UNIVERSITY OF CALGARY

A Comparison of Relative and Absolute Change Detection for

Measuring Forest Disturbance

by

Benjamin Ljungkull Curry

A DOCUMENT

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF GEOGRAPHIC INFORMATION SYSTEMS

DEPARTMENT OF GEOGRAPHY

CALGARY, ALBERTA

AUGUST, 2008

© Benjamin Ljungkull Curry 2008

UNIVERSITY OF CALGARY

FACULTY OF GRADUATE STUDIES

The undersigned certify that they have read, and recommend to the Faculty of Graduate

Studies for acceptance, a document entitled "A Comparison of Relative and Absolute

Change Detection for Measuring Forest Disturbance" submitted by Benjamin Ljungkull

Curry in partial fulfilment of the requirements of the degree of Master of Geographic

Information Systems.

Supervisor, Dr. Greg McDermid, Department of Geography

Dr. John Yackel, Department of Geography

Dr. Cormack Gates, Faculty of Environmental Design

Date

ii

Abstract

A comparison of absolute and relative indices in classifying degrees of forest disturbance

has been completed in a modeling environment under ideal conditions, and for a real

environment under natural conditions. In remote sensing, estimation of forest disturbance

in most commonly done using image differencing, where the earlier image is subtracted

from the more recent image resulting in higher values where higher amounts of change

has occurred. This method provides an absolute difference between the two dates and is

useful when the end product desired is also absolute, like in estimating total biomass

changes or carbon release. However, if mapping degrees of forest disturbance is required

then absolute differencing may not be the appropriate index to use, given that the

calculated difference will be correlated with forest biomass originally present. For

example, an image pixel with a value of 40 reduced to 10 will result in an absolute

change of 30 and a relative percent change of 75%. Using reflectance output from a

canopy reflectance model three relative indices found in the literature were compared

with absolute differencing in their respective abilities to classify four degrees of change

(0-25%, 26-50%, 51-75%, and 76-100%). All three relative indices provided significant

improvements (p <0.001) in classification over the absolute index. As a result, the same

experiment was undertaken using bi-temporal Landsat images and associated ground data

to classify degree of forest disturbance caused by mountain pine beetle infestation.

Classification accuracies of beetle damage using both absolute and relative indices were

poor overall. Significant differences between the techniques were not seen, as was

predicted form the modeling results. Possible reasons for insignificant results are

discussed and recommendations for improving future research are given.

iii

Acknowledgements

First and foremost I would like to thank Dr. Greg McDermid for supervising my

final project and providing clear guidance throughout it. Secondly, I would like to

acknowledge Katie Yalte for the work she did on her Master of Geographic Information

Systems final project, which laid the groundwork for this final project. Next, I would like

to extend my thanks to Mike Wulder and Joanne White, at Canadian Forest Service, for

providing data to undertake the final analysis of this project. I would like to especially

thank Mike for his feedback and comments to a rough draft of this document. Finally, I

would like to thank Sylvain Leblanc, at Natural Resources Canada, who provided me

with the Five-Scale software required for canopy reflectance modeling.

iv

Table of Contents

Approval Page..................................................................................................................... ii Abstract .............................................................................................................................. iii Acknowledgements............................................................................................................ iv Table of Contents.................................................................................................................v List of Tables .................................................................................................................... vii List of Figures .................................................................................................................... ix List of Abbreviations and Symbols......................................................................................x 

CHAPTER ONE: INTRODUCTION..................................................................................1 

CHAPTER TWO: LITERATURE REVIEW......................................................................4 2.1 Overview of Change Detection..................................................................................4 2.2 Review of Absolute Change Detection......................................................................9 2.3 Review of Relative Change Detection.....................................................................12 

2.3.1 Percent Change................................................................................................13 2.3.2 Normalized Change .........................................................................................15 2.3.3 Log Change .....................................................................................................15 

2.4 Estimating Mountain Pine Beetle Damage..............................................................17 2.4.1 Background......................................................................................................17 2.4.2 Remote Sensing Methods for Detection..........................................................19 2.4.3 Ground Verification Data ................................................................................23 

CHAPTER THREE: MODELING FOREST DISTURBANCE .......................................26 3.1 Introduction..............................................................................................................26 3.2 Theoretical Foundations ..........................................................................................27 

3.2.1 Theoretical Relationship..................................................................................27 3.2.2 Theoretical Classification................................................................................33 

3.3 Methods: 5-Scale Canopy Reflectance Model.........................................................36 3.3.1 Model Parameters............................................................................................37 3.3.2 Nonlinear Transformation ...............................................................................39 3.3.3 Classification Accuracy...................................................................................41 

3.4 Results & Discussion ...............................................................................................43 3.4.1 Percent Change in Ground Data ......................................................................43 3.4.2 Normalized Change in Ground Data ...............................................................46 3.4.3 Log Change in Ground Data............................................................................47 3.4.4 Comparison of Indices.....................................................................................49 

3.5 Conclusion ...............................................................................................................51 

CHAPTER FOUR: CLASSIFYING MOUNTAIN PINE BEETLE DAMAGE ..............52 4.1 Introduction..............................................................................................................52 4.2 Methods ...................................................................................................................53 

4.2.1 Study Area .......................................................................................................53 4.2.2 Satellite Images ...............................................................................................53 4.2.3 Absolute and Relative Indices .........................................................................55 4.2.4 Ground Verification Data ................................................................................56 

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4.2.5 Accuracy Assessment ......................................................................................59 4.3 Results & Discussion ...............................................................................................59 

4.3.1 Relation between TCW and Green Trees........................................................59 4.3.2 Accuracy Assessment ......................................................................................63 4.3.3 Spectral Response to Mountain Pine Beetle Damage .....................................67 

4.4 Conclusion ...............................................................................................................71 

REFERENCES ..................................................................................................................73 

vi

List of Tables

Table 1. Commonly seen remote sensing change detection techniques with examples from the literature. ...................................................................................................... 7 

Table 2. Indices used for classifying change in remotely sensed data and ground data... 23 

Table 3. Crown closure and TCW values were generated for Date1 and Date2, using the linear relationship in Figure 1. Different methods of measuing change in ground data: PΔ, NΔ, LΔ, and remotely sensed data: EWDI, PEWDI, NEWDI, and LEWDI are calculated........................................................................................ 29 

Table 4. Classification of PΔ in canopy using EWDI. Kappa statistic is 0.37. ................ 35 

Table 5. Classification of PΔ in canopy using: PEWDI, NEWDI, and LEWDI. Kappa statistic is 1.00........................................................................................................... 35 

Table 6. Wavelength range for Landsat ETM+ Bands 1-5 and 7, along with the midpoint. ................................................................................................................... 38 

Table 7. Paramters used in 5-Scale. .................................................................................. 39 

Table 8. 5-Scale output produced when an LAI of 3.0 is used as input. .......................... 39 

Table 9. Thresholds between PΔ categories of change..................................................... 43 

Table 10. Classification of PΔ and EWDI. Kappa statistic is 0.39................................... 44 

Table 11. Classification of PΔ and PEWDI. Kappa statistic is 0.77................................. 44 

Table 12. Classification of PΔ and NEWDI. Kappa statistic is 0.79................................ 44 

Table 13. Classification of PΔ and LEWDI. Kappa statistic is 0.72. ............................... 44 

Table 14. Z-test to identify signifiicant differences between classification methods, Z-score less than 1.96 is not significant at the 95% confidence interval...................... 46 

Table 15. Thresholds between NΔ categories of change. ................................................. 46 

Table 16. Classification of NΔ and EWDI. Kappa statistic is 0.34. ................................. 47 

Table 17. Classification of NΔ and NEWDI. Kappa statistic is 0.73. .............................. 47 

Table 18. Thresholds between LΔ categories of change. ................................................. 48 

Table 19. Classification of LΔ and EWDI. Kappa statistic is 0.25................................... 48 

Table 20. Classification of LΔ and LEWDI. Kappa statistic is 0.79. ............................... 48 

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Table 21. Summary Statistics for TCW 2002 and 2004. .................................................. 63 

Table 22. Thresholds between categories of change. ....................................................... 66 

Table 23. Classification of EWDI. Kappa statistic is 0.012. ............................................ 66 

Table 24. Classification of PEWDI. Kappa statistic is 0.024. .......................................... 66 

Table 25. Classification of NEWDI. Kappa statistic is 0.059. ......................................... 66 

Table 26. Z-test to identify signifiicant differences between classification methods, a Z-score less than 1.96 is not considered significant at a 95% confidence interval... 67 

Table 27. Correlation (R) between different resolution (5 m, 15 m, and 30 m) bands (Red, Green, and Blue) for both the number of green trees and red trees in 2004. .. 69 

viii

List of Figures

Figure 1. An arbitrary linear relationship between crown closure and TCW................... 28 

Figure 2. The data from Table 3 plotted to show the relationship between PΔ in canopy with EWDI (a), PEWDI (b), NEWDI (c), and LEWDI (d).......................... 31 

Figure 3. The data from Table 3 plotted to show the relationship between NΔ in canopy cover with EWDI (a), and NEWDI (b). ....................................................... 32 

Figure 4. The data from Table 3 plotted to show the relationship between LΔ in canopy cover with EWDI (a), and LEWDI (b)......................................................... 33 

Figure 5. The linear regression of LAI versus wetness (a), followed by the logarithmic regression (b), and a log transformation of LAI (c). ............................. 40 

Figure 6. The log transformed LAI (Figure 5c) with an adjustment of +1.0 (a), and an adjusted wetness (+0.261) versus adjusted log transformed LAI (b). ...................... 41 

Figure 7. Study area located at Angstad Creek, 25 km south of Merritt, British Columbia................................................................................................................... 54 

Figure 8. Map showing a close-up view of the 30 m plots used in measuring red attack mountain pine beetle damage. ........................................................................ 58 

Figure 9. The linear regression of Number of Trees in 2002 versus Wetness 2002 (a), followed by the logarithmic regression (b), and finally the log transformed number of trees versus wetness 2002 (c). ................................................................. 60 

Figure 10. The linear regression of Number of Trees in 2004 versus Wetness 2004 (a), followed by the logarithmic regression (b), and finally the log transformed number of trees versus wetness 2002 (c). ................................................................. 61 

Figure 11. The linear regression of EWDI versus Percent Change in Trees (a), PEWDI versus Percent Change in Trees (b), and NEWDI versus Percent Change in Trees...................................................................................................................... 64 

ix

List of Abbreviations and Symbols

Symbol AD

Definition Average Difference

AΔ Absolute Change BRDF Bidirectional Reflectance Distribution Function CBI Composite Burn Index DI Difference Index ETM+ Enhanced Thematic Mapper Plus EWDI Enhanced Wetness Difference Index GTT Green Trees Transformed HR VIR High Resolution Visible and Infrared K Kappa LAI Leaf Area Index LEWDI Log Enhanced Wetness Difference Index LIBERTY Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields LΔ Log Change MPB Mountain Pine Beetle MSS Multispectral Scanner NBR Normalized Burn Ratio NDMI Normalized Difference Moisture Index NDVI Normalized Difference Vegetation Index NEWDI Normalized Enhanced Wetness Difference Index NIR Near Infrared NΔ Normalized Change PCA Principal Compnents Analysis PEWDI Percent Enhanced Wetness Difference Index PΔ Percent Change RMSE Root Mean Square Error SPCA Selective Principal Compnents Analysis SWIR Shortwave Infrared TCB Tasseled Cap Brightness TCG Tasseled Cap Greenness TCT Tasseled Cap Transformation TCW Tasseled Cap Wetness TM Thematic Mapper

x

1

Chapter One: Introduction

Being able to detect subtle changes in forest structure is becoming increasingly important

with the spread of insect caused mortality, monitoring the effects of climate change,

managing at risk species, and timber harvest verification (Healey et al. 2006). Detecting

forest disturbance is important because these changes affect volume and growth of timber

which will in turn change forest inventory data (Aldrich 1975). Therefore, accurate

detection of forest changes is of significant interest to ecosystem managers and scientists.

Making informed management decisions relies on change detection techniques which can

quickly and accurately identify forest canopy changes (Nelson 1983).

Change detection is one of the primary uses of remotely sensed images from

Earth-orbiting satellites, due to their repetitive coverage at short intervals and consistent

image quality (Healey et al. 2005; Ridd and Liu 1998; Singh 1989). Practical applications

of remote sensing change detection include: assessing deforestation, monitoring

cultivation, land use change, capturing vegetation phenology, disaster monitoring,

monitoring snow-melt, and thermal changes, to name a few (Singh 1989). Aldrich (1975)

organized forest disturbance detectable by remote sensing into nine categories: no

disturbance, harvesting, silvicultural treatments, land clearing, insect and disease damage,

fire, flooding, regeneration, and other.

A review of the remote sensing literature indicates that estimating forest

disturbance is most often done using image differencing, where change is calculated by

subtracting the more recent image from an earlier image (Change = Date1 – Date 2).

