A COMPARISON OF LANDSAT, IKONOS AND RADARSAT SATELLITE IMAGERY FOR SUBURBAN LAND COVER MAPPING IN THE TOWNSHIP OF LANGLEY, BRITISH COLUMBIA Sarbjeet Kaur Mann B.Sc., University of Victoria 1999 RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTEROFRESOURCEMANAGEMENT in the School of Resource and Environmental Management Report No. 356 O Sarbjeet Kaur Mann 2004 SIMON FRASER UNIVERSITY April 2004 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author
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A COMPARISON OF LANDSAT, IKONOS AND RADARSAT SATELLITE IMAGERY FOR SUBURBAN LAND COVER MAPPING
IN THE TOWNSHIP OF LANGLEY, BRITISH COLUMBIA
Sarbjeet Kaur Mann
B.Sc., University of Victoria 1999
RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTEROFRESOURCEMANAGEMENT
in the School of Resource and Environmental Management
Report No. 356
O Sarbjeet Kaur Mann 2004
SIMON FRASER UNIVERSITY
April 2004
All rights reserved. This work may not be reproduced in whole or in part, by photocopy
or other means, without permission of the author
Approval
Name:
Degree:
Title of Research Project:
Report No.
Examining Committee:
Chair:
Date Approved:
Sarbjeet Kaur Mann
Master of Resource Management
A comparison of Landsat, IKONOS and RADARSAT satellite imagery for suburban land cover mapping in the Township of Langley, British Columbia
Marcela Olguin-Alvarez
Dr. Kristina D. Rothley, Assistant Professor School of Resource and Environmental Management Simon Fraser University Senior Supervisor
Dr. Suzana Dragicevic, Assistant Professor Department of Geography Simon Fraser University Committee Member
Pamela Zevit, Co-ordinator Greater Vancouver Region Biodiversity Strategy BC Ministry of Water, Land & Air Protection Committee Member
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Bennett Library Simon Fraser University
Burnaby, BC, Canada
Increasing pressure from urban growth is placing heavy demands on local
planners to ensure that biodiversity is maintained in the Greater Vancouver Regional
District. Tools and approaches for identifying and mapping the remaining natural areas
are necessary. Traditionally, planners have identified land cover by aerial surveys,
which are costly, time consuming and conducted on an as-needed basis. The current
study tests and compares the feasibility of medium resolution Landsat (ETM+) and high-
resolution IKONOS and RADARSAT satellite imagery for identification of land cover
(coniferous, deciduous, disturbed, water and wetland) at a study site in the Township of
Langley, British Columbia. Preliminary analysis showed that overall accuracy results for
the classified RADARSAT image were marginal (64%). RADARSAT is therefore
excluded from the main analysis. Maximum likelihood classification of principal
components is used to classify the Landsat and IKONOS images. Air-photo interpreted
polygons are used as reference data. Kappa analyses show that because of its
additional mid-IR bands, the classified Landsat image has a significantly higher overall
classification accuracy (79.8%) than IKONOS (70.7%). Overall accuracy increased with
increasing minimum polygon size of the reference data. The highest classification
accuracy (87.6%) was attained for the classified Landsat image when it was evaluated
against test points from reference data polygons larger than 0.216ha.
iii
Dedication
To my mother, for being everything a mother is supposed to be.
Acknowledgements
Kristina Rothley has been a great mentor and I thank her for her generous
guidance. I also thank Suzana Dragicevic and Pamela Zevit for their excellent advice
and suggestions, and Dan Buffet, Arthur Roberts, Rob Knight, Marcela Olguin-Alvarez,
Ilona Naujokaitis-Lewis, Billie Gowans and the REM Departmental Staff for their
assistance.
The RADARSAT images were obtained through the Canadian Space Agency
and RADARSAT International administered RADARSAT-1 Data for Research Use
program. Air-photo interpreted reference polygons were supplied by the Langley
Environmental Partners Society. Funding for this project was provided by Simon Fraser
University Graduate Fellowships and Applied Sciences Graduate Fellowships, and the
BC Ministry of Water, Land & Air Protection.
Finally, I thank my family and friends for their encouragement and support.
... ........................................................................................................................ Abstract III
Dedication ..................................................................................................................... iv
Acknowledgements ....................................................................................................... v
Table of Contents ......................................................................................................... vi . . List of Tables ............................................................................................................... VII
... List of Figures ............................................................................................................ VIII
List of Acronyms .......................................................................................................... ix
Chapter One: Introduction ........................................................................................... I 1 . 1 Context of Research ............................................................................................. 1 1.2 Research Objectives .............................................................................................. 3
Chapter Two: Methods .................................................................................................. 5 2.1 Study Site Selection ............................................................................................... 5 2.2 Image Acquisition ................................................................................................... 6 2.3 Image Pre-processing ............................................................................................ 7 2.4 Classification Scheme Development ...................................................................... 8 2.5 Creation of Training Data ....................................................................................... 8
4.2 Misclassification and Individual Class Performance ............................................. 19 4.3 Other Sources of Error ......................................................................................... 21
.................................................................................... 4.3.1 Co-registration Errors 22 4.3.2 Change in Land Cover ................................................................................... 22 4.3.3 Errors in Reference Data ............................................................................... 23 4.3.4 Boundary Error .............................................................................................. 25
Characteristics of the satellite imagery. ......................................................... 42
Land cover classification scheme. ................................................................. 43
........................... Area (ha) of the training regions for each land cover class. 44
Percentage (%) of the study site identified as each land cover class for ............................................... each air-photo interpreted reference data set. 45
Error matrices for the classified Landsat image (7 original classes; all test points used regardless of the size of the reference data polygons; test point sampling interval = 100m). ............................................................. 46
Error matrices for the classified IKONOS image (7 original classes; all test points used regardless of the size of the reference data polygons; test point sampling interval = 100m). ............................................................. 48
Error matrices for the classified Landsat image as evaluated against .................... interpretation 4 (5 classes; test point sampling interval = 100m) 50
Error matrices for the classified Landsat image as evaluated against .................... interpretation 3 (5 classes; test point sampling interval = 100m) 52
Error matrices for the classified IKONOS image as evaluated against .................... interpretation 4 (5 classes; test point sampling interval = 100m) 54
Table 10. Error matrices for the classified IKONOS image as evaluated against interpretation 3 (5 classes; test point sampling interval = 100m) .................... 56
Table 11. Error matrices for the classified Landsat image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 1 OOm). ........................................................................................................... 58
Table 12. Error matrices for the classified IKONOS image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 1 OOm). .......................................................................................................... .60
Table 13. Error matrices for the classified Landsat image as evaluated against .................... interpretation 4 (5 classes; test point sampling interval = 150m) 62
Table 14. Error matrices for the classified IKONOS image as evaluated against .................... interpretation 4 (5 classes; test point sampling interval = 150m) 64
Table 15. Z-statistic values for kappa analysis comparisons between error matrices. ...................................................................................................... .66
Table 16. Principal components of the Landsat and IKONOS satellite images. ............. 70
Table 17. Habitat types identified by Lee & Rudd (2002) as important for the ..................................................... conservation of biodiversity in the GVRD. 71
vii
List of Figures
Figure 1. Maps of the GVRD and the Langley study site. ............................................. 76
Figure 2. The classified Landsat image of the study site showing the seven original land cover classes. ........................................................................... 77
Figure 3. The classified IKONOS image of the study site showing the seven original land cover classes. ........................................................................... 78
Figure 4. The classified Landsat image of the study site showing the disturbed land cover class. ........................................................................................... 79
Figure 5. The classified IKONOS image of the study site showing the disturbed land cover class. ........................................................................................... 80
Figure 6. Overall accuracy (%) as a function of the minimum polygon size (ha) of the reference data. ........................................................................................ 81
Figure 7. Producer's accuracies for the land cover classes. ......................................... 82
Figure 8. Producer's accuracies for individual land cover classes as a function of .......................................... the minimum polygon size of the reference data. 83
Figure 9. Scattergram of the training regions used in the classification of the Landsat image. ............................................................................................. 84
Figure 10.Scattergram of the training regions used in the classification of the IKONOS image. ............................................................................................ 85
Figure 11 .Close-up showing differences in the detail and resolution of reference data sets interpretation 4 and interpretation 3, and the corresponding
.................................. areas on the classified Landsat and IKONOS images. 86
List of Acronyms
GFOV: Ground Field of View
GVRD: Greater Vancouver Regional District
LEPS: Langley Environmental Partners Society
MWLAP: Ministry of Water, Land & Air Protection
NIR: Near Infrared
RMS: Root Mean Square
Chapter One: Introduction
1 .I Context of Research
The Greater Vancouver Regional District (GVRD), a 3292 km2 area in south-
western British Columbia, is situated within one of the most productive and diverse
natural settings in Canada. The Fraser River is the richest salmon producing freshwater
river in the world and on average over 100 000 salmon spawn in streams within the
GVRD. The estuary of the Fraser River is a stopover point for several million birds
annually as they migrate along the Pacific Flyway. North of the Fraser River estuary and
the Lower Fraser Valley, forested uplands, mountains, valleys and river systems provide
habitat for numerous plant and animal species. The GVRD, however, is also located
within one of the fastest growing regions of North America and has emerged to become
the premier commercial, industrial and transportation center in western Canada. The
population of the region now exceeds two million (2,016,000 people; BC Ministry of
Water, Air & Land Protection 2001). By 2021, an additional 800,000 people are
expected. As this region has become more populated, significant changes have taken
place on this landscape. Conversion of land for housing, industrial development,
agriculture and other uses has resulted in fragmentation and alteration of much of the
area that once provided habitat to a diverse array of species (BC Ministry of Water, Air &
Land Protection 2001 ).
Conservation planning and policy for the protection of biodiversity and its
associated social and economic values has been identified as a priority at the federal,
provincial and regional levels in Canada (Environment Canada 1998). As a result of this
policy direction and the future expected growth in the region, the GVRD Biodiversity
Conservation Strategy was started in 1999 with an objective of assessing the status of
remaining green spaces and linkages in the GVRD and developing a strategy for
preserving and enhancing biodiversity throughout the region. Although green spaces in
urban areas may seem to be small and insignificant contributors to biodiversity,
collectively these areas can have a major effect on the integrity of urban ecosystems
and can represent a surprisingly high degree of biodiversity (Lee & Rudd 2002; Niemela
1999). Natural areas in urban settings provide habitat for plants and animals and
conduits for their dispersal. Equally important, natural areas, greenways and open
spaces provide human services such as storage and filtration for surface and
groundwater and opportunities for recreation.
The need to identify and protect natural areas in the GVRD and other urban
areas is urgent and requires accurate, up-to-date land cover maps. Reliable land cover
information, especially in map form, is not readily available for the GVRD nor is it easy to
acquire. An objective of the GVRD Biodiversity Conservation Strategy is to support
development of a comprehensive land cover map of the entire GVRD. These baseline
maps will be further used to produce maps describing currently undeveloped sites
according to their value as habitats and corridors for plants and animals, as reservoirs
for biodiversity, and as providers of human services (recreation and water quality).
Ultimately, these maps will serve as input to the planning process of the local 21
member municipalities comprising the GVRD, and form a central repository of easily
accessible information.
To create these maps tools are required to analyze and update spatial
information quickly and efficiently and to assess their accuracy. Remote sensing and
geographic information systems (GIs) are attractive options for the cost-effective
production of land cover maps. Because there is a high correlation between variation in
remotely sensed data and variation across the earth's surface remotely sensed data
provides an excellent basis for making maps of land cover (Lillesand & Kieffer 2000).
We use remotely sensed data to make maps because:
land cover maps derived from ground-based surveys are time-consuming and expensive to produce and become quickly outdated as the landscape is altered.
it offers a perspective from above (the 'bird's eye view'), allowing for a better understanding of spatial relationships at the landscape scale
it permits capturing types of data undectectable by the human eye such as the infrared portions of the electromagnetic spectrum, which allow for superior discrimination of certain land cover types (Congalton & Green 1999).
Remote sensing is available at a range of spatial and temporal scales and offers
a means for repetitive mapping of natural resources in a cost-effective manner. Its
application for sustainable resource management has been widely demonstrated and
the production of thematic maps, such as those depicting land cover, using an
appropriate image classification is one of the most common applications of remote
sensing (Foody 2002). Before remote sensing technology can be applied, however,
analysis is required to identify and refine appropriate procedures in order to produce
satisfactory mapping results for the region of interest (Green et al. 1994; Yang & Lo
2002). Further, if decisions based upon map information are to have reliable results,
then the accuracy of the maps must be known. Otherwise decisions based on these
maps may yield unexpected and unacceptable results (Congalton & Green 1999; Foody
2002).
1.2 Research Objectives
A critical component to ensuring effective management and conservation of
natural areas in the GVRD is an up-to-date, high-resolution spatial data set describing
current land cover (forest, water bodies, impervious surfaces, etc.). The automated
classification of satellite images can efficiently generate up-to-date land cover maps.
However, given the accuracy required for the land cover maps, the costs associated with
obtaining the satellite images, and the challenges presented by spectrally
heterogeneous urban landscapes, it is first necessary to demonstrate the accuracy of
this technique. In this study images from three satellites, I ) the Landsat Enhanced
Thematic Mapper Plus (ETM+) carried by the Landsat 7 satellite, 2) the IKONOS carried
by IKONOS 2, and 3) the Synthetic Aperture Radar (SAR) carried by RADARSATI, are
compared for identification of land cover types at a study site in the Township of
Langley, British Columbia, to determine the most effective imagery for discerning land
cover in the region.
