ANALYSIS OF LAND USE AND LAND COVER CHANGE IN KISKATINAW RIVER
WATERSHED: A REMOTE SENSING, GIS & MODELING APPROACH
by

Siddhartho Shekhar Paul
B.Sc. (Honors), University of Dhaka, Bangladesh, 2008
M.Sc., University of Dhaka, Bangladesh, 2009

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES
(ENVIRONMENTAL SCIENCE)

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
August, 2013

© Siddhartho Shekhar Paul, 2013

UMI Number: 1525672

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ABSTRACT
This thesis study w as conducted to capture the land use and land cover
(LULC) change dynamics in Kiskatinaw River W atershed, BC, Canada. A
combination of remote sensing, GIS and m odeling approach was utilized for this
purpose. Landsat TM and ETM+ satellite images of the years 1984, 1999 and 2010
were analyzed using object oriented image classification technique to produce LULC
m aps and detect the associated changes. The dynamic nature of different forest
types, increase in built-up area and significant depletion of wetlands were found to
be notable among the detected LULC changes. Thereafter, a multi-layer perception
neural netw ork technique was used to m odel transition potentials of various LULC
types, which was later realized with a Markov Chain land use m odel to predict
future changes. The integration of advanced satellite remote sensing tools and
neural netw ork aided M arkov Chain modeling w as illustrated to be an effective
means for LULC change detection and prediction in Kiskatinaw River W atershed.

Table of Contents

ABSTRACT.............................................................................................................................. ii
Table of C ontents...................................................................................................................iii
List of Figures.......................................................................................................................... v
List of Tables..........................................................................................................................vii
ACKNOWLEDGEMENT................................................................................................... viii
DEDICATION........................................................................................................................ ix
CHAPTER 1: INTRODUCTION........................................................................................... 1
1.1 B ackground.................................................................................................................... 1
1.2 Purpose of study ............................................................................................................ 4
1.3 Organization of the th esis............................................................................................ 5
CHAPTER 2: LITERATURE REVIEW................................................................................. 6
2.1 Land use and land cover (LULC) changes................................................................6
2.1.1 Concepts of LULC change..................................................................................... 6
2.1.2 Remote sensing (RS) and GIS techniques in LULC change an aly sis............. 8
2.2 Land use m odeling...................................................................................................... 15
2.2.1 Artificial neural netw ork (ANN) m odels.......................................................... 16
2.2.2 Spatial statistical m odels...................................................................................... 16
2.2.3 Cellular A utom ata (CA) m odels......................................................................... 17
2.2.4 Application of land use m odels.......................................................................... 17
CHAPTER 3: DATA AND METHODS.............................................................................. 20
3.1 Overview of Kiskatinaw River W atershed............................................................ 20
3.1.1 Location and extent.............................................................................................. 20
3.1.2 Kiskatinaw R iv er.................................................................................................. 22
3.1.3 Study area sub-basin............................................................................................ 24
3.1.4 Surficial Geology, Soil & Biophysical characteristics..................................... 25
iii

3.1.5 W ater use values in KRW.................................................................................... 27
3.2 M ethodology................................................................................................................ 28
3.2.1 Reconnaissance su rv e y ........................................................................................ 28
3.2.2 Data selection and collection............................................................................... 28
3.2.3 Description of the land use and land cover classes.........................................32
3.2.4 Image pre-processing and analysis.................................................................... 40
CHAPTER 4: RESULTS AND DISCUSSION.................................................................... 71
4.1 Results of RS & GIS analysis of satellite im ages.....................................................71
4.2 Accuracy assessm ent.................................................................................................. 78
4.3 Discussion on LULC changes.................................................................................... 81
4.3.1 LULC change in individual sub-basin...............................................................85
4.3.2 W etland depletion.............................................................................................. 108
4.3.3 N atural gas developm ent infrastructure.........................................................I l l
4.4 Land use m odeling.................................................................................................... 113
4.4.1 Results of modeling analysis............................................................................. 113
4.4.2 Discussion on m odeled outcom es.................................................................... 117
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS.....................................126
5.1 S um m ary .................................................................................................................... 126
5.2 Limitations and recommendations for future w ork.............................................130
B ibliography.........................................................................................................................132

List of Figures
Figure 1: Variation in spectral reflectance for different LULC ty p e s............................12
Figure 2: Difference in PBC and O O IC .............................................................................. 13
Figure 3: Study area- Kiskatinaw River W atershed.........................................................21
Figure 4: Channel netw ork w ithin Kiskatinaw River W atershed..................................23
Figure 5: Kiskatinaw River W atershed sub-basins..........................................................24
Figure 6: Surficial geology of KRW .................................................................................... 26
Figure 7: Different wavelengths of Landsat b a n d s..........................................................32
Figure 8: Cropland sam pled during ground truth survey..............................................33
Figure 9: Coniferous forest sampled during ground truth survey................................33
Figure 10: Deciduous forest sampled during ground truth su rv ey ..............................34
Figure 11: Mixed forest sampled during ground truth survey......................................35
Figure 12: Planted or re-growth forest sam pled during ground truth su rv e y ........... 36
Figure 13: Forest fire area sampled during ground truth survey..................................36
Figure 14: Cut block sampled during ground truth su rv e y ...........................................37
Figure 15: Pasture land sampled during ground truth su rv ey......................................38
Figure 16: View of One Island lake sampled during ground truth su rv ey ................. 38
Figure 17: W etland sam pled during ground truth su rv e y .............................................39
Figure 18: A gas development infrastructure sam pled during ground truth survey 40
Figure 19: Band- 4 of Landsat 2010 image before processing.........................................42
Figure 20: Mosaiced 2010 image w ith 5-4-3 band com bination

............................43

Figure 21:1984 Landsat satellite image used in this stu d y .............................................45
Figure 22:1999 Landsat satellite image used in this stu d y .............................................46
Figure 23:2010 Landsat satellite image used in this stu d y .............................................47
Figure 24: Num ber of segments and fineness varying with ST value...........................51
Figure 25:1984 image classification; A) MAXLIKE, B) SEGCLASS..............................54
Figure 26:1999 image classification; A) MAXLIKE, B) SEGCLASS..............................55
Figure 27: 2010 image classification; A) MAXLIKE, B) SEGCLASS..............................56
Figure 28: Landsat image analysis fram ew ork.................................................................60
Figure 29: MLP neural netw ork (after Eastman, 2012)....................................................64
Figure 30: Land use modeling fram ew ork........................................................................70
Figure 31: Area covered by each LULC type in 1984.......................................................72
Figure 32: KRW LULC m ap of 1984................................................................................... 73

Figure 33: Area covered by each LULC type in 1999.......................................................74
Figure 34: KRW LULC m ap of 1999................................................................................... 75
Figure 35: Area covered by each LULC type in 2010.......................................................76
Figure 36: KRW LULC m ap of 2010................................................................................... 77
Figure 37:1984-1999 change analysis for LULC ty p e s....................................................83
Figure 38:1999-2010 change analysis for LULC ty p e s....................................................84
Figure 39: LULC m ap of Mainstem sub-basin in 1984.................................................. 87
Figure 40: LULC m ap of Mainstem sub-basin in 1999.................................................. 88
Figure 41: LULC m ap of Mainstem sub-basin in 2010.................................................. 89
Figure 42: Change in LULC w ithin M ainstem sub-basin..............................................90
Figure 43: LULC m ap of Brassey sub-basin in 1984....................................................... 91
Figure 44: LULC m ap of Brassey sub-basin in 1999....................................................... 92
Figure 45: LULC m ap of Brassey sub-basin in 2010....................................................... 93
Figure 46: Change in LULC w ithin Brassey sub-basin....................................................93
Figure 47: LULC m ap of Flalfmoon-Oetata sub-basin in 1984..................................... 95
Figure 48: LULC m ap of Halfmoon-Oetata sub-basin in 1999..................................... 96
Figure 49: LULC m ap of Halfmoon-Oetata sub-basin in 2010..................................... 97
Figure 50: Change in LULC within Halfmoon-Oetata sub-basin..................................97
Figure 51: LULC m ap of East KRW sub-basin in 1984....................................................99
Figure 52: LULC m ap of East KRW sub-basin in 1999..................................................100
Figure 53: LULC m ap of East KRW sub-basin in 2010..................................................101
Figure 54: Change in LULC within East KRW sub-basin............................................. 102
Figure 55: LULC m ap of West KRW sub-basin in 1984.................................................104
Figure 56: LULC m ap of West KRW sub-basin in 1999.................................................105
Figure 57: LULC m ap of West KRW sub-basin in 2010.................................................106
Figure 58: Change in LULC w ithin W est KRW sub-basin............................................107
Figure 59: W etlands converted to other land-use type (1984-2010).............................109
Figure 60: Gains and losses in w etland area (1984-2010).............................................. 110
Figure 61: Changes in built-up area and cut block (1984-2010)...................................I l l
Figure 62: Transition probabilities from planted/re-grow th forest to deciduous forest
116
Figure 63: Interm ediate stage LULC m ap from hard prediction.................................120
Figure 64: Final stage LULC m ap from hard prediction...............................................121

Figure 65: Difference in area for each LULC type between existing and predicted
m ap s...................................................................................................................................