Therefore, image differencing results in measures of absolute change, that is, the amount

of change is dependent on the quantity of forest originally present. Absolute change may

2

be appropriate for estimating changes in total biomass or nutrients; however, it may be

unsuitable for estimating degrees of disturbance to an ecosystem (Miller and Thode

2007). Relative change techniques that account for the amount of forest present before

any change has occurred (Change = Date1 – Date 2/ Date1) should be more correct at

classifying relative categories of change like ordinal (e.g. low, medium, high, and

extreme), or percent (e.g. 0-25%, 26-50%, 51-75%, and 76-100%). For example, if an

image pixel with 40 trees is reduced to 10 trees this will result in an absolute decrease of

30 trees, while the relative percent change is 75%.

The objective of this study is to test the hypothesis that using relative change

techniques will lead to increased classification accuracy over absolute change detection

techniques, in detecting relative categories of change (0-25%, 26-50%, 51-75%, and 76-

100%) for forest disturbance. To do this, classification results using three types of

relative change indices found in the literature: percent change, normalized change, and

log percent change, will be compared and contrasted with absolute change and with each

other.

A current example of forest disturbance in western North America is the

infestation of lodgepole pine (Pinus contorta) trees by mountain pine beetles

(Dentroctonus ponderosa Hopkins) (Coops et al. 2006a; Franklin et al. 2003; Skakun et

al. 2003; Wulder et al. 2006a). Forest managers require information about the mountain

pine beetle at different scales in order to detect, map, predict infestation, and accurately

update inventories (Wulder et al. 2004). Those change detection studies in the literature

that have captured beetle infestation have done so using absolute difference in

measurements between two points in time (Coops et al. 2006b; Franklin et al. 2005;

3

Franklin et al. 2002a; Skakun et al. 2003; Wulder et al. 2006a). While detecting degree of

insect damage using relative difference in measurements has not been seen.

To fulfill the main objectives of this study three chapters follow, each

contributing to a better understanding of the uses of absolute and relative change in

classifying forest disturbance. Chapter Two is a literature review of the commonly used

change detection techniques for characterising forest disturbance, and more specifically,

those studies that have used absolute and relative change indices. Chapter Three

introduces the theoretical background to how relative change provides improved

classification over absolute change. As well, a canopy reflectance model is used to test

this theoretical background in a simulated environment. Finally, Chapter Four provides

an example of the theory and modeling results applied to actual remotely sensed data in

classifying degrees of forest damage caused by mountain pine beetles on the ground.

4

Chapter Two: Literature Review

The following literature review has two main goals: first, to provide an overview of the

concept of absolute and relative change detection as it applies to remote sensing, and

second, to use forest disturbance caused by the mountain pine beetle as a case study for

demonstrating how applying relative change can improve classification accuracies of

relative categories of disturbance. The review begins with a summary of commonly used

change detection indices in monitoring forests. Then, absolute change detection is

examined using one of the most commonly chosen change detection techniques, image

differencing. Next, relative change detection is introduced and three techniques found in

the literature are examined; percent change, normalized change, and log percent change.

An overview of the pine beetle life cycle is given along with the successful use of the

Tasseled Cap Transformation (TCT) vegetation index for detecting damage due to

infestation. Finally, a review of biophysical parameters commonly collected on the

ground to measure forest health and beetle damage is given.

2.1 Overview of Change Detection

Changes on the ground can be continuous, or discontinuous, but in general land cover

change occurring gradually is considered more common than complete conversion

(Coppin et al. 2004). While some changes are considered natural, such as flooding,

disease outbreaks, and growth, others are considered anthropogenic, like tree harvesting

for example (Coppin et al. 2004). Whether changes are gradual or rapid, natural or

anthropogenic, the need for accurate information on land-use change for management and

5

policy development is vital. Change detection is one of the primary uses of remotely

sensed images (Healey et al. 2005; Ridd and Liu 1998; Singh 1989).

A great deal of research has been done on the spectral appearance of forest

changes and the multispectral remote sensing methods which can be used to detect these

changes (Collins and Woodcock 1996). The general understanding is that changes in land

cover within the object scene will result in considerable changes in the electromagnetic

spectrum captured by satellites as radiance values, which are greater than differences due

to atmospheric conditions, sun angle, and moisture (Singh 1989). It is assumed that larger

changes on the ground will result in a larger change in radiance values (Mas 1999). By

observing the object of interest at two points in time (bi-temporal) the presence of change

can be identified (Ridd and Liu 1998). For example, the Landsat program is the United

States’ is the oldest land observation satellite, having captured moderate spatial resolution

images of the entire earth’s surface since 1972 (Jensen 2005). This vast library of images

makes it a very suitable satellite for bi-temporal change detection studies.

Choice of change detection technique will ultimately depend on the type of

change that has occurred on the ground (Franklin et al. 2001; Jensen 2005). For forest

disturbance this could include: stand replacing disturbance, partial harvesting, insect

infestation, and other natural disturbances. In a review of remote sensing change

detection techniques, Singh (1989) concluded that different change detection algorithms

can produce different change maps for the same environment. One of the challenges

facing the remote sensing community is a better understanding of which change detection

methods should be used for specific applications (Collins and Woodcock 1996). Often,

6

the choice of change detection technique is pragmatic rather than scientific, with a focus

on success of a chosen approach rather than the shortcomings (Coppin et al. 2004).

Table 1 summarizes some of the more common indices developed for vegetation

change detection. This list is by no means exhaustive, as well, new algorithms are

continuously being developed (Healey et al. 2005). In general, change detection

techniques fall into one of two categories: image enhancement and postclassification

comparison (Yuan and Elvidge 1998). Image enhancement, such as image differencing or

ratioing, attempts to detect spectral changes directly, while post classification comparison

labels all land cover types found in Date1 and Date2 and then compares classified images

(Yuan and Elvidge 1998).

In one study, 75 change detection techniques were evaluated both visually and

statistically using bi-temporal Landsat images of the Washington, D.C./Baltimore

corridor for 1973 and 1990 (Yuan and Elvidge 1998). The goal was to find a technique

that would best detect vegetation changes related to CO2 models and that would be easy

to use. Results showed that band differencing methods outperformed band ratioing

methods. The best performance was obtained using Normalized Difference Vegetation

Index (NDVI) differencing (NDVIDate1-NDVIDate2) on images with clouds removed, while

automated scattergram controlled regression differencing performed best when clouds

were included in the analysis.

7

Table 1. Commonly seen remote sensing change detection techniques with examples from the literature.

Methods Description Reference

Image Differencing

Image differencing subtracts the imagery of one date from another date. Areas that have changed result in positive and negative values, while no change areas have a value of zero (Jensen 2005).

Ridd and Liu 1998; Nelson 1983; Mas 1999; Jin and Sader 2005; Wulder et al. 

2006a;

Image Ratioing

Similar logic as image differencing, but instead of subtraction the images are divided. Therefore, no change areas have a ratio of 1 (Jensen 2005).

Nelson 1983;              Yuan and Elvidge 1998

Image Regression

Mathematical model describing the fit between two multi‐date images of the same area developed through stepwise‐regression (Coppin et al. 2004). Ridd and Liu 1998

Write Function Memory Insertion

A visual change detection method, were individual bands from different dates are displayed using red, green, and blue (Jensen 2005). Franklin et al. 2002a

Chi‐square Transformation

Uses six reflective bands to create a single change image, then thresholding is used to highlight pixels of change (Ridd and Liu 1998) Ridd and Liu 1998

Multi‐date Composite Image

Take multiple dates of a remotely sensed image and place into a single dataset. Change information can then be extracted by using a traditional classification, or by subjecting it to principal components analysis (Jensen 2005).

Mas 1999

Post‐classification Comparison

Requires rectification and classification of each image, the two images are then compared pixel‐by‐pixel using a change detection matrix (Jensen 2005).

Mas 1999;                Yuan and Elvidge 1998

Composite Analysis

Uses combined registered datasets collected under similar conditions but from different years. Statistics where vegetative canopy change is occurring should be different from no change (Coppin et al. 2004).

Healey et al. 2005

Bi‐temporal Linear Data 

Transformation

Linear data transformation techniques are applied to two dates of imagery that has been stacked, such as principal components analysis (PCA) (Coppin et al. 2004). Mas 1999

Spectral Change Vector Analysis

A multivariate change detection technique that processes the spectral and temporal component of the data and outputs change magnitude and direction (Coppin et al. 2004). Chen et al. 2003

8

In monitoring land cover changes in Campeche, Mexico, Mas (1999) compared

six change detection methods using Landsat Multispectral Scanner (MSS) scenes from

February 1974 and April 1992. Change detection techniques included: image

differencing, vegetation index differencing, selective principal components analysis

(SPCA), direct multi-date unsupervised classification, post-classification change

differencing, and a combination of image enhancement and post-classification

comparison. The purpose of the study was twofold: first, identification of change/no

change categories, and second identifying the type of change that had occurred. The most

accurate techniques were determined respectively to be: post-classification, SPCA band

2, band 2 differencing, and NDVI differencing. Results were contradictory to

comparative studies, with explanation being due to different soil moisture and vegetation

phenology for the two Landsat scenes.

In another study, four change detection algorithms were applied to bi-temporal

Landsat images over Salt Lake Valley, Utah (Ridd and Liu 1998). The change images

were thresholded and compared with ground data for the study area. The indices

compared include: single band differencing, single band regression, TCT differencing,

and Chi square transformation. Results showed that differencing bands 2 and 3, and the

regression of band 2 and 3 were best at separating change and no change on the ground.

This study concluded that none of the indices was absolutely superior to the others and

that the final choice should depend on the environmental conditions and the application

objectives.

Increasingly accurate information on forest health is being expected by the public,

government, and industry (Coops et al. 2006a). Detecting forest disturbance is

9

particularly important with respect to estimating global carbon cycles, local and regional

forest management (Healey et al. 2005), percent canopy cover, area of forest loss, and

presence of insect attack (Franklin et al. 2001). Remote sensing satellites which monitor

forest change in a repetitive manner provide the ideal opportunity for estimating forest

structure changes (Healey et al. 2005). Coppin et al. (2004) highlight several challenges

monitoring ecosystem change using satellite remote sensing, the first of which is

detecting modifications and conversions to an ecosystem accurately, such as estimating

changes to a forest which is not stand replacing. By understanding the change process

occurring, it allows for a more advanced detection and categorization than just the

presence and absence of change, such as, degrees of change to an ecosystem.

2.2 Review of Absolute Change Detection

In reviewing change detection techniques, univariate image differencing was found to be

one of the most commonly chosen change detection techniques, used for a variety of

environments (Coppin et al. 2004; Jin and Sader 2005; Miller and Thode 2007; Singh

1989; Skakun et al. 2003; Wulder et al. 2006b). Image differencing uses spatially

registered images of the same area collected at different times, one digital image is

subtracted from the other to obtain a third image showing the difference between pairs of

pixels (Jensen 2005; Mas 1999; Ridd and Liu 1998; Singh 1989):

Absolute Change (1) 21 DateDate −=

10

where Date1 is the image from the earlier point in time and Date 2 is the image from the

recent point in time. The pixel values within the change image usually have a Gaussian

distribution with pixels showing no change distributed around the mean and high change

pixels found in the tails of the distribution (Jensen 2005; Mas 1999). That is, pixels that

have seen a high amount of vegetation growth since the earlier point in time will be

located in the far left (negative) tail of the Gaussian distribution, while pixels that have

undergone high vegetation loss will be located in the far right (positive) tail.

Absolute image differencing can be performed on individual bands of an image as

well as on vegetation index images, such as the NDVI (Lyon et al. 1998; Nelson 1983;

Song et al. 2001) and all three TCT images: brightness (TCB), greenness (TCG), and

wetness (TCW) images (Franklin et al. 2002b; Ridd and Liu 1998).

Wulder et al. (2006b) recommends using the difference between two image dates

to better detect pine beetle infested trees, rather than a single date image which contains a

spectral response made up of several stand elements. The elements present in a pixel

include trees that are healthy as well as trees at different stages of attack. Skakun et al.

(2003) used absolute image differencing between multiple Landsat images to capture the

difference in reflectance patterns over 1 and 2 years from when dead trees were observed

by aerial surveys.

In a study by Franklin et al. (2001) bi-temporal Landsat images were transformed

into TCB, TCG, TCW, NDVI, and into the first three components in the principal

components analysis (PCA1, PCA2, and PCA3). The absolute differences in mean

percent reflectance were determined on a per pixel basis and were then averaged to a

forest stand level. Results showed that absolute change in TCW was most sensitive to

11

changes in forest structure. In another study, Franklin et al. (2002a) used image

differencing of TCW images over a 15 year time interval to effectively detect forest

structure changes in the Fundy Model Forest, New Brunswick.