The primary motivation and goal for this project is to provide key technical advice
to support management directions for biodiversity conservation in the GVRD. The
specific research objectives guiding this study are:
1. To determine the mapping accuracy of each classified satellite image relative to reference data, using a commonly applied classification method.
2. To compare how the classified satellite images perform relative to each other
3. To determine the accuracy with which each land cover class is mapped.
4. To analyze how decreasing the resolution of the reference data affects the accuracy of the classified satellite images.
5. To analyze how accuracy changes with different sources of reference data.
This report describes the fundamental procedures used to extract land cover
data from remotely sensed images and assess accuracy of the land cover maps that are
produced. Chapter 2 begins with the classification and accuracy assessment methods.
Chapter 3 provides the analysis results. Chapter 4 is devoted to the discussion and
provides recommendations for future research and for management.
Chapter Two: Methods
2.1 Study Site Selection
The GVRD lies in the Fraser Lowlands, a physiographic area that extends from
the Georgia Strait to Chilliwack (Figure 1). This area consists of extensive upland
separated by wide, flat-bottomed valleys. These low elevation lands are mostly in the
Coastal Western Hemlock biogeoclimatic zone. The GVRD is also situated in the Coast
Forest Region where the dominant natural tree species are coastal Douglas fir, western
hemlock and western red cedar (Meidinger & Pojar 1991).
The Langley study site was chosen as the focus for this study because the
Langley Environmental Partners Society (LEPS) provided a ground-truthed land cover
map for this area in the form of GIs-based polygons (minimum polygon size = 0.01 ha)
based on aerial photograph interpretation of 1:20000 air photos from 2002. This map
was to be used as the reference data in this study. Five percent of the polygons in this
map were ground-truthed and the interpretation was approximately 80% correct
(Caroline Astley 2003, personal communication). Furthermore, the variety and relative
abundance of land cover classes in the bounds of the Langley study site are considered
characteristic of many other locations across GVRD. Langley is also one of the fastest
growing municipalities in the GVRD and therefore a high priority for the GVRD
Biodiversity Conservation Strategy. The study site (2.7km x 4.4 km) borders the
Canadian - US border and encompasses Little Campbell River Regional Park (Figure
1 >.
Topography in Langley varies from level areas to gently rolling hillsides to ravines
along major watercourses. Most of Langley has been logged or cleared, and treed
areas are now a mixture of second growth coniferous and deciduous trees. Langley is a
major agricultural community in the province and approximately three-quarters of the
municipality is in the Agricultural Land Reserve. The complex geological history of the
area has resulted in a variety of deposits, landforms and soil types, and this diversity in
soil types combined with the long growing season and proximity to the Vancouver
market results in production of a large variety of agricultural products (Township of
Langley 1979).
2.2 Image Acquisition
Summer and winter RADARSAT and Landsat images and a summer IKONOS
image were purchased for the study site (a winter IKONOS image was unavailable;
Table 1). The first IKONOS satellite was launched in 1999 and only more recent
publications describe its applicability for land cover mapping (Zanoni & Goward 2003).
In particular, mapping of impervious vs. non-impervious areas and forested vs. non-
forested areas has been successful, with reported overall classification accuracies
greater than 90% and 84% (Cablk & Minor 2003; Goetz et. al2003). Sugumaran et. al
(2002) reported an overall IKONOS classification accuracy of greater than 85% for the
mapping of seven different land cover types in Columbia, Missouri. A weakness of the
high resolution (4m pixels) IKONOS imagery is that clouds, a frequent occurrence in the
GVRD skies, can obscure the images. Further, spectral information is recorded in only
four bands.
The Landsat series of satellites is much older (the first Landsat satellite was
launched in 1972) and numerous published studies demonstrate the usefulness of
Landsat images for a wide range of thematic mapping (Lillesand & Kieffer 2000),
including land cover mapping in urban areas. Yang and Lo (2002) and Seto et. al (2002)
reported overall Landsat classification accuracies greater than 85% for the mapping of
land-uselland cover change in urban areas. Landsat has moderate resolution 30 meter
pixels, detects radiation in a larger range of the electromagnetic spectrum and may also
be distorted by clouds. The most cloud free IKONOS and Landsat images were
purchased for this study.
RADARSAT was designed for ice reconnaissance, coastal surveillance, land
cover mapping, and agricultural and forestry monitoring (Lillesand & Kieffer 2000).
Applications of RADARSAT for mapping of wetlands have been widely studied and
overall classification accuracies greater than 80% have been reported (Parmuchi et. a1
2002). The RADARSAT-1 sensors generate and record radiation in a single band of the
microwave range, providing high resolution 8m pixels regardless of weather conditions
but do not allow for the statistical, multi-band land cover discrimination possible with
multi-spectral images. However, previous research has found that classification
accuracies based on Landsat TM data may increase when RADARSAT image tone and
texture data is included (Presutti et al. 2001).
2.3 Image Pre-processing
The Landsat, IKONOS and RADARSAT satellite images were clipped to match
the boundaries of the Langley study site and then georeferenced to the reference data
(polygons derived from air-photo interpretations) by applying a polynomial transformation
(ER Mapper 2002). Root mean square (RMS) errors were kept below 1 pixel. Pixel
sizes for the satellite images are provided in Table 1.
2.4 Classification Scheme Development
Classification schemes are fundamental to any mapping project because they
reduce the total number of land cover types that must be dealt with to some reasonably
small number. The classification scheme (Table 2) used to describe land cover in the
test sites was based on a ground truthed land coverlland use map recently derived by
LEPS for the Langley study site, but modified to accommodate the anticipated uses of
the land cover maps to be produced. The detail of the adopted scheme was also
dictated by the land cover types that can be discerned with satellite data. The scheme
was relatively simple so that it could be applied across the GVRD, but exhaustive and
exclusive with hierarchical elements.
2.5 Creation of Training Data
To run the statistical classifications of the satellite images, areas of known land
cover in the images, called training regions, must first be identified. For this project, the
training data set was derived by comparing polygons from air-photo interpretations to
Red-Green-Blue (RGB) colour composites of the images and to the results of
unsupervised classifications (maximum number of classes: 25) (ER Mapper 2002). A
small subset of the interpreted polygons was used as training data (Table 3). These
areas were distributed across the study site, and apart from a few exceptions, were
excluded from the accuracy assessment.
2.6 Image Classification
Land cover type was predicted for the study site using the standard maximum
likelihood classifier. Two RADARSAT (summer and winter) images, one IKONOS image
(summer), and two Landsat images (summer and winter) were analyzed using ER
Mapper 6.3 (ER Mapper 2002).
Before the RADARSAT images could be classified it was necessary to remove
speckle from the images. Mean spectral values and standard deviation statistics of the
different land cover training regions were used to determine the appropriate filter and/or
texture analyses to do this. The objective was to have distinct non-overlapping means
and confidence intervals for the spectral signatures of each class. Results (not reported)
showed that applying the Average 5x5 filter and extracting Maximum Probability texture
data allows for the best distinction between spectral means of the land cover training
regions in the RADARSAT images. Therefore, speckle was removed from the
RADARSAT images with the Average 5x5 filter, and Maximum Probability texture data
was extracted. A supervised classification (maximum likelihood enhanced) was then
performed on each RADARSAT image.
Before the supervised classification (maximum likelihood enhanced) was
performed on the Landsat and IKONOS images, principle components analysis was
used to derive new axes that would improve the explanatory power of the raw image
data (Singh & Harrison 1985). Two principal components were derived from the multi-
spectral bands of each image and were classified using the maximum likelihood
classifier. Two principal components explained 90% of the spectral variation in the
Landsat image and 99% of the spectral variation in the IKONOS image.
The summer Landsat image was also combined with the filtered summer
RADARSAT image and texture data, and classified using principal components to
determine if there was an improvement in classification accuracy. The winter and
summer Landsat images were also combined and classified in a similar manner.
2.7 Accuracy Assessment
Accuracy assessment is not an easy task as it is necessary to balance the
requirements for rigor and defensibility with practical limitations of cost and time. In this
study, reference data was collected for the study site through air photo interpretation.
Air photos are a good reference data source because they allow for more consistent
measurements over large areas as the interpretation is done in the laboratory with one
or a few well-trained interpreters rather than in the field by many, frequently volunteer,
observers (Congalton & Green 1999).
As I began the accuracy assessment of the classified images using the LEPS
reference data (described above), it soon became apparent that this reference data was
of limited use because the LEPS classification scheme was quite different to the one
adopted for this study. As a result three different individuals completed additional air-
photo interpretations of a 1 :24000 August 2002 colour air photo for the study site based
on the classification scheme that was used to generate the classified satellite images.
These air-photo interpretations were completed using table stereoscopes and grease
pencils, and the resulting low-resolution interpretations were later scanned and digitized.
These three reference data sets are in the form of GIs-based polygons and are referred
to as: interpretation 1, interpretation 2, interpretation 3. The exact spatial resolution of
these three reference data sets is not known. Later on, the 1 :24000 August 2002 air
photo of the study site was scanned to produce a digital air-photo with high resolution
4.1 m pixels. An on-screen interpretation of this digital air photo was completed to
produce an additional polygon-based reference data set, which is referred to as
interpretation 4. lnterpretation 4 has a minimum polygon size 0.01 2ha (1 1 m2)'.
Each of the 1 :24000 air-photo interpreted reference data sets differ in the amount
of coverage allocated to each land cover class in the study site (Table 4). lnterpretation
' Ground resolution is often incorrectly equated with Ground Field of View (GFOV). Spatial resolution is defined as the minimum separation of two objects that can be actually separated in an image. Separation requires at least one pixel to be between two separate objects. Thus the objects need to be more than twice the square root of two, times the GFOV, to be resolved. Thus a 4. lm pixel digital image offers 11 m spatial resolution (Hastings 2001).
I and interpretation 2 identified very few coniferous polygons compared to interpretation
3 and interpretation 4. Interpretation I also identified very few deciduous polygons. This
highlights the fact that different interpreters can introduce different degrees of error for
particular land cover types. Reference data sets interpretation I and interpretation 2
were not used in the accuracy assessment because based on my familiarity with the
study site, they lacked sufficient coverage of the coniferous and deciduous land cover
classes.
ArcView 3.2 (ESRI 2000) was used to complete the accuracy assessment. The
classification accuracy of the classified Landsat and IKONOS images was evaluated
using test points from interpretation 3 and interpretation 4 reference data sets. The test
points were distributed across the study site in a grid style where the points were spaced
a) 100m apart, and b) 150m apart. The required number of test points to be extracted
from the test point grid was calculated according to the following formula (Congalton &
Green 1999):
where:
land cover type with the greatest coverage in the study site
B - - a constant derived from the Chi-squared distribution
rri - - the proportion of land covered by i
bi - - the desired level of precision
Based on the formula above, I calculated that 717 was the minimum number of
test points required to adequately assess the accuracy of the classified images. For the
rarer classes where the test point grid did not yield at least 50 test points, I manually
added test points until 50 test points were attained for each class. The test point grid in
combination with the test points that were added manually yielded a total of
approximately 950 test points for the 100m grid. Of these 950 test points, up to 95
points were added manually to the water, wetland and coniferous categories. Similarly,
166 points were added manually to the approximately 550 points of the 150m grid.
Reference data test points in larger homogenous areas are more likely to be
correctly labelled by air-photo interpreters than test points in smaller heterogenous
areas. Furthermore, in satellite imagery, larger objects have proportionally fewer mixed
pixels and georeferencing inaccuracies than smaller objects and are more likely to be
correctly classified. For these reasons it is expected that as the minimum polygon size
of the reference data increases, so will overall accuracy results. To test this, I
successively assessed accuracy of the classified images using only those test points
that fell within minimum sized interpreted polygons of 0.024 ha, 0.096 ha, and 0.216 ha.
To complement the analysis, the IKONOS and Landsat classified satellite images
were also evaluated against the LEPS air-photo interpretation (from now on referred to
as LEPS interpretation) for the area where all three data sets overlapped (area
approximately 2.7km x 6.2km). Again, test points were distributed in a grid style where
the points were spaced 100m apart. Where a minimum of 50 test points was not
selected for rare classes, test points were added manually to reach this amount. LEPS
used different criteria for their classification scheme and only those LEPS polygons that
had labels analogous to labels in my classification scheme were included in the analysis.
These labels included: deciduous, coniferous, water, wetland, herbs, and soil. The
LEPS interpretation did not label any polygons analogous to impervious.
The classified RADARSAT images, the combined LandsatlRadarsat image and
the combined summerlwinter Landsat image were only evaluated against the LEPS
interpretation using an accuracy assessment methodology similar to the one described
above. Preliminary accuracy assessments against the LEPS interpretation data
indicated that the classified RADARSAT images performed poorly relative to the
IKONOS and Landsat images, with overall accuracy ranging from 54% to 64%.
