Special Technology Development Program
Progress Report
PROJECT NUMBER: R10-2008-01
PROJECT TITLE: Estimating insect distributions in Alaskan landscapes not covered in
aerial surveys.
PROJECT STATUS: Continuing.
ORIGINAL EXPECTED COMPLETION DATE OF PROJECT: March 2010.
EXPECTED COMPLETION DATE OF THE PROJECT: March 2010.
SUBJECT: Pest modeling (10%), survey (30%), monitoring (10%), satellite imagery
(10%), risk/hazard (20%), assessments (20%).
STATUS OF SUBJECT SPECIES: Native and non-native pests.
PROJECT OBJECTIVES: The objectives of this study are:
1) To determine how existing aerial survey methods and results can be integrated with
recently developed spatial modeling techniques to predict insect pest distributions in
remote areas of Alaska where aerial surveys are logistically difficult, expensive, and
currently impractical.
2) To adapt a spatial modeling/aerial survey approach, aimed at measuring climate change
impact on forest insects statewide, and establish baseline conditions for future assessments
of insect pest migrations and intensification.
BRIEF DESCRIPTION OF PROJECT: The primary activities will include the following,
FY 2008:
1) Identify and select pilot study sites (April).
2) Acquire satellite imagery, GIS layers, conduct field surveys (April to August).
3) Conduct spatial analysis and generate spatial models (September to June).
FY 2009:
1) Select plots and use methods/techniques developed above to cover the rest of Alaska (April).
2) Collect data – Satellite imagery, GIS layers, FIA data, etc, and conduct surveys (July to
September).
3) Develop predictive models and potential/actual pest distribution models (September to June).
5) Determine estimates of accuracy and error (September to June).
6) Progress reports, inclusion of insect distribution maps into Forest Conditions Report (July).
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FY 2009:
1) Manuscript preparation and submission (August to March).
2) Operational use for Region 10 annual pest surveys of predictive modeling in R10 (beginning
May).
CHANGES TO ORIGINAL PROJECT SCOPE OR OBJECTIVES: None.
ADDITIONS TO THE ORIGINAL PROJECT SCOPE OR OBJECTIVES. Although the
aspen leaf miner is still at high levels (755,393 infested acres in 2007 vs 210,234 ac in
2008), the large aspen tortrix (40,395 ac in 2007 vs 7,184 ac in 2008) is not. In the
Tortrix’s place, we substituted spruce mortality, mainly caused by spruce beetle and
willow leaf blotch, presumably all caused by Micrurapteryx salicifoliella. Based on the
latest R10 FHP aerial survey, spruce mortality occurs over 129,126 acres and willow leaf
blotch 72,382 acres. These two conditions have become our 2nd and 3rd largest after aspen
leaf miner.
BRIEF DESCRIPTION OF TASKS ACCOMPLISHED THIS YEAR
We are on schedule.
We completed the following tasks
FY 2008:
1) We identified and selected pilot study sites;
2) We acquired all the satellite imagery and we spent 3 weeks on site out of Fairbanks
collecting data. The project has acquired a set of six Landsat ETM+ (2001) images
for the area between Fairbanks and Homer, Alaska. A set of similar satellite
imagery will be acquired for 2008. Information was collected on the
presence/absence and severity of aspen leaf miner near Fairbanks, Alaska and
presence/absence and severity of spruce mortality near Homer, Alaska. Because of
the continual heavy rains and slick roads, for several days we were unable to make
ground assessments on dirt roads;
3) We have begun analyzing the data. In addition, we continued to examine last year’s
field data for refinements needed for the spatial models. We examined the use of
MODIS imagery and compared it to the models based on Landsat.
FY 2009:
1) The severity of defoliation of willow and aspen was modeled based upon roadside
surveys. There was a strong correlation between willow and aspen defoliation.
2) Vegetation modeling was completed for the Kenai Peninsula using Landsat-5 TM
images. The 1986 land-cover classes were used to predict the land cover classes in
2008.
3) Mapping done for 2008 spruce mortality using 1986 data, Landsat-5 TM, spatial
modeling techniques, and ground points.
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PRODUCTS AND DUE DATES:
Our originally proposed products and due dates are still current, with the exception of the SAF
presentation/poster which would be a little premature in 2008:
1) Spatial models and incidence/severity estimates for aspen leaf miner spruce mortality (2008)
2) Spatial models and incidence/severity estimates for various major forest insect pests (2009)
3) Inclusion of some results in the Region 10 Forest Health Conditions Report (2008, 2009)
4) Society of American Foresters Convention poster or presentation (2009)
5) Entomological Society of America poster or presentation (2009)
6) Publication in peer reviewed journal (2010)
STATUS OF PRODUCTS/PRESENTATIONS.