In a study by Jin and Sader (2005), the normalized difference moisture index

(NDMI) and TCW were compared for their ability to detect forest disturbances for

different forests and harvest intensities at one, two, and five year intervals for Landsat

acquisition. The TCW and NDMI difference images were obtained by simple subtraction

(Equation 1) of the more recent date from the earlier date. The two vegetation indices

were determined to be highly correlated and no significant difference was seen between

methods in detecting forest disturbance. For detection of partial forest harvests, images

collected each year were suggested to minimize classification errors, and for detecting

clear cuts up to 5 year Landsat interval was concluded to be appropriate.

(Lyon et al. 1998) compared seven vegetation indices for sensitivity in detecting

land cover changes including: deforestation, fire, and cropping activities. These

vegetation indices all take advantage of the different absorption, reflectance, and

transmittance of vegetation for the red (band 3) and near-infrared (band 4) parts of the

electromagnetic spectrum captured by the Landsat satellite. This study concluded that of

the seven indices compared, NDVI image differencing was the best overall change

detection technique given laboratory and field results. In another study, which compared

image differencing, image ratioing, and vegetation index differencing, for their ability to

detect gypsy moth defoliation it was also concluded that vegetation index differencing

provided the most accurate measures of forest canopy change (Nelson 1983).

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2.3 Review of Relative Change Detection

The advantage of relative differences over absolute differences is that relative numbers

are pure and independent of units of measurement, allowing for direct comparison of

differences (Tornqvist et al. 1985). For example, the final output from Equation 1 is a

measure of absolute change which is correlated to the biomass in the pre-change image,

making it difficult to compare areas with different amounts of original biomass (Miller

and Thode 2007). That is, a pixel containing a small amount of vegetation will experience

quite a different spectral response change compared to a pixel with a high amount of

vegetation, when for example 90% of the biomass is lost. Both pixels in the above

example would have experienced a “high” loss in biomass, but this information could be

lost or distorted if absolute difference was used to determine degree of change (Miller

and Thode 2007). Coppin and Bauer (1994) suggested the use of relative image

differencing in order to reduce the occurrence of obtaining identical change values for

different change events.

Few studies have been done using relative measures of change with either remote

sensing images (Miller and Thode 2007) or with associated ground data (Coppin and

Bauer 1994). Those studies that were found to use relative change applied a variety of

algorithms to achieve relative measures of change. Miller and Thode (2007) along with

Healey et al. (2006) successfully employed percent change to increase accuracy in

change detection, while Coppin and Bauer (1994) used normalized change, and Galal and

Qureshi (1997) used log percent change, first proposed by (Tornqvist et al. 1985).

13

2.3.1 Percent Change

The standard approach to measuring relative change or difference is to calculate the

percent change (Tornqvist et al. 1985) using:

Percent Change 1001

21 ∗⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

DateDateDate (2)

where in remote sensing Date1 would be the pixel value from the early time period, and

Date2 the pixel value from the more recent time period (Healey et al. 2006; Miller and

Thode 2007).

In a recent study by Miller and Thode (2007), burn severity across heterogeneous

landscapes of California were quantified using both absolute (Equation 1) and relative

(Equation 2) vegetation index differencing. The rational for comparing both techniques

was that a pixel with a small amount of trees and another with a large amount of trees

should produce quite different spectral changes even if both undergo stand-replacing fire.

The index chosen to capture the spectral signal of pre and post-fire was the normalized

burn ratio (NBR), while the field data used to quantify burn severity was the Composite

Burn Index (CBI). Initial results were unexpected, in that using Equation 2 to calculate

relative change showed a clear correlation with the amount of pre-fire vegetation. Areas

with low vegetation experienced higher degrees of relative change, which as mentioned

before was an effect that was suppose to be removed using Equation 2. As a first order

correction they took the square root of the denominator in Equation 2, which therefore

removed the unwanted trend in their results. Final results indicated that overall accuracies

and Kappa statistics were not significantly different for absolute and relative change.

However, classification of high severity burns had improved user and producer

14

accuracies, which they argued is the most important burn severity category for ecological,

wildlife, and management issues. They concluded that the use of relative rather than

absolute index may be more appropriate given that it allows comparison of fires across

space and time, and results in high classification accuracies for the most important

category, high burn severity.

In another study which utilized Equation 2, Healey et al. (2006) assessed the

potential to map intensity of partial forest harvests in Washington State, USA, using bi-

temporal Landsat images. Field plots were used to collect ground data on live tree basal

area and percent canopy cover using aerial photos. Two approaches to change detection

were explored in this study. The first approach created a date-specific relation between

spectral response and the associated field measurements for a point in time. Successive

dates were then differenced to obtain an absolute change estimate. The second approach

took the difference between two dates for a field measure and divided it by the first field

measure (Equation 2), therefore obtaining a relative change in field measurements, which

were then regressed against the spectral difference between two images (Equation 1). In

this second method, relative measures of change on the ground were used to predict

absolute changes in spectral values. Of the two modeling methods just mentioned, using

relative change as a field measure also had the lowest root mean squared error (RMSE)

for predicting loss of basal area and change in canopy cover. Relative difference in

ground data (basal area and percent canopy cover) were used instead of absolute

difference because linear models which used relative change were said to be consistently

stronger; however, no data or statistics were provided to support this claim.

15

2.3.2 Normalized Change

To reduce the occurrence of obtaining identical change values for different change

events, Coppin and Bauer (1994) suggested a standardization (or normalization) of the

image differencing algorithm. In this study, bi-temporal Landsat images were converted

into seven vegetation indices and then a normalized version of the image differencing

algorithm was applied. This normalization method took the difference of the two images

and divided it by the sum of the two images:

Normalized Change 10021

21 ∗⎟⎟⎠

⎞⎜⎜⎝

⎛+−

=DateDateDateDate (3)

where Date1 is the image from the earlier point in time, and Date2 is the image from the

more recent point in time. Using aerial photos of canopy cover as ground truth, they

concluded that the normalized version of differencing TCB and TCG were best able to

identify changes to forest canopy structure. While Coppin and Bauer (1994) say that a

normalized version of change (Equation 3) was used to reduce confusion of obtaining

similar change values for different change events, no reference data for absolute change

results is offered for comparison (Equation 1). Preferably, this study would have included

final results for both absolute image differencing and for normalized image differencing.

2.3.3 Log Change

In economics, information is often unclear due to difficulties in measuring changes and

differences (Tornqvist et al. 1985). Similarly, in remote sensing measuring change

accurately is an ongoing challenge. Relative differences are important in estimating and

comparing changes because they are pure numbers, independent of the units of

16

measurement (Tornqvist et al. 1985). However, at least two problems are identified in

using the most common algorithm (Equation 2) for measuring relative change. First,

change depends on which of the two images (Date1 or Date2) is placed in the

denominator and therefore lacks symmetry (Tornqvist et al. 1985). Second, is that the

percent differences are not additive over consecutive time periods (Tornqvist et al. 1985).

For example, two successive 10% decreases of forest canopy cover results in a total

decrease of 21% using Equation 2, not an expected 20% reduction.

Tornqvist et al. (1985) proposed a solution to the above mentioned problems

when the relative differences between values X (Dateα) and Y (Dateβ) of a ratio-scale

variable are to be measured. They proved that if indicators of relative change are to be

symmetric, additive, and normed, then only one possible indicator remains:

Log Change 100log ∗⎟⎟⎠

⎞⎜⎜⎝

⎛=

β

α

DateDate

e (4)

where Dateα can be the older or more recent point in time, and Dateβ is which ever point

in time that Dateα is not . Log change does not depend on which of the two values (Dateα

or Dateβ) are used as a point of comparison (Galal and Qureshi 1997). Dateα can not be

equal to zero because it is invalid to take the log of zero, and Dateβ can not be zero

because it is invalid to divide by zero.

Log change has been observed in the literature for making relative comparisons

between indices used for calculating geographical inequality in the distribution of health

status indicators across space and time (Galal and Qureshi 1997). In this study, relative

changes were calculated between 1980 and 1994 using three unique indices, for the

Middle East-North African region. By using log percent change, accurate comparisons

17

between the three indices were possible for their unique ability to measure changes in

distribution of health.

2.4 Estimating Mountain Pine Beetle Damage

2.4.1 Background

Forest damage caused by mountain pine beetle was chosen as a case study dataset for

evaluating the ability of absolute and relative change indices to capture degree of

disturbance. The total area impacted by the mountain pine beetle has increased

dramatically over the last decade in British Columbia (Coops et al. 2006a), and western

USA (Wulder et al. 2006b). Accurately estimating loss of timber due to infestation is

important for forest managers (Safranyik et al. 1974), wildlife habitat loss, and fire

prevention (Wulder et al. 2005), to indicate just a few motivations. Since the 1960s,

research into the use of remote sensing to detect mountain pine beetle damage has been

ongoing (Wulder et al. 2006a). In British Columbia, satellite images have been used since

the 1990s to detect, map, and predict damage (Skakun et al. 2003).

The life cycle of the mountain pine beetle is well documented by Safranyik et al.

(1974). The basic cycle takes 1 year to complete, and begins with its dispersion in mid-

summer, when beetles emerge from host trees searching for new tree stands to colonize.

Two possible techniques are used by the beetles to attack potential trees. The first,

involves the cooperation of many beetles in the attack of a few trees, therefore exhausting

the trees defence. The second, uses blue-stain fungus (Ophiostoma clavegerum and

Ophiostoma montium) carried into the tree by the beetles spreading through the phloem

and xylem which eventually kills the tree. Once healthy trees are found, the beetles bore

18

through the bark to feed on phloem and lay eggs which will hatch shortly after. Due to

feeding by the larvae and adults, the tree eventually dies when water and nutrients are

blocked due to the fungus. Susceptibility to beetle attack increases when several winters

of warm weather occur consecutively, along with routine fire suppression increasing

suitable hosts. Generally, mortality is higher for large-diameter trees, high stand density,

trees older than 80 years, and sunnier aspects (Shore et al. 2006).

Four distinct stages of mountain pine beetle infestation exist: endemic, incipient,

outbreak, and collapse (Safranyik and Carroll 2006). A small population of beetles

(endemic) will expand to the incipient stage, and then into the outbreak stage, finally,

collapse occurs when the supply of pine trees are worn out. Of particular interest to the

remote sensing analyst are color changes to the crown of the pine tree. The first stage of

attack is referred to as the green-attack stage (Skakun et al. 2003; Wulder et al. 2006a).

This stage of attack can be difficult to observe visually given that the reflectance overlaps

considerably with healthy non-attacked trees. However, approximately 2 to 3 months

after the initial attack it can begin to be detectable (Safranyik and Carroll 2006); the

following year after the attack has occurred will see the foliage fade from green to yellow

and eventually to red, known as the red-attack stage (Safranyik et al. 1974; Wulder et al.

2006a). The red-attack stage appears as a reddish colour in the normal-colour composite

Landsat image (Skakun et al. 2003). This stage shows a drop in green wavelength

reflectance and an increase in red reflectance, as well as higher reflectance in the 850-

1100nm wavelengths (Ahern 1988). The gray-attack stage occurs approximately 2 years

after the initial attack has occurred, when the tree has lost all of its needles (Safranik et al.

1974; Wulder et al. 2006a). The reflectance for this final stage is similar to defoliated

19

trees, and therefore care should be taken not to mistake gray-attack trees with defoliated

trees (Wulder et al. 2006a). In following 15 lodgepole pine trees over a 3 year period it

was found that after 12 months all trees had passed through the green-attack stage and on

to the red-attack stage, and at 13 months the grey-attack stage had begun (Wulder et al.

2006a).

2.4.2 Remote Sensing Methods for Detection

Use of remote sensing techniques to detect insect defoliation over time is well

documented in the literature and is commonly done using images from Landsat satellites

(Hall et al. 1983; McDermid et al. 1993; Nelson 1983; Wulder et al. 2006a). An

advantage of this medium resolution sensor in detecting red-attack levels of infestation is

that identification occurs at the landscape level in a cost-effective manner, highlighting

areas that may require more expensive high resolution images in the future (Wulder et al.

2006b). Depending on the final information needs, other advantages of using satellite

images to map red-attack trees include: all trees are sampled for red-attack regardless of

location, and the images are unbiased compared to an individual doing the same work

(Wulder et al. 2004).

Combining Landsat bands into vegetation indices for increased sensitivity to

forest structure changes has been shown to be more effective than using single bands

(Collins and Woodcock 1996; Healey et al. 2006; Nelson 1983). One of the most

successful indices for detecting mountain pine beetle damage is TCW (Coops et al.

2006a; Franklin et al. 2003; Skakun et al. 2003). This is because color changes that occur

during insect infestation are grouped along the principal directions of brightness,

20

greenness, and wetness (Franklin et al. 2001). The TCT reduces six Landsat reflectance

bands into three orthogonal indices: TCB, TCG, TCW (Crist and Cicone 1984; Crist and

Kauth 1986; Kauth and Thomas 1976). For the Landsat Enhanced Thematic Mapper Plus

(ETM+) satellite, TCW is calculated using (Huang et al. 2002):

TCW = 0.2626(band1) + 0.2141(band2) + 0.0926(band3)

+ 0.0656(band4) - 0.7629(band5) - 0.5388(band7) (5)

where bands 1-5 and 7 indicate the corresponding Landsat ETM+ band.