123

Figure 66: Soft prediction output for 2020....................................................................... 125

List o f Tables
Table 1: Description of satellite imageries used in LULC change detection............... 31
Table 2: Spectral features of Landsat b a n d s......................................................................31
Table 3: Driver variables for transition potential m odeling...........................................67
Table 4: Surface area covered by each LULC type in a particular y e a r........................78
Table 5: Accuracy assessment error matrix for 2010 image classification................... 79
Table 6: Accuracy assessment su m m ary ...........................................................................80
Table 7: Sensitivity of transition m odel for forcing independent variables...............115
Table 8: Transition probability m atrix ............................................................................. 117
Table 9: Expected transition of pixels............................................................................... 119
Table 10: Area calculated for predicted LULC m aps.....................................................123

ACKNOWLEDGEMENT
W hen you leave home for an uncertain period of time and travel half w ay of
the w orld to achieve something, you m ust know that you are starting a difficult
journey to reach that 'special achievement'. Pursuing the graduate study at UNBC
was that special to me. Not to mention, this precious journey w ould not have been
possible w ithout the gracious support from some great people.

First and foremost, I w ould like to express my sincere gratitude to my
supervisor Dr. Jianbing Li whose unreserved support and guidance at every stage of
doing this research m entored me to reach the goal. His elaborate efforts and patience
for my intellectual developm ent are truly appreciated. I would also like to offer my
indebtedness to my committee m embers Dr. Roger W heate and Dr. Liang Chen for
their guidance throughout this research. Despite busy schedules, they were more
than willing to meet me when I needed advice.

I am grateful to Scott Emmons, Senior Instructor, GIS lab for his various sorts
of technical support. My sincere thankfulness goes to m y research team member,
Gopal Saha and others for being beside me for every single need. I would like to
offer my appreciation to m y family and friends who are w ith me to share my laughs
and cries and who I can feel every moment. The research was funded by Peace River
Regional District, City of Dawson Creek, Geoscience BC, Encana, and BP Canada.

DEDICATION

I dedicate this w ork to the people w ho feel and fight for a partition free world and
who care for unbiased goodwill tow ards hum ankind

ix

CHAPTER 1: INTRODUCTION

1.1 Background
Hum an beings, since the earliest stage of settlement, are dependent on land
for their food production and various sorts of economic developm ent which have
been constantly modifying the global landscape. The relentless pressure to m eet the
needs of burgeoning population and dem and driven developm ent activities have
amplified the stress on earth's land (Foley et al., 2011; Weinzettel et al., 2013). In this
context, anthropogenic activity and its concomitant land use and land cover (LULC)
changes have become an inevitable issue for the present time and accentuating the
risks of environm ental degradation around the globe (Paiboonvorachat, 2008;
Stabile, 2012).

Over half of the w orld's landscape is influenced by hum an activities or under
some sort of anthropogenic developm ent and since the historic past, m any natural
resources have been heavily used or even depleted in the w orst cases (Foley, et al.,
2005, Goldewijk, et al., 2011). The impacts of this widespread LULC change on the
natural environm ent are m ulti faceted, including climate change, alteration of
hydrological cycle, increased w ater extraction, im pairm ent of w ater quality,
degradation of soil nutrients, amplified surface erosion, and loss of biodiversity
(Turner, et al., 2007, Paiboonvorachat, 2008). Therefore, information on land use and
1

land cover, changing trends and optimal use of the land resources have become
predestined criteria for land use planning and effective natural resources
managem ent of an area.

W atersheds in the north-eastern part of British Columbia (BC), Canada have
been experiencing w idespread LULC changes over the past few years due to the
convergence of various industrial interests, for example, logging, mining, oil and gas
development, large scale hydro developm ent etc. (Lee & Hanneman, 2012). Among
the north-eastern BC's watersheds, Kiskatinaw River watershed (KRW) which is the
study area of this research, features a significant portion of industrial developm ent
activities and associated LULC changes. Being the only source of drinking w ater
supply to the City of the Dawson Creek (DC) and neighbouring village of Pouce
Coupe, KRW plays a dom inant role in north-eastern BC's life and environm ent
(Saha, et al., 2013), bu t unfortunately, information on LULC changes w ithin the
watershed is scanty. Therefore, there is a pressing need to understand the LULC
system within the w atershed to assess its impact on the overall w atershed dynamics.

Researchers around the globe have long been enjoying the effectiveness of
remote sensing (RS) technology for extracting current and previous land use and
land cover (LULC) information and for providing robust inventory of LULC
changes (Ridd and Liu, 1998; Mas, 1999; Paul et al., 2012; Chen et al., 2013). Recent

2

advancem ent of RS tools and combination of Geographic Information System (GIS)
w ith RS makes this technique m ore successful and introduces a w ider scope of
research including LULC change detection, LULC modeling and prediction (Araya,
2009, Paul et al., 2012). Every change in land cover can be reflected by the alteration
of radiance value captured by the remote sensor, e.g. satellite image sensor (Mas,
1999). Later, this variation in radiance value is gauged by comparing multi-tem poral
satellite images or aerial photographs (Chen et al., 2013; Saha et al., 2013) and LULC
m aps are produced for change detection. The information gathered from the change
detection analysis can be further realized by a land use modeling approach. The
simulation of land use m odels has recently proven valuable in land use planning,
environmental impact assessment, policy making etc. (Schulp, et al., 2008; Kline, et
al., 2007) and thus, being widely used globally. Land use models are capable of
exploring the transition potentials of various LULC types for a given set of driver
variables (Kamusoko, et al., 2009). This information can then be used for predicting
future LULC information for a study area.

It is anticipated that this thesis study will provide a comprehensive insight
about the land use-land cover system within the Kiskatinaw River watershed. The
LULC information congregated by RS, GIS and m odeling analysis of this research
will update decision m akers and developm ent practitioners about the m agnitude

3

and nature of long term LULC change in KRW. As a result, informed environm ental
policies and m anagem ent strategies could be im plem ented and practiced.

1.2 Purpose of study
This thesis study aims to capture the LULC change in Kiskatinaw River
watershed (KRW) to compare scenarios before and after the economic grow th in this
vicinity using RS, GIS and modeling techniques. So, the specific objectives
encompassed by this study are:

• To assess the changes in land use and land cover occurring w ithin KRW
based on the analysis of remotely sensed satellite imagery
• To model the transition potentials for each LULC type
• To predict future LULC scenarios

4

1.3 Organization of the thesis

Chapter 1: Introduction
Introduces the research

Background of
the study

Purpose of the
study

Structure of the
thesis

Chapter 2: Literature Review
Explains existing literatures and context of the study,
introduces available m ethods of analysis

Chapter 3: Data and Methods
Provides details about the chosen study methods

Overview on the
study area

Detail of the data
used

Describes the data
analysis process

Chapter 4: Results and Discussion
Describes the analyzed results

Presents the results

Explains the deliverables

__________ i____________
Chapter 5: Conclusion
Summarizes the research outputs,
explains limitations and provides directions for future research
5

CHAPTER 2: LITERATURE REVIEW

2.1 Land use and land cover (LULC) changes

2.1.1 Concepts of LULC change
Although the term s 'land use' and 'land cover' are sometimes used
interchangeably, each has a distinct meaning. Land cover is the bio-physical layer
covering the earth surface, while land use represents the hum an utilization of the
land cover. Land cover includes earth's land surface distribution of vegetation,
water, desert and ice as well as the biota, soil, topography etc. in the im m ediate
subsurface, and it also includes hum an activity areas, such as settlement, mine
exposure etc. (Lambin, et al., 2003; Oumer, 2009). On the other hand, land use is
attributed to how hum ans exploit the land cover to serve their ow n purposes and
includes features such as residential zones, agricultural farms, logging areas etc.
(Zubair, 2006; Oumer, 2009). In this context, land use influences the changes in land
cover; therefore, LULC change can be defined as the modification of surface features
on earth's landscape which is realized by the difference in their surface appearance
assessed at two different times (Ayele, 2011).