Because of its poor preliminary performance, RADARSAT was excluded from further
analysis. Preliminary analyses also indicated that the classification of the Landsat image
with the addition of the RADARSAT data did not result in an improvement of overall
accuracy results. Further, the classification of the combined summer and winter Landsat
images did not result in an improvement in overall accuracy either. These classifications
were also excluded from further analysis.
In summary, a classified Landsat image and a classified IKONOS image were
evaluated in detail against test points from three reference data sets: 1) interpretation 4
(minimum polygon 0.012 ha), 2) interpretation 3 (minimum polygon size not known), and
3) the LEPS interpretation (minimum polygon size = 0.01 ha). For all of the reference
data sets, accuracy was also assessed using only the subset of test points that fell within
minimum sized interpreted polygons of 0.024 ha, 0.096 ha, and 0.216 ha. For all of the
analyses, the classified image and reference data labels for the test points were
compared to one another in an error matrix, from which the overall accuracies, user and
producer's accuracies, and kappa values were computed.
A kappa analysis is used in accuracy assessment for statistically determining if
two kappa values, and therefore if two error matrices, are significantly different. This
allows one to statistically compare two images, classification algorithms, etc., to
determine which produces statistically higher overall accuracy results. A kappa value is
computed for each error matrix and is a measure of how well the remotely sensed
classification agrees with the reference data (Bishop et al. 1975; Congalton & Green
1999). The measure of agreement is based on the difference between the actual
agreement between the classification and the reference data (as indicated by the major
diagonal) and the chance agreement which is indicated by the row and column totals. A
kappa value greater than 0.80 represents strong agreement, a value between 0.40 and
0.80 represents moderate agreement, and a value below 0.40 represents poor
agreement.
The kappa value for an error matrix is calculated as follows (Congalton & Green
1999):
Let k = the number of classes
i = row number
j = column number
n = total number of test points
nV = number of test points falling in the cell corresponding to row i and
column j
pii = nVln
Then let
(the actual agreement)
k
PC = 1 P~+P+, (the chance agreement) i = I
Finally,
Chapter Three: Results
Despite applying the Average 5x5 filter and extracting the Maximum Probability
texture data, it became apparent early on in the study that classified RADARSAT images
performed poorly relative to IKONOS and Landsat images. Preliminary accuracy
assessments against the LEPS interpretation indicated that overall accuracy for the
classified RADARSAT images ranged from 54% to 64%. Because of its poor preliminary
performance, RADARSAT was excluded from further analysis. Preliminary analyses
also showed that the classification of the Landsat image with the addition of the
RADARSAT data did not result in an improvement of overall accuracy results. The
classification of the combined summer and winter Landsat images did not result in an
improvement in overall accuracy either.
Individual class results for the classified Landsat and IKONOS images (Figures 2
& 3) are provided in Tables 5-1 2, as evaluated against interpretation 4, interpretation 3
and LEPS interpretation reference data sets. Several of the land cover classes tend to
have high reflectance (so called "bright" pixels). These include impervious surfaces,
soils and herbs (which often have lots of bare soil mixed in with the vegetation). These
"bright" feature classes have similar spectral properties resulting in their being confused
with one another and ultimately being misclassified in both the Landsat and IKONOS
images (Tables 5 and 6). For example, in the classified Landsat image 11 1 of the 132
impervious test points are misclassified as herbs, and 49 of the 56 soil test points are
misclassified as herbs (Table 5a). In the classified IKONOS image, out of the 132
impervious test points, 58 are misclassified as soil and 35 are misclassified as herbs.
Twenty-two of the 57 soil test points are misclassified as herbs (Table 6a). Because of
this confusion, the herbs, soil and impervious classes were merged. The resultant new
class was called disturbed and subsequent accuracy assessments and kappa analyses
were performed using this new class (Figures 4 & 5; Tables 7 to 12; Table 15). Bright
feature confusion has been reported in other studies (Sawaya et. al 2003) between
concrete, bare fields and recreational fields.
A kappa analysis showed that Landsat has a significantly higher overall
classification accuracy (79.8%) than IKONOS (70.7% overall accuracy) when evaluated
against all the test points from interpretation 3, interpretation 4 and the LEPS
interpretation (Figure 6; Tables 8a, IOa, 15a). Overall accuracy for both Landsat and
IKONOS was a function of the resolution of the reference data against which the
classified image was evaluated: overall accuracy increased with increasing minimum
size of the polygons in which the test points were located (Figure 6; Table 15b). The
highest overall accuracy (90.7%) was attained for the classified Landsat image when it
was evaluated against test points from LEPS interpretation reference polygons larger
than 0.21 6ha (Table I Id). This result exceeds the 85% level that was set as a target for
overall classification accuracy by Anderson et. al (1976), although not all classes exceed
70% accuracy. The highest accuracy level for IKONOS (80.2%) was also attained when
it was evaluated against test points from LEPS interpretation reference polygons larger
than 0.216ha (Table 12d).
When compared to accuracy assessments results assessed against
interpretation 4, overall accuracy results increased marginally when the classified
Landsat and IKONOS images were assessed against the LEPS interpretation and
decreased marginally when they were assessed against interpretation 3 (Figure 6; Table
15c). However, the differences were generally not significant. Interpretation 4 is
considered to be the most accurate reference data set since it has more precise higher
resolution polygons than interpretation 3, and since it was based on the same
classification scheme that was used to create the classified satellite images. The LEPS
interpretation was based on a classification scheme different to the one used in this
study and did not map impervious areas.
When all of the test points are considered, the error matrices (and derived
producer's and user's accuracy's) indicate that the match between the test points and
the classified IKONOS and Landsat images was poor for the water and wetland classes
(accuracy <56%) (Figure 7; Tables 7a & 8a). Both Landsat and IKONOS mapped
coniferous and deciduous areas reasonably well (60%-80%). Disturbed areas were
mapped very well by Landsat (92.2% accuracy) and reasonably well by IKONOS (76.9%
accuracy). Before the soil, impervious and herbs classes were merged, Landsat
mapped herbs very well (87.6% accuracy) and IKONOS mapped them poorly (56.5%
accuracy).
As already described, producer's and user's accuracies for each class generally
increased with increasing minimum size of reference polygons, especially for the
coniferous and deciduous classes (Figure 8; Tables 7 & 8). However, this was not the
case for the classification for wetlands, the accuracy of which remained around 50% for
Landsat and 30% for IKONOS, regardless of the size of the reference polygons. The
accuracy of the disturbed class for the classified Landsat image remained around 92%.
It was not possible to assess how the accuracy of water changed, as the number of test
points dropped dramatically with increasing minimum polygon size.
Overall accuracy results were not significantly different when evaluated against
test points spaced 150m apart (as opposed to 100m apart) for both the classified
Landsat and IKONOS image, regardless of the minimum size of the reference polygon
data (Tables 13, 14 & 15d).
Chapter Four: Discussion
In this chapter, after a brief explanation of the RADARSAT results, individual
class results and sources of error will be discussed. This discussion will provide
background information to explain the differences in performance between Landsat and
IKONOS. Recommendations to improve any future work on this project will follow. This
chapter ends with conclusions and recommendations for management.
4.1 Radarsat
Early analyses indicated that RADARSAT consistently performed poorly (overall
accuracy ~65%). The poor performance could be attributed to the fact that RADARSAT
captures information in a single band, as opposed to the multi-spectral Landsat and
IKONOS sensors. Artificially bright pixels caused by corner reflection may also be a
factor in the poor performance of RADARSAT (Lillesand and Kiefer 2000). A
considerable level of corner reflection was obvious throughout the RADARSAT images.
Buildings with distinctly vertical surfaces adjacent to distinctly horizontal surfaces
produced corner reflection as would be expected. These bright corners were simply
merged with the bright pixels normally associated with impervious surfaces. However,
corner reflection also occurred along the edges of forested patches that were adjacent to
disturbedlwetland patches. These bright corners were mistakenly classified as being
impervious surfaces. In a less disturbed landscape where the transition between land
cover classes would be smoother, these errors would be less likely to occur.
The poor RADARSAT results were supported by Presutti et al. (2001) who
reported that the use of Landsat data alone provided superior classification accuracy
compared to the use of RADARSAT data alone, and that the use of texture improved
RADARSAT classification marginally. Still, the filtered summer and winter RADARSAT
images offered revealing visual information on the study site. Building tops and water
bodies were easily discernable, and vegetated versus non-vegetated areas could be
readily distinguished.
4.2 Misclassification and Individual Class Performance
Suburban and urban environments represent one of the most challenging areas
for remote sensing analysis due to high spatial and spectral diversity of land cover types.
Major types of spectral confusion and misclassification can be identified in the current
study, especially in regards to the herbs category. In the classified Landsat image: (1)
impervious areas are misclassified as herbs, (2) soil is misclassified as herbs, (3)
wetlands are misclassified as herbs, (4) water is misclassified as herbs, and (5) water is
misclassified as coniferous (Table 5). In the classified IKONOS image: (1) impervious
areas are misclassified as herbs and soil, (2) soil is misclassified as herbs, (3) wetlands
are misclassified as herbs, (4) water is misclassified as coniferous, and (5) herbs are
misclassified as soil (Table 6).
The spectral variation in the herbs training region is very large (Figures 9 & 10)
for both IKONOS and Landsat, accounting for the confusion associated with this
category and the need to create the new disturbed class. An ideal scattergram should
have no spectral gaps and no spectral overlap between the class ellipses. The variation
for herbs is so large, that 'when in doubt' it makes statistical sense to label a pixel as
herbs as opposed to another class. One land cover class is not enough to describe the
variation within the herbs category. It is recommended that the existing class be split
further into more specific classes (i.e. sparse herbs, dense herbs, etc.) where practical,
to help alleviate spectral confusion. These new spectral classes representing a similar
class can be later regrouped (Ma et al. 2001). Training data for these more specific
classes should be collected through field surveys. Reference and satellite data should
also be collected at the same time since the herbs class changes dramatically between
the different seasons.
The analyst creating the reference data had familiarity with the site that let her
distinguish wetland from herbs. However, during the summer these wetlands are very
herbaceous and another analyst unfamiliar with the site would have likely labeled the
wetland areas as herbs. As indicated by Figures 9 & 10, it is indeed difficult to
distinguish the two classes spectrally, accounting for the poor accuracy results for the
wetland class.
For IKONOS, the spectral variation in the soil training region is also large (Figure
1 O), accounting for the confusion between impervious and soil, and herbs and soil. This
variation is not present for Landsat indicating that either Landsat is better than IKONOS
at distinguishing this land cover type or perhaps that improper areas were included in the
soil training regions for IKONOS. This highlights the need for ground-truthing of areas
chosen as training regions.
It is difficult and/or impossible for a statistically based classification algorithm like
the maximum likelihood classifier to label areas in the study site that have reflectance
values characterized by the unlabelled spectral zones in the scattergrams. These
reflectance values are not represented well enough by the training regions making it
difficult to statistically assign a land cover label to areas characterized by these
reflectance values. Such is the case for areas on the ground that are spectrally
characterized by the zone between the coniferous and water ellipses in the Landsat
scattergram (Figure 9). This accounts for the misclassification between these two
classes in the classified Landsat image. It is normally expected that these two classes
should be easy to distinguish spectrally (Lillesand and Kieffer 2000). The confusion is
less for IKONOS, as the gap between the coniferous and water ellipses is small in the
IKONOS scattergram (Figure 10). Part of the confusion between impen/ious and herbs
land cover classes can also be attributed to a gap between the two ellipses in the
Landsat scattergram.
The coniferous, deciduous, and disturbed classes were identified with reasonable
accuracy by Landsat, as was expected from the literature. IKONOS identified coniferous
and disturbed areas well. These results can be applied with a high level of confidence to
immediately derive a land cover map of suburban GVRD for these classes.
4.3 Other Sources of Error
Map inaccuracies or error can occur at many steps throughout any remote
sensing project. Accuracy assessment is conducted to understand the quality of map
information by identifying and assessing map errors (Congalton & Green 1999).
Accuracy assessment is never an easy task. It requires obtaining reference data of
higher quality with adequate coverage of space and classes to test a map. However, the
ability to obtain an ideal reference data set is constrained by practical limits of
technology, logistics, and cost (Crist & Deittner 2000). Disagreements between the
classified image and the reference data are typically interpreted as errors in the land
cover map derived from the remotely sensed data (Congalton 1991). This interpretation
has driven research that aims to decrease the error in image classification. This
research has typically focused on the derivation and assessment of different
classification algorithms. However, there are many other possible sources of error, in
addition to misclassification. These include co-registration errors, error in the reference
data, change in land cover between the collection date of the reference data and the
collection date of the satellite images, and difficulty in assessing boundary areas (Foody
2002).
4.3.1 Co-registration Errors
Even if the classified satellite image and the reference data are perfect, error can
result from misregistration of the two data sets (Czaplewski 1992; Stehman 1997a;
Foody 2002). This problem is most apparent in heterogeneous landscapes with a
complex land cover mosaic, such as the Langley study site (Scepan 1999). Locational
accuracy is important if we are trying to match up small polygons. Unfortunately, such
landscapes are frequently the ones for which it is most important to map and monitor
land cover. Without perfect co-registration, however, the confusion matrix may contain
errors due to misregistration as well as thematic mislabeling which will complicate the
interpretation of derived accuracy metrics. Co-registration error is assumed to be
minimal in the Langley study site, which was relatively flat (as noted above, all RMS
pixel errors were kept below 1 during co-registration).
