Transcript Using GIS to

Detecting Emerald Ash Borer Infestation with
Hyperspectral data using Spectral Mixture Analysis
Silvia Petrova
Acknowledgment : The data for this
analysis is provided from Clark Labs.
Tools: All analysis are performed with
Idrisi Kilimanjaro.
Advanced Remote Sensing – Spring 2004
Instructor: John Rogan
Objective
Background
Study area
The objective of this project is to develop a methodology for classification of healthy, affected and
dead ash trees using hyperspectral images collected from an area west of Detroit, Michigan, USA.
According to the literature research, hyperspectral remote sensing is being used for detecting the
early stages of infestation or disease over a large area. It could provide methods for more quickly
and efficiently assessing the pest infestation than by ground observation.
Spectral Mixture Analysis (SMA) was performed with set of 107 hyperspectral bands to separate
four endmembers: tree cover, green grass, senescent grass and non-vegetation. In addition, ground
truth data was collected and used in the analysis to identify the range of membership for healthy,
affected and dead ash trees from the forest fraction.
Hyperspectral data
Ground
Truth Data
Principle Component
Analysis
128 bands,
Spectral
resolution
0.4-2.5µm
The study area is located south of the village
of Brooklyn in Michigan. The village is
located in the beautiful Irish Hill section
of southern Jackson County. The landscape
offers a variety of scenes from farmland, dotted by many lakes,
to the state forest, which occupies a significant portion of the
northeastern corner.
The galleries excavated
by EAB larvae disturb
the transport of water
and nutrients within the
tree. Decline of the
canopy continues until
the tree is dead.
According to the Michigan Department of Agriculture on July 11,
2003, the area around the village of Brooklyn was included in
the list of isolated infestation areas by Emerald Ash Borer (EAB).
For the past several years, ash trees have been declining
and dying by the hundreds of thousands in southeast
Michigan. In the spring of 2002, scientists discovered that
a small green beetle was causing the destruction. The
insect was identified as Agrilus planipennis with the help
of experts from Eastern Europe. The US scientists named
it “Emerald Ash Borer” (EAB). This beetle is native to
eastern Asia, but not previously found in North America.
The insect destroys ash trees which has significant
economic impact in Michigan.
Source : http://www.msue.msu.edu/reg_se/roberts/ash/
Data
Most adult
beetles live for
two to four
weeks. They are
active fliers and
occasionally eat
small amounts of
foliage.
Unsupervised
classification on PCA
Tree Cover Area
Green vegetation is incorrectly
classified as tree cover.
Masked out image with
non-tree cover area.
FCC Bands 10 ,16, 28
The hyperspectral imagery was acquired on September 05, 2003 by Earth Search
Sciences, Inc. using a Probe-1 spectrometer, which covers a 128 channel swatch range
from 0.4µm to 2.5µm. The raw data was processed and imported into Idrisi Kilimanjaro
software, where the data was rescaled in the range of 0 to 1. Additionally, ground truth
data was collected for ash trees with different health status.
No geometric correction was performed.
Methodology:
Band 119
An unsupervised classification was performed on the first seven PCA components. Five
classes were derived from 20 clusters in order to define an area without tree cover.
Mid-point 2.3µm
Band 77
Mid-point 1.5µm
The hyperspectral images were masked out with an area that is not tree covered.
Spectral Mixture Analysis was performed on masked out images.
C3
Band 28
Mid-point 0.83µm
The ground truth data
was collected from
urban and forest area.
Band 16
Mid-point 0.65µm
Band 10
Ground truth data was used to determine the membership of different health status
of the ash trees.
C2
C1
Mid-point 0.56µm
Tree Cover
Fraction
Non vegetation
Fraction
Senescent Grass
Fraction
Green Grass
Fraction
Conclusion and Discussions
A linear mixture modeling was performed with
masked out hyperspectral images in order to
describe mixed pixels contained in the tree
scene. This is with regards to the spectral
variability between the selected endmembers.
HYPERUNMIX module is applied to separate
tree cover from the other spectral components
that have not been separated by unsupervised
classification.
Ground truth data was not sufficient (10 points) in order to
allow extraction of endmembers for different health status of
ash trees on the scene. The ground truth data was used to
determine the range of distribution of percent membership
for different health status trees from the tree cover fraction.
The box-plot diagram bellow shows a slight difference in the
range of membership for affected and dying trees. The
healthy trees have high values of membership in the fraction.
As a result four fraction images were produced:
Tree cover, Non-Vegetation, Senescent Grass
and Green Grass fraction.
Mean and Standard Deviation of Endmembers
The graph on
0.8
the right shows
0.6
that the Green
0.4
and Senescent
0.2
Vegetation
0
are not well
separable.
The result of above
analysis indicates
that detection of
EAB infestation in
the study area is
difficult because of
similarity in the
reflectance from
affected and dying
trees.
Forest
Green
Vegetation
Yellow
Vegetation
Non Vegetation
Mean
0.165
0.101
0.084
0.649
Standard Dev
0.282
0.221
0.203
0.328
Endmembers
SMA modeled the tree area with
a high value and green vegetated
area with a lower value.
SMA modeled some pixels
from the forest area as
non-vegetation.
SMA modeled senescent
grass among the forest area.
SMA modeled green grass
correctly with percent distribution
among the tree cover.
Percentage membership of Tree Fraction for Ash
Trees per status of health
Percentage membership
Mean/ Standard Dev
Spectral Mixture Analysis
1
0.8
0.6
0.4
0.2
0
Healthy
Ash
Affected
Ash
Dying
Ash