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Ancillary data such as slope, aspect, and 
elevation, as well as soil moisture, depth to 
frost, and tree crown cover are other data that 
must be included as attributes to measure or 
observe. 
Characterization of forest stressors 
FIA teams will collect data and information on 
forest vegetation health, using forest stressor 
guides provided by Forest Pest Management and 
Forest Pest Research. Each tree will be observed 
for stressors, including: 
o Disease; 
o Insects; 
o Foliar health and vigor; 
o Other physical damage or tree 
stress indicators such as fire 
scars; and 
o Animal damage (due to possible 
changes in plant species density 
and composition). 
Researchers also will evaluate the foliar health 
and vigor of non-tree vegetation. Particular 
observation emphasis will be given to plants 
identified as stress indicators by the 
literature, FIA cooperators, and other scientists 
working in the field. Location of any stressed 
vegetation on the horizontal-vertical profile 
plot will be mapped on the H-V plot sketch map. 
Quantification of changes in forest conditions 
over time. 
Changes in tree data and plant health and vigor 
may be apparent from remeasurement observations. 
Data will be quantified and stored in a format 
allowing evaluations of correlations with forest 
stressors. 
Correlation of changes with forest stressors. 
Change information will be evaluated against 
forest stressors following each re-measurement 
observation. A statistical analysis will be used 
to identify significant relationships. Analyses 
of these relationships will be performed in 
conjunction with cooperating agencies. 
Cooperators primarily will be responsible for 
final statistical analyses of soil chemical 
content and vegetation data collected, and for 
their respective statistical relationships to 
changes in vegetation health and vigor. 
Cooperators also will evaluate data from any site 
instrumentation. 
USE OF REMOTE SENSING IN THE MONITORING 
SYSTEM 
Remote sensing will play a major role in this 
monitoring system. Since most of the original 
data were collected using AIRIS (LaBau and 
Winterberger, 1988), we already have much 
remotely sensed data. For example, at each 
eight-hectare AIRIS site, Landsat multispectral 
scanner (MSS)-based land cover classifications 
(LCC) were extracted from the LCC maps of the 
entire inventory unit. In addition, 
more-detailed remotely sensed LCC data were 
collected on 1:60,000 color infrared photos, and 
still more intense data were collected on photos 
at 1:3,500 to 1:7,500 scales. 
We will continue using satellite- based LCC (MSS, 
thematic mapper, and SPOT) whenever appropriate 
and cost effective. 
Newer satellite technology also will be used as 
it is developed. One of the more promising and 
rapidly developing technologies is NOAA's 
Advanced Very High Resolution Radiometer (AVHRR). 
AVHRR has some excellent applications for gross 
monitoring of change in vegetation cover 
(Hastings et al, 1988), as long as resolution 
finer than 1,000 meters is not required. AVHRR 
may work well for measuring areas impacted by 
fire, extensive insect attack of forests, major 
wind damage of vegetation, such as large timber 
blowdowns, and similar catastrophic effects. 
Other possible remote sensing tools include Side 
Looking Airborne Radar (SLAR) and Synthetic 
Aperture Radar (SAR) now under development. 
Other applications may include such innovative 
remote sensing applications as use of aerial 
video remote sensors (Maggio and Baker, 1988), 
aerial stand volume tables (Setzer and Mead, 
1988), PC-driven stereo plotters, Geographic 
Information Systems applications and continued 
use of standard photogrammetric applications. 
The monitoring design will be kept flexible 
enough to permit use of new remote sensing 
developments as they occur. 
SUMMARY 
The monitoring design proposed here uses 
remeasurement of existing timber and non-timber 
vegetation plots to detect changes in vegetation 
health and vigor. The proposed monitoring grid 
and monitoring techniques used to collect tree 
and non-tree vegetation data are described. 
The monitored plots initially are characterized 
as to vegetation and plot condition. Then, 
remeasurements are made to detect vegetation 
changes that may be related to global climate 
change. Next, correlations are derived to 
establish relationships between the observed 
changes and forest stress criteria. Forest 
stress criteria are provided by Forest Pest 
Management, forest researchers in the field, 
cooperating academic study teams, and syntheses 
of scientific literature on the subject. 
Finally, cooperating research teams will conduct 
additional analyses of the change relationships 
in an attempt to model further expected changes. 
Part of these analyses may involve inter-relating 
chemical analyses of soil and vegetation with the 
observed changes. Cooperation with other global 
change research programs in Alaska will help 
improve understanding of the impact of global 
change in the Arctic, and the contribution of 
boreal and sub-boreal forest ecosystems to global 
models. 
Hopefully, this type of ground-based monitoring 
system will allow scientists to establish some 
basic relationships between changes in forest 
vegetation health and vigor and forest stressors 
that may be related to global climate change. 
This paper also advocates continued use of 
several remote sensing techniques already being 
used by Alaska Forest Inventory and Analysis, 
including use of Landsat MSS vegetation 
classifications on Landsat MSS, high- and low- 
altitude color infrared aerial photos, use of