Download mapping oak wilt in texas - The University of Texas at San Antonio

MAPPING OAK WILT
IN TEXAS
Amuche Ezeilo
Wendy Cooley
OAK WILT (Ceratocystis
fagacearum)

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Oak wilt is an arboreal disease that affects
oaks in Texas and the Northeastern part of the
U.S.
Central Texas has been the hardest hitthousands of oak trees have died over the past
20 years
DISTRIBUTION IN THE U.S.
Figure 1. 2005 Oak wilt distribution map in the United States (USDA Forest Service)
DISTRIBUTION IN TEXAS
Fort Worth
Dallas
College Station
Austin
San
Antonio
Houston
Figure 2. Oak wilt coverage in Texas (The Texas Forest Service)
WHAT IS OAK WILT
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Oak wilt is a vascular fungal disease that
develops in the water conducting vessels
(xylem)
The fungus plugs up the vessels, reducing
water flow in trees
Due to a lack of water, the tree begins to wilt
and often times die
All oaks are vulnerable but red oaks are more
susceptible than white oaks
TRANSMISSION ROUTE 1

One method of transmission is through root
grafts


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Oak trees, esp. live oaks, tend to grow in large
groups
Roots in these groups are all interconnected
through root grafting
Therefore, it is easy for an infected oak to pass the
disease to healthy oaks
Grafting can also be between live oaks and red
oaks
TRANSMISSION ROUTE 2

The other method of transmission is through an insect
vector
 Fungal mats produced on red oak bark emit an
odor that attracts sap feeding insects of the
Nitidulidae family as well as the Oak Bark Beetle
 Beetles carry fungal spores on their bodies from
the spore mat of an infected tree to a fresh wound
on a healthy oak
 The beetle feeds on the sap from a fresh wound of
a healthy oak and, thus, spreads the infection to the
healthy tree
CURE?

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There is no known cure for oak wilt
Prevention is the key to fighting this disease
Early detection and rapid removal of infected
trees including breaking grafted roots
Avoid wounding oak trees and when wounding
cannot be avoided, paint immediately with
pruning paint
Cutting deep trenches around infection centers
OAK WILT SUPPRESSION
PROJECT


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Created by the Texas Forest Service to detect
oak wilt centers
They conduct aerial survey flights annually
over 59 counties to locate possible centers
These centers are then confirmed on ground
Using remote sensing on current aerials will
help TFS to classify these areas
Data used were 1 meter orthophotos from
2004, Kerr County, after resizing
AIMS
Detect areas of Oak Wilt in Kerr County
 Classify and map these areas
 Compare results of various classifications
 Thus enabling easier monitoring and control
of the disease
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METHODS

Supervised and Unsupervised ENVI Methods
Supervised: makes use of researcher’s a priori
knowledge.
Training areas of gray/grayish magenta created, representing
dead or severely affected forest.
This training area spectral information is input to maximum
likelihood technique
Which determines probability of each image pixel
belonging in the training areas, and therefore of each pixel
being either healthy or diseased

METHODS contd
Unsupervised: These methods use only
statistical techniques to classify the image
 Two techniques

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1. K-Means Clustering
2. Isodata
METHODS_K-MEANS
K-Means Clustering
 Clustering analysis, requiring analyst to
select # of clusters
 Technique then arbitrarily locates this #
and iteratively repositions them until optimum
separability is achieved
(Univ of Lethbridge)

METHODS_ ISODATA
Iterative Self-Organizing Data Analysis Technique
 Iterative-repeatedly performs entire classification and
recalculates statistics.
 Self-organizing refers to way in which it locates
inherent data clusters.
 Minimum spectral distance formula is used to form
clusters
(Univ of Lethbridge)
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ISODATA contd
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Means shift with each iteration
Until either

1. Maximum # of iterations achieved, OR
2. Maximum percentage of unchanged pixels has
been reached between 2 iterations
(Univ of Lethbridge)
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K-Means
15 Means Selected, 3 Iterations
Sample Location
Same Area on Image
RESULTS
Isodata
3 Iterations, Sample Location
Same Area on Image
Supervised Classification
Maximum Likelihood, Sample
Location
Same Area on Image
Discussion
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Comparisons made by observing linked
images of each classification and orthophoto
Then determining which classification best
fit the affected orthophoto vegetation
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Summary
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Supervised maximum likelihood classification
seems to best classify the data
Unsupervised Isodata classification was
second best
Thirdly, Unsupervised K-Means classification
However, no methods could separate water
from diseased vegetation