However, as an example, several months were spent trying to analyze another
study site on the North Shore, BC. Satellite images were classified and air-photos were
interpreted as reference data. However, due to the higher elevations and complex
terrain of the North Shore study site, it proved impossible at that time to line up the two
data sets accurately enough to complete a proper accuracy assessment.
4.3.2 Change in Land Cover
It is unreasonable to report as errors those areas where land cover changes
have occurred after the collection date of the satellite imagery. In practice, the reference
data may not be collected until long after (or long before) the satellite imagery is
acquired. Therefore, it may not be obvious whether the discrepancy between the map
and the reference data is due to temporal land cover change or simply to
misclassification (Crist & Deittner 2000).
Temporal error is most definitely present in the reference data, as up to two
years passed between collection date of the satellite imagery and the collection date of
the reference data. Also, part of the study site is situated in an agricultural area where
land cover tends to change rapidly, not only over the long term, but also over seasonal
cycles.
4.3.3 Errors in Reference Data
A meaningful accuracy assessment requires that the ground data are accurate.
However, ground data sets are themselves a classification which may contain error and
sometimes more error than the remotely sensed product they are being used to evaluate
(Congalton & Green 1999; Foody 2002). The two most obvious sources include
misidentification of the class by the interpreter and data entry errors.
In this study, except for the LEPS interpretation, no accuracy assessment was
performed on reference data sets interpretation 4 and interpretation 3. Without an
accuracy assessment, it is impossible to estimate the degree to which error exists in the
reference data. During the air-photo interpretation, it was difficult to distinguish between
coniferous and deciduous land cover types in mixed forest areas and it was difficult to
distinguish between soil and herbs land cover types in agricultural areas. To avoid
mislabelling areas, polygons were labelled as unknown when in doubt as to the correct
land cover type. Comparatively, because it was derived from an on-screen interpretation
of air-photos, reference data set interpretation 4 is assumed to be the more accurate and
precise than reference data set interpretation 3, particularly for the water, wetland, and
impervious land cover types. On-screen interpretation allowed for greater magnification
and precise delineation of boundaries. Interpretation 3 was derived by tracing areas on
the air-photos under limited magnification and was particularly prone to error in boundary
areas (to be discussed below) because the grease pencils used to do the tracing were
not fine enough to create thin polygon borders along boundaries and along narrow
features such as roads. Furthermore, Interpretation 4 coverage values for coniferous,
deciduous, and soil land cover types differ from interpretation 3 coverage values (Table
4). This indicates that error, or at least differences, exists in the different reference data
sets and requires consideration.
Overall accuracy results were marginally higher when the classified images were
evaluated against interpretation 4 (minimum polygon size = 0.01 2ha) as opposed to
interpretation 3 (minimum polygon size not known). This is expected because a more
generalized interpretation masks fine-scale heterogeneity in the landscape and a given
point within a polygon may actually be incorrectly labelled even though the polygon as a
whole is correct. One of the challenges of accuracy assessment of high-resolution
images is to match the resolution of the reference data and the classified image. Ideally,
the method of collecting the reference data must identify land cover at the same level of
detail as the map (Crist & Deittner 2000). Reference data that might be appropriate for
evaluating moderate spatial resolution imagery (10 - 30m pixels) may be inadequate for
high-resolution imagery (1- 10m pixels). A reference data set mapped at a resolution of
at least 0.01 2 ha ( I I m) is necessary to adequately evaluate IKONOS. As discussed
previously, ground resolution is often incorrectly equated with Ground Field of View
(GFOV). Spatial resolution is defined as the minimum separation of two objects that can
be actually separated in an image. Separation requires at least one pixel to be between
two separate objects. Thus the objects need to be more than twice the square root of
two, times the GFOV, to be resolved. Thus a 4m IKONOS image offers I I m spatial
resolution, just as a 10m SPOT image offers 29m spatial resolution (Hastings 2001).
Reference data sets interpretation 4 and the LEPS interpretation were of adequate
resolution (minimum polygon sizes of 0.01 ha and 0.012 ha) to assess the accuracy of
IKONOS. However, the minimum polygon size of interpretation 3 is not known.
4.3.4 Boundary Error
Boundary errors occur at class boundaries due to the occurrence of spectral
mixing within a pixel. Sampling is often consciously constrained to large homogeneous
regions of the classes with regions in and around the vicinity of complexities such as
boundaries excluded (Dicks & Lo 1990; Richards 1996; Wickham et al. 1997) as a
deliberate action to minimize misregistration problems and ensure a high degree of
confidence in the reference data labels. However, as a result of this type of strategy, the
accuracy statement derived may be optimistically biased (Hammond & Verbyla 1996;
Zhu et al. 2000) and only relevant to a small part of the image. Further, as polygons
become smaller in highly heterogeneous landscapes, edge avoidance becomes very
difficult and removes even more of the map from the sampling pool. To be applicable to
the entire map, the test points used in forming the confusion matrix have to be
representative of the conditions found in the region (Foody 2002)
If boundary areas are masked or excluded from the analysis, we might not be
able to adequately compare the capabilities of IKONOS and Landsat in classifying
boundary areas. It is expected that because of its coarser spatial resolution, the Landsat
image contains more mixed pixels than the IKONOS image. A problem with the
reference data set used in this study is that areas that were difficult to identify were
labeled as unknown and removed from the analysis. The study site is spatially complex
and approximately 30% of the study site was removed from the analysis (Table 4).
These areas that were removed from the current study often included mixed forests,
residential areas, and boundary areas near edges and transition zones that did not
accurately represent the polygon.
4.4 Landsat vs. IKONOS
For this study site, Landsat consistently outperforms IKONOS, regardless of the
resolution or source of the reference data. The spatial and spectral resolution of the
satellite images will determine the types of patches that may be extracted. An
examination of the principal components used in the classifications is very revealing
(Table 16). Landsat principal component 1, which explains 63% of the variation, heavily
weights bands 5 and 6, which are mid-IR bands that IKONOS lacks. Landsat principal
component 2 heavily weights band 4, the near-lR (NIR) band. IKONOS principal
component 1 also very heavily weights the NIR band. It appears that the infrared bands
are very important in explaining the spectral variation in the study site, and that the
higher overall accuracy results for Landsat can be attributed in part to its additional
infrared bands. The spectral limitations of IKONOS for urban land cover mapping has
been confirmed by other studies (Herold et. al 2003; Goetz et. al 2003). In general,
vegetation discrimination, which is important for identification of wildlife habitat, is
enhanced through the incorporation of data from one of the mid-IR bands (band 5 or 7)
(Lillesand & Kieffer 2000). The present results support this fact as Landsat especially
outperforms IKONOS in the deciduous and herbs categories.
Given that suburban environments are generally characterized by highly
heterogeneous surface covers with substantial inter-pixel and intra-pixel changes, it is
generally believed that higher spatial resolution is better for suburban land cover
mapping. Therefore, the lower overall accuracy of the classified IKONOS image was a
surprise given its fine resolution and multi-spectral quality. However, the usefulness of a
given type of imagery for suburban and urban applications should not be based solely on
its spatial characteristics (Jensens and Cowen 1999; Yang & Lo 2002). Higher spatial
resolution imagery has not improved classification accuracy in rural-urban fringe settings
because each feature in a rural-urban fringe scene can have its own spectrally unique
signature. Research in the past has shown that improved spatial resolution can lead to
an increase not only in the inter-class variability but also in the intra-class variability,
which can produce poor image classification accuracy if a classic pixel based
classification method (such as the maximum likelihood classifier) is used (Irons et
a1.1985, Haack et al. 1987; Malcolm et al. 2001). Yang & Lo (2002) demonstrated that it
was the spectral and radiometric resolution and not the spatial resolution that was most
relevant for land cover assessment.
Accuracy may be assessed using a range of spatial units and the unit selected
can have a major impact on the estimated magnitude of classification accuracy (Zhu et
al. 2000; Foody 2002; Knight & Lunetta 2003). In this study, overall accuracy for both
Landsat and IKONOS is a function of the minimum polygon size of the reference data.
The larger the homogeneous area around a test point, the greater the probability it will
be correctly classified. There are many reasons why this may be, and most have
already been touched upon. First, it is easier for the interpreter to correctly identify
areas, which tend to be prone to misinterpretation and misclassification. Lastly, although
it is not thought to be significant in this study site, larger polygons are less affected by
poor misregistration.
4.5 Future Analyses
In this study the widely used maximum likelihood statistical classifier was applied
in the image classification. However, in this complex suburban landscape, some cover
types likely vary in distribution of digital numbers, where one simple mean value may not
provide the best description, such that two or more spectral classes are associated with
a single cover type. For instance, herb coverage can vary from very sparse to very
dense, or impervious surfaces may be very dull or extremely bright. It is recommended
that the existing classes be split further where practical, to help alleviate spectral
confusion. These new spectral classes representing a similar class can be later
regrouped (Ma et al. 2001). Further, if the probability of a land cover type occurring in
the landscape is known beforehand, Bayesian classifiers can be used to further refine
the classification. This is expected to be most effective in classifications at the local
level (Hepinstall and Sader 1997).
The automated image classification method, which is preferred for large amounts
of data over large study areas, relies mainly upon brightness and spectral elements with
limited use of image spatial contents. These types of classification methods generally
work well in spectrally homogeneous areas, such as forests, but not in highly
heterogeneous regions, such as urban landscapes (Yang & Lo 2002). Many other
strategies have been developed for improving automated classification, including
decision tree classifiers (de Colstoun et al. 2003; Oetter et al. 2000; Rogan et al. 2002),
and artificial neural networks (Civco 1993). However, few have found their way into
routine use because these techniques can vary greatly in terms of their performance,
depending on image characteristics and mapping objectives (Campbell 1996). Several
other classification techniques or procedures are also quite promising because they
have been shown experimentally to be not only accurate but also comparatively simple
and easy to implement in a conventional image processing platform. For example, the
present analysis could benefit from the incorporation of spatially referenced ancillary
data (i.e. a transportation layer) in the classification procedure (Oetter et al. 2000).
Alternative classifiers were not included in the present study because the objective of
this study was to test a non-specific spectrally based methodology that could be easily
transferred and applied at a regional level.
The study could benefit from the application of post-classification spatial
processing. This could be ( I ) localized contextual reclassification (Barnsley & Barr
1996), for example by overlaying a drainage network and identifying a buffer zone as
'riparian', or (2) modal filtering reclassification where areas smaller than a user identified
threshold are identified, declassified, and re-labelled on the basis of their surrounding
pixels/polygons (Presutti et al 2001 ; Yang & Lo 2002). For example, a modal filter could
be applied to the IKONOS image to remove the 'speckle' pixels and replace them with
class values of their surroundings. The high resolution IKONOS sensors pick up more
variation in land cover than do the interpreters creating the reference data polygons
(Figure 11). The result is a classified IKONOS image that is highly 'speckled' compared
to the original reference polygons. Landsat on the other hand, because it is
characterized by lower resolution 30m pixels, produces a smoother image that is less
'speckled' and agrees more frequently with the reference data.
Newer accuracy assessment techniques also attempt to address the mixed pixel
problem. For example, a source of error is the implicit assumption that the image is
composed of pure pixels. Unfortunately, remotely sensed data are often dominated by
pixels that represent areas containing more than one class and these are a major
problem in accuracy assessment (Foody 1996, 1999). As already discussed, mixed
pixels are common especially in coarse spatial resolution data sets and/or where the
land cover mosaic is complex, such as the Langley study site (Campbell 1996; Foody
2002). In a standard classification of data containing mixed pixels, the interpretation of
the class labels is difficult as many of the errors observed may be only partial errors
because the pixel may represent an area that is partially comprised of the allocated
class. Similarly however, some of the apparently correct class labels may be partly
erroneous. In attempting to solve the mixed pixel problem, fuzzy classifications have
been used increasingly (Gopal & Woodcock 1994). These typically are fuzzy in the
sense that they allow each pixel to have multiple and partial class membership. Since
mixed pixels often dominate remotely sensed imagery and will not disappear with the
use of fine spatial resolution data, techniques that allow their inclusion into the
assessment of classification accuracy are required (Foody 2002). Fuzzy logic may also
provide more useful information where, for example, a given point within a polygon may
actually be incorrectly classified even though the polygon as a whole is correct, or where
different magnitudes of error exist. For instance, misclassifying a polygon as coniferous
instead of deciduous is much less dramatic than misclassifying it as water (Crist &
Deittner 2002).
Some users might benefit from having a measure of accuracy by polygon or
geographic area indicating the level of reliability (Corves & Place 1994; Crist & Deittner
2000). Often there is a distinct pattern to the spatial distribution of thematic errors with,
for example, errors spatially correlated at the boundaries of classes (Congalton 1988;
Steele et al. 1998). Much of the error occurring at the boundaries is associated with
misregistration of the data sets and mixed pixels. Classified Landsat and IKONOS
images may differ in the spatial distribution of error. Unfortunately, the confusion matrix
and the accuracy metrics do not provide this kind of information (Steele et al. 1998).
