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MAPPING OAK WILT IN TEXAS

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MAPPING OAK WILT IN TEXAS Powered By Docstoc
					MAPPING OAK WILT
    IN TEXAS
     Amuche Ezeilo
     Wendy Cooley
         OAK WILT (Ceratocystis
             fagacearum)
   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 hit-
    thousands 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
                                                               Houston
                                       San
                                       Antonio




Figure 2. Oak wilt coverage in Texas (The Texas Forest Service)
         WHAT IS OAK WILT
   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
       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?
   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
   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
                     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



   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)
                ISODATA contd
   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)
          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
   Comparisons made by observing linked
    images of each classification and orthophoto

  Then determining which classification best
fit the affected orthophoto vegetation
                  Summary
 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

				
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posted:1/2/2012
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