By comparing several linear change detection methods, Collins and Woodcock

(1996) evaluated which methods were best suited for measuring forest canopy changes

due to drought. Methods compared included: multitemporal TCT, Gramm-Schmidt

orthonalization, and multidate PCA. Index output was evaluated using ground truth on

loss in basal area on a per-stand basis. The benefit of this technique is that the relation

between spectral and forest change can be taken further than the change/no change

categorization. TCW difference was found to be the most reliable single indicator of

forest change.

Healey et al. (2005) successfully used TCT to identify vegetation stand-replacing

disturbance. While Healey et al. (2006) investigated the association of changes in forest

structure, due to partial harvesting in Oregon, USA, and spectral response by Landsat

bands (1-5, and 7) as well as specific vegetation indices. The indices included: TCB,

TCG, TCW, NDVI, Difference Index (DI), and NDMI. The final results indicated that the

variables most dominated by shortwave infrared (SWIR), bands 5 and 7, demonstrated

21

the closest relationship to changes in forest structure. TCW and DI indices are highly

influenced by SWIR and therefore showed very strong relationships (R2) with forest

structure changes. Other studies have also confirmed that as forest canopy is removed,

the visible and shortwave infrared (SWIR) wavelength reflectance increases while the

near infrared (NIR) decreases (Collins and Woodcock 1996; Franklin et al. 2000; Olsson

1994).

Differencing vegetation indices, rather than single bands, have been shown to

perform better in detecting forest structure changes (Coppin et al. 2004; Lyon et al. 1998;

Nelson 1983). Differencing indices uses band ratioing at each point in time, which

enhances certain spectral responses while suppressing others, and subtracts the two

images (Singh 1989). As well, indices are able to condense information from multiple

bands into a single band, which provides more information than any single band alone is

able to (Coppin et al. 2004).

A TCW differencing technique called the Enhanced Wetness Difference Index

(EWDI) was developed specifically for mapping trees killed by the mountain pine beetle

(Skakun et al. 2003). This differencing technique uses the low moisture content of dead

trees to separate them from healthy trees (Wulder et al. 2004). The EWDI is calculated

using Equation 1, where TCW1 is the older image (Date1) and TCW2 is the more recent

image (Date2), then a linear stretch to increase contrast across the available digital

numbers (Wulder et al. 2006b). Areas where moisture has decreased over time will result

in positive EWDI values, negative values will be observed where moisture has increased,

and EWDI values near zero will be seen for no change in moisture between dates

(Wulder et al. 2006b).

22

Skakun et al. (2003) successfully used absolute TCW differencing (EWDI)

between multiple Landsat images to capture the difference in reflectance patterns over 1

and 2 year intervals from when pine beetle red-attack was observed by aerial surveys.

The classification accuracy for both subtraction methods was similar for overall accuracy

and Kappa accuracy. However, samples with high red-attack damage showed the highest

classification accuracies, especially when 2 year wetness subtraction was used. Samples

with low red-attack damage showed the lowest classification accuracies, due likely to the

increased variance in spectral response with inclusion of healthy vegetation. Overall

accuracies ranged from 67% to 78%. More recently, EWDI has been included in logistic

regressions to produce a probability map for predicting red-attack (Wulder et al. 2006b).

For this study, the final predictive model for red-attack damage had an 86% accuracy

level, with less reliance on threshold setting and an ability to easily include ancillary

variables.

The above mentioned studies which used EWDI relied on absolute measures of

change between TCW to estimate red-attack damage. It is hypothesized that by using

relative change indices accuracies for relative change classification (e.g. 0-25%, 26-50%,

51-75%, and 76-100%) should increase significantly over absolute classification. That is,

the distribution of non-attack and red-attack pixels from EWDI will be more distinct

allowing greater accuracy in thresholding relative change categories. Three methods of

relative change were proposed in Section 2.3. By substituting TCW1 and TCW2 images

into Equations 2, 3, and 4, the relative version of EWDI would respectively be: percent

EWDI (PEWDI), normalized EWDI (NEWDI), and log EWDI (LEWDI) (Table 2).

23

Table 2. Indices used for classifying change in remotely sensed data and ground data.

Method Equation ReferenceRemote Sensing 

Ground Data 

Absolute

Image Differencing

#1 Ridd and Liu 1998; Nelson 1983; Mas 1999; Yuan and Elvidge 1998; Jin and Sader 2005; Coops et al. 2006b; Franklin et al. 2005; Franklin et al 2002a; Skakun et al. 2003; Wulder et al. 2006a; Franklin et al. 2001; Lyon et al. 1998

EWDI AΔ

Percent Change

#2Miller and Thode 2007;                   

Healey et al. 2006PEWDI PΔ

Normalized Change

#3

Coppin and Bauer 1994 NEWDI NΔ

Log Change

#4Galal and Qureshi 1997;                  Tornqvist et al. 1985

LEWDI LΔ

Relative

⎟⎟⎠

⎞⎜⎜⎝

⎛

β

α

DateDate

elog

1

21

DateDateDate −

21 DateDate −

21

21

DateDateDateDate

+−

2.4.3 Ground Verification Data

Output from the EWDI is usually calibrated with ground plots so that what is

occurring on the ground can be accurately tied to spectral changes measured by the

satellite (Wulder et al. 2004). The accuracy of a final EWDI map is then tested using

similar ground plots where the values of ground variables are known (Wulder et al.

2004). Establishing a relationship between a biophysical parameter and remote sensing

spectral response is commonly done for insect defoliation using Landsat images

(McDermid et al. 1993).

24

Methods used for collecting ground data on defoliation range from broad

overviews to identifying precise location and extent. Aerial sketch mapping is a broad

survey used to approximate the location and extent of damage to a forest (Coops et al.

2006a). Helicopter Global Positioning System surveys are more detailed, occurring over

smaller areas of beetle infestation (Coops et al. 2006a). Colour aerial photography is

another detailed way for capturing beetle damage to forests (Ahern 1988). Finally, the

most precise way to measure insect damage is through ground sampling techniques

(Coops et al. 2006a; Nelson 1983).

Ground surveys usually involve establishing field plots of a specific dimension in

the forest floor, using predetermined sampling techniques to choose locations.

Biophysical parameters commonly measured at each plot include: diameter at breast

height, degree of attack for each tree (non-attack, green-attack, red-attack, and gray-

attack) (Wulder et al. 2006b), crown closure, Leaf Area Index (LAI), volume, height,

stem density, age, and species, just to name a few (Franklin 2001).

Ground surveys that measure forest health done in regular intervals allow for the

calculation of change in a biophysical parameter, which can then be used to classify the

spectral changes in a differenced image (Franklin 2001). Most commonly this is done

using absolute change (AΔ) in the ground parameter (Table 2). For example, a crown

closure of 80 is reduced to 30 resulting in an absolute decrease of 50. However, it is

hypothesised that relative changes in forest parameters will be significantly better at

predicting relative change categories in remotely sensed images. Using the same above

example where 80 crown closure is reduced to 30 would give: percent change (PΔ) of 62,

normalized change (NΔ) of 45, and log change (LΔ) of 98 (Table 2). The use of NΔ in

25

field data has been observed in the literature (Healey et al. 2006), however, its

improvement over other relative indices was not supported by any data or statistical

analysis. Therefore, along with PΔ in biophysical parameters relative change methods:

NΔ, and LΔ, should be investigated (Table 2).

26

Chapter Three: Modeling Forest Disturbance

3.1 Introduction

Detecting varying degrees of forest disturbance is central to making informed

management decisions (Aldrich 1975; Healey et al. 2006; Nelson 1983). A review of the

remote sensing literature indicates that estimating forest disturbance is typically done

using an absolute change index, where change is calculated by subtracting data from a

more recent date from that of an earlier date (Coppin et al. 2004; Jin and Sader 2005;

Miller and Thode 2007; Singh 1989; Skakun et al. 2003; Wulder et al. 2006b). Therefore,

absolute change results are correlated to the amount of pre-change forest biomass (Miller

and Thode 2007). If the purpose of calculating forest change is to estimate absolute

change in biomass then this is a suitable index, however, if it is to classify disturbance

into relative categories (e.g. 0-25%, 26-50%, 51-75%, 76-100%) then it may be

inadequate. More accurate classification is expected by accounting for the amount of pre-

change forest present through use of relative change indices.

The objective of this chapter is twofold. First, is to test the hypothesis that relative

categories of forest disturbance, PΔ in ground data, will be more accurately classified

using relative change techniques rather than the traditional absolute change technique. To

test this hypothesis, EWDI, PEWDI, NEWDI, and LEWDI will be calculated for the

remotely sensed data and accuracy results accessed using PΔ in ground data. Second, is

to test the hypothesis that higher classification accuracies will be obtained when the same

relative change algorithm is used on both remotely sensed data and ground data, that is,

PΔ with PEWDI, NΔ with NEWDI, and LΔ with LEWDI.

27

Two models were used to investigate the above hypotheses, with the first providing

a theoretical foundation for the second. The first uses a hypothetical linear relationship

between TCW and forest crown closure to test the change techniques under completely

controlled conditions. The second uses a canopy reflectance model, which simulates

spectral reflectance of the forest using input for known reflectance values of forest

canopy and understory. Forest canopy reflectance models are tools used to predict the

bidirectional reflectance distribution function (BRDF), or anisotropic behaviour of

reflected solar radiation from vegetation (Leblanc and Chen 2000). 5-Scale is one

example of a reflectance model with an improved ability to simulate canopy conditions

compared to earlier reflectance models (Peddle et al. 2004). Its main purpose is to predict

the reflectance of vegetation from the remote sensing point of view (Leblanc and Chen

2002).

3.2 Theoretical Foundations

3.2.1 Theoretical Relationship

To highlight the difference between absolute and relative change indices, consider the

following circumstances; assume that a linear relationship exists between the measured

ground data crown closure and the remotely sensed TCW, such that a 20% decrease in

crown closure will equal a corresponding 20% decrease in TCW (Figure 1). Forest crown

closure is defined as the percent of ground covered by tree crowns (Franklin 2001). It is

worth highlighting that in the real world as full crown closure is approached its

correlation with spectral reflectance decreases, as seen for all biophysical parameters

(Franklin 2001).

28

Figure 1. An arbitrary linear relationship between crown closure and TCW.

This hypothetical linear relationship (Figure 1) was then used to create 30 forest

disturbance examples to highlight the differences and similarities between absolute

change (EWDI) and relative change (PEWDI, NEWDI, and LEWDI) (Table 2). As well,

a comparison of the three relative change techniques for measuring change in ground

data: PΔ, NΔ, and LΔ (Table 2), was completed (Table 3).

From Table 3, it becomes clear that choice of method for measuring change will

have a large impact on results obtained, therefore impacting the ability to accurately

classify relative categories of change such as: low (0-25%), medium (26-50%), high (51-

75%), and extreme (76-100%). The relative indices (PEWDI, NEWDI, and LEWDI)

report consistent values for constant values of PΔ, in comparison to the absolute index

(EWDI). For example, when PΔ is 40% EWDI produces very different results (0.40 to

2.00) depending on the original amount of crown closure (Table 3). On the other hand,

PEWDI, NEWDI, and LEWDI produce consistent results: 0.40, 0.25, and 0.51

respectively. Similar trends are seen when NΔ and LΔ are used for ground data.

29

Table 3. Crown closure and TCW values were generated for Date1 and Date2, using the linear relationship in Figure 1. Different methods of measuing change in ground data: PΔ, NΔ, LΔ, and remotely sensed data: EWDI, PEWDI, NEWDI, and LEWDI are calculated.