The

current

global

condition

of

mass

environm ental

change

and

sustainability issues elucidates the gravity of LULC change detection research in
different parts of the world. Though LULC changes entail both natural (e.g. weather,
6

flooding, earthquake etc.) and anthropogenic causes, the ever-increasing dem and of
the m ushroom ing population has designated the anthropogenic influences as the
most dram atic (Turner, et al., 2007; Foley, et al., 2011; Weinzettel, et al., 2013).

At present, the undisturbed pristine areas cover less than 50% of the total
earth's landscape; forest cover is only 30% which was around 50% some 8000 years
ago (Oumer, 2009). Diverse and intense anthropogenic activities around the w orld
are attributed for most of these LULC changes. For this reason, research is
conducted around the world to study this dynamic LULC alteration and devoted
efforts are underw ay to explore its connection w ith the disturbances happening in
the earth system.

7

2.1.2 Remote sensing (RS) and GIS techniques in LULC change analysis
2.1.2.1 Definition of RS and GIS:

Remote sensing is defined in various ways in the literature, but w ith similar
meaning. For example, United Nations (1986) defined remote sensing as

'the sensing o f the Earth’s surface from space by making use o f the properties o f
electromagnetic waves emitted, reflected or diffracted by the sensed objects, fo r the purpose o f
improving natural resources management, land use and the protection o f the environm ent'

Lillesand, et al. (2008), defined rem ote sensing as (p. 1):

'the science and art o f obtaining information about an object, area, or phenomenon
through the analysis o f data acquired by a device that is not in contact w ith the object, area,
or phenomenon under investigation'

In short, remote sensing is the study of satellite images or aerial photographs which
are capable of differentiating earth's land use and land cover types by variation in
their electromagnetic signature. On the other hand, geographic information systems
(GIS) refer to any scientific effort that incorporates geographical data to visualize,
analyze, and explore geographically referenced information of a location.

8

The United States Geological Survey (USGS, 2007) defined GIS as:

'In the strictest sense, a GIS is a computer system capable o f assembling, storing,
manipulating, and displaying geographically referenced information, that is data identified
according to their locations'

2.1.2.2 Application in LULC mapping

In combination, RS and GIS serve efficiently for earth observations and
associated information analysis (Paiboonvorachat, 2008; Araya, 2009; Paul, et al.,
2012). Viewing earth from space enables us to com prehend the cum ulative influence
of hum an activities on earth surface's natural state. Capturing and analyzing this
information by the RS and GIS tools provides a cost effective record of LULC in an
accurate and timely m anner (Ridd & Liu, 1998; Chen, et al., 2013).

Availability of m ultiple satellite sensors offering image data w ith fine spatial
resolution, high geometric precision and short revisit intervals has m ade satellite
remote sensing m ore appealing than aerial photography and m anual data collection
m ethods for LULC change detection and m odeling (Aplin, et al., 1997; Stabile, 2012).
With the advancem ent of satellite image analysis tools and ease-of-access to various
off-the-shelf image processing software, satellite remote sensing has been gaining
9

wide popularity for investigating LULC change. For example, Musaoglu, et al.,
(2005) analyzed Landsat and SPOT satellite images for land use change monitoring
(1975-2001) in the Beykoz region of Istanbul. Land cover change dynamics were
m onitored in Africa using high spatial resolution satellite data by Brink & Eva
(2009). Supervised classification of Landsat image was performed by El-Kawy, et al.
(2011) to provide recent and historical LULC conditions for the w estern Nile delta.
Satellite remote sensing was also em ployed in New Zealand for estim ating change
in forest cover, i.e. area of afforestation and deforestaion to m eet the reporting
obligation under Kyoto protocol (Dymond, et al., 2012). Land use change derived by
shrub cover grow th in northern slope of Alaska was m apped by Beck et al. (2011)
using IKONOS and SPOT satellite data. Thus, satellite remote sensing is being vastly
utilized at different parts of the w orld for diverse LULC change detection
approaches.

2.1.2.3 Satellite image analysis
Satellite image analysis entails digital image processing which involves
m anipulation and interpretation of the digital image data by specific com puter
program s to display and extract meaningful information about the surface of the
earth. Digital image classification which is among the basic image analysis processes

10

governs m ost of the LULC change detection study (Matinfar, et al., 2007). Image
classification which is norm ally perform ed on multispectral images, i.e. images with
more than one spectral band, automatically categorizes all the image pixels into
various land cover classes based on their similar digital num ber (DN) values
(Lillesand, et al., 2008).

The satellite sensors record the variation in the electromagnetic radiation
from each part of the earth surface and assign it with a distinct DN value for each
spectral band (Oumer, 2009). The range of digital num ber varies from sensor to
sensor and depends on the radiometric resolution, which is attributed to the sensor's
sensitivity to various level of incoming energy (Ayele, 2011). For example, Landsat
Multispectral Scanner (MSS) satellite sensor detects radiation in the range from 0 to
63 DN; while Landsat Thematic M apper (TM) sensor's DN value ranges from 0 to
255 (NASA, 2011). The variation in the spectral reflectance of a particular LULC type
is captured during the digital image classification process and thus the LULC m ap
of an area is constructed. For instance, the spectral signature of w ater is different
from that of vegetation for each band of a multi-spectral imagery and vice-versa.
Figure 1 explains the variation in spectral reflectance for three LULC types: water,
vegetation and soil in Landsat TM imagery.

11

L«^TMb«di n i

" mm fit m
50 -

Sail

L

Vegetation

30

Water

o

04

Oui

U

14

2.0

23

Figure 1: Variation in spectral reflectance for different LULC types
(adapted from Richards & Jia, 1999)

Various classification approaches and algorithm s have been adopted by
researchers around the globe for classifying satellite imagery (Gao & Mas, 2008). The
conventional m ethod is a pixel based classification (PBC) technique which classifies
the image based on each single image pixel (Dean & Smith, 2003) (Figure 2). The
remotely sensed satellite imagery comprises rows and columns of pixels whose
spectral similarity and dissimilarity w ork as the basis of PBC. The classification
process groups the like-pixels under distinct LULC types (Casals-Carrasco, et al.,
2000). Though the PBC technique has been well developed and successfully applied
in m any cases, it has some limitations essentially because spatial photo-interpretive
elements namely texture, context and shape are disregarded during PBC and the

12

pixels do not represent true geographical objects (Hay & Gastilla, 2006; Blaschke,
2010). These issues contribute to lower classification accuracy in m any studies.

a) Pixel based classification

b) Object oriented classification

Green = Vegetation, Blue = Water
a) Individual pixels have been identified for vegetation and water classes based on spectral
reflectance i.e. DN values; b) Image objects or segments comprising several pixels have been
identified for vegetation and water classes based on homogeneities of spectral, spatial and
other characteristics.
Figure 2: Difference in PBC and OOIC

PBC classification techniques which employ the supervised m ethod,
unsupervised m ethod or some combinations (Enderle & Weih, 2005), do not
consider the spatial and contexual information of the pixels of interest; bu t this
information could be used to produce more accurate LULC classification output,

13

particularly when using high resolution satellite data (De Jong, et al., 2001; Benz, et
al., 2003; Dwivedi, et al., 2004; Matinfar et al., 2007; MacLean et al., 2013). In this
context, the object oriented image classification (OOIC) technique m ade a timely
arrival in the research era of remote sensing. Although the concept of OOIC was
introduced in 1970s, OOIC started to attract the dem and of researcher community
after mid-1990s w ith the advancem ent of remote sensing data processing software
and hardw are as well as w ith the increased availability of high spatial and spectral
resolution imagery (De Kok, et al., 1999).

Over time, m any faceted issues regarding PBC have increased dissatisfaction
among the remote sening users which has been compensated by the object oriented
image classification (OOIC) gaining w ide popularity over the last few years
(Blaschke, 2010; Chen et al., 2013). Unlike per-pixel classification, OOIC classifies the
imagery by image segments which comprise groups of spectrally hom ogeneous
pixels. Segments are building blocks in OOIC (Hay and Castilla, 2008; Blaschke,
2010) and govern the classification process by their ow n characteristics (such as:
segment size, shape, texture, zonal statistics etc.) instead of the individual
characteristics of each pixel (MacLean et al., 2013). Image segm entation which is the
basic step in object oriented classification, divides the imagery into homogeneous,
continuous and contiguous objects (Gao & Mas, 2008). Figure 2 dem onstrates the
difference between PBC and OOIC. In Figure 2a vegetation and w ater were
14

classified pixel by pixel, but in Figure 2b, vegetation and w ater objects were
identified by segm ent based classification.

