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. 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 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 Houston Austin 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) 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 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 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 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 One method of transmission is through root grafts Oak trees, esp. live oaks, tend to grow in large groups Oak trees, esp. live oaks, tend to grow in large groups Roots in these groups are all interconnected through root grafting 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 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 Grafting can also be between live oaks and red oaks

7. TRANSMISSION ROUTE 2 The other method of transmission is through an insect vector 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 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 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 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 There is no known cure for oak wilt Prevention is the key to fighting this disease Prevention is the key to fighting this disease Early detection and rapid removal of infected trees including breaking grafted roots 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 Avoid wounding oak trees and when wounding cannot be avoided, paint immediately with pruning paint Cutting deep trenches around infection centers Cutting deep trenches around infection centers

9. OAK WILT SUPPRESSION PROJECT Created by the Texas Forest Service to detect oak wilt centers Created by the Texas Forest Service to detect oak wilt centers They conduct aerial survey flights annually over 59 counties to locate possible centers They conduct aerial survey flights annually over 59 counties to locate possible centers These centers are then confirmed on ground These centers are then confirmed on ground Using remote sensing on current aerials will help TFS to classify these areas 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 Data used were 1 meter orthophotos from 2004, Kerr County, after resizing

10. AIMS Detect areas of Oak Wilt in Kerr County Detect areas of Oak Wilt in Kerr County Classify and map these areas Classify and map these areas Compare results of various classifications Compare results of various classifications Thus enabling easier monitoring and control Thus enabling easier monitoring and control of the disease

11. METHODS Supervised and Unsupervised ENVI Methods Supervised and Unsupervised ENVI Methods Supervised: makes use of researcher’s a priori Supervised: makes use of researcher’s a prioriknowledge. Training areas of gray/grayish magenta created, representing dead or severely affected forest. This training area spectral information is input to maximum 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 Unsupervised: These methods use only statistical techniques to classify the image Two techniques Two techniques 1. K-Means Clustering 1. K-Means Clustering 2. Isodata 2. Isodata

13. METHODS_K-MEANS K-Means Clustering K-Means Clustering Clustering analysis, requiring analyst to Clustering analysis, requiring analyst to select # of clusters Technique then arbitrarily locates this # 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 Self-Organizing Data Analysis Technique Iterative-repeatedly performs entire classification and recalculates statistics. Iterative-repeatedly performs entire classification and recalculates statistics. Self-organizing refers to way in which it locates inherent data clusters. Self-organizing refers to way in which it locates inherent data clusters. Minimum spectral distance formula is used to form Minimum spectral distance formula is used to formclusters (Univ of Lethbridge)

15. ISODATA contd Means shift with each iteration Means shift with each iteration Until either Until either 1. Maximum # of iterations achieved, OR 1. Maximum # of iterations achieved, OR 2. Maximum percentage of unchanged pixels has 2. Maximum percentage of unchanged pixels has been reached between 2 iterations (Univ of Lethbridge)

16. K-Means 15 Means Selected, 3 Iterations Sample Location

17. Same Area on Image

18. RESULTS Isodata 3 Iterations, Sample Location

19. Same Area on Image

20. Supervised Classification Maximum Likelihood, Sample Location

21. Same Area on Image

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

23. Summary Supervised maximum likelihood classification seems to best classify the data Supervised maximum likelihood classification seems to best classify the data Unsupervised Isodata classification was Unsupervised Isodata classification was second best second best Thirdly, Unsupervised K-Means classification Thirdly, Unsupervised K-Means classification However, no methods could separate water from diseased vegetation However, no methods could separate water from diseased vegetation