Ex. Date1 Date2 Date1 Date2 PΔ NΔ   LΔ EWDI PEWDI NEWDI LEWDI1 100 95 5.00 4.75 5.00 2.56 5.13 0.25 0.05 0.03 0.052 80 69.6 4.00 3.48 13.00 6.95 13.93 0.52 0.13 0.07 0.143 60 49.8 3.00 2.49 17.00 9.29 18.63 0.51 0.17 0.09 0.194 40 32 2.00 1.60 20.00 11.11 22.31 0.40 0.20 0.11 0.225 20 15 1.00 0.75 25.00 14.29 28.77 0.25 0.25 0.14 0.296 100 70 5.00 3.50 30.00 17.65 35.67 1.50 0.30 0.18 0.367 80 56 4.00 2.80 30.00 17.65 35.67 1.20 0.30 0.18 0.368 60 42 3.00 2.10 30.00 17.65 35.67 0.90 0.30 0.18 0.369 40 28 2.00 1.40 30.00 17.65 35.67 0.60 0.30 0.18 0.3610 20 14 1.00 0.70 30.00 17.65 35.67 0.30 0.30 0.18 0.3611 100 60 5.00 3.00 40.00 25.00 51.08 2.00 0.40 0.25 0.5112 80 48 4.00 2.40 40.00 25.00 51.08 1.60 0.40 0.25 0.5113 60 36 3.00 1.80 40.00 25.00 51.08 1.20 0.40 0.25 0.5114 40 24 2.00 1.20 40.00 25.00 51.08 0.80 0.40 0.25 0.5115 20 12 1.00 0.60 40.00 25.00 51.08 0.40 0.40 0.25 0.5116 100 50 5.00 2.50 50.00 33.33 69.31 2.50 0.50 0.33 0.6917 80 40 4.00 2.00 50.00 33.33 69.31 2.00 0.50 0.33 0.6918 60 30 3.00 1.50 50.00 33.33 69.31 1.50 0.50 0.33 0.6919 40 20 2.00 1.00 50.00 33.33 69.31 1.00 0.50 0.33 0.6920 20 10 1.00 0.50 50.00 33.33 69.31 0.50 0.50 0.33 0.6921 100 40 5.00 2.00 60.00 42.86 91.63 3.00 0.60 0.43 0.9222 80 32 4.00 1.60 60.00 42.86 91.63 2.40 0.60 0.43 0.9223 60 24 3.00 1.20 60.00 42.86 91.63 1.80 0.60 0.43 0.9224 40 16 2.00 0.80 60.00 42.86 91.63 1.20 0.60 0.43 0.9225 20 8 1.00 0.40 60.00 42.86 91.63 0.60 0.60 0.43 0.9226 100 37 5.00 1.85 63.00 45.99 99.43 3.15 0.63 0.46 0.9927 80 23.2 4.00 1.16 71.00 55.04 123.79 2.84 0.71 0.55 1.2428 60 10.8 3.00 0.54 82.00 69.49 171.48 2.46 0.82 0.69 1.7129 40 4.8 2.00 0.24 88.00 78.57 212.03 1.76 0.88 0.79 2.1230 20 1.4 1.00 0.07 93.00 86.92 265.93 0.93 0.93 0.87 2.66

Change in Remotely Sensed DataGround Data (Crown Closure)

Remote Data (Wetness)

Change in Ground Data

30

Data plots were then created using the same 30 disturbance simulations in Table 3

to observe visually the differences and similarities (Figures 2-4). An exceptionally

inconsistent relationship between PΔ and EWDI is seen visually in Figure 2a. A linear

relationship between PΔ and PEWDI is observed in Figure 2b as expected, given that

both change in ground data (crown closure) and remote sensing data (TCW) is being

calculated using the same formula (Equation 2). As for PΔ and NEWDI (Figure 2c), a

slightly nonlinear relationship is observed, but the relationship is consistent throughout

the range of canopy losses. Finally, LEWDI begins with an almost linear relationship up

to about 50% canopy loss, but then turns into an exponential relationship where high

canopy loss results in much larger LEWDI values (Figure 2d). These results visually

highlight the strengths of relationships which ultimately will impact the ability to classify

change accurately.

The simulated data in Table 3 was used again to plot the relationship between

absolute and relative change indices, but this time using NΔ (Figures 3a, b). When

absolute change in remote sensing data (EWDI) is plotted against relative changes in

ground data (NΔ) an inconsistent relationship is observed (Figure 3a), similar to that seen

in Figure 2a. When the normalized relative change in remote sensing data (NEWDI) is

compared to normalized canopy loss (NΔ), a perfect linear relationship is seen (Figure

2b). This is expected given that both ground and remotely sensed data changes are being

calculated using Equation 3. The x-axis for NΔ in Figure 3 has the same range of values

(0-100%) as seen for PΔ in Figure 2 even though the values represent different amounts

of relative change.

31

Figure 2. The data from Table 3 plotted to show the relationship between PΔ in canopy with EWDI (a), PEWDI (b), NEWDI (c), and LEWDI (d).

32

Figure 3. The data from Table 3 plotted to show the relationship between NΔ in canopy cover with EWDI (a), and NEWDI (b).

Finally, the relationship for log change in Table 3 was plotted to visualize the

difference between absolute and relative change indices (Figures 4a, b). The observed

relationships are almost identical to those seen in Figures 3a and 3b; however, in this case

Equation 4 was used to calculate relative change in the ground data (LΔ) and remotely

sensed data (LEWDI). The x-axis for LΔ has a different range of values (0-300%) than

seen for PΔ and NΔ (0-100%), due to the different equation used (Equation 2-4).

33

Figure 4. The data from Table 3 plotted to show the relationship between LΔ in canopy cover with EWDI (a), and LEWDI (b).

3.2.2 Theoretical Classification

To further explore the hypothetical linear relationship between crown closure and

wetness introduced in Figure 1, a total of 120 scenarios were created to simulate forest

disturbances ranging from 1% to 99% canopy loss, for the original canopy closures of:

100, 90, 80, 70, 60, 50, 40, 30, 20, and 10. The corresponding TCW values, obtained

from Figure 1, were used to calculate the remote sensing change indices: EWDI, PEWDI,

NEWDI, and LEWDI. While the corresponding canopy closures from Figure 1, were

used to calculate change in ground data: PΔ, NΔ, and LΔ.

Thresholds were then determined using output for each of the remote sensing

change indices for four relative categories of change: low (0-25%), medium (26-50%),

34

high (51-75%), and extreme (76-100%). The four categories of change were established

by calculating average difference (AD) index values for each category. Thresholds were

determined using the midpoint between the average values of the upper and lower class:

Thresholdlow-medium lowlowmedium ADADAD

+⎟⎠⎞

⎜⎝⎛ −

=2

(6)

where Thresholdlow-medium is the threshold between the low and medium categories, ADlow

is the average difference index values for all scenarios in the low category, and ADmedium

is for all scenarios in the medium category.

Using the thresholds for EWDI, PEWDI, NEWDI, and LEWDI, four error

matrices were generated to evaluate the accuracy of each classification method, using PΔ

in canopy cover. With the same 120 change scenarios, the NΔ change in ground data was

used to classify changes in remotely sensed data using EWDI and NEWDI. Finally, the

same scenarios were used to classify LΔ change in ground data with and EWDI and

LEWDI changes in remotely sensed data.

For each matrix the overall accuracy (i.e., number of samples correctly classified

divided by the total number of samples in the error matrix) and Kappa (K) was

calculated:

∑

∑ ∑

=++

= =++

−

−= r

iii

r

i

r

iiiii

xxN

xxxNK

1

2

1 1

)*(

)*( (7)

where r is the number of rows in the matrix, xii is the number of observations in row i and

column i , xi+ and x+i are marginal totals of row i and column i, and N is the total number

of samples (Congalton 1991).

35

The classification accuracy assessment for PΔ and EWDI (Table 4) showed an

overall accuracy of 53%, and Kappa of 0.37, which is very low given the optimal

conditions and relationship (Figure 1). The lowest degree of change (0-25%) was found

to have the highest producer (63%) and user (90%) accuracies, which can easily be

explained by referring to the 0-25% range in Figure 2a, which is quite linear. As well, by

referring to Figure 2a it is clear why user (33%) and producer (20%) accuracies were so

low for the 51-75% change category. For the relative indices (PEWDI, NEWDI, and

LEWDI), overall accuracy for each was found to be 100%, with a Kappa coefficient of

1.00. These results were expected given the strong relations being classified for PEWDI

(Figure 2b), NEWDI (Figure 2c), and LEWDI (Figure 2d).

Table 4. Classification of PΔ in canopy using EWDI. Kappa statistic is 0.37.

Categories 0-25% 26-50% 51-75% 76-100% Total User's (%)0-25% 27 9 4 3 43 6326-50% 3 14 8 5 30 4751-75% 0 7 6 5 18 3376-100% 0 0 12 17 29 59Total 30 30 30 30 120Producer's (%) 90 47 20 57

Reference Data

Overall: 53%

Table 5. Classification of PΔ in canopy using: PEWDI, NEWDI, and LEWDI. Kappa statistic is 1.00.

Categories 0-25% 26-50% 51-75% 76-100% Total User's (%)0-25% 30 0 0 0 30 10026-50% 0 30 0 0 30 10051-75% 0 0 30 0 30 10076-100% 0 0 0 30 30 100Total 30 30 30 30 120Producer's (%) 100 100 100 100

Reference Data

Overall: 100%

36

The error matrix for NΔ and EWDI was identical to Table 4, as was the matrix for

NΔ and NEWDI to Table 5; therefore, they were not reproduced. The same was observed

for the error matrices of LΔ and EWDI (Table 4), along with LΔ and LEWDI (Table 5).

These results provide support for the concept of using the same index to calculate both

change in remotely sensed data and ground data in order to obtain higher classification

accuracies (Table 5). As well, the results help establish a baseline for when a linear

relationship exists between the ground and remote sensing data. Therefore, when

simulated data from a canopy reflectance model, or actual satellite images, is used it can

be compared to these baseline values (Tables 4, 5).

3.3 Methods: 5-Scale Canopy Reflectance Model

5-Scale is a geometric-optical radiative-transfer model which provides BRDF simulations

by modeling five scales of canopy architecture (Leblanc and Chen 2002). Leblanc and

Chen (2000) developed the 5-Scale model by combining the 4-Scale model (Chen and

Leblanc 1997) with the Leaf Incorporating Biochemistry Exhibiting Reflectance and

Transmittance Yields (LIBERTY) model (Dawson et al. 1998). The 4-Scale model

focuses on four scales of canopy structure: tree groups, crowns, branches, and shoots

(Chen and Leblanc 1997). The fifth scale, LIBERTY model, simulates the reflectance

and transmittance spectra of a leaf at different wavelengths (Dawson et al. 1998).

To predict vegetation reflectance 5-Scale makes use of the following six

methodologies (Leblanc and Chen 2002). First, tree crowns are described using

geometrical objects, such as a cone and cylinder for conifers. The distribution of trees is

non-random and simulated using a Neyman type A distribution. When trees are found in

37

clusters the crown size decreases corresponding to the cluster size. Second, the branch

architecture inside the crown is described by a single inclination angle, along with an

angle distribution pattern for the foliage elements. Third, the bright spot where the viewer

sees only sunlit elements is calculated on the ground and foliage with gap size

distributions between and inside the crowns. Fourth, the tree surface is considered as

complex medium so shadowed foliage is seen on the sunlit side and sunlit foliage is seen

on the shadow side of the tree. Fifth, shaded reflectivities are calculated using a multiple

scattering scheme with view factors. Sixth, the bidirectional hyperspectral reflectance for

any combination of sun and viewing geometries can be computed if canopy and

background spectra are provided, otherwise LIBERTY can be used for simulation.

3.3.1 Model Parameters

The 5-Scale model allows for input parameters to be changed easily, therefore different

changes in forest canopy can be easily generated (Leblanc and Chen 2000). For this

study, canopy reflectance was required at specific wavelengths in order to imitate the

wavelengths used by the Landsat ETM+ satellite (Table 6), which is required in the

calculation of TCW. The midpoint in the range of each Landsat ETM+ band was used as

input into the model.

38

Table 6. Wavelength range for Landsat ETM+ Bands 1-5 and 7, along with the midpoint.

Band Minimum Maximum Midpoint1 450 515 4832 525 605 5653 630 690 6604 750 900 8255 1550 1750 16507 2080 2350 2215

Wavelength (nm)

To simulate canopy loss for forest stands of lodgepole pine due to pine beetle

infestation, reflectance data was obtained from the United States Geological Survey

(USGS) Digital Spectral Library (Clark et al. 2007). Lodgepole pine reflectance data was

collected in Yellowstone National Park, Wyoming, USA. The 5-Scale model allows for

inclusion of understory, therefore reflectance data for lichen Licedea from San Juan

Mountain, Colorado, was included. The 5-Scale model requires transmissivity values for

lodgepole pine at chosen wavelengths (Table 6), however these were unavailable, so

estimates were made based on the typical values given by 5-Scale for the chosen

wavelengths. The parameters were kept constant in 5-Scale except for LAI, which was

decreased at specific intervals to imitate loss of forest canopy. LAI is defined as the leaf

area per unit ground area, an important measure of energy, gas, and water, in forest

ecosystems (Franklin 2001). The parameters used for 5-Scale are shown in Table 7. An

example of canopy reflectance output values for a LAI of 3 is shown in Table 8. These

output values were then entered into Equation 5 to calculate TCW.

39

Table 7. Paramters used in 5-Scale.

Size 10000Number of Stem 3000Stick Height 3mCrown Height 8mCrown Radius 1.3mSpecies Conifer

Illumination Solar Zenith Angle 40VZA MAX 80Angle Step 5Landsat Wavelength MidpointFoliage USGSBackground USGS

Transmittance Foliage EstimateCanopy Cover LAI  Variable

Pixel

Crown Geometry

Plot Parameters

Reflectivities

Table 8. 5-Scale output produced when an LAI of 3.0 is used as input.