The image segments or objects provide OOIC the leverage of using spatial,
spectral, textural and contextual inform ation during LULC classification.

2.2 Land use modeling
Land use modeling, at the present time, plays a pivotal role in m any natural
resources m anagem ent and decision m aking processes. Land use models are
effective tools to analyze the causes and consequences of land use-land cover change
and create an enhanced understanding of the land use system in an area (Verburg, et
al., 2004; Stabile, 2012). The use of land change models is multi-dimensional. For
example, they were used in biodiversity m onitoring (Verburg, et al., 2008), for
estimating loss of vegetation cover (Echeverria, et al., 2008), for forest m anagem ent
(Kamusoko, et al., 2013), in urban expansion and planning (Sun, et al., 2007) etc..

Researchers around the globe have been devising and utilizing a w ide variety
of land use models, all of which are diverse in their formulations, objectives and
capabilities. There are whole landscape models, distributional landscape models as
well as spatial landscape models (Baker, 1989; Singh, 2003). Since the spatial details
including natural and hum an processes have greater impacts on land use change
15

system, spatial modeling has taken over other modeling m ethods in m any studies.
Progress in remote sensing and GIS research has m ade significant contributions in
these spatial landscape modeling m ethods (Singh, 2003).

Spatial land use modeling research has em ployed various approaches, a few
of which has been explained below.

2.2.1 Artificial neural network (ANN) models
ANN serves as a machine learning tool which is capable of quantifying and
forecasting complex behaviour and patterns of LULC change (Pijanowski, et al.,
2002). ANN imitates the interconnected neural system in the hum an brain. The basic
element or nodes, called neurons are connected in layers and perform the processing
in an ANN model. The neuron's output is derived from m ultiplying the input signal
by specific weights which are determ ined by using training algorithm s of which
back propagation is the most popular approach (Pijanowski, et al., 2002; Singh, 2003;
Pijanowski, et al., 2005). ANN is used to identify the pattern of land use change and
hence, transition from one land use type to another can be predicted.

2.2.2 Spatial statistical models
Spatially explicit statistical m odeling of LULC changes is a widely used
approach for understanding processes related to LULC change and quantifying their
16

influences on the change dynamics (Semeels & Lambin, 2001). Various spatial
statistical modeling techniques have been adopted by researchers for understanding
current LULC change and projecting the future change scenarios. Multiple linear
regression, Markov Chain methods, M ultivariate m odeling tools etc. are just a few.
W hen spatial information is aggregated w ith statistical analysis, the land use
modeling becomes more realistic and effective (Veldkamp & Lambin, 2001).

2.2.3 Cellular Automata (CA) models
The cellular autom ata (CA) is a popular spatially explicit land use modeling
tool. The output of the CA m odel emerges from interactions betw een individual
cells which are the fundam ental m odeling unit (Batty, 2005); this is why, CA is
frequently considered as powerful technique for modeling complex land use change
(Hasbani, 2008). CA is enhanced by its natural affinity w ith GIS and remotely sensed
data use (Torrens & O'Sullivan, 2001).

2.2.4 Application of land use models
Specific land use change process was focused in many land use modeling
approaches (Mas, et al., 2004; He, et al., 2008), whereas some other m odels integrated
m ultiple change dynamics (Dietzel & Clarke, 2007; Overmars, et al., 2007). A
Regressional statistical land use m odel w as used by Aspinall (2004); a cellular
17

autom ata mechanistic m odel was applied by Walsh et al. (2008) for modeling
agricultural expansion and deforestation; a combination of various models has also
been used in m any studies as in Kamusoko et al. (2013) for m odeling m ultiple land
use processes. Though complex models are capable of generating robust output,
simulation of these models entails rigorous and difficult param eterization as well as
large cost and time (Benito et al., 2010). However, M arkov Chain as a simple land
use model, is a useful and popular tool in this context and covers large spatial
extent (Weng, 2002). The M arkov m odel calculates transition matrix for various land
use features based on current driving factors and predicts the future land use change
pattern if the driving forces continue in future (Mubea et al., 2010). M arkov Chain
has been successfully used in m any studies with few reported issues in varied
settings; for example Weng (2002) em ployed M arkov model along with remote
sensing and GIS analysis to m odel land use dynamics in a coastal region of China;
Islam & Ahmed (2011) modeled urban spraw l in Dhaka city using GIS aided
Markovian modeling, while Freier, et al. (2011) used Markov Chain for modeling
rangelands under climate change scenarios in semi-arid environm ent of Morocco.

The transition matrix which comprises all the estimated transition potentials
serves as one of the basics in M arkov Chain land use modeling. The non-param etric
multi-layer perception (MLP) technique which is an artificial neural netw ork has the
m erit to fit complex non-linear relationships between driving variables and land use
18

for producing accurate transition potential estimation (Sangermano, et al., 2010;
Eastman, 2012). M ulti-layer perception tool has the ability to perform efficiently
even with less training data which has m ade it a convenient and preferred technique
(Civco, 1993; Chan, et al., 2001; Martinuzzi, et al., 2007). The augm entation of
computing power and performance as well as availability of user friendly software
packages have supported and substantially increased the use of MLP neural
netw ork techniques in land use studies (Li & Yeh, 2002; Pijanowski, et al., 2002). A
num ber of studies have reported the effectiveness of artificial neural netw orks in
land use modeling (Pijanowski, et al., 2005; Almeida, 2008; Lin, et al., 2011). It is
claimed in various studies that a MLP netw ork w ith 3 layers - input, hidden and
output is capable of estim ating any polynomial function and its ability is almost
unequivocal for solving very complex regression and land use classification and
modeling problems (Eastman, 2012). So the integration of MLP neural netw ork and
M arkov Chain model could reinforce the land use m odeling study by aggregating
statistical and spatial characteristics of LULC variations.

19

CHAPTER 3: DATA AND METHODS

3.1 Overview of Kiskatinaw River Watershed
3.1.1 Location and extent
Kiskatinaw River W atershed (KRW) is located on the Alberta Plateau of
north-eastern British Columbia, near the British Columbia-Alberta border (Figure 3).
KRW lies between longitude 119° 59' W to 121° 7' W and latitude 54° 58' N to 56° 5'
N. The total area of the w atershed is 4097 km 2, although this study focuses on the
upper Kiskatinaw w atershed which is 2836 km 2 (Figure 3). The City of Dawson
Creek and the village of Pouce Coupe are located on the north-east of the study area.
The m unicipality of Tumbler Ridge is near the south-western periphery of KRW.
Arras, located at the northernm ost edge of the study area serves as Dawson Creek's
w ater intake station. Steep slopes of the Rocky M ountain Foothills characterize the
w estern portion of the watershed, while undulating plains projecting into BC from
Alberta delineate the eastern portion (Kiskatinaw River IWMP, 1991).

20

120°0'0"W

55°40'0"N

56°0*0M
N

56°0'0"N

120°50'0M
W

55°0’0"N

55°0'0HN

55°20'0"N

55°20'0"N

55°40,0"N

Poucacoupe

A Major location
Major roads______________________
120°50,0,,W

L

120W W

Figure 3: Study area- Kiskatinaw River Watershed

3.1.2 Kiskatinaw River
The Kiskatinaw River is a tributary of the Peace River. The River originates in
the foothills of the Rocky Mountains, near Tumbler Ridge, and flows approxim ately
200 km north before joining the Peace River at the Alberta border w ith BC (Peace
Forest District, 2010) (Figure 4). The w ater supply area rises from an elevation of
680m at Arras in the northernm ost edge to 1,300m at Bear Hole Lake in the
southernm ost boundary (Dobson Engineering Ltd., 2007). The eastern and w estern
confluences m eet the main confluence of the river almost at the m iddle of the
watershed near its eastern border (Figure 4). The average annual flow rate is 10 m 3/s,
but in January it drops to 0.052 m 3/s which m akes it m ore complicated to establish
an effective w ater resources m anagem ent for the w atershed (City of Dawson Creek,
2009). The watershed receives an average annual precipitation of 499 mm,
comprising 320 mm of rain and 179 m m of snow.

22

Legend
Main c h a n n e l o f K iskatinaw R iver
T ributaries of K iskatinaw R iver

Elevation (m)

Figure 4: Channel network within Kiskatinaw River Watershed

23

3.1.3 Study area sub-basin
For study purposes, the KRW study area has been divided into five sub­
basins which are Mainstem, Brassey, Halfmoon-Oetata, East KRW and W est KRW
(Figure 5). Among these, West KRW covers the largest area of 1005 km 2, followed by
East KRW (996 km 2), Mainstem (433 km 2), Brassey (208 km 2) and Halfmoon-Oetata
(194 km 2).

