Ranked-Set Sampling Potential for Disaster Assessment; G. Johnson & W. Myers
GypsES: A Decision Support System for Gypsy Moth Management; M. Foster
A Decision-Support System for Spruce Budworm in Canada; D. MacLean
Vegetation Inventory and Monitoring in Alaska; B. Mead & T. Setzer
Estimating Spruce Beetle Infestation Trends and Dynamics; W. van Hees
HYPERFORMS FOR RAPID ACCESS TO DISPARATELY STRUCTURED INFORMATION
AND META-INFORMATION
Dr. Wayne L. Myers & Dr. Michael Foster
The Pennsylvania State Univ.
Univ. Park, PA 16802
ABSTRACT
Catastrophes create needs to draw upon several kinds of disparately
structured information quickly. Such needs typically encompass both
computerized databases and document-based sources. Relevant computerized
databases may reside on different hardware platforms, under different software
systems, controlled by different database administrators. Specialized formats
and access protocols may elevate the problem to an order that approaches need
for a panel of experts. Flexible hyperforms can provide the functionality of
compound expert systems managing information about information
(meta-information) that are both inexpensive and easily updated as databases
change. When structured as generalized objects, complex hyperform systems can
be linked retrospectively and recursively into higher order network objects.
If desired, demons can be imbedded in such systems to reach directly into
other systems and extract content. Multisystems of this nature can handle
complexity of virtually any order within the confines of PC-compatible
computer technology.
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POTENTIAL OF RANKED-SET SAMPLING FOR DISASTER ASSESSMENT
Glen Johnson & Dr. Wayne Myers
The Pennsylvania State University
University Park, PA 16802
ABSTRACT
Parsimony of field work in forest inventory is even more important for the
disaster assessment context than for regularly scheduled forest survey
scenarios. Interest thus lies in sample designs which enable rapid assessment
to augment detailed ground work. Double sampling for stratification is a
relatively familiar strategy for achieving this end, wherein remote sensing or
direct aerial reconnaissance can serve the purpose of stratification. The
less familiar strategy of ranked set sampling becomes a candidate approach in
cases where rapid assessment extends beyond categorization to ordination. The
Center for Statistical Ecology and Environmental Statistics at Penn State has
done considerable work in extending the concepts of ranked set sampling and
developing scenarios for application. Fundamentals of ranked set sampling are
presented, and scenarios considered for its application in the context of
inventories that are mounted in response to catastrophes.
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GypsES: A KNOWLEDGE BASED DECISION SUPPORT SYSTEM FOR GYPSY MOTH
MANAGEMENT IN FORESTED LANDSCAPES
Dr. Michael A. Foster
Laboratory for AI Applications
501 ASI Building, College of Agricultural Sciences
Penn State University
Univ. Park, PA 16802 USA
ABSTRACT
Management of gypsy moth (GM), Lymantria dispar L. (Lepidoptera:
Lymantriidae) is currently ad-hoc because of insufficient technology transfer
from reseaarch to implementation, unstandardized management and population
monitoring practices, and inadequate database management systems and
geographic information systems. Recently, a team funded by the USDA Forest
Service (Penn State University, Virginia Tech University, West Virginia
University, and USDA-FS-NEFES) has developed GypsES (Gypsy Moth Expert
System), a knowledge based, spatially referenced system for gypsy moth
management. The GypsES architecture consists of 5 knowledge based advisors
for Monitoring (moths and egg masses), Defoliation Projection, Phenology,
Risk Assessment, and Treatment, along with an X-Windows based graphical user
interface and the GRASS geographic information system running under UNIX.
The GypsES knowledge bases are encoded through generation of C language code
from graphical dependency networks, enabling customization even by non-
programmers. The forested landscape is represented hierarchically:
administrative units managed under a single budget, management or land use
units homogenous in management objective, and treatment units homogeneous in
treatment method and timing.
We focus here on the Treatment Advisor's support of decision and
implementation. Treatment Decision assists in (1) configuring potential
treatment units and choosing treatment methods; (2) assigning treatment
priorities based on risk assessment; and (3) determining the final set of
affordable treatment units. Treatment Implementation advises on: (1)
scheduling aerial application activities; (2) evaluation of aerial
applicator's bids; and (3) calibration of spray nozzles. GypsES has several
unique features among forest pest management software systems: (1) an
extensive hierarchy of landscape elements; (2) detailed assistance for
scheduling aerial suppression activities; (3) within-consultation digitizing
and editing of polygons; and (4) user-editable heuristics. The customizable
nature of GypsES may speed adoption by enabling users to tailor it within
limits to their own unique needs.