Band Reflectance 1 0.007932 0.011303 0.011814 0.095315 0.074227 0.05023

3.3.2 Nonlinear Transformation

Wetness was calculated for 132 LAI values ranging from 0 to 5 (Figure 5a). A strong

relation between variables was observed (Figure 5a), coefficient of determination (R2)

equal to 0.82, but the logarithmic regression better fit the data (R2 = 0.97), Figure 5b). As

a result, a log transformation of LAI was completed to provide a stronger linear relation

with spectral response (Figure 5c), also seen in the literature (Healey et al. 2006). Then a

value of 1.0 was added to all log-linear LAI values to ensure only positive values existed

(Figure 6a). Finally, a value of 0.261 was added to all wetness values (Figure 6b) so that

a wetness value of zero approximately corresponds to a LAI value near zero.

40

Figure 5. The linear regression of LAI versus wetness (a), followed by the logarithmic regression (b), and a log transformation of LAI (c).

41

Figure 6. The log transformed LAI (Figure 5c) with an adjustment of +1.0 (a), and an adjusted wetness (+0.261) versus adjusted log transformed LAI (b).

3.3.3 Classification Accuracy

Given the nonlinear relationship between LAI and wetness (Figures 5, 6), the transformed

and adjusted version of LAI was used in the classification of change for both absolute

and relative indices. A total of 506 forest disturbance scenarios were created using the

original 132 LAI values. To simulate different degrees of LAI loss, a total of 11 different

Date1 LAI values were used. For each of the 11 Date1 LAI values, 46 degrees of

disturbance were simulated by using the appropriate Date2 LAI.

42

Classification of the following PΔ categories of forest disturbance was performed:

0-25%, 26-50%, 51-75%, and 76-100%. Error matrices were created for classification of

PΔ in ground data using EWDI, PEWDI, NEWDI, and LEWDI, as change in remote

sensing data. As well, confusion matrices were created for classification using NΔ in

ground data with EWDI, and NEWDI for change in remote sensing data. In the case of

NΔ, the forest disturbance categories include: 0-25%, 26-50%, 51-75%, and 76-100%.

Matrices were also created for LΔ in ground data with EWDI, and LEWDI. For LΔ, the

forest disturbance categories include: 0-114%, 115-230%, 231-345%, and 346-460%. For

each matrix the overall accuracy and Kappa (Equation 7) was calculated.

To statistically compare the difference between overall classifications for

different error matrices the Z score for difference between two proportions was estimated

using:

⎟⎟⎠

⎞⎜⎜⎝

⎛+−

−=

21

2

2

1

1

11)1(nn

pp

nx

nx

Z (8)

where x1 and x2 are the number of correctly classified samples within the two independent

samples of size n1 and n2, respectively, and p = (x1 +x2)/(n1 + n2) (Foody 2004). A 95%

confidence interval was used for this two-sided test, resulting in critical Z values of -1.96

and +1.96 (Rogerson 2006).

43

3.4 Results & Discussion

3.4.1 Percent Change in Ground Data

Table 9 shows the calculated thresholds between categories of change. For classification

of PΔ in ground data, the best performance was seen by NEWDI (Overall: 84%, Kappa

0.79), followed by PEWDI (Overall: 83%, Kappa 0.77), then LEWDI (Overall: 79%,

Kappa 0.72), and finally EWDI (Overall: 54%, Kappa 0.38) (Tables 10-13).

The performance by EWDI (Table 10) was almost identical to that seen

previously in the theoretical background section (Table 4, Overall: 53%, Kappa 0.37).

The highest producer and user accuracies are seen for the the lowest change category (0-

25%) and lowest producer and user accuracies are seen for the the high change category

(51-75%). This outcome makes sense given the relationship being classified where it is

more stable for lower levels of change (Figure 2a). However, other studies that have

successfully used image differencing to classify degrees of forest change have found

consistently higher user and producer accuracies for higher degrees of forest change

(Franklin et al. 2003; Jin and Sader 2005; Skakun et al. 2003). This is explained by larger

degrees of change produce stronger spectral changes which is why lowest classification

accuracies are usually found for the lower degrees of change (Skakun et al. 2003).

Table 9. Thresholds between PΔ categories of change.

Categories PΔ EWDI PEWDI NEWDI LEWDI

Low 0‐25% < 0.0539 < 0.3146 < 0.2030 < 0.4224Medium 26‐50% 0.0539 to 0.1021 0.3146 to 0.5945 0.203 to 0.4453 0.4224 to 1.007High 51‐75% 0.1021 to 0.1428 0.5945 to 0.8420 0.4453 to 0.7498 1.007 to 2.5965Extreme 76‐100% > 0.1428 > 0.8420 > 0.7498 > 2.5965

44

Table 10. Classification of PΔ and EWDI. Kappa statistic is 0.39.

Categories 0‐25% 26‐50% 51‐75% 76‐100% Total User's (%)0‐25% 117 33 11 0 161 7326‐50% 15 50 35 25 125 4051‐75% 0 38 33 22 93 3576‐100% 0 0 53 74 127 58Total 132 121 132 121 506Producer's (%) 89 41 25 61

Reference Data

Overall: 54%

Table 11. Classification of PΔ and PEWDI. Kappa statistic is 0.77.

Categories 0‐25% 26‐50% 51‐75% 76‐100% Total User's (%)0‐25% 115 21 0 0 136 8526‐50% 17 79 13 0 109 7251‐75% 0 21 105 1 127 8376‐100% 0 0 14 120 134 90Total 132 121 132 121 506Producer's (%) 87 65 80 99

Reference Data

Overall: 83%

Table 12. Classification of PΔ and NEWDI. Kappa statistic is 0.79.

Categories 0‐25% 26‐50% 51‐75% 76‐100% Total User's (%)0‐25% 121 23 0 0 144 8426‐50% 11 79 13 0 103 7751‐75% 0 19 108 2 129 8476‐100% 0 0 11 119 130 92Total 132 121 132 121 506Producer's (%) 92 65 82 98

Reference Data

Overall: 84%

Table 13. Classification of PΔ and LEWDI. Kappa statistic is 0.72.

Categories 0‐25% 26‐50% 51‐75% 76‐100% Total User's (%)0‐25% 122 23 0 0 145 8426‐50% 10 84 18 0 112 7551‐75% 0 14 114 39 167 6876‐100% 0 0 0 82 82 100Total 132 121 132 121 506Producer's (%) 92 69 86 68

Reference Data

Overall:79%

45

For the highest classification accuracies, PEWDI (Table 11) and NEWDI (Table

12), the producer and user accuracies were lower for the midrange change categories (26-

50% and 51-75%) than observed for the low change (0-25%) and the extreme change

(76-100%). This outcome may be explained by refering to Figure 6b, where the

relationship between independent and dependent variables is not as linear throughout

certain regions, which when substituted into Equations 2 and 3 result in less accurate

calculations. The highest classification accuracies for both relative indices was seen for

the extreme change category (Tables 11, 12), similar to results by Miller and Thode

(2007) who consider this class to be of highest importance to land managers.

For LEWDI (Table 13), the highest producer accuracy was seen for 0-25%

change, and lowest accuracy for the 76-100% change. This outcome is supported by

Figure 2d which shows the relationship between PΔ ground data and TCW is linear up to

around the mid values, after which the TCW increases exponentially making calcualtion

of thresholds for a high degree of change (Equation 6) more difficult.

Significant improvements in overall classification (p < 0.001) were seen for all

three relative indices (PEWDI, NEWDI, and LEWDI) when compared to the absolute

index (EWDI) (Table 14). No significant difference was seen between PEWDI and

NEWDI (p = 0.334), or PEWDI and LEWDI (p = 0.105), however, NEWDI showed a

significant improvement over LEWDI in classifying PΔ ground data (p = 0.040).

46

Table 14. Z-test to identify signifiicant differences between classification methods, Z-score less than 1.96 is not significant at the 95% confidence interval.

Ground Data Comparison Z‐Score p‐value SignificantEWDI vs. PEWDI 9.930 < 0.001 YesEWDI vs. NEWDI 10.318 < 0.001 YesEWDI vs. LEWDI 8.425 < 0.001 YesPEWDI vs. NEWDI 0.429 0.334 NoPEWDI vs. LEWDI 1.622 0.105 NoNEWDI vs. LEWDI 2.048 0.040 Yes

NΔ EWDI vs. NEWDI 9.472 < 0.001 YesLΔ EWDI vs. LEWDI 11.755 < 0.001 Yes

PΔ

3.4.2 Normalized Change in Ground Data

Table 15 shows the thresholds between categories of change, determined using Equation

10, but this time the classification was done using NΔ in ground data. The best

performance was seen by NEWDI (Overall: 81%, Kappa 0.73), followed by EWDI

(Overall: 53%, Kappa 0.33) (Tables 14, 15). Once again significant improvements were

seen for the relative index (NEWDI) over the absolute index (EWDI) (Table 14). Similar

trends in the user and producer accuracies as seen for PΔ in ground data (Tables 10, 12)

were also observed for NΔ (Tables 16, 17).

Table 15. Thresholds between NΔ categories of change.

Categories NΔ EWDI NEWDI

Low 0‐25% < 0.0772 < 0.3223Medium 26‐50% 0.0772 to 0.1286 0.3223 to 0.6082High 51‐75% 0.1286 to 0.1569 0.6082 to 0.8519Extreme 76‐100% > 0.1569 > 0.8519

47

Table 16. Classification of NΔ and EWDI. Kappa statistic is 0.34.

Categories Low Medium High Extreme Total User's (%)Low 173 38 13 6 230 75Medium 35 44 21 12 112 39High 1 30 17 12 60 28Extreme 0 20 48 36 104 35Total 209 132 99 66 506Producer's (%) 83 33 17 55

Reference Data

Overall: 53%

Table 17. Classification of NΔ and NEWDI. Kappa statistic is 0.73.

Categories Low Medium High Extreme Total User's (%)Low 183 14 0 0 197 93Medium 26 95 14 0 135 70High 0 23 65 0 88 74Extreme 0 0 20 66 86 77Total 209 132 99 66 506Producer's (%) 88 72 66 100

Reference Data

Overall: 81%

In Table 16 the categories for NΔ remain the same (0-25%, 26-50%, 51-75%, 76-

100%) as those in PΔ (Table10). It is recognized that a PΔ in ground data of 50% is

equivalent on the ground to NΔ change of 33%, and LΔ change of 69% (Table 3).

However, if these equivalent divisions were used to separate classes, then the same

overall accuracy and Kappa value would be obtained for Table 16 and 17 as seen for

Tables 10 and 12. As a result, notice the unequal distribution of the 506 samples in the

four categories of change (Table 16, 17) compared with PΔ (Tables 10-12).

3.4.3 Log Change in Ground Data

Table 18 shows the thresholds between categories of change, but this time the

classification was done using LΔ in ground data. The best performance was seen by

48

LEWDI (Overall: 90%, Kappa 0.79), followed by EWDI (Overall: 59%, Kappa 0.25)

(Tables 19, 20). A significant improvement was seen for the relative index (LEWDI) over

the absolute index (EWDI) (Table 14). Similar to NΔ, comparable trends in the user and

producer accuracies as seen for PΔ in ground data (Tables 10, 13) were also observed for

LΔ (Tables 19, 20). Once again notice the unequal distribution of the 506 samples in the

four user categories of change, with much fewer samples in the extreme change category

(Table 19, 20).

Table 18. Thresholds between LΔ categories of change.

Categories LΔ EWDI LEWDILow 0‐114% < 0.1096 < 1.5706Medium 115‐230% 0.1096 to 0.1593 1.5706 to 3.8517High 231‐345% 0.1593 to 0.1691 3.8517 to 5.3381Extreme 346‐460% > 0.1691 > 5.3381

Table 19. Classification of LΔ and EWDI. Kappa statistic is 0.25.

Categories Low Medium High Extreme Total User's (%)Low 261 32 6 2 301 87Medium 59 31 9 3 102 30High 9 8 0 0 17 0Extreme 12 50 18 6 86 7Total 341 121 33 11 506Producer's (%) 77 26 0 55

Reference Data

Overall: 59%

Table 20. Classification of LΔ and LEWDI. Kappa statistic is 0.79.

Categories Low Medium High Extreme Total User's (%)Low 334 19 0 0 353 95Medium 7 82 0 0 89 92High 0 20 33 4 57 58Extreme 0 0 0 7 7 100Total 341 121 33 11 506Producer's (%) 98 68 100 64

Reference Data

Overall: 90%

49

3.4.4 Comparison of Indices

The results of classifying PΔ (Tables 10-13), NΔ (Tables 16, 17), and LΔ (Tables 19, 20),

had many similar trends within the error matrices. In all three cases using relative change

indices (PEWDI, NEWDI, and LEWDI) to capture relative categories performed

significantly better than using an absolute change index (EWDI), however, no significant

difference was seen between relative indices (Table 14). These results support other

studies which have identified advantages of relative indices in classifying relative change

(Coppin and Bauer 1994; Healey et al. 2006; Miller and Thode 2007). For overall

classification accuracy the highest value (91%) was seen for LΔ using LEWDI (Table

20), followed by PΔ using NEWDI (84%, Table 12), and PΔ using PEWDI (83%, Table

11). For Kappa values LΔ using LEWDI and PΔ using NEWDI were tied (Kappa: 0.79),

followed by PΔ using PEWDI (Kappa: 0.77).

