MAPPING OAK WILT MAPPING OAK WILT IN TEXASIN TEXAS

Amuche EzeiloAmuche Ezeilo

Wendy CooleyWendy Cooley

OAK WILT (OAK WILT (Ceratocystis Ceratocystis fagacearumfagacearum))

Oak wilt is an arboreal disease that affects Oak wilt is an arboreal disease that affects oaks in Texas and the Northeastern part of the oaks in Texas and the Northeastern part of the U.S. U.S.

Central Texas has been the hardest hit-Central Texas has been the hardest hit-thousands of oak trees have died over the past thousands of oak trees have died over the past 20 years 20 years

DISTRIBUTION IN THE U.S.DISTRIBUTION IN THE U.S.

Figure 1.  2005 Oak wilt distribution map in the United States (USDA Forest Service)

DISTRIBUTION IN TEXASDISTRIBUTION IN TEXAS

Fort Worth Dallas

College Station

HoustonAustin

San Antonio

Figure 2. Oak wilt coverage in Texas (The Texas Forest Service)

WHAT IS OAK WILTWHAT IS OAK WILT

Oak wilt is a vascular fungal disease that Oak wilt is a vascular fungal disease that develops in the water conducting vessels develops in the water conducting vessels (xylem)(xylem)

The fungus plugs up the vessels, reducing The fungus plugs up the vessels, reducing water flow in treeswater flow in trees

Due to a lack of water, the tree begins to wilt Due to a lack of water, the tree begins to wilt and often times dieand often times die

All oaks are vulnerable but red oaks are more All oaks are vulnerable but red oaks are more susceptible than white oakssusceptible than white oaks

TRANSMISSION ROUTE 1TRANSMISSION ROUTE 1

One method of transmission is through root One method of transmission is through root graftsgrafts Oak trees, esp. live oaks, tend to grow in large Oak trees, esp. live oaks, tend to grow in large

groupsgroups Roots in these groups are all interconnected Roots in these groups are all interconnected

through root graftingthrough root grafting Therefore, it is easy for an infected oak to pass the Therefore, it is easy for an infected oak to pass the

disease to healthy oaksdisease to healthy oaks Grafting can also be between live oaks and red Grafting can also be between live oaks and red

oaksoaks

TRANSMISSION ROUTE 2TRANSMISSION ROUTE 2 The other method of transmission is through an insect The other method of transmission is through an insect

vectorvector Fungal mats produced on red oak bark emit an Fungal mats produced on red oak bark emit an

odor that attracts sap feeding insects of the odor that attracts sap feeding insects of the Nitidulidae family as well as the Oak Bark BeetleNitidulidae family as well as the Oak Bark Beetle

Beetles carry fungal spores on their bodies from Beetles carry fungal spores on their bodies from the spore mat of an infected tree to a fresh wound the spore mat of an infected tree to a fresh wound on a healthy oakon a healthy oak

The beetle feeds on the sap from a fresh wound of The beetle feeds on the sap from a fresh wound of a healthy oak and, thus, spreads the infection to the a healthy oak and, thus, spreads the infection to the healthy treehealthy tree

CURE?CURE?

There is no known cure for oak wiltThere is no known cure for oak wilt Prevention is the key to fighting this diseasePrevention is the key to fighting this disease Early detection and rapid removal of infected Early detection and rapid removal of infected

trees including breaking grafted roots trees including breaking grafted roots Avoid wounding oak trees and when Avoid wounding oak trees and when

wounding cannot be avoided, paint wounding cannot be avoided, paint immediately with pruning paintimmediately with pruning paint

Cutting deep trenches around infection centersCutting deep trenches around infection centers

OAK WILT SUPPRESSION OAK WILT SUPPRESSION PROJECTPROJECT

Created by the Texas Forest Service to detect Created by the Texas Forest Service to detect oak wilt centersoak wilt centers

They conduct aerial survey flights annually They conduct aerial survey flights annually over 59 counties to locate possible centersover 59 counties to locate possible centers