DEALING WITH A RECURRENT CATASTROPHIC EVENT: A DECISION-SUPPORT SYSTEM
FOR SPRUCE BUDWORM AND FOREST MANAGEMENT PLANNING IN CANADA
David A. MacLean
Forestry Canada - Maritimes Region
P.O. Box 4000, Fredericton, N.B., Canada
In certain regions and stand types in the world, forest insect outbreaks
are the primary determinants of forest structure and development. Forest
composition and age structure of much of the spruce-fir in eastern Canada is
related more to past spruce budworm (Choristoneura fumiferana) outbreaks
(beginning about 1910, 1940, 1970) than to harvesting or wildfires. These
outbreaks are often a natural component of forest succession, and do not just
occur because we are doing something "wrong". However, they are clearly a
"catastrophic event" (e.g., an estimated 220 million cubic meters of timber
was killed by spruce budworm in Canada from 1977-81) and must be taken into
account in forest management planning if plans are to be accurate. Managers
need tools to predict outbreak occurrence and effects on forest development,
in order to ensure that expected timber supply/stand types will be present at
the expected time of harvest/other usage, and in order to utilize silviculture
and management planning to reduce the severity of future outbreaks.
I will describe a budworm and forest management decision-support system
(DSS) under development in a 5 year multi-agency project supported by Canada's
Green Plan. A pest management DSS is defined as an integration of current
knowledge, available data, and software applications to provide graphical,
tabular, and textual support for decisions made by forest managers. As such,
it requires a multi-faceted approach involving database acquisition/creation
and organization, knowledge engineering, model development, interface
development, as well as systems analysis and design. Our objective is to
develop a DSS which integrates the use of harvest scheduling and silviculture
to minimize losses and the need for protection (insecticide use) during
budworm epidemics, and sets priorities for application of limited forest
protection, as desired, to increase forest yields and maintain environmental
integrity.
The DSS will allow forest managers to relate pest and forest management
decisions to consequences on budworm population levels and forest inventory.
The DSS integrates pest monitoring and a population prediction model; spatial
risk and vulnerability (severity of damage) assessment models; models
predicting long-term stand- and forest-level development and timber supply
under alternative pest/forest management scenarios; remote sensing (Landsat
and MEIS) methods to assess current and cumulative damage (stand growing
condition) for all stands; a protection planning system based on marginal
timber supply benefits; a dynamic inventory projection system to update
inventory data and allow "what-if" exploration of possible futures; and forest
management planning (silviculture, harvest scheduling, protection planning)
options to reduce the damage caused by future spruce budworm outbreaks.
The system is being implemented on a Unix workstation and ARC/Info GIS
environment.
A figure associated with this presentation is in the MACLEAN.PCX file.
VEGETATION INVENTORY AND MONITORING IN ALASKA
Bert Mead & Ted Setzer
Forestry Sciences Lab
201 East 9th Ave., Suite 303
Anchorage, AK 99501
ABSTRACT
Permanent vegetation measurement plots are located on a systematic grid
throughout Alaska as part of a statewide inventory. Data collected include
plant species lists and species foliar cover profiles as well as tree volumes
and size distributions. These data have been used to estimate species biomass
and composition and to make preliminary plant community classifications. The
permanently located plots make it possible to monitor species composition and
cover changes resulting from catastrophic events such as widescale volcanic
ash deposition, flood, fire and insect epidemics.
The ability to estimate both immediate and gradual biomass fluctuations
permits monitoring of changes in productivity or other management concerns
resulting from such events. In addition, community descriptions resulting
from a broad-scale, statewide inventory will assist in estimating impacts on
species diversity and wildlife habitat due to catastrophic events. These
abilities will depend on the type of catastrophic event, the time period
involved, and the ability to control for the effects of other environmental
influences.
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USE OF POINT-IN-TIME, EXTENSIVE FOREST INVENTORY DATA FOR ESTIMATING
SPRUCE BEETLE INFESTATION TRENDS AND DYNAMICS
Willem W. S. van Hees, Research Forester
USDA, Forest Service
Anchorage, AK
ABSTRACT
Results of two studies conducted to investigate use and utility of
point-in-time forest inventory for assessment of spruce beetle infestation
trends and characterization of infestation dynamics are summarized. The first
study was conducted to develop an analytical method of plot classification for
identification of stage of infestation.
With plots thus classified, areas of forestland and volumes of timber
affected by the infestation were estimated. The likely near-term (5 years)
trend of the infestation was projected. Subsequent changes in the infestation
supported the projection. The second study was conducted to show the utility
of extensive forest inventory data in characterization of infestation
dynamics. Results of this investigation agreed well with other, smaller-scale
studies conducted in the same area. Extensive inventories are less expensive
than intensive inventories and an extensive sample can be used to characterize
infestation dynamics. Thus, it may be possible, through use of extensive
forest inventories, to identify changes in dynamics useful in forest health
monitoring over large areas.
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