Status of Product Development 2009 –
Fairbanks Roadside Survey
Willow was a dominant vegetation type that occurred along the three highways surveyed.
The severity of damage to willow ranged from 0 to 5 with an average of 1.7. The severity of damage
to willow decreased significantly with elevation (m), independent of the geographical location of the
sample points, or distance from the city of Fairbanks (Fig. 1).
Fig. 1. Predicted severity of damage to willow along three major highways
near Fairbanks, Alaska.
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Aspen occurred in clusters along the three highways surveyed; willow was more continuously. The
severity of damage to aspen ranged from 0 to 5 with an average severity of 1.7. The severity of
damage to aspen was spatially independent along the three highways (Moran’s I = 0.088, p-value =
0.128). The damage to aspen was independent of elevation, the geographical location of the sample
points and distance from the city of Fairbanks. Aspen damage was correlated to the severity of
damage to willow (Fig. 2).
Fig. 2. Predicted severity of damage to aspen along three major highways near Fairbanks, Alaska.
Spruce Mortality on the Kenai Peninsula – 1986
A binary classification tree was used to predict the vegetation types on the Kenai Peninsula in 1986.
Important variables in the model included various Landsat-5-MS-bands and elevation.
The classification tree was able to accurately predict all land cover types except birch and black
spruce (Table 1). Black spruce was misclassified as bogs most of the time. Since black spruce can
occur as scattered individuals throughout the bogs, the satellite imagery was not able to discriminate
between the two types. Birch was misclassified as aspen and black spruce. Birch and aspen have
similar spectral properties making it difficult to discriminate between the two species, unless the
aspen trees are infested with aspen leaf minor.
The predicted land cover types for the Peninsula in 1986 are displayed in Fig. 3. The Kenai
Peninsula is dominated by black and white spruce. Bogs are scattered throughout the western part of
the Kenai Peninsula with black and white spruce, while birch and aspen dominate the higher
elevations in the eastern and southern part of the Kenai Peninsula.
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Fig. 3. Land cover types of the Kenai Peninsula in 1986 based on Landsat-5 MS imagery and
elevation.
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Spruce Mortality on the Kenai Peninsula – 2008
The sample data used to develop the land cover map in 1986 was used in combination with the 2008
satellite imagery to predict the land cover types in 2008. While the accuracy of some classes are
low (Table 1), the classification errors represent changes from 1986 to 2008. Sample points of
black spruce in the western part of the Kenai Peninsula were classified as bogs while some white
spruce sample points were classified as birch or aspen. Some birch sample points were also
classified as aspen. Comparing the land cover maps one can observe the decrease in the area
covered by spruce, and an increase in area classified as bogs and birch/aspen. The changes
correspond to the areas identified on aerial surveys as spruce mortality from 1999 to 2007 (Fig. 4).
Fig. 4. Land-cover types of the Kenai Peninsula in 2008 based on Landsat-5 MS imagery, elevation
and slope.
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ACCOMPLISHMENTS TO DATE:
Products: Several models. A little too early yet.
Publications: None yet.
Technology Transfer: The results of this study are being used as a basis to develop a
theoretical spatial and temporal model of the interaction between the pests, in this case the aspen
leaf minor, and its environment that will allow researchers to generate simple geographic models to
predict the spatial nature of the outbreak.
While a general model will probably not be possible, a class of models researchers could use to
predict insect outbreaks is slowly emerging. In each case researchers must develop a deep
understanding of the specific local interactions between the pest and its resource to be able to make
good predictions.
FHP LEAD CONTACT:
Name
John E. Lundquist
Affiliation (Office or Dept.)
Phone, E-mail, Fax
Alaska Region, Anchorage
S&PF, FHP
(907) 743-9453
jlundquist@fs.fed.us
PRINCIPAL INVESTIGATORS:
Name
Affiliation (Office or Dept.)
Phone, E-mail, Fax
Robin Reich
Colorado State University
Fort Collins, Colorado
(970) 491-6980
robin@cnr.colostate.edu
John E. Lundquist
Alaska Region, Anchorage
S&PF, FHP
(907) 743-9453
jlundquist@fs.fed.us
James Kruse
Alaska Region, Fairbanks
S&PF, FHP
(907) 451-2701
jkruse@fs.fed.us
Mark Schultz
Alaska Region, Juneau
S&PF, FHP
(907) 586-8883
mschultz01@fs.fed.us
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