Figure 5: Kiskatinaw River Watershed sub-basins

24

3.1.4 Surficial Geology, Soil & Biophysical characteristics
Surficial Geology m ap of the KRW area is displayed in Figure 6. Seven types
of surficial deposits are identified in this watershed, namely alluvial, colluvial,
eolian,

glaciofluvial,

lacustrine,

morainal,

and

organic.

M orainal

deposits

predom inate the area whereas alluvial deposits exist m ostly along the confluences of
the river. Eolian deposits are observed in the central-eastern areas.

Lower slopes and valley bottom s are predom inately covered in thick
sequences of fine-grained lacustrine deposits. Glaciofluvial, colluvial and organic
deposits are not w idespread and can be found locally. Colluvial deposits which are
derived mainly from mass-wasting processes are found on m id-slopes of the
w atershed (Dobson Engineering Ltd., 2007). Clay and silt loams are the dom inant
soil type in this area although sandy loam is also found.

Most of the watershed belongs to Boreal White and Black Spruce
biogeoclimatic zones with a m inor com ponent of Engelmann Spruce Sub-alpine Fir
(Dobson Engineering Ltd., 2007).

25

Surficial Deposit
Alluvial
Colluvial
Eolian
Glaciofluvial
Lacustrine
Morainal
Organic

I
20 km

Figure 6: Surficial geology of KRW

26

3.1.5 Water use values in KRW
Kiskatinaw River watershed in northern-eastern BC serves as a dynam ic and
crucial w ater resource for the Peace River Regional District (PRRD) area. It is the
primary drinking w ater source for the City of Dawson Creek, village of Pouce
Coupe and thousands of rural inhabitants in PRRD. Over decades, the dom inant
land use activity in this area has been agriculture including grain production,
livestock and mixed farming (Jacklin, et al., 2003). Forestry is also a major land use
activity in this densely forested watershed. But over the past few years, natural gas
development has been dom inating the scene by its intensity and rapid expansion
(Forest Practices Board , 2011). KRW is included in the Montney shale gas play
which is one of the major shale basins in N orth America. Recent gas developm ent
within the w atershed is dom inating the land use and land cover dynamics in this
area. As a result, the w ater dem and in the w atershed is increasing at an average rate
of about 3.2% per year (Saha, et al., 2013).

27

3.2 Methodology
3.2.1 Reconnaissance survey
The first im portant step in this research was the reconnaissance survey in the
study area. Before actual data collection, this survey provided overall information
on the study watershed and allowed to comprehend the gravity of land use and
land cover change w ithin it. Field trips were perform ed at a num ber of sites in the
w atershed and general discussion w as conducted w ith the Dawson Creek city
authority, individuals performing

diverse

research

in this w atershed

and

community representatives who belong to various stakeholders of the watershed.
All of these created a deep insight about the study area and its ongoing activities
which w hetted the overall research enthusiasm and strategy.

3.2.2 Data selection and collection
The research is based on the analysis of satellite imagery of the study
watershed. Therefore, the first and foremost task w as the selection of satellite sensor
and associated images. D uring this process, the prior considerations w ere the
purpose of the study, objects to be identified, and the availability of images.

Based on literature review and previous experiences, Landsat satellite images
were selected for this study for a num ber of reasons.
28

a) For long term change detection, Landsat data are available since 1972. This
robust and continuous data inventory stores satellite data for every part of the w orld
from 1972 till today. Since this study aims to detect the LULC changes in KRW from
1984 to 2010, Landsat data was the best available option.

b) Landsat satellite has a repeat interval of 16 days. This property of this
sensor has increased the flexibility of data selection, especially w hen cloud cover is a
major limitation in satellite data selection.

c) Last but not least, Landsat data are freely available and it w as a huge
support to this graduate research.

After selecting the satellite sensor, the next task was to collect necessary
Landsat imagery for this study. This step was also governed by these factors:

i) The objective of the study which w as to capture developm ent activity
driven LULC change w ithin KRW from early 1980's to 2010,

ii) Image quality: the main hindrance was to obtain cloud free analyzable
imagery

iii) Image acquisition time: to obtain images captured at more or less the same
time of the year is important, because seasonal variability changes the appearance of
land use features and this can impact the quality of analysis.

29

After considering all of these factors, three Landsat images w ere selected for
analysis and downloaded from the data warehouse of USGS Earth Resources
Observation and Science Center (EROS):

1) 1984 Landsat 4/5 TM imagery: represents early stage of industrial
developm ent in this forested w atershed

2) 1999 Landsat ETM+ imagery: represents the status at the beginning of the
gas industry booming within the w atershed

3) 2010 L andsat 4/5 TM imagery: represents current status w ithin KRW

All the images dow nloaded w ere either from late July or early A ugust within
a span of 10-18 days to keep the analysis free from seasonal variability impact. Table
1 sum m arizes the data description. Two separate scenes for each year had to be
dow nloaded to cover the whole watershed, one from path 48 row 21 and another
from path 48 row 22.

30

Table 1: Description of satellite imageries used in LULC change detection

Year

Day

Satellite imagery

Spectral resolution1

Spatial resolution

1984

July 17

Landsat 4/5 TM

Band 1 to 5 & 7

30 m

1999

A ugust 4

Landsat ETM+

Band 1 to 5 & 7

30 m

2010

July 25

Landsat 4/5 TM

Band 1 to 5 & 7

30 m

1 thermal band 6 w as excluded in this analysis

Landsat data are multi-spectral w ith seven different color bands although the
Landsat Enhanced Thematic M apper Plus (ETM+) sensor has an additional
panchromatic band 8 which was not used in this study, along w ith therm al band 6
for both TM and ETM+ sensors. Table 2 and Figure 7 describe in detail the spectral
features of different Landsat bands. Landsat images have spatial resolution of 30 m
i.e. each pixel of the image covers 30m X 30m area on land.

Table 2: Spectral features of Landsat bands

Band Num ber

W avelength Interval

Spectral Response

1

0.45-0.52 p m

Blue-Green

2

0.52-0.60 p m

Green

3

0.63-0.69 p m

Red

4

0.76-0.90 p m

Near IR

5

1.55-1.75 p m

Mid-IR

6

10.40-12.50 p m

Thermal IR

7

2.08-2.35 p m

Mid-IR

31

ULTRA­

NEAR-IR SOLAR
■IVISIBLEl 1 I REFLECTEDP I

VIOLET

1.0

2.0 3j0

M1DIR

5.0

! FAR-IR-

10 15 20 30

WAVELENGTH, Jim____________
Figure 7: Different wavelengths of Landsat bands (after NASA, 2011)

3.2.3 Description of the land use and land cover classes
Based on the reconnaissance field observation and local information, it was
decided to concentrate on 11 land use and land cover (LULC) classes during the
satellite data analysis. The selected LULC classes are cropland, coniferous forest,
deciduous forest, mixed forest, planted or re-growth forest, forest fire, cut block,
pasture, water, wetland, built-up area. Each of the classes is described below.

Cropland: This class includes all the cultivated lands used for crop
production. This comprises mostly flat areas and also some steep slopes where
various crops are grown (Figure 8).

32

Figure 8: Cropland sampled during ground truth survey

Coniferous Forest: The forested lands that predom inantly comprise
evergreen trees throughout the year are defined as coniferous forest in this
classification scheme (Figure 9).

Figure 9: Coniferous forest sampled during ground truth survey

33

Deciduous Forest: The forested lands w ith predom inance of broadleaf trees
that lose their leaves seasonally, particularly at the end of the frost-free season, are
classified as deciduous forest in this study (Figure 10).

Figure 10: Deciduous forest sampled during ground truth survey

Mixed Forest: The forested lands which have both evergreen coniferous and
broadleaf deciduous trees and no predom inance of one category are defined as
mixed forest in this classification scheme (Figure 11).

34

Coniferous tree

D eciduous tree

Figure 11: Mixed forest sampled during ground truth survey

Planted or Re-growth Forest: The forested lands that comprise young
coniferous and/or deciduous plants which have re-grown or been planted after
forest fire or clear cutting or any other decay event are classified in this class. Some
herb-shrub m ay be included in this class since these are hard to differentiate during
digital image classification with imagery of 30 m resolution. Planted and re-grow th
forests are very common in this w atershed (Figure 12).

35

Figure 12: Planted or re-growth forest sampled during ground truth survey

Forest Fire: This class comprises fire affected forest land w ith burnt, dead
trees. A fire event occurred in the Hourglass area of this watershed in 2006. So this
class only appeared in 2010 image classification (Figure 13).