The utility of this study to decide the most efficient strategy for the development
of a GVRD land cover map is dependent upon the degree to which land cover conditions
in the Langley site are characteristic of the rest of the GVRD. As already mentioned, it
would be beneficial to repeat the analysis for another study sites in the GVRD, for
example the North Shore or Delta, where different land cover types may be present.
A classified satellite image would either replace or provide additional data for air-
photo interpretations. In order to provide a complete analysis of all similar alternatives, it
would be ideal to compare results of this type of study with the classification of multi-
spectral aerial photography (Arthur Roberts 2003, personal communication).
Recent studies have also examined the applications of IKONOS and other high
resolution imagery for mapping of only one or two land cover types at a time, such as
impervious surface or water quality mapping (Cablk & Minor 2003; Sawaya et. al2003;
Masuoka et. al 2003). For instance, mapping of transportation surfaces has shown
significant improvement as the spectral resolution of the sensor improves (Herold et. al.
2003).
This study was started one year prior to another GVRD Biodiversity Conservation
Initiative project which identified specific habitat types (Table 17) that were of particular
importance to maintaining biodiversity in the region (Lee & Rudd 2002). The results of
this study can be used to determine the suitability of remotely sensed images for the
mapping of these specific habitat types, because the land cover types mapped in this
study overlapped with some of these identified habitat types. Table 17 explains which of
the GVRD identified habitat types were mapped in this study, which ones were not
mapped but have the potential to be mapped through satellite imagery, and which ones
could be mapped with the addition of ancillary data layers to the satellite imagery.
These are my opinions as pertaining to the GVRD, and the references provided in Table
17 explore the mapping of these habitat types, but may not necessarily provide the best
approach.
4.6 Conclusions and Recommendations
Monitoring and decision support tools are important in the management and
planning of natural resources, especially in urban areas like the GVRD where growth
and change is occurring rapidly. Determining the applicability of satellite remote sensing
for land cover mapping is thus a valuable undertaking, as it has the potential to offer
information on land cover in a timely fashion. For the GVRD Biodiversity Conservation
Strategy, remotely sensed data has the potential to provide information that will lead to
(1) better understanding of the state of existing biodiversity values and conservation
within the region, (2) better refinement of policy and planning priorities through
development of realistic management objectives for conservation and protection, and (3)
more effective allocation of financial, technological and human resources needed to
achieve desired outcomes (BC Ministry of Water, Land & Air Protection 2001)
There are over a dozen major research journals devoted to the field of remote
sensing. With all of the research in this growing field, numerous image classification
methods have been developed. In this study, I applied the widely used maximum
likelihood statistical classifier on the principal components derived from the Landsat and
IKONOS images. In order to maintain transferability of the methodology to other parts of
the GVRD, ancillary data layers were not used in the classification.
This study has demonstrated the usefulness of satellite remote sensing, digital
image processing and GIs techniques in producing land cover maps for the GVRD. The
results show that the spectral resolution of the satellite images and the spatial resolution
of the reference data affect the accuracy of computer based image classifications.
Because of its fine spatial resolution, the classified IKONOS image was initially expected
to be superior over the classified Landsat image. However, the reference data used for
this study suggest that the lower resolution classified Landsat image giver higher
classification accuracy results than IKONOS. It is thought that the spectral resolution of
Landsat, particularly the presence of the mid-IR bands, gives Landsat the edge over
IKONOS.
The utility of this study to decide the most efficient strategy for the development
of a GVRD land cover map is dependent upon the degree to which land cover conditions
in the Langley site are characteristic of the rest of the GVRD. The approach used in the
study is expected to be transferable to other suburban areas of the GVRD, however, this
has not yet been assessed. A digital elevation model (DEM) would be necessary for
similar studies of the mountainous North Shore area.
From a biodiversity conservation management and planning perspective, the
present study indicates that Landsat offers greater potential than IKONOS in accurate
land cover classification of suburban areas in the GVRD. In particular, the study shows
that the disturbed, coniferous and deciduous classes were mapped accurately enough,
such that the results could be applied immediately across suburban areas of the GVRD
for these classes. A better study site, with more coverage of open water, is necessary to
assess the ability to classify water, which is normally expected to be easy to classify.
The wetland class was mapped poorly, indicating that air-photo interpretation is
necessary to identify this class correctly. Further, if other habitat types (Table 17) are to
be mapped, air-photo interpretation or alternative digital image processing methods may
be required. If it is not to be used for classification purposes, satellite imagery is still an
excellent aid and complementary interpretive tool during manual air-photo interpretation.
Lillesand and Kieffer (2000) recommend that Landsat images should not be a
replacement for low altitude aerial photographs.
The increasing classification accuracy with increasing minimum reference data
polygon size for the classified Landsat and IKONOS images suggests that it may be
possible to obtain an acceptable overall accuracy rating (85%; see Anderson et. al 1976)
if larger minimum test polygon sizes are acceptable, or if the landscape is characterized
by land cover types with larger patch sizes. As long as the scale of resolution at which
the classified image meets accuracy requirements is consistent with planning needs, the
classified satellite image will be a useful tool for planning.
Of course, local biodiversity conservation planners would prefer to map smaller
features because small parcels are more consistent with cadastral maps and tend to be
more susceptible to impacts than larger parcels. However, it is important to ensure that
land use decisions are based on correct information. Therefore, it is more important to
have correctly identified parcels, even if they are larger than desired. As discussed
previously, the classification of small parcels in urban settings is expected to be
problematic using pixel based statistical classifiers because improved spatial resolution
can lead to (I) an increase in the inter-class spectral variability and the intra-class
spectral variability, (2) an increase in the mixed pixel problem, and (3) greater
misregistration problems in accuracy assessments. Until the efficacy of IKONOS is
proven in the classification of small parcels, it is recommended that the lower resolution
Landsat imagery be used to produce classified land cover maps of the disturbed,
coniferous and deciduous classes. The relative cost of Landsat images is considerably
cheaper than IKONOS images (Table I), and in the meantime, managers can wait for
the development of more effective high resolution technology, wait for the results of
similar studies in the literature, or provide support for studies to improve upon the
present methodology. For instance, it would be valuable to compare present results to
the classification of multi-spectral aerial photography. For now, because of its crisp
image and detail, it is recommended that IKONOS images, while not the best for
creating land cover maps, be used as an aid to air-photo interpretation.
There is considerable interest in the use of remote sensing to study thematic
change. However, a variety of factors influence the accuracy of land cover change
products. Basic issues are the accuracies of the component classifications as well as
more subtle issues associated with the sensors and data preprocessing methods used,
together with the atmospheric conditions at the times of the different image acquisitions.
When mapping land cover change, the problems discussed previously in relation to the
registration of data sets and boundaries are generally magnified. Error in the individual
classifications may also be confused with change (Khorram 1999). As a consequence of
these and other issues, the estimation of the accuracy of a change product is a
substantially more difficult and challenging task than the assessment of the accuracy of
a single image classification (Congalton & Green 1999). This is a major limitation in
environmental studies where the magnitude of change is often important. (Foody, 2002).
In conclusion, the classified Landsat map approached the 85% accuracy level
stipulated by the Anderson classification (Anderson et al. 1976). This is good evidence
that the image processing approach adopted in this study has been effective, and that
satellite imagery does provide a viable source of data from which updated land cover
information can be extracted to improve the effectiveness and efficiency of conservation
efforts in suburban areas of the GVRD.
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Tables
Tab
le 1
. C
hara
cter
istic
s of
the
sate
llite
imag
ery.
'A
ctiv
e' r
efer
s to
sen
sors
that
gen
erat
e an
d di
rect
radi
atio
n to
war
d th
e E
arth
and
reco
rd it
s ba
cksc
atte
r. 'P
assi
ve' r
efer
s to
se
nsor
s th
at r
ecor
d re
flect
ed ra
diat
ion
orig
inal
ly p
rodu
ced
by th
e S
un.
Sen
sor
Dat
es
App
rox.
cos
t of
Act
ive/
Pas
sive
B
ands
S
pect
ral
Pix
el s
ize
(m)
Rad
iom
etric
co
vera
ge fo
r R
esol
utio
n R
esol
utio
n G
VR
D
(mic
rons
)
RA
DA
RS
AT
-1
911 6
/00,
$8
,000
A
ctiv
e 1
3800
0 -7
7000
1 1
/27/
00
8m
0-64
000
Land
sat E
TM
6/
28/0
0,
$1,0
00
Pas
sive
1 1
2210
1
1 (B
lue)
0.
45 -
0.52
30
0-
255
2 (G
reen
) 0.
53 -
0.61
30
0-
255
3 (R
ed)
0.63
- 0
.69
30
0-25
5
4 (N
IR)
0.75
- 0.
90
30
0-25
5
5 (M
id-I
R)
1.55
- 1.
75
30
0-25
5
7 (M
id-I
R)
2.09
- 2
.35
30
0-25
5
Pan
chro
mat
ic
0.52
- 0.
90
15
0-25
5
IKO
NO
S
6/25
/00
$1 00
,000
P
assi
ve
1 (R
ed)
0.45
- 0.
52
4 0-
255
2 (G
reen
) 0.
52 -
0.60
4
0-25
5
3 (B
lue)
0.
63 -
0.69
4
0-25
5
4 (N
IR)
0.76
- 0.
90
4 0-
255
Table 2. Land cover classification scheme. The mapped land cover classes are in italicized bold.
Mapped land cover classes
I. water water: all areas of open water, including rivers, ponds, lakes and ocean
II. natural vegetation a. non-forest wetland: wetlands including swamps, marshes and fens.
b. forest deciduous: deciduous trees
coniferous: coniferous trees
Ill. disturbed impervious: asphalt, concrete and construction material (i.e. buildings, roads, parking lots)
soil: areas of sparse vegetation, cultivated land without crops and sediments along shorelines
herbs: grasses and other non-woody herbaceous vegetation including crops, pasture, golf courses, recreational fields.
Table 3. Area (ha) of the training regions for each land cover class. The maximum number of test points that were located in training regions and mistakenly used in the accuracy assessments is also provided.
Landsat IKONOS
Land cover class Area (ha) Area (ha)
herbs
soil
wetland
coniferous
deciduous
water
im~ervious
Maximum no. of test points located in training regions 10 15
Table 4. Percentage (%) of the study site identified as each land cover class for each air-photo interpreted reference data set.
Reference data source
Land cover class interpretation I interpretation 2 interpretation 3 interpretation 4
coniferous 0.2 0.2 4.7 .5
deciduous 0.2 5.0 6.8 13.4
soil 17.9 16.6 6.3 6.5
herbs 31 .I 26.6 8.5 30.5
impervious 8.7 16.8 1.3 7.2
water 0.4 0.5 0.4 0.4
wetlands 1.5 1.3 1.6 1.9
unknown 40.0 33.0 30.3 32.7
Total 100.0 100.0 100.0 100.0
Tab
le 5
. E
rror
mat
rices
for
the
clas
sifie
d La
ndsa
t im
age
(7 o
rigin
al c
lass
es; a
ll te
st p
oint
s us
ed re
gard
less
of t
he s
ize
of th
e re
fere
nce
data
pol
ygon
s; te
st p
oint
sam
plin
g in
terv
al =
100
m).
5a.
As
eval
uate
d ag
ains
t int
erpr
etat
ion
4.
Kap
pa =
0.4
3
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
herb
s im
perv
ious
so
il w
ater
w
etla
nd
Row
tota
l ac
cura
cy (
%)
coni
fero
us
68
8 3
5 0
28
1 11
3 60
.2
deci
duou
s 4
124
13
2 0
1 2
146
84.9
herb
s 27
24
33
9 11
1 49
20
21
59
1
57.4
impe
rvio
us
0 0
0 4
0 0
0 4
100.
0
soil
2 0
13
8 6
0 0
29
20.7
wat
er
0 0
0 0
0 5
0 5
100.
0
wet
land
1
6 19
2
1 0
25
54
46.3
Col
umn
tota
l 10
2 16
2 38
7 13
2 56
54
49
94
2
Pro
duce
r's
Ove
rall
accu
racy
(%
) 66
.7
76.5
87
.6
3.0
10.7
9.
3 51
.0
accu
racy
(%
):
60.6
5b.
As
eval
uate
d ag
ains
t int
erpr
etat
ion
3.
Kap
pa =
0.2
9
Cla
ssifi
ed
Inte
rpre
tatio
n 3
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
herb
s im
perv
ious
so
il w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
43
6 7
1 2
26
4 89
48
.3
deci
duou
s 11
88
20
1
8 2
8 13
8 63
.8
herb
s 38
15
25
1 1 3
4 13
7 17
17
60
9 41
.2
impe
rvio
us
0 0
0 9
0 0
0 9
100.
0
soil
1 0
9 7
11
0 0
28
39.3
wat
er
0 0
0 0
0 5
0 5
100.
0
wet
land
2
1 13
0
7 0
25
48
52.1
Col
umn
tota
l 95
11
0 30
0 15
2 16
5 50
54
92
6
Pro
duce
r's
Ove
rall
P
4
accu
racy
(%
) 45
.3
80.0
83
.7
5.9
6.7
10.0
46
.3
accu
racy
(%):
46
.7
Tab
le 6.
Err
or m
atric
es fo
r th
e cl
assi
fied
IKO
NO
S im
age (7 o
rigin
al c
lass
es; a
ll te
st p
oint
s us
ed re
gard
less
of t
he s
ize
of th
e re
fere
nce
data
pol
ygon
s; te
st p
oint
sam
plin
g in
terv
al =
100m).