These centers are then confirmed on groundThese centers are then confirmed on ground Using remote sensing on current aerials will Using remote sensing on current aerials will

help TFS to classify these areas help TFS to classify these areas Data used were 1 meter orthophotos from Data used were 1 meter orthophotos from

2004, Kerr County, after resizing 2004, Kerr County, after resizing

AIMSAIMS

Detect areas of Oak Wilt in Kerr CountyDetect areas of Oak Wilt in Kerr County Classify and map these areasClassify and map these areas Compare results of various classificationsCompare results of various classifications Thus enabling easier monitoring and controlThus enabling easier monitoring and control

of the diseaseof the disease

METHODSMETHODS

Supervised and Unsupervised ENVI MethodsSupervised and Unsupervised ENVI Methods

Supervised: makes use of researcher’s a prioriSupervised: makes use of researcher’s a prioriknowledge.knowledge.Training areas of gray/grayish magenta created, representingTraining areas of gray/grayish magenta created, representingdead or severely affected forest. dead or severely affected forest. This training area spectral information is input to maximumThis training area spectral information is input to maximumlikelihood technique likelihood technique Which determines probability of each image pixelWhich determines probability of each image pixelbelonging in the training areas, and therefore of each pixelbelonging in the training areas, and therefore of each pixelbeing either healthy or diseased being either healthy or diseased

METHODS contdMETHODS contd

Unsupervised: These methods use onlyUnsupervised: These methods use only

statistical techniques to classify the image statistical techniques to classify the image Two techniques Two techniques

1. K-Means Clustering1. K-Means Clustering 2. Isodata2. Isodata

METHODS_K-MEANSMETHODS_K-MEANS

K-Means ClusteringK-Means Clustering Clustering analysis, requiring analyst toClustering analysis, requiring analyst to

select # of clustersselect # of clusters Technique then arbitrarily locates this #Technique then arbitrarily locates this #

and iteratively repositions them until optimumand iteratively repositions them until optimum

separability is achievedseparability is achieved

(Univ of Lethbridge)(Univ of Lethbridge)

METHODS_ ISODATAMETHODS_ ISODATA

Iterative Self-Organizing Data Analysis TechniqueIterative Self-Organizing Data Analysis Technique Iterative-repeatedly performs entire classification and Iterative-repeatedly performs entire classification and

recalculates statistics.recalculates statistics. Self-organizing refers to way in which it locates Self-organizing refers to way in which it locates

inherent data clusters.inherent data clusters. Minimum spectral distance formula is used to formMinimum spectral distance formula is used to form

clustersclusters

(Univ of Lethbridge)(Univ of Lethbridge)

ISODATA contdISODATA contd

Means shift with each iterationMeans shift with each iteration Until eitherUntil either

1. Maximum # of iterations achieved, OR1. Maximum # of iterations achieved, OR

2. Maximum percentage of unchanged pixels has2. Maximum percentage of unchanged pixels has

been reached between 2 iterationsbeen reached between 2 iterations

(Univ of Lethbridge)(Univ of Lethbridge)

K-MeansK-Means15 Means Selected, 3 Iterations15 Means Selected, 3 Iterations

Sample Location Sample Location

Same Area on ImageSame Area on Image

RESULTSRESULTSIsodataIsodata

3 Iterations, Sample Location3 Iterations, Sample Location

Same Area on ImageSame Area on Image

Supervised ClassificationSupervised ClassificationMaximum Likelihood, Sample Maximum Likelihood, Sample

LocationLocation

Same Area on ImageSame Area on Image

DiscussionDiscussion

Comparisons made by observing linked Comparisons made by observing linked images of each classification and orthophotoimages of each classification and orthophoto

Then determining which classification bestThen determining which classification best

fit the affected orthophoto vegetationfit the affected orthophoto vegetation

SummarySummary

Supervised maximum likelihood classification Supervised maximum likelihood classification seems to best classify the dataseems to best classify the data

Unsupervised Isodata classification was Unsupervised Isodata classification was

second bestsecond best Thirdly, Unsupervised K-Means classificationThirdly, Unsupervised K-Means classification

However, no methods could separate water However, no methods could separate water from diseased vegetationfrom diseased vegetation