Figure 13: Forest fire area sampled during ground truth survey

36

Cut block: This class represents the forest clear cut area which w as removed
for industrial (mostly) or other purposes. Cut blocks are copious in this watershed.
Figure 14 shows a cut block in KRW which was cleared by the gas developm ent
industry.

Figure 14: Cut block sampled during ground truth survey

Pasture: The lands which are m aintained for livestock production as well as
used for perennial hay or forage cultivation are defined as pasture in this
classification scheme. Figure 15 shows a typical pasture land in the study area.
Pasture and croplands may be interchanged after several years.

37

Figure 15: Pasture land sampled during ground truth survey

Water: This class comprises open w ater bodies w ith m ore than 95% w ater
surfaces. It includes river channels, lakes etc. (Figure 16).

Figure 16: View of One Island lake sampled during ground truth survey

38

Wetland: In this classification scheme, w etlands are non-forested and/or
slightly forested marshes, swamps etc. w here the groundw ater table is at, near or
above the surface for significant part of the year. W etlands are one of the common
features in KRW (Figure 17).

Figure 17: Wetland sampled during ground truth survey

Built-up Area: This class includes lands covered w ith hum an-built structures
like: houses, roads, industrial infra-structures etc. (Figure 18).

39

Figure 18: A gas development infrastructure sampled during ground truth survey

3.2.4 Image pre-processing and analysis
The image pre-processing and analysis entail a num ber of steps before
generating the final output. During this process, several com puter software
packages were used. Pre-processing was mostly performed w ith PCI Geomatica
10.2, image analysis was conducted w ith IDRISI Selva 17.0 while ArcGIS 10.1 and
Q uantum GIS 1.7 were used at different phases of the analysis and m ap generation.

40

3.2.4.1 Pre-processing of Landsat image
The dow nloaded Landsat images for each band needed to undergo several
pre-processing steps. The pre-processing of images prepares them for the
classification analysis. All the bands of the two Landsat scenes w ere dow nloaded as
separate image files (.tiff) which were layer stacked together for classification
analysis.

Figure 19 shows an image of an individual Landsat band dow nloaded from
the USGS EROS database. These individual bands were then stacked sequentially
from 1 to 7 using the 'transfer' function in PCI. Stacked bands w ere then translated
to PCI default image format (.pix). W hen layer stacking of all the bands from each
scene into two separate image files w as executed, they were ready for the next step
of 'mosaicing'. During mosaicing, both of the new image files w ere joined together
to form a single image file which was later clipped to get the full extent of the study
area. Figure 20 displays mosaiced output of 2010 Landsat image w ith 5-4-3 band
combination.

The downloaded Landsat images have been already georeferenced to
projection system Universal Traverse Mercator (UTM), zone 10 w ith datum WGS 84
which was utilized throughout the analysis.

41

Sheene frdn*
•

■ <►

.

25
’. .

.

Scene from path 48 & row, 22

Figure 19: Band- 4 of Landsat 2010 image before processing

Figure 20: Mosaiced 2010 image with 5-4-3 band combination (KRW area in red boundary)

Each band of the Landsat image has its own characteristics as discussed
above and contains distinct signatures for the associated LULC features. Each LULC
type absorbs or reflects a particular range of wavelength. This phenom enon is
recorded in each Landsat band w ith a particular w avelength range. This is why, the
43

nature of these different Landsat bands had to be thoroughly studied to m ake a
decision as to which combination of three bands w ould be most interpretive during
classification analysis and visual elucidation. After literature surveying and lab
examination, 5-4-3 band combination was selected for RGB color composite, i.e.
band 5 in the red, band 4 in the green and band 3 in the blue. This combination
provides the user w ith the greatest am ount of information and color contrast which
makes it easier to differentiate different LULC features (NASA, 2011). Healthy
vegetation and forest cover appear w ith strong green color, while soil is m auve. It
clearly contrasts w ater bodies with distinct blue color where range of blue color
varies w ith depth and turbidity of water. It also provides robust agricultural
information. Built-up areas appear in dark purple or pink. The 5-4-3 band
combination was used for all three Landsat images for the years 1984,1999, 2010 and
color composite images were produced accordingly for classification. Figure 21,
Figure 22 and Figure 23 display the Landsat images for the respective years in 5-4-3
band combination and clipped to the study area extent.

44

RGB

Red: Band 5
Green: Band 4
Blue: Band 3

V
2 0 km

Figure 21:1984 Landsat satellite image used in this study

45

Band 5
Green: Band 4

20 km

Figure 22:1999 Landsat satellite image used in this study

46

Red: Band 5
Green: Band 4
Blue: Band 3
Figure 23:2010 Landsat satellite image used in this study

47

3.2.4.2 Image classification

Digital image classification in remote sensing is the detection and clustering
of similar image pixels into the same information categories which are produced
from several spectral bands of a satellite image (Campbell, 2002; Paiboonvorachat,
2008). The object oriented image classification (OOIC) m ethod was used in this
analysis. OOIC, as described in the literature review section, has the ability to
generate m ore accurate and m eaningful result than conventional pixel based
methods.

Image classification in this study was conducted using the segm ent classifier
in IDRISI Selva which comprises three distinct and m utually dependent modules,
namely SEGMENTATION, SEGTRAIN and SEGCLASS. SEGMENTATION is the
process by which spectrally similar, hom ogeneous pixels are grouped into
individual image segments or polygons. SEGTRAIN generates training and
signature files for the final classification step. Finally, SEGCLASS is a majority rule
classifier which uses segmentation, training files and a pixel based classification
output for its performance.

48

3.2.4.2.1 First step - segmentation of the image
During the segmentation process in IDRISI, the spectral similarity of the
image pixels are quantified using variance of pixel values within a m oving w indow
which is a user defined filter. Both spectral and spatial characteristics of the imagery
are critical for properly delineating image segments. Accordingly, all six Landsat
bands (1 to 5 & 7) were used for image segm entation in this study.

In its first step, the SEGMENTATION m odule uses the m oving w indow to
generate a variance image. The hom ogeneity of pixels controls the variance value;
the more the homogeneity, the lower the variance value. The hom ogeneous pixels
are grouped into image segments based on their variance values and assigned with
discrete IDs. The smaller the threshold value, the smaller the size of the segments as
smaller threshold value searches for m ore homogeneity.

In this analysis, all three Landsat images for the years 1984, 1999 and 2010
were segm ented using these param eters: w indow w idth and height 3 x 3 , weight
m ean factor 0.5, weight variance factor 0.5, similarity tolerance 10. The w idth and
height of the moving w indow assists IDRISI to derive variance image for each layer;
the weights for the m ean and the variance factors evaluate the similarity between
neighbouring

segments;

similarity

tolerance

(ST)

is

used

to

control

the

generalization level during the segm entation process given that the smaller the

49

tolerance value, the higher the num ber of image segments and the finer the
segmentation output (Egberth and Nilsson, 2010). Figure 24 shows how the num ber
of segments and fineness vary w ith similarity tolerance value of 10 and 50.

50

Figure 24: Number of segments and fineness varying with ST value
A) Original Image (5-4-3 band combination), B) Segments generated w ith ST=50 (red
outline), C) Segments generated with ST=10 (red outline)

51

3.2A.2.2 Second step - generating training profile

After perform ing segmentation of all the images, the SEGTRAIN m odule was
used to create training profiles to be used for classification. The segm ented images
w ere overlaid on the respective RGB composite images and segments were
identified for its particular LULC classes.

The training class generation entailed a rigorous use of field sam pling data,
higher resolution aerial photograph for some parts as well as personal knowledge
about this w atershed since m any parts of this rem ote densely forested w atershed are
physically inaccessible. Several factors were considered while selecting the sam pling
locations by onscreen viewing of the imagery - a) at least 30 sampling polygons for
each LULC type, b) spatial distribution of the data polygons, c) accessibility to
sample location etc.. A t the end of this process and based on the sam pled data,
SEGTRAIN m odule generated a training file which w as forw arded for further
analysis in the next step.

52

3.2A2.3 Third step - classification of images
This is the final step in the digital OOIC procedure. The SEGCLASS m odule
on IDRISI requires a supervised classification image for its final analysis.
Accordingly, supervised classification was perform ed for each of the years using
m aximum likelihood (MAXLIKE) algorithm where training classes from the
previous step were utilized. In the MAXLIKE process, pixels are assigned to the
most likely class based on a comparison of the subsequent probability that it belongs
to each of the signatures being considered. Then, the SEGCLASS m odule executed
the final classification using the MAXLIKE output, segmented image from the first
step and segment-based training and signature files from the second step. The
SEGCLASS resulted in less noisy, smoother and im proved classified output
compared to the MAXLIKE output. Figures 25 - 27 dem onstrate the classification
outputs generated for the 1984,1999 and 2010 images respectively.