6a.
As
eval
uate
d ag
ains
t int
erpr
etat
ion
4. K
appa
= 0.36
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s he
rbs
impe
rvio
us
soil
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
79
28
37
12
4 12
4 176
44.9
deci
duou
s 9
109
11
14
1 2
9 155
70.3
herb
s 9
2 1
221
35
22
2 21
331
66.8
impe
rvio
us
0 0
6 1
3
0 0
10
10.0
soil
3 9
79
58
21
8 2
180
11.7
wat
er
0 0
0 0
1 30
0
3 1
96.8
wet
land
2
6 37
12
5 0
14
76
18.4
P
03
Col
umn
tota
l 102
173
39 1
132
57
54
50
959
Pro
duce
r's
Ove
rall
accu
racy
(%
) 77.5
63.0
56.5
0.8
36.8
55.6
28.0
accu
racy
(%):
49
.5
6b.
As
eval
uate
d ag
ains
t int
erpr
etat
ion
3.
Kap
pa =
0.27
Cla
ssifi
ed
Inte
rpre
tatio
n 3
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s he
rbs
impe
rvio
us
soil
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
Con
ifero
us
62
26
23
18
15
17
11
172
36.0
Dec
iduo
us
8 76
16
11
3 1
10
125
60.8
Her
bs
14
7
162
45
77
2 21
328
49.4
Impe
rvio
us
0 0
6 2
4 0
0 12
16.7
soil
6 3
63
65
49
6 2
1 94
25.3
wat
er
0 0
4 0
2 24
0
30
80.0
wet
land
6
3 3 1
12
17
0
12
81
14.8
Col
umn
tota
l 96
1 15
305
153
167
50
56
942
Pro
duce
r's
Ove
rall
P
a
accu
racy
(%)
64.6
66.1
53.1
1.3
29.3
48.0
21.4
accu
racy
(%):
41
.I
Tab
le 7
. E
rror
mat
rices
for
the
clas
sifie
d La
ndsa
t im
age
as e
valu
ated
aga
inst
inte
rpre
tatio
n 4
(5 c
lass
es; t
est p
oint
sam
plin
g in
terv
al
= 10
0m).
7a.
All
test
poi
nts
used
rega
rdle
ss o
f the
siz
e of
the
refe
renc
e da
ta p
olyg
ons.
Kap
pa =
0.6
4
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (
%)
coni
fero
us
68
8 8
28
1 1 1
3 60
.2
deci
duou
s 4
124
15
1 2
146
84.9
dist
urbe
d 29
24
53
0 20
21
62
4 84
.9
wat
er
0 0
0 5
0 5
100.
0
wet
land
1
6 22
0
2 5
54
46.3
-
-
--
-
-
-- -
Col
umn
tota
l 10
2 16
2 57
5 54
49
94
2
Pro
duce
r's a
ccur
acy
(%)
66.7
76
.5
92.2
9.
3 51
.0
Ove
rall
accu
racy
(%
):
79.8
Cn 0
7b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha. K
appa
= 0
.68.
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (
%)
Con
ifero
us
56
7 2
24
1 90
62
.2%
Dec
iduo
us
3 10
6 4
1
2 1 1
6 91
.4%
Dis
turb
ed
9 9
270
17
20
325
83.1
%
wat
er
0 0
0 5
0 5
100.
0%
wet
land
1
3 19
0
23
46
50.0
%
Col
umn
tota
l 69
12
5 29
5 47
46
58
2
Pro
duce
r's a
ccur
acy
(%)
81.2
84
.8
91.5
1 0
.6
50.0
O
vera
ll ac
cura
cy (%
): 79
.0
7c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.7
8
Cla
ssifi
ed
Inte
rmet
atio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
Con
ifero
us
37
5 1
6 1
50
74.0
%
Dec
iduo
us
2 78
1
0 1
82
95.1
%
Dis
turb
ed
3 1
162
3 19
18
8 86
.2%
wat
er
0 0
0 3
0 3
100.
0%
wet
land
0
0 9
0 21
30
70
.0%
Col
umn
tota
l 42
84
17
3 12
42
35
3
Pro
duce
r's a
ccur
acy
(%)
88.1
92
.9
93.6
25
.0
50.0
O
vera
ll ac
cura
cy (%
):
85.3
7d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.8
1
5
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
Con
ifero
us
26
1 1
0 1
29
89.7
%
Dec
iduo
us
1 56
1
0 1
59
94.9
%
Dis
turb
ed
1 1
98
1 13
11
4 86
.0%
wat
er
wet
land
Col
umn
tota
l 28
58
10
6 3
31
226
Pro
duce
r's a
ccur
acy
(%)
92.9
96
.6
92.5
66
.7
51.6
O
vera
ll ac
cura
cy (%
):
87.6
Tab
le 8
. E
rror
mat
rices
for t
he c
lass
ified
Lan
dsat
imag
e as
eva
luat
ed a
gain
st in
terp
reta
tion
3 (5
cla
sses
; tes
t poi
nt s
ampl
ing
inte
rval
=
100m
).
8a.
All
test
poi
nts
used
reg
ardl
ess
of th
e si
ze o
f the
ref
eren
ce d
ata
poly
gons
. K
appa
= 0
.56
Cla
ssifi
ed
Inte
rpre
tatio
n 3
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
43
6 10
26
4
89
48.3
deci
duou
s 11
88
29
2
8 13
8 63
.8
dist
urbe
d 39
15
55
8 17
17
64
6 86
.4
wat
er
wet
land
Col
umn
tota
l 95
11
0 61
7 50
54
92
6
Pro
duce
r's a
ccur
acy
(%)
45.3
80
.0
90.4
1 0
.0
46.3
O
vera
ll ac
cura
cy (%
): 77
.6
8b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha.
Kap
pa =
0.6
1
Cla
ssifi
ed
Inte
rpre
tatio
n 3
U
ser's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
40
5 7
22
2 76
52
.6
deci
duou
s 7
75
14
1 6
103
72.8
dist
urbe
d 26
10
38
0 11
14
44
1
86.2
wat
er
0 0
0 4
0 4
100.
0
wet
land
1
0 15
0
22
38
57.9
Col
umn
tota
l 74
90
41
6 38
44
66
2
Pro
duce
r's a
ccur
acy
(%)
54.1
83
.3
91.3
1 0
.5
50.0
O
vera
ll ac
cura
cy (%
):
78.7
8c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.6
9
Cla
ssifi
ed
- -
lnte
rpre
tatio
n 3
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
37
3 4
14
1 59
62
.7
deci
duou
s 3
65
2 1
4 75
86
.7
dist
urbe
d 15
2
21 8
3 11
24
9
wat
er
0 0
0 3
0 3
wet
land
1
0 12
0
14
27
Col
umn
tota
l 56
70
23
6 21
30
41
3
Pro
duce
r's a
ccur
acy
(%)
66.1
92
.9
92.4
14
.3
46.7
O
vera
ll ac
cura
cy (%
):
81.6
8d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.8
0
Cn
W
Cla
ssifi
ed
Inte
rpre
tatio
n 3
U
ser's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
Con
ifero
us
27
2 0
1 1
31
87.1
Dec
iduo
us
0 46
0
0 2
48
95.8
Dis
turb
ed
9 0
135
0 11
15
5 87
.1
wat
er
0 0
0 3
0 3
100.
0
wet
land
0
0 4
0 11
15
73
.3
Col
umn
tota
l 36
48
13
9 4
25
252
Pro
duce
r's a
ccur
acy
(%)
75.0
95
.8
97.1
75
.0
44.0
O
vera
ll ac
cura
cy (%
):
88.1
Tab
le 9
. E
rror
mat
rices
for
the
clas
sifie
d IK
ON
OS
imag
e as
eva
luat
ed a
gain
st in
terp
reta
tion
4 (5
cla
sses
; tes
t poi
nt s
ampl
ing
inte
rval
= 1
OO
m).
9a.
All
test
poi
nts
used
rega
rdle
ss o
f the
siz
e of
the
refe
renc
e da
ta p
olyg
ons.
Kap
pa =
0.5
2
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
79
28
53
12
4 17
6 44
.9
deci
duou
s 9
109
26
2 9
155
70.3
dist
urbe
d 12
30
44
6 10
23
52
1 85
.6
wat
er
0 0
1 30
0
3 1
96.8
wet
land
2
6 54
0
14
76
18.4
Col
umn
tota
l 10
2 17
3 58
0 54
50
95
9
Pro
duce
r's a
ccur
acy
(%)
77.5
63
.0
76.9
55
.6
28.0
O
vera
ll ac
cura
cy (%
):
70.7
U1
P
9b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha.
Kap
pa =
0.5
9
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
Con
ifero
us
58
23
15
7 4
107
54.2
Dec
iduo
us
8 89
10
2
7 1 1
6 76
.7
dist
urbe
d 2
16
239
9 23
28
9 82
.7
wat
er
0 0
0 29
0
29
100.
0
wet
land
1
5 31
0
13
50
26.0
Col
umn
tota
l 69
13
3 29
5 47
47
59
1
Pro
duce
r's a
ccur
acy
(%)
84.1
66
.9
81 .O
61
.7
27.7
O
vera
ll ac
cura
cy (
%):
72
.4
9c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.6
6
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
36
12
4 2
3 57
63
.2
deci
duou
s 4
7 1
1 0
6 82
86
.6
dist
urbe
d 2
4 14
8 0
2 1
175
84.6
wat
er
0 0
0 10
0
10
100.
0
wet
land
0
3 20
0
13
36
36.1
Col
umn
tota
l 42
90
17
3 12
43
36
0
Pro
duce
r's a
ccur
acy
(%)
85.7
78
.9
85.5
83
.3
30.2
O
vera
ll ac
cura
cy (%
):
77.2
9d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.6
2
ul
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
ul
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
Con
ifero
us
23
8 3
0 1
35
65.7
Dec
iduo
us
3 50
0
0 3
56
dist
urbe
d 2
2 85
0
18
107
wat
er
0 0
0 3
0 3
wet
land
0
2 18
0
9 29
Col
umn
tota
l 28
62
10
6 3
3 1
230
Pro
duce
r's a
ccur
acy
(%)
82.1
80
.6
80.2
10
0.0
29.0
O
vera
ll ac
cura
cy (%
):
73.9
Tab
le 10. E
rror
mat
rices
for
the
clas
sifie
d IK
ON
OS
imag
e as
eva
luat
ed a
gain
st in
terp
reta
tion
3 (5
clas
ses;
test
poi
nt s
ampl
ing
inte
rval
= 100m).
10a. A
ll te
st p
oint
use
d re
gard
less
of t
he s
ize
of th
e re
fere
nce
data
pol
ygon
s. K
appa
= 0.46
Cla
ssifi
ed
Inte
rpre
tatio
n 3
U
ser's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
62
26
56
17
11
172
36.0
deci
duou
s 8
76
30
1 10
125
60.8
dist
urbe
d 20
10
473
8 23
534
88.6
wat
er
0 0
6 24
0
30
80.0
wet
land
6
3 60
0 12
8 1
14.8
Col
umn
tota
l 96
1 15
625
50
56
942
Pro
duce
r's a
ccur
acy
(%)
64.6
66.1
75.7
48.0
21.4
Ove
rall
accu
racy
(%):
68
.7
UI
Q,
lob.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0.024ha.
Kap
pa =
0.53
Cla
ssifi
ed
Inte
rpre
tatio
n 3
U
ser's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
53
24
28
10
9 1 24
42.7
deci
duou
s 5
62
15
0 9
9 1
68.1
dist
urbe
d 13
5 33
5 6
18
377
88.9
wat
er
0 0
6 22
0
28
78.6
wet
land
4
2 37
0 10
53
18.9
Col
umn
tota
l 75
93
42 1
38
46
673
Pro
duce
r's a
ccur
acy
(%)
70.7
66.7
79.6
57.9
21.7
Ove
rall
accu
racy
(%):
71
.6
10c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.5
8
Cla
ssifi
ed
Inte
rpre
tatio
n 3
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
41
17
11
5 6
80
51.3
deci
duou
s 3
55
7 0
7 72
76
.4
dist
urbe
d 11
0
195
4 13
22
3 87
.4
wat
er
0 0
3 12
0
15
80.0
wet
land
2
1 23
0
6 32
18
.8
Col
umn
tota
l 57
73
23
9 21
32
42
2
Pro
duce
r's a
ccur
acy
(%)
71.9
75
.3
81.6
57
.1
18.8
O
vera
ll ac
cura
cy (%
):
73.2
10d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.6
3
U1
4
Cla
ssifi
ed
Inte
rpre
tatio
n 3
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
26
6 3
0 4
39
66.7
deci
duou
s 2
44
3 0
7 56
78
.6
dist
urbe
d 7
0 11
8 1
11
137
86.1
wat
er
0 0
0 3
0 3
100.
