsat systems of land categorization are 
comparable, and is recommended for all subsequent surveys utilizing 
MAGI System data. This is noteworthy because the present capability to 
extract the primary and secondary acreages from the MAGI System by 
category and to recombine them is cumbersome, reinforcing a user ten 
dency to extract unadjusted primary data only. 
For this study area, good correspondence between the MAGI System and 
Landsat data was achieved for four categories—forest, water, crop/ 
pasture, and MDR—without _a priori knowledge of the MAGI land use 
designations. However, a priori knowledge of and accommodation for 
the MAGI designation was necessary to achieve high correspondence for 
two of the three remaining cover categories, CII and transitional, 
which were not handled uniformly throughout the study area by the DSP 
classification scheme. For convenience, large and well-defined areas 
(i.e., commercial, institutional and industrial facilities), which 
together comprised roughly 15 percent of the total study area, were 
blocked out at the photointerpretive stage and given a 100 percent CII 
(commercial, institutional or industrial) label regardless of the 
actual land cover categories occurring at these locations, a practice 
consistent with DSP's traditional emphasis on land use. Although cor 
rectly designated by functional land use, these areas also contained 
within their property boundaries substantial portions of LDR, the third 
category which did not exhibit good correspondence, as well as grass 
and forest. For example, 46 percent of the LDR, a spatially dispersed 
category by definition, was located on these CII blocks. Roughly half 
the ground cover was forest and grassland on individual CII blocks such 
as the Baltimore Washington International Airport, Fort Meade, the 
National Security Agency, and many other private, state, and federal 
institutions. These were solely designated as governmental/institu 
tional by MAGI on both the primary and secondary data layers. The 
transitional category was also represented only as a primary category 
since it was recorded at 60 percent or greater proportions/cell where 
it existed, even if representation was actually less. This important 
category, though small proportionally, was actually weighted toward 
overestimation by DSP, except where it was not counted on the CII 
blocks. For these areas where the DSP land use categories were direc 
tly compared to the Landsat land cover categories, the correspondence 
could not be expected to be large. The result was that LDR as a cover 
type (and with much less proportional impact on other categories, such 
as forest, as well), was severely underreported with a corresponding 
overestimation of CII as a cover type in the MAGI System data base. 
For the remaining 85 percent of the study area where both surveys 
represented similar land categories, the correspondence of the Landsat 
and MAGI results was good to excellent. These results further support 
the premise that the observed discrepancies between the MAGI System and 
Landsat data on the CII blocks were indeed related to the differences 
in the two land interpretation and classification methodologies, a 
finding consistent with other research (Fitzpatrick-Lins, 1978). 
Overall, the Landsat data provided more reliable "cover type" acreage 
estimates than did the acreages from MAGI. Since land cover infor 
mation is increasingly important for environmental planning, hydro- 
logical models and development strategies, the Landsat data could be 
used by DSP to provide information not currently available through MAGI 
for some geographical areas. This approach could ultimately yield a 
comprehensive means of categorizing all land areas within the state.