These outputs were then clipped to the study area extent and input into
ArcGIS for m ap creation and were analyzed on IDRISI for accuracy assessment,
change detection and land use modeling.

53

■ Cropland
I I Coniferous Forest
■ Deciduous Forest
■ Mixed Forest
■ Ptaitfed_Regrowti Forest
■

Cut Bloc*

I I Pasture
■ Water
■ Wetend
H i But-upArea

H i Cropland

B

I I Coniferous Forest
■ D eciduous Forest
^ i Mixed forest

Hi Ptartted_RegrowthForest
■ Cut Block
I I P asture
■ W ater
■ Wetland
H i Buft-upArea

»

Figure 25:1984 image classification; A) MAXLIKE, B) SEGCLASS

54

■
Cropland
I I coniferous Forest
■
Deciduous Forest
H Mixed Forest
■
Ptarted.Regroifrth Forest
■
Cut Block
I I Fasfcve
■
Water
■
Wetland
■
Buift-upArea

R

■

Cropland

I I Coniferous Forest
H I Deciduous Forest
■
Mixed Forest
H i Planted Regrowrih Forest
■
CutBtodc
Pasture
Water
Wetland
BuitapA iea
" i* 1

Figure 26:1999 image classification; A) MAXLIKE, B) SEGCLASS

55

■
Cropland
I I CorifcrouB F orest
■
D eciduous Forest
■
M ixed Forest
■ Ptantedjtegrowth Forest
■
F o rest fire
I I Cut Block
■ Pasture
□

B u t-qpA rea

■ Cropland
I I C oniferous Forest
B D eciduous Forest
■ M ixed F o rest
■ Pfaflted.R egraw lti F orest
■ F orest fire
| | cut Block
I
P asture

B

■ w etland
I 1 B u t-u p A ree

Figure 27: 2010 image classification; A) MAXLIKE, B) SEGCLASS

56

3.2.4.3 Accuracy assessment
Assessing accuracy of digital image classification output is very im portant.
Accuracy assessment is usually perform ed either using a new set of ground truth
data or by comparing w ith a previously classified reference m ap for selected
sampling points.

In this study, accuracy assessment was an intricate task as there w as no
reference LULC m ap available for the study area for the years 1984 and 1999. Thus,
there were no available ground truth data for those years. But it was possible to
perform the accuracy assessment using ground tru th data for the 2010 classification
output. Since the same signature and training information were used for classifying
all three images, the accuracy assessment of 2010 confidently confirmed the accuracy
of the other two, assuming that the land cover was consistent over the years.

The design of the sampling program is critical. In this study, stratified
random sampling m ethod was applied for the ground data collection. The stratified
random scheme works by dividing the area into a rectangular matrix of cells and
then chooses a random location w ithin each cell for sampling. Spatially distributed
20 sample points w ere selected for each class in LULC classification scheme. Each of
these points was checked in the field or w ith higher resolution images (Google
earth), w here locations were inaccessible. Every match between classified LULC

57

m ap and ground truth information w as counted as 1 and for mis-match, it resulted
in 0. All of these information w ere sum m arized in an error matrix which is the most
common m ethod used by researchers for classification accuracy assessm ent (Oumer,
2009). The overall accuracy, user's accuracy, producer's accuracy as well as Kappa
coefficient w ere calculated.

Overall accuracy is defined as the ratio betw een the total num ber of samples
which are correctly classified and the total num ber of samples considered for the
accuracy assessment. U ser's accuracy corresponds to error of commission. It refers
to the m easurem ent of how m any of the samples of a particular class matched
correctly. It is defined by the following ratio:

Total number o f samples that are correctly classified in a given category

User's accuracy =----------------- —— :-------;------:------;— :— ;------------------------------Total number of samples in that category

On the other hand, producer's accuracy corresponds to errors of omission. It
is a m easure of how m uch of land in each LULC category was classified correctly. It
is calculated as:

58

Total number o f samples which are correctly classified for a given category

Producer's accuracy = ---------------------------------------------------------------------————— (2)
Total number o f samples that are classified to thatparticular category

The kappa coefficient estimates the agreem ent between a m odeled scenario
and reality (Congalton R. G., 1991). It determ ines if the results displayed in an error
matrix are significantly better than random (Lillesand, et al., 2008). For an error
matrix w ith num ber of rows and column, kappa coefficient is com puted as:

K = (NA - B ) / (N2 - B)

(3)

Where, N = total num ber of observations included in the error matrix

A =the sum of correct classifications contained in the diagonal elements

B = the sum of the products of row total and colum n total for each LULC type

in the error matrix

Figure 28 sum m arizes all the Landsat image analysis steps in a flow chart:

59

Landsat imagery dow nload for 1984,1999 & 2010
(scene from Path 48, Row 21 & Path 48, Row 22)

Pre-processing

Layer stacking of bands (1-5 &

Mosaicing of scenes,

7): Transfer & Translate

Clipping to KRW area

Image classification

Generation of training profile

Segmentation of the image

MAXLIKE classification

Post-classification
analysis

OOIC Classification

Accuracy assessment

M anual editing of the classified

LULC maps generation

output to include m issing features

for 1984,1999 & 2010

Figure 28: Landsat image analysis framework

60

3.2AA Land use m odeling

RS and GIS along with com puter based m odeling tools have become a
popular and efficient m eans of sim ulating current and future land use and land
cover change and hence, assist in land use planning and natural resources
m anagem ent (Herold, et al., 2003; Araya, 2009).

Various tools exist in the era of land use modeling which are diverse in
design, spatial scale, tem poral dimension, data availability etc. The present study
utilizes M arkov Chain m odel for simulating LULC changes in Kiskatinaw River
W atershed.

The M arkov Chain model is a unique and widely used tool in land use
modeling which dem onstrates the LULC changes as a stochastic process (Weng,
2002). In the M arkovian system, the future state of a land use system is modeled on
the basis of the immediate proceeding state (Araya, 2009). The M arkov Chain
analysis describes the probability of LULC changes from one period to another by
constructing a transition probability matrix between period-1 and period-2.

The

basic hypothesis of M arkov Chain prediction is that future land use at time (t+1), X m
is a function of current land use at time t, Xr, i.e.

X m = f (Xt)

(4)

61

The transition probability Pm* is represented by the probability that a cell of
land cover type Um alters into land cover type m in between the m odel period.
Therefore, if the transition probabilities are congregated in a transition Matrix, P,
then Xm can be derived from the following equation (Benito, et al., 2010):

X w = X t .P

(5)

The whole land use m odeling task was perform ed in the 'Land Change M odeler
(LCM)' on IDRISI Selva.

3.2.4.4.1 Land Change Modeler (LCM)
LCM is a powerful application in IDRISI which integrates a set of tool to
understand the dynamics of land use-land cover conversion and its associated
impacts. In this study, LCM provided support for LULC change analysis, transition
potential m odeling and LULC change prediction. For all these tasks, LCM used the
LULC m aps generated for the years 1984, 1999 and 2010. The change analysis was
perform ed for two separate periods, one from 1984 to 1999 and another from 1999 to
2010. But the transition potential modeling and change prediction were carried out
for the whole period from 1984 to 2010.

62

The 'Change Analysis' on LCM assesses changes between time Ti and T2 . In
the first set, changes w ere evaluated for T 1 = 1984 and T 2 = 1999; in the second set, it
was for Ti = 1999 and T 2 = 2010. As a result, an in-depth comprehension could be
gained about the LULC change dynam ic of the study area. The changes that are
identified are transitions from one state of land use-land cover to another. The gain
and loss in area for each LULC type as well as the net change were calculated and
corresponding graphics were generated. Outside of LCM, changes in individual sub­
watershed were also evaluated and m aps were created using ArcGIS.

After the change analysis, the next task on LCM w as to m odel the potential of
land transitions. At first, the LCM project was set for Ti = 1984, T2 = 2010 and
corresponding LULC m aps were loaded to the project. Then, LCM calculated the
transitions occurring between 1984 and 2010. At this stage, transition m aps were
created which basically shows the LULC changes from one type to another. The
transition m aps were organized w ithin empirically evaluated transition sub-models
that were driven by the same underlying variables. These driver variables were used
to model the chronological change processes.