0
wet
land
1
0 16
0
5 22
22
.7
Col
umn
tota
l 36
50
14
0 4
27
257
Pro
duce
r's a
ccur
acy
(%)
72.2
88
.0
84.3
75
.0
18.5
O
vera
ll ac
cura
cy (%
): 76
.3
Tab
le 1
1. E
rror
mat
rices
for
the
clas
sifie
d La
ndsa
t im
age
as e
valu
ated
aga
inst
the
LEP
S in
terp
reta
tion (5 c
lass
es; t
est
poin
t sa
mpl
ing
inte
rval
= 100m).
I I a
. A
ll te
st p
oint
s us
ed r
egar
dles
s of
the
size
of t
he r
efer
ence
dat
a po
lygo
ns.
Kap
pa =
0.61
Cla
ssifi
ed
LEP
S In
terp
reta
tion
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
90
12
5 11
6
1 24
72.6
deci
duou
s 9
100
22
1 4
136
73.5
dist
urbe
d 52
34
485
7 18
596
81.4
wat
er
0 0
1 31
0
32
96.9
wet
land
1
6 30
0 21
58
36.2
Col
umn
tota
l 152
152
543
50
49
946
Pro
duce
r's a
ccur
acy
(%)
59.2
65.8
89.3
62.0
42.9
Ove
rall
accu
racy
(%
):
76.8
I I b
. T
est p
oint
s fr
om r
efer
ence
dat
a po
lygo
ns la
rger
than
0.024ha.
Kap
pa =
0.72
Cla
ssifi
ed
LEP
S In
terp
reta
tion
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
78
6 0
10
5 99
78.8%
deci
duou
s 6
80
13
0 3
102
78.4%
dist
urbe
d 13
11
391
6 16
437
89.5%
wat
er
0 0
0 30
0
30
100.0%
wet
land
0
0 28
0 20
48
41.7%
Col
umn
tota
l 97
97
432
46
44
71 6
Pro
duce
r's a
ccur
acy
(%)
80.4
82.5
90.5
65.2
45.5
Ove
rall
accu
racy
(%):
83
.7
I I c. T
est p
oint
s fr
om r
efer
ence
dat
a po
lygo
ns la
rger
than
0.0
96ha
. K
appa
= 0
.79
Cla
ssifi
ed
LEP
S In
terp
reta
tion
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
53
4 0
9 3
69
76.8
%
deci
duou
s 2
60
1 0
2 65
92
.3%
dist
urbe
d 3
4 29
1
4 11
31
3 93
.0%
wat
er
0 0
0 29
0
29
100.
0%
wet
land
0
0 20
0
16
36
44.4
%
Col
umn
tota
l 58
68
31
2 42
32
51
2
Pro
duce
r's a
ccur
acy
(%)
91.4
88
.2
93.3
69
.0
50.0
O
vera
ll ac
cura
cy (%
):
87.7
I Id
. T
est p
oint
s fr
om r
efer
ence
dat
a po
lygo
ns la
rger
than
0.2
16ha
. K
appa
= 0
.84
Cla
ssifi
ed
LEP
S In
terp
reta
tion
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
45
2 0
5 3
55
81.8
%
deci
duou
s 0
44
0 0
0 44
10
0.0%
dist
urbe
d 2
3 22
0 2
8 23
5 93
.6%
wat
er
0 0
0 27
0
27
100.
0%
wet
land
0
0 11
0
15
26
57.7
%
Col
umn
tota
l 47
49
23
1
34
26
387
Pro
duce
r's a
ccur
acv
(%)
95.7
89
.8
95.2
79
.4
57.7
O
vera
ll ac
cura
cy (%
):
90.7
Tab
le 1
2. E
rror
mat
rices
for
the
clas
sifie
d IK
ON
OS
imag
e as
eva
luat
ed a
gain
st th
e LE
PS
inte
rpre
tatio
n (5
cla
sses
; tes
t poi
nt
sam
plin
g in
terv
al =
100
m).
12a.
All
test
poi
nts
used
rega
rdle
ss o
f the
siz
e of
the
refe
renc
e da
ta p
olyg
ons.
Kap
pa =
0.4
8
Cla
ssifi
ed
LEP
S I
nte
r~re
tatio
n
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
99
37
49
2 11
19
8 50
.0
deci
duou
s 14
89
32
1
10
146
61 .O
dist
urbe
d 38
27
39
9 5
17
486
82.1
wat
er
0 0
2 42
2
46
91.3
wet
land
0
7 6 5
0
10
82
12.2
Col
umn
tota
l 15
1 16
0 54
7 50
50
95
8
Pro
duce
r's a
ccur
acy
(%)
65.6
55
.6
72.9
84
.0
20.0
O
vera
ll ac
cura
cy (%
):
66.7
cn 0
12b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha.
Kap
pa =
0.5
8
Cla
ssifi
ed
L EP
S In
terp
reta
tion
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
75
21
29
2 9
136
55.1
%
deci
duou
s 8
69
2 1
0 9
107
64.5
%
dist
urbe
d 13
10
33
5 3
15
376
89.1
%
wat
er
0 0
0 41
2
43
95.3
%
wet
land
0
3 48
0
9 6
0
15.0
%
Col
umn
tota
l 96
10
3 43
3 46
44
72
2
Pro
duce
r's a
ccur
acv
(%)
78.1
67
.0
77.4
89
.1
20.5
O
vera
ll ac
cura
cv (%
):
73.3
12c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.6
4
Cla
ssifi
ed
LEP
S In
terp
reta
tion
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
49
14
14
2 5
84
58.3
%
deci
duou
s 5
51
10
0 7
73
69.9
%
dist
urbe
d 4
7 25
7 3
12
283
90.8
%
wat
er
0 0
0 37
1
38
97.4
%
wet
land
0
1 3
1 0
7 39
17
.9%
Col
umn
tota
l 58
73
31
2 4
2
32
51 7
Pro
duce
r's a
ccur
acy
(%)
84.5
69
.9
82.4
88
.1
21.9
O
vera
ll ac
cura
cy (%
):
77.6
12d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.6
9
Cla
ssifi
ed
L EP
S In
terp
reta
tion
Use
r's
2
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
42
7 10
1
5 6 5
64
.6%
deci
duou
s 4
40
4 0
5 53
75
.5%
dist
urbe
d 1
5 19
6 0
10
21 2
92.5
%
wat
er
0 0
0 33
1
34
97.1
%
wet
land
0
1 22
0
5 28
17
.9%
Col
umn
tota
l 47
53
23
2 34
26
39
2
Pro
duce
r's a
ccur
acy
(%)
89.4
75
.5
84.5
97
.1
19.2
O
vera
ll ac
cura
cy (%
):
80.6
Tab
le 1
3. E
rror
mat
rices
for
the
clas
sifie
d La
ndsa
t im
age
as e
valu
ated
aga
inst
inte
rpre
tatio
n 4
(5 c
lass
es; t
est p
oint
sam
plin
g in
terv
al
= 15
0m).
13a.
All
test
poi
nts
used
rega
rdle
ss o
f the
siz
e of
the
refe
renc
e da
ta p
olyg
ons.
Kap
pa =
0.6
0
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
34
2 5
29
2 72
47
.2
deci
duou
s 3
60
5 1
2 7
1 84
.5
dist
urbe
d 13
14
28
4 23
19
35
3 80
.5
wat
er
0 0
0 5
0 5
100.
0
wet
land
0
1 9
0 3
1 41
75
.6
Col
umn
tota
l 50
77
30
3 58
54
54
2
Pro
duce
r's a
ccur
acy
(%)
68.0
77
.9
93.7
8.
6 57
.4
Ove
rall
accu
racy
(%):
76
.4
ln 10
13b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha.
Kap
pa =
0.6
5
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
29
0 1
26
1 57
50
.9
deci
duou
s 2
49
1 1
1 54
90
.7
dist
urbe
d 4
7 16
7 17
18
21
3 78
.4
wat
er
0 0
0 5
0 5
100.
0
wet
land
0
1 5
0 3 1
37
83
.8
Col
umn
tota
l 35
57
1 7
4 49
51
36
6
Pro
duce
r's a
ccur
acy
(%)
82.9
86
.0
96.0
1 0
.2
60.8
O
vera
ll ac
cura
cy (
%):
76
.8
13c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.7
7
Cla
ssifi
ed
lnte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (%
)
coni
fero
us
24
0 1
6 1
32
75.0
deci
duou
s 1
37
0 0
1 39
94
.9
dist
urbe
d 2
1 96
1
16
116
82.8
wat
er
0 0
0 3
0 3
100.
0
wet
land
0
1 4
0 30
35
85
.7
Col
umn
tota
l 27
39
10
1 10
48
22
5
Pro
duce
r's a
ccur
acy
(%)
88.9
94
.9
95.0
30
.0
62.5
O
vera
ll ac
cura
cy (%
):
84.4
13d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.8
0
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
Land
sat i
mag
e co
nife
rous
de
cidu
ous
dist
urbe
d w
ater
w
etla
nd
Row
tota
l ac
cura
cy (
%)
coni
fero
us
17
0 1
2 I
21
81 .O
deci
duou
s 1
26
0 0
1 28
92
.9
dist
urbe
d 1
0 52
0
9 62
83
.9
wat
er
wet
land
Col
umn
tota
l 19
27
55
2
34
137
Pro
duce
r's a
ccur
acy
(%)
89.5
96
.3
94.5
0.
0 67
.6
Ove
rall
accu
racy
(%):
86
.1
Tab
le 1
4. E
rror
mat
rices
for
the
clas
sifie
d IK
ON
OS
imag
e as
eva
luat
ed a
gain
st in
terp
reta
tion
4 (5
cla
sses
; tes
t poi
nt s
ampl
ing
inte
rval
= 1
50m
).
14a.
All
test
poi
nts
used
reg
ardl
ess
of th
e si
ze o
f the
ref
eren
ce d
ata
poly
gons
. K
appa
= 0
.51
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
35
19
30
12
6 10
2 34
.3
deci
duou
s 8
48
10
0 16
82
58
.5
dist
urbe
d 6
9 24
8 12
25
30
0 82
.7
wat
er
0 0
0 34
0
34
100.
0
wet
land
1
5 16
0
10
32
31.3
Col
umn
tota
l 50
8
1 30
4 58
57
55
0
Pro
duce
r's a
ccur
acy
(%)
70.0
59
.3
81.6
58
.6
17.5
O
vera
ll ac
cura
cy (%
):
68.2
cn P
14b.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.024
ha.
Kap
pa =
0.5
9
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
30
12
5 8
5 60
50
.0
deci
duou
s 5
39
4 0
16
64
60.9
dist
urbe
d 0
6 15
9 10
25
20
0 79
.5
wat
er
0 0
0 3 1
0
3 1
100.
0
wet
land
0
3 6
0 8
17
47.1
Col
umn
tota
l 35
60
1 7
4 49
54
37
2
Pro
duce
r's a
ccur
acy
(%)
85.7
65
.0
91.4
63
.3
14.8
O
vera
ll ac
cura
cy (%
):
71.8
14c.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.096
ha.
Kap
pa =
0.6
3
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
24
5 1
1 5
36
66.7
deci
duou
s 3
33
1 0
15
52
63.5
dist
urbe
d 0
2 96
0
23
121
79.3
wat
er
0 0
0 9
0 9
100.
0
wet
land
0
1 3
0 8
12
66.7
Col
umn
tota
l 27
41
10
1 10
51
23
0
Pro
duce
r's a
ccur
acy
(%)
88.9
80
.5
95.0
90
.0
15.7
O
vera
ll ac
cura
cy (%
):
73.9
14d.
Tes
t poi
nts
from
ref
eren
ce d
ata
poly
gons
larg
er th
an 0
.216
ha.
Kap
pa =
0.6
4
Cla
ssifi
ed
Inte
rpre
tatio
n 4
Use
r's
IKO
NO
S im
age
coni
fero
us
deci
duou
s di
stur
bed
wat
er
wet
land
R
ow to
tal
accu
racy
(%)
coni
fero
us
18
1 1
0 2
22
deci
duou
s 1
27
0 0
9 37
dist
urbe
d 0
0 52
0
19
7 1
wat
er
0
0 0
2 0
2
wet
land
0
1 2
0 5
8
Col
umn
tota
l 19
29
55
2
35
140
Pro
duce
r's a
ccur
acy
(%)
94.7
93
.1
94.5
10
0.0
14.3
O
vera
ll ac
cura
cy (%
):
74.3
Tab
le 1
5. Z
-sta
tistic
val
ues
for
kapp
a an
alys
is c
ompa
rison
s be
twee
n er
ror
mat
rices
. T
he e
rror
mat
rices
bei
ng c
ompa
red
are
indi
cate
d by
the
corr
espo
ndin
g ta
ble
num
bers
. The
crit
ical
z-v
alue
is 1
.96.
E
rror
m
atric
es a
re s
igni
fican
tly d
iffer
ent f
rom
eac
h ot
her w
here
the
z-st
atis
tic is
gre
ater
than
1.9
6 (h
ighl
ight
ed in
bol
d).
a =
all t
est p
oint
s us
ed
b =
test
poi
nts
from
ref
eren
ce p
olyg
ons
> 0
.024
ha
c =
test
poi
nts
from
ref
eren
ce p
olyg
ons
> 0
.096
ha
d =
test
poi
nts
from
ref
eren
ce p
olyg
ons
> 0.
21 6
ha
15a.
