IMAGE CLASSIFICATION APPROACHES FOR MAPPING ARUNDO DONAX ALONG THE SAN ...

100 Pages·2012·5.73 MB·English

IMAGE CLASSIFICATION APPROACHES FOR MAPPING ARUNDO DONAX
ALONG THE SAN DIEGO RIVER
USING HIGH SPATIAL RESOLUTION IMAGERY

A Thesis
Presented to the
Faculty of
San Diego State University

In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
in
Geography

by
Tim Tidwell
Fall 2012

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Copyright © 2012

by

Tim Tidwell

All Rights Reserved
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ABSTRACT OF THE THESIS

Image Classification Approaches for Mapping Arundo Donax Along the San Diego River
Using High Spatial Resolution Imagery
by
Tim Tidwell
Master of Science
San Diego State University, 2012

Exotic plant invasion is a major environmental issue and is particularly critical for
riparian ecosystems of California. Early detection of invasive plants is crucial for effective weed
management. In this study, high spatial resolution multispectral imagery was captured and
utilized for detecting the invasive plant Arundo donax. This invasive plant was detected and
delineated using imagery captured with a digital color infrared imaging sensor mounted on an
unmanned aerial system (UAS) in August of 2011 and a light manned aircraft along the San
Diego River in May 2009. The multispectral image data are characterized by 12 cm and 81cm
spatial resolutions respectively with spectral coverage in the visible (V) and near infrared (NIR)
portions of the light spectrum. Classification techniques were tested to exploit different attributes
of the very high spatial resolution imagery. Three classification approaches were evaluated
including: (1) a per-pixel maximum likelihood approach using ERDAS Imagine software, (2) a
neighborhood based or pseudo-object oriented approach using Feature Analyst software, and (3)
a true object-based approach using Imagine Objective software. To test the reliability and
accuracy of the three different classification routines, the accuracy assessment included a
stratified random sampling design scheme utilizing a 16 m point-to-point distance rule.
Results indicate A. donax detection varied substantially throughout both study sites,
between image datasets, and across a range of image classification algorithms. A. donax exhibits
a wide range of biophysical characteristics within the riparian environment which make it
difficult to determine a single optimal detection routine. Results derived from the statistical
accuracy assessment suggest that: (1) A. donax patches as small as 3.5 m2 can be detected by off-
the-shelf multispectral imaging sensors aboard low cost, flexible imaging systems based on
unmanned aircraft systems (UAS) and light-sport aircraft (LSA) platforms, (2) a maximum
likelihood classifier can accurately detect expansive A. donax colonies using 81 cm multispectral
imagery, (3) the spatial contextual classifier of Feature Analyst was more efficient at detecting
small and mixed A. donax patches, (4) the object-based Imagine Objective classifier utilizing
image segmentation consistently yielded less accurate detection results, (5) ultra-high spatial
resolution imagery obtained by a UAS can achieve similar A. donax detection results compared
to coarser spatial resolution imagery obtained by a manned LSA in small and sparse
communities, and (6) detection accuracy of all classification routines decreases when A. donax
distributions are sparse, relatively small and mixed within other riparian vegetation types. Based
on these findings an optimal routine to map A. donax would utilize a maximum likelihood
classifier to accurately detect existing, large colonies combined with the spatial contextual
classifier of Feature Analyst to more efficiently map small and sparse distributions.
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TABLE OF CONTENTS
PAGE
ABSTRACT……………………………………………………………………………...iv
LIST OF TABLES……………………………………………………………………...viii
LIST OF FIGURES……………………………………………………………………....ix
ACKNOWLEDGEMENTS………………............................…….…………….....….....xi
CHAPTER
1 INTRODUCTION……………………………………………..............……………...1
2 BACKGROUND AND LITERATURE REVIEW……............………..…………….4
Arundo Donax…...…………….............…………………………..……………..4
Remote Sensing and Mapping of Invasive Species……............…..…………….7
Remote Sensing of Arundo Donax……………............………..………………..9
Unmanned Aerial Systems (UAS), Light-Sport Aircraft (LSA),
Multispectral Sensors and Data………………………………...........................10
Range of Classification Routines………………..…………....………...............10
3 PROBLEM STATEMENT AND RESEARCH QUESTIONS…..…………............12
Research Questions…………………………………..……............…………...12
Goals and Objectives………………………………………..………............….13
4 RESEARCH METHODS.............................................................................................14
Study Sites..........................................................................................................14
Data.....................................................................................................................19
Image Data...........................................................................................19
Invasive Species Distribution Maps.....................................................20
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Reference Data.....................................................................................20
Image Preprocessing...........................................................................................24
Geometric Correction and Georeferencing..........................................24
Radiometric Correction........................................................................26
NEOS MS-4100 Image Dataset...............................................26
LOUIS UAS Image Dataset.....................................................30
Image Classification.........................................................................................36
ERDAS Imagine Maximum Likelihood Classifier..............................36
Feature Analyst Machine Learning Classifier.....................................37
Admiral Baker Golf Course Site: NEOS MS-4100
Image Dataset..........................................................................38
Cactus County Park Site: LOUIS UAS
Image Dataset...........................................................................40

