154 - 
Forest stocking equations can also be used with other information to 
set management operational priorities (Ffolliott and Worley, 1973). This 
application would combine knowledge of the portions of wildland units Ave: 
in a forest that support minimum forest density levels, the output of the 
forest stocking equations, with selected criteria characterizing alternative 
management opportunities (minimum forest density levels, minimum portions 
of wildland units meeting specified minimum forest density levels, etc.). Ave 
Then, for a given wildland management system, wildland units are eliminated 
from consideration, or ranked in terms of suitability by interpretations 
of the appropriate frequency distributions and the selected criteria. Bru 
Continual collection and assessment of basic source data by remote 
sensing techniques will allow all of the analyses outlined above to be 
frequently up-dated. Such re-evaluations will provide information as to Ffo 
changes in wildland management potentials with time by identifying the 
management status at given points in time. Consequently, the objectives 
of the study may be satisfied in a dynamic sense, yielding more sensitive 
inputs to efficient wildland management decision-making. Lar 
CONCLUSIONS 
Forest stocking equations describing the portion of the forested areas Moe 
on the south one-half of the Prescott National Forest in central Arizona 
that supports arbitrarily defined minimum basal area levels can be developed 
by multiple-stage remote sensing techniques. The methodologies utilized 
in this study may be suitable for the synthesis of source data from high 
altitude imagery that are required to develop forest stocking equations 
elsewhere. 
The precision of the methodologies employed was within the desired 
levels of confidence. However, shadow effects caused by the angle of the 
sun at the time the 1:120000 imagery was taken may, in part, account for 
added observer variation. 
Forest stocking equations, as developed herein, can be used alone or 
in conjunction with other information to assist in natural resource 
decision-making. 
ACKNOWLEDGEMENTS 
Funding for this study was provided, in part, by a NASA grant, 
Applications of Remote Sensing to State and Local Governments , and by 
the Department of Watershed Management, University of Arizona, Tucson. 
The assistance of the USDA Forest Service is also appreciated.