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