Cactus County Park Site: NEOS MS-4100
Image Dataset..........................................................................42

Imagine Objective Object-Based Approach........................................43
Accuracy Assessment......................................................................................46
Calibration and Validation Polygon Selection.....................................46
Proportion of non-Arundo Polygons....................................................49
MMU, Sampling Unit and 16 Meter Distance Rule............................50
Pooling of NEOS MS-4100 Samples...................................................52
5 RESULTS AND DISCUSSION...................................................................................53
Results..............................................................................................................53
Visual Assessment...............................................................................53
Accuracy Assessment Results..............................................................59
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Overall Accuracy.....................................................................59
Producer's and User's Accuracies............................................62
Discussion........................................................................................................64
Study Site Characteristics....................................................................64
Detection Capability of Image Datasets..............................................65
Detection Capability of Classification Routines..................................67
6 CONCLUSIONS..........................................................................................................70
Summary of Findings.......................................................................................70
Limitations and Recommendations..................................................................73
Research Significance......................................................................................74
REFERENCES........................................................................................................................76
APPENDICES
A ACCURACY ASSESSMENT TABLES.....................................................................82
B CLASSIFICATION PRODUCTS................................................................................86
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LIST OF TABLES
PAGE
Table 1. Data Sources. All sources of data used or generated for this study..................... ......23
Table 2. The description and value of each cue metric used in Imagine Objective
for each image dataset at each site...............................................................................45
Table 3. Error matrix of the Feature Analyst classification product of the Admiral
Baker Golf Course site using NEOS MS-4100 image data.........................................83
Table 4. Error matrix of the Feature Analyst classification product of the Cactus
County Park site using LOUIS image data..................................................................83
Table 5. Error matrix of the Feature Analyst classification product of the Cactus
County Park site using NEOS MS-4100 image data...................................................83
Table 6. Error matrix of the Imagine Objective classification product of the Admiral
Baker Golf Course site using NEOS MS-4100 image data.........................................83
Table 7. Error matrix of the Imagine Objective classification product of the Cactus
County Park site using LOUIS image data..................................................................84
Table 8. Error matrix of the Imagine Objective classification product of the Cactus
County Park site using NEOS MS-4100 image data...................................................84
Table 9. Error matrix of the Maximum Likelihood classification product of the
Admiral Baker Golf Course site using NEOS MS-4100 image data...........................84
Table 10. Error matrix of the Maximum Likelihood classification product of the
Cactus County Park site using LOUIS image data......................................................84
Table 11. Error matrix of the Maximum Likelihood classification product of the
Cactus County Park site using NEOS MS-4100 image data.......................................85
Table 12. Error matrix of the Maximum Likelihood classification product with
pooled samples of both sites using NEOS MS-4100 image data.................................85
Table 13. Error matrix of the Feature Analyst classification product with
pooled samples of both sites using NEOS MS-4100 image data.................................85
Table 14. Error matrix of the Imagine Objective classification product with
pooled samples of both sites using NEOS MS-4100 image data.................................85
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LIST OF FIGURES
PAGE
Figure 1. A. donax in the environment. (a) Healthy green stand along a river,
(b) flowering patch, (c) A. donax shading the in stream environment
and (d) yellowing of dormant patch during winter........................................................5
Figure 2. Study sites for this research. (a)The location of the both study sites in red
within San Diego County, (b) both study sites within the southern section
of San Diego River in yellow, NEOS MS-4100 multispectral image data of,
(c) the Admiral Baker Golf Course site and, (d) Cactus County Park site