After comparing plots and error matrices, the most important factor influencing

the ability to classify relative change is thought to be the strength of the original

relationship (Figure 1, Figure 6b) between the satellite response (TCW) to the ground

measurement (LAI, crown closure, stem count, etc.). Healey et al. (2006) performed

logarithmic transformations on ground data measurements in order to improve the

relationship between forest measurement and spectral response, before calculating PΔ in

ground data. This is important to do if spectral changes are to be used to estimate degrees

of forest disturbance, especially for lower degrees of disturbance. That is, the spectral

change going from a 90% canopy closure to 70% needs to be similar for a change of 30%

canopy closure to 10%, however, this is not often the case (Coppin and Bauer 1994;

Healey et al. 2005). This can be attributed to the fact that the spectral response to a

50

biophysical measurement is strong only up to a certain point after which it weakens

considerably (Franklin 2001; Healey et al. 2006).

If a near linear relationship exists between satellite response and ground data then

matching the same relative change index for ground data (PΔ, NΔ, and LΔ) with the

respective remote sensing index (PEWDI, NEWDI, LEWDI) will produce high

classification accuracies (Tables 5, 11, 17, and 20). One of the disadvantages of using NΔ

and LΔ change in ground data for classification is that their true values are not

understood as easily compared to PΔ. The average person will understand a map showing

pine beetle damage between Date1 and Date2 if it has the following PΔ categories: 0-25%,

26-50%, 51-75%, 76-100%. It is however more difficult to understand what NΔ

categories: 0-25%, 26-50%, 51-75%, 76-100% actually means in terms of dead trees on

the ground. Even more challenging, is understanding what LΔ categories: 0-114%, 115-

230%, 231-345%, and 346-460%, means in terms of lost trees. While overall

classification accuracy was highest for LΔ using LEWDI, when Kappa is considered then

no difference is seen with PΔ classification using NEWDI. Also, if a more even

distribution of the 506 samples across the four categories of change is desired for LΔ

using LEWDI (Table 18) then a biased sample of extreme changes in ground data would

be required.

51

3.5 Conclusion

In remote sensing, degrees of forest disturbance are most often captured using image

differencing, which is correlated with the amount of biomass initally present (Coppin and

Bauer 1994; Miller and Thode 2007). Using an arbitrary linear relationship between

wetness and crown closure, a better understanding of the improvement relative indices

provide over abolute was seen using visual plots and tables. Classifcation of the arbitrary

linear relationship further demonstrated the advantage provided by relative indices.

Significant improvements in classification were seen (Table 14) using output from a

canopy reflectance model for all three relative indices (PEWDI, NEWDI, LEWDI) over

the absolute index (EWDI) in classifying three types of change in ground data (PΔ, NΔ,

LΔ). However, significantly higher classifications were not obtained when both change in

ground data and remote sensing data were calculated using the same index (Table 14).

Therefore, improved classification is seen when any of the relative indices are used on

remote sensing data to estimate relative ground changes.

One of the most important factors impacting relative classification is considered

to be the strength of the linear relation between ground data and spectral response. If a

strong linear relation is observed between datasets, then PΔ in ground data (LAI, crown

closure, stem count, etc.) is suggested to be the most practical method of identifying

relative categories of forest disturbance. This is supported by strong classification results

when PΔ is used in conjunction with NEWDI and PEWDI (Tables 11, 12). Future

research should focus on implementing the recommendations made in this chapter with

different suitable forest disturbance scenarios captured by actual remote sensing

technology and appropriate ground validation data.

52

Chapter Four: Classifying Mountain Pine Beetle Damage

4.1 Introduction

Infestation of lodgepole pine (Pinus contorta) by mountain pine beetles (MPB)

(Dentroctonus ponderosa Hopkins) has reached epidemic proportions in western North

America (Coops et al. 2006a; Franklin et al. 2003; Skakun et al. 2003; Wulder et al.

2006a). In the interior region of British Columbia, lodgepole pine accounts for more than

half of the growing stock, and is considered a dominant species for commercial

harvesting (Franklin et al. 2003).

In remote sensing, degree of MPB damage is often classified using image

differencing (Coops et al. 2006b; Franklin et al. 2005; Franklin et al. 2002a; Skakun et al.

2003; Wulder et al. 2006a). Image differencing is an example of absolute change

detection since it is directly related to the amount of forest present prior to infestation

(Miller and Thode 2007). Forest managers frequently require maps showing relative

degrees of damage (e.g. 0-25%, 26-50%, 51-75%, 76-100%). Results from forest

disturbance modeling (Chapter 3) show a significant improvement by relative indices

over absolute in classifying relative degrees of damage.

The objective of this chapter is to use satellite images before MPB infestation

(Date1), and after MPB infestation (Date2), to test the hypothesis that using relative

change techniques will lead to increased classification accuracy over absolute change

techniques for percent change in red-attack trees. Classification results using two relative

change algorithms, PEWDI and NEWDI, will be contrasted with a commonly used

absolute change algorithm, EWDI, in their abilities to each classify MPB damage.

53

4.2 Methods

4.2.1 Study Area

The study area chosen was originally selected for use in another study whose aim was to

monitor the outbreak of MPB in Western Canada, and is thoroughly described in Wulder

et al. (2008) and White et al. (2007). Figure 7 shows the full extent of the study area (32

km × 64 km) located 25 km south of Merritt, British Columbia, at Angstad Creek

centered on 49.84° N and 120.75° W. This site was selected in 2002 because it contained

a suitable climate for MPB and no observed attacked trees within a 10 km radius of the

site. From 2002 to 2005, this study area was monitored for the spread of MPB as it

transitioned from absent, to incipient, and finally to endemic. In the studies by Wulder et

al. (2008) and White et al. (2007), specific plots were established within Figure 7 to

collect detailed biophysical measurements of forest structure throughout the MPB

infestation. However, in this study the entire study area (Figure 7) was used.

4.2.2 Satellite Images

Two Landsat images (Path/Row: 46/25) covering the study area (Figure 7) was obtained

from the Canadian Forest Service, Pacific Forestry Centre. The pre-infestation scene

(Date1) was acquired by the Landsat 7 ETM+ satellite on August 14, 2002. The post-

infestation scene (Date2) was acquired by the Landsat 5 Thematic Mapper (TM) satellite

on September 28, 2004. This 2 year lag between image acquisition dates is suggested for

successful mapping of red-attack trees using Landsat images (Wulder et al. 2006c). The

optimal months for capturing change of tree crowns from a green to red colour are: July,

August, and September (Wulder et al. 2006c).

54

Figure 7. Study area located at Angstad Creek, 25 km south of Merritt, British Columbia.

55

The satellite images were obtained after pre-processing had been completed, with

all steps undertaken detailed in a Canadian Forest Service Information Report by Wulder

et al. (2006c). Image-to-image co-registration was performed on both images, using the

pre-infestation image (ETM+) as the master image and post-infestation image (TM) as

the slave. To reduce the difference between TM and ETM+ and make calculation of the

TCT easier, the TM images are converted to ETM+ by applying a set of gains and offsets.

Both images then had their raw digital numbers converted to at-satellite radiance, which

were then converted to top of the atmosphere reflectance. Finally, both images were

normalized using pseudo invariant targets. After completing the pre-processing, the

corresponding TCW was calculated for both images using Equation 5.

4.2.3 Absolute and Relative Indices

The absolute change index most commonly used for detecting red-attack beetle damage is

the EWDI (Skakun et al. 2003; Wulder et al. 2006a; Wulder et al. 2006b) which is

calculated using the formula:

EWDI (9) 21 TCWTCW −=

where TCW1 is the older image and TCW2 is the more recent image.

The two relative versions of EWDI being tested are PEWDI (Equation 10) and

NEWDI (Equation 11):

56

PEWDI 1001

21 ∗⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

TCWTCWTCW (10)

NEWDI 10021

21 ∗⎟⎟⎠

⎞⎜⎜⎝

⎛+−

=TCWTCWTCWTCW (11)

where TCW1 and TCW2 have the same definition as above.

4.2.4 Ground Verification Data

Tree stem counts from aerial photography were used as verification data in the ability of

EWDI, PEWDI, and NEWDI, to classify percent disturbance due to MPB between 2002

and 2004. Stem counts were done by counting the number of green trees and red-attack

trees in a plot area. A single colour air photo (Figure 7), taken September 2004 by

Terrasaurus Aerial Photography, was provided by the Canadian Forest Service, Pacific

Forestry Centre. The image was taken using three colour bands: red, green, and blue, with

a 0.3 meter pixel resolution. Unfortunately, no air photo was available for 2002, so it was

assumed that any red-attack tree counted in the 2004 air photo would have been green in

2002. This assumption was supported by the fact that it was verified on the ground in

2002 that no MPB infestation had occurred in the study area (Wulder et al. 2008; White

et al. 2007), and that the grey-attack stage is reached at least 2 years from initial attack

(Wulder et al. 2006a).

A grid of 30 meter plots was created using the border of the Landsat pixels as an

outline, and was then overlaid on the air photo (Figure 8). Only those squares with at

least one red-attack tree found within its borders were kept, leaving a total of 169 squares

scattered throughout the study area. Only pixels containing some degree of change were

57

used in this study because the purpose of this study is to better classify relative change

categories and not the no-change areas which can be difficult to distinguish from low

degrees of change. Figure 8 is a close up of a high beetle damaged area and demonstrates

how some 30 meter plots have only a few pine trees that can be damaged within it and

others have many. Each of the 169 plots was zoomed into and the total number of trees

(green trees + red trees) within each counted and added to the corresponding attribute

table, along with the number of red-attack trees. Next, the grid of 169 plots and

associated attribute table was transformed into 169 centroids representing the center of

each plot. Then, TCW values were extracted at each of the 169 points for TCW1, TCW2,

and added to the same attribute table. Finally, the attributes were exported to an Excel

spreadsheet to access the accuracy of different indices at classifying percent change.

58

Figure 8. Map showing a close-up view of the 30 m plots used in measuring red attack mountain pine beetle damage.

59

4.2.5 Accuracy Assessment

The number of green trees versus TCW was plotted for both 2002 and 2004. This was

important to do in checking if a nonlinear relation existed and if a transformation of the

data was required before attempting classification. As well, summary statistics were

produced for the 169 points for both TCW1, and TCW2. Before classification, the strength

of the relationship between each index (EWDI, PEWDI, and NEWDI) and the percent

change in green trees was analyzed.

Classification of the following categories of percent change was performed: 0-

25%, 26-50%, 51-75%, and 76-100%. Threshold values between categories were

calculated using Equation 6. Error matrices were created for classification of percent

change in ground data using EWDI, PEWDI, and NEWDI. Overall accuracy and Kappa

were calculated for each matrix using Equation 7. Finally, Z-score values were calculated

(Equation 8) for overall classifications to test whether or not the difference between

change techniques was statistically significant at the 95% confidence interval for a two-

tailed test.

4.3 Results & Discussion

4.3.1 Relation between TCW and Green Trees

For the year 2002 the linear relationship between green trees in a 30 m plot (Figure 8)

and the corresponding TCW value was found to be weak, with only 27.5% of the

variation in TCW explained by green tree count (Figure 9a). However, when a

logarithmic regression was carried out the relation improved, with 40% of the variability

explained (Figure 9b). Therefore, a logarithmic transformation of the number of green

60

Figure 9. The linear regression of Number of Trees in 2002 versus Wetness 2002 (a), followed by the logarithmic regression (b), and finally the log transformed number of trees versus wetness 2002 (c).

61

Figure 10. The linear regression of Number of Trees in 2004 versus Wetness 2004 (a), followed by the logarithmic regression (b), and finally the log transformed number of trees versus wetness 2002 (c).

62

trees in 2002 was completed before classification of damage (Figure 9c). A similar trend

in the data was observed for the year 2004. For a linear regression, 22.3% of the variation

was explained (Figure 10a), and with the logarithmic regression 32.9% was explained

(Figure 10b). Therefore, a log transformation of the 2004 green trees was also completed

before classification (Figure 10c).

Summary statistics were produced for both remotely sensed data and ground

verification data using the 169 samples (Table 19). The 2002 wetness values were higher

than 2004 for: minimum, mean, median, and maximum (Table 21). As well, the standard

deviation was lower in 2002 than 2004. This suggests that in 2004 the samples are much

drier than in 2002 and as a consequence increased variance is observed. When looking at

Figure 8, these results come as no surprise given that in 2002 all of the red-attack trees

were green, and therefore should contain much more moisture than in 2004. The same

trend in the data is seen after improving the data fit by using green trees transformed

(GTT) (Table 21). In general the relationship between green trees and TCW in 2002 is

stronger and clearer than in 2004 (Table 21). For example, the correlation coefficient (R

= 0.63), which measures the departure of the two variables from independence (Rogerson

2006), is highest for 2002 remotely sensed data and ground Data. Similarly, the

coefficient of determination (R2 = 0.40), measuring variation in the TCW explained by

the tree count (Rogerson 2006), is highest for 2002. While in 2004 the number of GTT

has a higher correlation (R = 0.58) with the 2002 TCW than the 2004 TCW (R = 0.57),

and the same coefficient of determination for 2002 and 2004 (R2 = 0.33, p < 0.001). This

blurred relationship between the number of green trees in 2004 and TCW values in 2004

makes accurate classification more challenging.

