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MAPPING OAK WILT IN TEXAS

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.

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MAPPING OAK WILT IN TEXAS

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  1. MAPPING OAK WILT IN TEXAS Amuche Ezeilo Wendy Cooley

  2. 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

  3. DISTRIBUTION IN THE U.S. Figure 1.  2005 Oak wilt distribution map in the United States (USDA Forest Service)

  4. DISTRIBUTION IN TEXAS Fort Worth Dallas College Station Austin Houston San Antonio Figure 2. Oak wilt coverage in Texas (The Texas Forest Service)

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. METHODS contd • Unsupervised: These methods use only statistical techniques to classify the image • Two techniques • 1. K-Means Clustering • 2. Isodata

  13. 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)

  14. 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)

  15. 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)

  16. K-Means15 Means Selected, 3 IterationsSample Location

  17. Same Area on Image

  18. RESULTSIsodata3 Iterations, Sample Location

  19. Same Area on Image

  20. Supervised ClassificationMaximum Likelihood, Sample Location

  21. Same Area on Image

  22. Discussion • Comparisons made by observing linked images of each classification and orthophoto • Then determining which classification best fit the affected orthophoto vegetation

  23. 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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