acquired on May 25, 2009...........................................................................................16
Figure 3. Indicates the presence of invasive species including A. donax in the
San Diego River Watershed (courtesy of the San Diego River Conservancy)............17
Figure 4. A map overlaid onto a CIR digital orthoimage depicts widespread
occurrence of invasive species including A. donax within the Admiral Baker
Golf Course study site (courtesy of the San Diego River Conservancy).....................18
Figure 5. A. donax reference polygons are numbered within the four section grid
overlaid onto the NEOS MS-4100 CIR multispectral imagery. Polygons
from each section were selected randomly to create the stratified random
sampling design scheme..............................................................................................22
Figure 6. 14mm lens CIR 47-Image Averages of 30% reflectance panel. (a) A 47
image stack corresponding to the near infrared (NIR) band wavelength
taken by the MS-4100 camera system. (b) 47 image stack corresponding
to the red band wavelength. (c) 47 image stack corresponding to the green
band wavelength. Note the substantial difference in DN values near the
center and edges of all images.....................................................................................29
Figure 7. Band 2 Bi-directional reflectance modeled zonal mean correction mask.
This band 2 correction mask was generated from the modeled means of
each of the 20 sectors using a 3rd order polynomial trend line. It has been
smoothed three times to minimize the cross-edge sector effect..................................34
Figure 8. Comparison of LOUIS multispectral image of the Cactus County
Park site before and after application of 20-sector modeled zonal mean
correction mask. (a) P1030472 original river image. (b) P1030472 river
image corrected using 20-sector modeled zonal mean correction mask.
Note the overall increase in brightness values on the right side of the
corrected image (b). Also note the cross-sector edge effect is not noticeable
due to using the 51x51 convolution filter....................................................................35
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Figure 9. A. donax maps for the Admiral Baker Golf Course site. (a) Field derived
reference maps of A. donax locations in green, non-arundo vegetation
locations in blue and riparian zone boundary, (b) Maximum Likelihood A. donax
classification product in yellow, (c) Feature Analyst A. donax classification
product in yellow, and (d) Imagine Objective A. donax classification product
in yellow.......................................................................................................................55
Figure 10. A. donax maps of the Cactus County Park site using NEOS MS-4100
image data. (a) Field derived reference maps of A. donax locations in green,
non-arundo vegetation locations in blue and riparian zone boundary, (b)
Maximum Likelihood A. donax classification product in yellow, (c) Feature
Analyst A. donax classification product in yellow, and (d) Imagine Objective
A. donax classification product in yellow....................................................................56
Figure 11. A. donax maps of the Cactus County Park site using LOUIS image data.
(a) Field derived reference maps of A. donax locations in green, non-arundo
vegetation locations in blue and riparian zone boundary, (b) Maximum
Likelihood A. donax classification product in yellow, (c) Feature Analyst
A. donax classification product in yellow, and (d) Imagine Objective A. donax
classification product in yellow...................................................................................57
Figure 12. A. donax detection accuracy at the Admiral Baker Golf Course site
using NEOS MS-4100 image data and pooled samples of both sites..........................61
Figure 13. A. donax detection accuracy at the Cactus County Park site using
NEOS MS-4100 and LOUIS image data.....................................................................61
Figure 14. A. donax maps for the Admiral Baker Golf Course site. (a) Field derived
reference maps of A. donax locations in green, non-arundo vegetation
locations in blue and riparian zone boundary, (b) Maximum Likelihood A. donax
classification product in yellow, (c) Feature Analyst A. donax classification
product in yellow, and (d) Imagine Objective A. donax classification product
in yellow.......................................................................................................................87
Figure 15. A. donax maps of the Cactus County Park site using NEOS MS-4100
image data. (a) Field derived reference maps of A. donax locations in green,
non-arundo vegetation locations in blue and riparian zone boundary, (b)
Maximum Likelihood A. donax classification product in yellow, (c) Feature
Analyst A. donax classification product in yellow, and (d) Imagine Objective
A. donax classification product in yellow....................................................................88
Figure 16. A. donax maps of the Cactus County Park site using LOUIS image data.
(a) Field derived reference maps of A. donax locations in green, non-arundo
vegetation locations in blue and riparian zone boundary, (b) Maximum
Likelihood A. donax classification product in yellow, (c) Feature Analyst
A. donax classification product in yellow, and (d) Imagine Objective A. donax
classification product in yellow...................................................................................89