63

Table 21. Summary Statistics for TCW 2002 and 2004.

TCW 2002 TCW 2004Minimum ‐69.39 ‐98.09Mean ‐17.89 ‐22.70Median ‐17.03 ‐20.31Maximum ‐1.71 0.00Standard Deviation 8.52 12.14R (GTT 2002) 0.63 0.60R (GTT 2004) 0.58 0.57

R2 (GTT 2002) 0.40 (p = 0) 0.36 (p = 0)

R2 (GTT 2004) 0.33 (p < 0.001) 0.33 (p < 0.001)

4.3.2 Accuracy Assessment

Before classifying results of the three indices into the four categories of relative change,

plots were created of the relationship between the output of each index and percent

change in green trees (Figure 11a-c). The expectation was that the separation between

lower and higher amounts of tree loss would be clearer for the relative indices (PEWDI,

and NEWDI) than for the absolute index (EWDI). However, the results indicate that none

of the three indices provide clear separation between degrees of change (Figure 11a-c).

EWDI was the weakest (R2 = 0.0014), followed by PEWDI (R2 = 0.0056), then NEWDI

(R2 = 0.0092). Possible reasons for these weak relations are addressed further on.

64

Figure 11. The linear regression of EWDI versus Percent Change in Trees (a), PEWDI versus Percent Change in Trees (b), and NEWDI versus Percent Change in Trees.

65

The calculated thresholds for each of the three indices are shown in Table 22.

These thresholds were used to classify the four categories of relative change for EWDI

(Table 23), PEWDI (Table 24), and NEWDI (Table 25). In each classification the

distribution of the original 169 samples is such that 125 samples are in the low change

category (0-25%), 31 are in the medium category (26-50%), 11 are in the high category

(51-75%), and 2 are in the extreme category (75-100%). Given the observed relationships

in Figures 11 a, b, and c, it comes as no surprise to observe the low classification

accuracies for EWDI (Overall: 41%, Kappa 0.012), PEWDI (Overall: 49%, Kappa

0.024), and NEWDI (Overall: 41%, Kappa 0.059). For all three indices, the highest

producer and user accuracies were seen for the lowest percent change (0-25%) in red-

attack trees, contrary to results obtained by other forest disturbance studies (Franklin et

al. 2003; Miller and Thode 2007; Skakun et al. 2003).

Similar to the results seen in Chapter 3, EWDI was good at classifying low

degrees of change but poor at classifying high degrees of change. However, it is worth

noting very few samples were found in the high and extreme change categories. PEWDI

showed improvement over EWDI for classifying the extreme category, but NEWDI

provided the greatest distribution of classification which is reflected in it also having the

highest Kappa value of the three. Kappa calculation incorporates off-diagonal elements of

the error matrix, unlike overall accuracy, by incorporating the product of row and

columns (Congalton 1991). Although, no significant difference between overall

accuracies was seen for the three indices, when the difference of two proportions test was

used (Table 26).

66

Table 22. Thresholds between categories of change.

Categories Percent Loss (%) EWDI PEWDI NEWDI

Low 0‐25% < 4.0832 >-0.3452 >-0.0982Medium 26‐50% 4.0832 to 5.5829 -0.3146 to -0.4088 -0.0982 to -0.1306High 51‐75% 5.5829 to 7.6534 -0.4088 to -0.4416 -0.1306 to -0.1756Extreme 76‐100% > 7.6534 < -0.4416 < -0.1756

Table 23. Classification of EWDI. Kappa statistic is 0.012.

Categories 0-25% 26-50% 51-75% 76-100% Total User's (%)0-25% 66 16 3 0 85 7826-50% 16 4 2 0 22 1851-75% 12 7 0 2 21 076-100% 31 4 6 0 41 0Total 125 31 11 2 169Producer's (%) 53 13 0 0

Reference Data

Overall: 41%

Table 24. Classification of PEWDI. Kappa statistic is 0.024.

Categories 0-25% 26-50% 51-75% 76-100% Total User's (%)0-25% 77 21 5 1 104 7426-50% 6 4 1 0 11 3651-75% 5 0 0 0 5 076-100% 37 6 5 1 49 2Total 125 31 11 2 169Producer's (%) 62 13 0 50

Reference Data

Overall: 49%

Table 25. Classification of NEWDI. Kappa statistic is 0.059.

Categories 0-25% 26-50% 51-75% 76-100% Total User's (%)0-25% 62 12 4 0 78 7926-50% 9 5 1 0 15 3351-75% 15 8 1 1 25 476-100% 39 6 5 1 51 2Total 125 31 11 2 169Producer's (%) 50 16 9 50

Reference Data

Overall: 41%

67

Table 26. Z-test to identify signifiicant differences between classification methods, a Z-score less than 1.96 is not considered significant at a 95% confidence interval.

Comparison Z‐Score p‐value SignificantEWDI vs. PEWDI 1.478 0.139 NoEWDI vs. NEWDI 0.000 1.000 NoPEWDI vs. NEWDI 1.478 0.139 No

4.3.3 Spectral Response to Mountain Pine Beetle Damage

In Chapter 3, it was concluded that relative classification of forest disturbance relied on a

strong linear relation between spectral response and change in ground data. In this

chapter, the relation was improved with TCW by using the log transformed version of the

green trees (Figures 9, 10). While the relationship was significant for both 2002 (R2 =

0.40, p = 0) and 2004 (R2 = 0.33, p < 0.001), the spectral response to green trees was still

far from being linear. Possible reasons for this poor relationship might include: spatial

resolution, spectral resolution, vegetation phenology, and choice of verification data.

Following, is a discussion of these potential influences on the recorded spectral response.

Landsat data has been commonly used at the regional level for identifying clear

cutting at the stand level, but rarely for mapping partial harvest (Healey et al. 2006). One

of the greatest challenges in pixel based change detection studies is the maximization of

signal to noise in order to minimize artificial changes (Coppin and Bauer 1994). Franklin

et al. (2003) emphasizes some of the challenges in detecting MPB damage, in that a

relatively small influence on the spectral response is observed for MPB damage,

especially for broadband satellites.

Several recent studies have emphasized the advantages of using high spatial

resolution imagery for detecting insect damaged trees (Coops et al. 2006a; Franklin et al.

68

2008; Wulder et al. 2006a; Wulder et al. 2008). Red-attack damage that is found in small

scattered patches should be detected using high spatial resolution images, in comparison

to detection of epidemic levels of MPB which can be successfully done with moderate

spatial resolution imagery (Wulder et al. 2006a). In the present study, the distribution of

red-attack trees are found in dispersed patches throughout the study area (Figure 8), and

125 of the 169 samples have low degrees of beetle damage (Tables 23-25). Moderate

spatial resolution pixels (30 m × 30 m, Figure 8) have response values which are a

combination of several factors including: red-attack trees, healthy trees, shadows, and

understory (Wulder et al. 2006a). Higher spatial resolution images, like IKONOS (4 m ×

4 m), have pixels responding to fewer features on the ground and therefore will have

higher accuracies in mapping red-attack trees.

To investigate whether a stronger relationship would be seen for smaller pixel

sizes, the original three bands (red, green, and blue) of the 2004 air photo were resampled

from 0.3 m to 5 m, 15 m, and 30 m pixels (Table 27). To do this, a cubic convolution

resampling technique was used, which determines the new pixel value using the distance

weighted average of the nearest 16 pixels (Jensen 2005). Then correlations were

determined between each of the three coloured bands and the number of green trees and

red trees in 2004.

69

Table 27. Correlation (R) between different resolution (5 m, 15 m, and 30 m) bands (Red, Green, and Blue) for both the number of green trees and red trees in 2004.

Pixel Size Band Green Trees 2004 Red Trees 2004Red ‐0.04 ‐0.09Green ‐0.01 ‐0.14Blue ‐0.09 ‐0.12Red ‐0.11 ‐0.13Green ‐0.06 ‐0.16Blue ‐0.15 ‐0.17Red ‐0.10 ‐0.06Green ‐0.06 ‐0.05Blue ‐0.14 ‐0.10

5 Meter

15 Meter

30 Meter

The correlations were negative for both green and red trees for all resolutions and

band colours (Table 27). A possible explanation for this is that those areas with more

trees, red or green, will be darker and have lower values compared to areas with fewer

trees. This relation was observed during close-ups of all three bands of the original air

photo image. The strongest correlation for the number of green trees was seen for the

blue band of the 15 m pixel (R = - 0.15), while the weakest was seen for the green band

of the 5 m pixel (R = - 0.01). For red trees, the strongest was seen for the 15 m blue band

(R = - 0.17), while the weakest was the 30 m green band (R = - 0.05). Franklin et al.

(2008) compared change indices calculated using SPOT 4 HR VIR (high resolution

visible and infrared) and other indices calculated using Landsat TM images (30 m × 30

m), and found higher levels of correlation to defoliation using SPOT images (20 m × 20

m). As well, the patterns seen in Table 27 suggest a higher resolution satellite might be

more appropriate for detecting relative damage by MPB.

70

Another issue of concern is difference in vegetation phenology between the two

Landsat scenes, August 14th 2002 and September 28th 2004. While the earlier scene is

from the middle of the summer when vegetation is at its peak the more recent is from the

fall which often have drier conditions with senescent leaves on understory vegetation and

deciduous trees. As well, the study area (Figure 7) contains pine trees in different initial

stages of attack before red-attack. Green-attack trees will pass through a yellow colour

stage in the progression to red-attack, beginning approximately 2 to 3 months after the

initial attack it will start to be detectable (Safranyik and Carroll 2006). These influences

will decrease the wetness measured even though no red-attack trees are counted in the

sample plot, as a result weakening the linear relationship observed with green tree count.

Use of high spectral resolution imagery in detecting lower amounts of red-attack

trees has been implemented successfully (White et al. 2007) for a portion of the exact

same study area used here (Figure 7). White et al. (2007) used a single date of Hyperion

image from 2005 to compare estimates of red-attack with those made from a 2005 Quick

Bird image. Hyperion is an imaging spectrometer on the Earth Observing satellite

platform with 30 m spatial resolution and a 10 nm spectral resolution ranging from 0.43

to 2.4 μm, while Quick Bird uses a 2.44 m spatial resolution for four multi-spectral

bands: blue (0.45-0.52 μm), green (0.52-0.60 μm), red (0.63-0.69 μm), and infra-red

(0.76-0.90 μm) (White et al. 2007). Results from this study suggest that the narrow bands

of Hyperion data may be able to better detect lower densities of red-attack damage at the

landscape level than previous studies using EWDI with Landsat data. These results may

indicate a way to improve the linear relation between spectral response and number red-

attack trees possibly improving the ability to classify relative degrees of change.

71

To assess classifications accurately, one needs accurate ground data (Congalton

1991). While stem counts have been successfully used to classify MPB damage in other

studies (Franklin et al. 2003; Skakun et al. 2003) in these cases it was determined through

field surveys not by photo interpretation (Figure 8). The choice of ground verification

data was due primarily to time and data constraints, and was not the optimal choice.

When trees were tightly spaced (Figure 8) it was difficult to separate tree crowns, which

would have resulted in underestimation of total number of green trees in a 30 m pixel. As

well, high amounts of shadow made counting shorter trees challenging. Ideally,

verification data would include use of ground data collected in the field at the same

points in time that both of the remote sensing images are captured.

4.4 Conclusion

The purpose of this chapter was to test the hypothesis that relative change indices

would be significantly better at classifying relative degrees of change in MPB damage

than an absolute index would. However, no significant difference in classification was

seen (Table 14) between the relative indices, PEWDI and NEWDI, and the absolute

index (EWDI). Though, classification accuracies for all three methods were found to be

very low (Tables 23-25). The strength of the relationship between spectral response and

number of green trees was not strong (R2 = 0.40, R2 = 0.33), as a result making

estimating degrees of change difficult. Possible causes of the weak relationship between

spectral response and verification data were discussed, and include: spatial resolution,

vegetation phenology, spectral resolution, and ground data choice.

72

Support from previous work (Chapter 3) suggests that significant improvements

will be obtained in classification when suitable datasets are used. Results and discussion

from this study indicate that choice of forest disturbance and supporting data is vital.

Future research should focus on exploring other types of forest disturbance (partial

harvesting, fire damage, etc.) to see if a stronger relationship between spectral response

and disturbance is observed. As well, exploring different spatial resolution, spectral

resolution, and ground verification, could help improve the strength of the relationship. If

these recommendations are undertaken in future research then significant improvements

in classifying relative change by using relative indices is expected, as previously

observed in a modeling environment.

73

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