Ove
rall
accu
racy
resu
lts a
re c
onsi
sten
tly s
igni
fican
tly h
ighe
r for
Lan
dsat
than
IKO
NO
S w
hen
eval
uate
d ag
ains
t all
of th
e re
fere
nce
data
set
s.
1 Tab
le
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 4.
9a
9b
9c
9d
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 3.
10a
lob
1 oc
10d
IKO
NO
S e
valu
ated
aga
inst
LEP
S in
terp
reta
tion.
12a
12b
12c
12d
Land
sat e
valu
ated
aga
inst
LE
PS
inte
rpre
tatio
n
Ila
Il
b
Ilc
Il
d
Land
sat e
valu
ated
aga
inst
in
terp
reta
tion
4.
7a
7b
7c
7d
Land
sat e
valu
ated
aga
inst
in
terp
reta
tion
3
8a
8b
8c
8d
15b.
Ove
rall
accu
racy
resu
lts fo
r La
ndsa
t and
IKO
NO
S a
re c
onsi
sten
tly s
igni
fican
tly h
ighe
r whe
n ev
alua
ted
agai
nst t
est p
oint
s fr
om
refe
renc
e po
lygo
ns la
rger
than
0.0
96ha
and
0.2
16ha
Land
sat e
valu
ated
aga
inst
inte
rpre
tatio
n 4.
Land
sat e
valu
ated
aga
inst
inte
rpre
tatio
n 3.
Land
sat e
valu
ated
aga
inst
LEP
S in
terp
reta
tion.
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 4.
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 3.
IKO
NO
S e
valu
ated
aga
inst
LEP
S in
terp
reta
tion.
Tab
le
Ilb
I lc
I Id
Land
sat
Land
sat
Land
sat
eval
uate
d ev
alua
ted
eval
uate
d ag
ains
t ag
ains
t ag
ains
t LE
PS
in
terp
reta
tion
4.
inte
rpre
tatio
n 3.
in
terp
reta
tion.
IKO
NO
S
eval
uate
d ag
ains
t in
terp
reta
tion
4.
9a
IKO
NO
S
IKO
NO
S
eval
uate
d ev
alua
ted
agai
nst
15c.
The
ref
eren
ce d
ata
set a
gain
st w
hich
the
clas
sifie
d La
ndsa
t and
IKO
NO
S im
ages
are
eva
luat
ed a
gain
st d
o no
t sig
nific
antly
af
fect
ove
rall
accu
racy
res
ults
. T
he o
nly
exce
ptio
ns a
re w
hen
the
clas
sifie
d La
ndsa
t im
age
is e
valu
ated
aga
inst
all
the
test
po
ints
from
inte
rpre
tatio
n 3 a
nd te
st p
oint
s fr
om in
terp
reta
tion
3 p
olyg
ons
larg
er th
an 0
.096
ha.
Land
sat e
valu
ated
aga
inst
inte
rpre
tatio
n 3.
Land
sat e
valu
ated
aga
inst
L EP
S in
terp
reta
tion.
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 3.
IKO
NO
S e
valu
ated
aga
inst
LEP
S in
terp
reta
tion.
Tab
le
8a
8b
8c
8d
I la
Ilb
I lc
I Id
10a
lob
1 Oc
10d
12a
12b
12c
12d - La
ndsa
t eva
luat
ed a
gain
st in
terp
reta
tion
4.
7a
7b
7c
7d
2.36
1.88
2.02
0.26
0.88
1.32
0.38
0.00
IKO
NO
S e
valu
ated
aga
inst
inte
rpre
tatio
n 4.
15d.
Ove
rall
accu
racy
resu
lts a
re n
ot s
igni
fican
tly a
ffect
ed b
y ch
angi
ng th
e te
st p
oint
sam
plin
g in
terv
al fr
om 1
00m
to l5
Om
.
Land
sat e
valu
ated
aga
inst
inte
rpre
tatio
n 4.
Tes
t po
int s
ampl
ing
inte
rval
= 1
50m
.
I IK
ON
OS
eva
luat
ed a
gain
st
inte
rpre
tatio
n 4.
Tes
t po
int s
ampl
ing
inte
rval
= 1
50m
.
Tab
le
Land
sat e
valu
ated
aga
inst
in
terp
reta
tion
4.
IKO
NO
S e
valu
ated
aga
inst
in
terp
reta
tion
4.
Tes
t poi
nt s
ampl
ing
inte
rval
= 1
00m
.
7a
7b
7c
7d
Tes
t po
int s
ampl
ing
inte
rval
= 1
00m
.
9a
9b
9c
9d
Table 16. Principal components of the Landsat and IKONOS satellite images.
16a. Landsat principal components 1 and 2 and the corresponding eigenvectors2 for each band.
Landsat bands Landsat principal Landsat principal component 1 component 2 eigenvectors eigenvectors
1 (Blue) 0.266 -0.1 10
2 (Green) 0.300 -0.024
3 (Red) 0.490 -0.110
4 (NIR)
5 (Mid IR)
7 (Mid IR)
8 (Panchromatic)
% of the total scene variance represented 63 27 in each principal component
16b. IKONOS principal components 1 and 2 and the corresponding eigenvectors of each band.
IKONOS Bands IKONOS principal IKONOS principal component 1 component 2 eigenvectors eigenvectors
1 (Red) 0.01 5 0.644
2 (Green) 0.038 0.61 1
3 (Blue) -0.01 0 0.459
4 (NIR) 0.999 -0.029
% of the total scene variance represented 67 32 in each principal component
Eigenvectors are the variance contribution from each original input band to each transformed principal component band.
70
Tab
le 1
7. H
abita
t typ
es id
entif
ied
by L
ee &
Rud
d (2
002)
as
impo
rtan
t for
the
cons
erva
tion
of b
iodi
vers
ity in
the
GV
RD
. T
his
tabl
e in
dica
tes
whe
ther
the
habi
tat t
ype
was
map
ped
in th
e pr
esen
t stu
dy, a
nd if
not
, whe
ther
I b
elie
ve it
is p
ossi
ble
to
map
dis
tinct
ly w
ith th
e us
e of
sat
ellit
e im
ager
y al
one
or w
ith th
at a
dditi
on o
f anc
illar
y da
ta la
yers
Hab
itat T
ypes
of
Inte
rest
M
appe
d in
P
ossi
ble
to m
ap u
sing
onl
y sa
telli
te
Pos
sibl
e to
map
with
sat
ellit
e im
ager
y an
d cu
rren
t stu
dy
imag
ery
addi
tion
of a
ncill
ary
data
laye
rs
WE
TLA
ND
EC
OS
YS
TE
MS
ye
s -
Mar
sh a
nd S
wam
p no
ye
s (P
arm
uchi
et.
a1 2
002)
Bog
no
ye
s (T
akeu
chi e
t. a
l20
03
) -
Ver
nal P
ool
no
no, n
eed
grou
nd s
ampl
ing
to id
entif
y -
OP
EN
WA
TE
R
Yes
- -
EC
OS
YS
TE
MS
Lake
no
no
, una
ble
to d
istin
guis
h fr
om o
ther
ye
s, w
ith a
dditi
on o
f hy
drol
ogic
dat
a la
yer
open
wat
er e
cosv
stem
s
Pon
d no
no
, una
ble
to d
istin
guis
h fr
om o
ther
ye
s, w
ith a
dditi
on o
f hy
drol
ogic
dat
a la
yer
open
wat
er e
cosy
stem
s
Riv
er
no
no, u
nabl
e to
dis
tingu
ish
from
oth
er
yes,
with
add
ition
of h
ydro
logi
c da
ta la
yer
open
wat
er e
cosy
stem
s
no
no, u
nabl
e to
dis
tingu
ish
from
oth
er
yes,
with
add
ition
of h
ydro
logi
c da
ta la
yer
open
wat
er e
cosy
stem
s
Res
ervo
ir no
no
, una
ble
to d
istin
guis
h fr
om o
ther
ye
s, w
ith a
dditi
on o
f hy
drol
ogic
dat
a la
yer
open
wat
er e
cosy
stem
s
Ditc
h an
d S
torm
wat
er
no
no, u
nabl
e to
dis
tingu
ish
from
oth
er
yes,
with
add
ition
of
hydr
olog
ic d
ata
laye
r D
eten
tion
Pon
d op
en w
ater
eco
syst
ems
Est
uary
no
ye
s (D
onog
hue
& M
ironn
et 2
002)
-
Hab
itat T
ypes
of
Inte
rest
M
appe
d in
P
ossi
ble
to m
ap u
sing
onl
y sa
telli
te
Pos
sibl
e to
map
with
sat
ellit
e im
ager
y an
d cu
rren
t stu
dy
imag
ery
addi
tion
of a
ncill
ary
data
laye
rs
UR
BA
N A
ND
RU
RA
L E
CO
SY
ST
EM
S
yes
(Sug
umar
an e
t. a
l20
02
)
Bou
leva
rd a
nd S
tree
t Tre
es
no
no, d
eter
min
ed b
y sp
atia
l pro
xim
ity to
ye
s, w
ith a
dditi
on o
f tra
nspo
rtat
ion
data
laye
r ro
ads
-- -
--
-
-
- -
-
Hed
gero
ws,
Rig
hts-
of-w
ay
no
no, d
iffic
ult t
o di
stin
guis
h fr
om o
ther
ye
s, w
ith a
dditi
on o
f tra
nspo
rtat
ion
data
laye
r ve
geta
tion
Shr
ub C
omm
uniti
es a
nd
no
yes
(Gos
lee
et. a
l 200
3)
- T
hick
ets
Law
ns a
nd G
arde
ns
no
no, d
eter
min
ed b
y sp
atia
l pro
xim
ity to
ye
s, w
ith a
dditi
on o
f cad
astr
al d
ata
laye
r re
side
ntia
l dev
elop
men
t
BLU
FF
AN
D B
ED
RO
CK
no
no
, con
fuse
d w
ith im
perv
ious
sur
face
s O
UT
CR
OP
PIN
GS
'1
Mar
ine
no
no, c
onfu
sed
with
impe
rvio
us s
urfa
ces
-
Scr
ee a
nd T
alus
Slo
pes
no
no, c
onfu
sed
with
impe
rvio
us s
urfa
ces
yes,
with
the
addi
tion
of a
dig
ital e
leva
tion
laye
r
Inla
nd a
nd U
plan
d B
luffs
no
no
, con
fuse
d w
ith im
perv
ious
sur
face
s
Hab
itat T
ypes
of
Inte
rest
M
appe
d in
P
ossi
ble
to m
ap u
sing
onl
y sa
telli
te
Pos
sibl
e to
map
with
sat
ellit
e im
ager
y an
d cu
rren
t stu
dy
imag
ery
addi
tion
of a
ncill
ary
data
laye
rs
HE
RB
AN
D G
RA
SS
Ye
s -
- E
CO
SY
ST
EM
S
Old
Fie
ld
no
no, n
eed
grou
nd s
ampl
ing
to id
entif
y -
Pas
ture
no
no
, con
fuse
d w
ith c
ropl
and
and
athl
etic
ye
s, w
ith a
dditi
on o
f agr
icul
tura
l lan
d us
e fie
lds
data
laye
r
Cro
plan
d no
no
, con
fuse
d w
ith p
astu
re a
nd a
thle
tic
yes,
with
add
ition
of a
gric
ultu
ral l
and
use
field
s da
ta la
ver
Ath
letic
Fie
lds
and
Gol
f C
ours
es
no
no, c
onfu
sed
with
pas
ture
and
cro
plan
d ye
s, w
ith a
dditi
on o
f la
nd u
se d
ata
laye
r
AR
TIF
ICIA
L S
TR
UC
TU
RE
S
Yes
- -
Bui
ldin
gs
no
no, c
onfu
sed
with
oth
er im
perv
ious
ye
s, w
ith a
dditi
on o
f lan
d us
e da
ta la
yer
surf
aces
4
P
Tra
nsm
issi
on T
ower
s no
no
, con
fuse
d w
ith o
ther
impe
rvio
us
yes,
with
add
ition
of
land
use
dat
a la
yer
surf
aces
Nes
t Box
es
no
no, t
oo s
mal
l of a
feat
ure
to id
entif
y -
Oth
er B
uilt
Env
ironm
ents
Ye
s -
- E
XP
OS
ED
OR
DIS
TU
RB
ED
Ye
s -
- S
ITE
S
Qua
rrie
s an
d G
rave
l Pits
no
no
, con
fuse
d w
ith o
ther
impe
rvio
us
yes,
with
the
addi
tion
of a
land
use
dat
a la
yer
surf
aces
Bar
ren
Land
Ye
s -
-
Figures
deciduous
0 1000 2000 Meters I I
impervious
soil
water
wetland
Figure 2. The classified Landsat image of the study site showing the seven original land cover classes. Pixel size = 30m.
0 1000 2000 Meters
I coniferous
I soil
7 water
1 wetland
Figure 3. The classified IKONOS image of the study site showing the seven original land cover classes. Pixel size = 4m.
deciduous
1 disturbed
1 wetland
0 1000 2000 Meters I
Figure 4. The classified Landsat image of the study site showing the disturbed land cover class. Pixel size = 30m.
coniferous - r l deciduous
1 disturbed
water
wetland
0 1000 2000 Meters I D l
Figure 5. The classified IKONOS image of the study site showing the disturbed land cover class. Pixel size = 4m.