LCM m odeled the transition potentials for each identified transition between
different LULC types by using a multi-layer perception (MLP) neural netw ork
technique. The non-linear neural networks can be regarded as a complex

63

mathematical function which has the ability to convert m ulti-variant input data to a
desired output. MLP in LCM uses a back propagation algorithm for transition
prediction. A typical MLP netw ork contains one input layer, one output layer and
one or more hidden layers, each containing m ultiple nodes which is akin to the
neural netw ork of brains (Lin, et al., 2011) (Figure 29). Each node in the three layers
is connected to other w ith varying weights and plays critical role during the
modeling

Input:
Image
data

► Transition

Hidden

Output

•av er

Layer

Figure 29: MLP neural network (after Eastman, 2012)

procedure (Eastman, 2012). The hidden layer nodes are crucial to the execution of
MLP such that it loses its ability to learn and make use of interaction effects w ithout

64

them (Chan, et al., 2001). At the beginning of the MLP process, sam pling size was
selected for each transition which is the num ber of change pixels that w ould be used
in the modeling. Half of the samples were used for training and half were utilized
for validation of the transition model. As m entioned before, each transition was
modeled under a set of input variables which are basically various GIS layers and
likely to affect the land use change within the watershed. Table 3 explains the input
variables used in this analysis. The selection of these variables entailed careful
consideration of KRW's land use activities and data availability, and they were
presented as different GIS layers in the m odeling process. All of the driving
variables were tested for their effects on modeling accuracy and skill statistics
calculated by using the following equations 6 and 7 (Eastman, 2012). The variables
which resulted in lower accuracy (i.e. below 50%) and skill statistics were rem oved
from analysis, and then the remaining variables were considered to govern the
transition process for each transition sub-model. Modeling parameters, e.g. hidden
layer nodes, learning rates, m om entum factor, sigmoid constant etc. were
interactively estim ated by the MLP modeling tool and produced the best result.
MLP provides the m easure of accuracy (in %) and skill statistics (value: -1 to +1) as
an appraisal of efficiency of the prediction process.

65

The expected accuracy can be determ ined by the following equation
(Eastman, 2012):

(6)

EA = 1 / ( T + P )

Where,

EA = expected accuracy,

T = the num ber of transitions in the sub-m odel

P = the num ber of persistence classes

And the m easure of model skill is then expressed as:

(7)

S = ( A - E A ) / (1-EA)

Where,

A = m easured accuracy from the analysis

EA = expected accuracy

66

Table 3: Driver variables for transition potential modeling

Driver variable layer
Distance
to
infrastructure

Role

gas

development Shale
gas
development
industry:
responsible for forest clear cutting, road
development, high am ount of w ater
extraction from the river etc.
Forest cut blocks planned for future Forestry industry: Cut blocks planned
harvesting
and m apped for future harvesting
dictates the plantation and regrow th
process, hence the shifting of forest
types.
Cum ulative kill by m ountain pine beetle Active m anagem ent of the m ountain
infestation
pine beetle attack is on action in this
w atershed since its detection in 2004
which
includes
aggressive
forest
harvesting.
This driver comprises
location and num ber of cumulative kill
by pine beetle infestation.
Distance to major channel netw ork
The channel network controls the
general hydrology, w etlands dynamics,
gas developm ent activities etc. in this
watershed.
Digital elevation m odel (DEM) and These two determine the hydrological
topographic w etness index (TWI)
flow path i.e. overall hydrological
process w ithin a watershed; hence these
control the wetland dynamics in the
watershed. TWI is defined as Ln(A/tanB)
(Sorensen, et al., 2005)where,
A = local upslope area draining through
a certain point per unit contour length,
tanB = the local slope

67

In the final step of LULC modeling, the transition probabilities estim ated
from MLP neural netw ork modeling w ere fed into the "Change Prediction" m odule
on LCM to generate future LULC scenario in 2020 by using M arkov Chain (MC)
modeling. The MC model in LCM could generate a transition probability m atrix and
a transition area matrix based on which the prediction process w as perform ed. The
probability of change from one LULC type to another was contained in the
transition probability matrix. The transition area matrix records the num ber of pixels
that are expected to convert from one LULC type to another, and it w as created
through the multiplication of each column in the transition probability matrix by the
num ber of pixels in a corresponding LULC type (Myint & Wang, 2006). Based on
these transition matrices, MC m odel was used to generate both hard and soft
predictions of LULC within the study area. The hard prediction produces a LULC
m ap derived by a multi-objective land allocation algorithm contained in LCM which
considers all of the calculated transitions for creating lists of host classes (i.e. losing
land area) and claimant classes (i.e. gaining land area) (Eastman, 2012). On the other
hand, soft prediction identifies the vulnerability of LULC change for a set of
transitions based on a m ethod of logical "OR" aggregation. This m ethod is based on
the principle that a location is m ore vulnerable to LULC change if it is subject to
several transitions than if it is only subject to a single transition. The output of
logical "OR" aggregation for a pixel is equal to (a+b-ab), where 'a ' represents the
68

probability of that pixel transition to one LULC type and 'b ' represents its transition
probability to another LULC type. For example, if a particular pixel has a probability
of 0.40 to be changed to one LULC type and 0.30 to another LULC type, the logical
"OR" operation w ould evaluate the LULC change vulnerability as (0.40 + 0.30 - 0.40
x 0.30 = 0.58). More detailed descriptions of transition probability estimation and
LULC change vulnerability assessment can be found in Eastman (2012). Both of the
hard and soft predictions in this study w ere perform ed w ith an interm ediate stage at
2015 and a final stage at 2020.

The land use modeling process has been sum m arized in the following flow
chart (Figure 30):

69

r

Inputs
LULC m ap of Ti

LULC m ap of T2

..........................................................

1—

...

...

Driver variables

Change Analysis

Transition Model

Change m ap/gain
& loss in area/net
change (T1-T2)

MLP neural
netw ork

Transition potentials:
map / matrix

Change Prediction
Figure 30: Land use m odeling fram ew ork

Markov
Chain m odel

Soft prediction:
projected
vulnerability of

70

H ard
prediction:
projected LULC
m ap of T3

I

CHAPTER 4: RESULTS AND DISCUSSION

4.1 Results of RS & GIS analysis of satellite images
The land use-land cover m aps produced by integration of remotely sensed
image classification and corresponding GIS editing have provided im portant LULC
information for this study area. Analysis of 1984 imagery has been sum m arized in
Figure 31 and Figure 32. It is shown that the major share of the land area was
covered by different types of forest among which coniferous forest com prised the
m axim um of 37.35%, followed by deciduous forest (28.09%) and mixed forest
(12.41%). In total, all of the forest types including planted and re-grow th forest
occupied 80% of the total land area of the study watershed in 1984. W etlands
covered a significant portion of the area (16.02%). Other LULC features, namely
cropland, cut block, pasture, water, and built-up area comprised 0.82%, 1.58%,
0.23%, 0.76% and 0.64% respectively of the study watershed.

71

Built-up area
W etland
W ater
P asture
C ut block
P lanted or regrow th forest
M ixed forest
D eciduous forest
Coniferous forest
C ropland

200

400

Figure 31: Area covered by each LULC type in 1984

72

1000

1200

Built-up area
Coniferous forest
Cropland
Cut block
Deciduous forest
Mixed forest
Pasture
Planted or regrowth forest
Water
Wetland

N
20 km

Figure 32: KRW LULC map of 1984

73

Figure 33 and Figure 34 display the output generated from the analysis of
1999 Landsat imagery. As in the 1984 analysis, forest types comprised the dom inant
portion (85%) of the land area among which coniferous forest covered the maximum
of 41.45%, followed by deciduous forest 23.30%, mixed forest 15.92%, and planted
and re-growth forest 4.94%. W etlands occupied only 7.79% of the w atershed area in
1999. Other LULC features, namely cropland, cut block, pasture, water, and built-up
area comprised 1.12 %, 1.54%, 1.82%, 0.75% and 1.39% respectively of the study
watershed.

Built-up area
Wetland
Water
Pasture
Cut block
' Wanted or regrow th forest
Mixed forest
Deciduous forest
Coniferous forest
Cropland
0

200

400

600

800

Figure 33: Area covered by each LULC type in 1999

74

1000

1200

Built-up area
Coniferous forest
Cropland
Cut block
Deciduous forest
Mixed forest
Pasture
Planted or regrowth forest
Water
Wetland

fj
20 km

Figure 34: KRW LULC map of 1999

75

Analysis of 2010 Landsat imagery is displayed in Figure 35 and Figure 36.
Kiskatinaw w atershed m aintained its LULC nature in 2010 w ith the principal
portion covered by various forest types. As before, the dom inant forest type is
coniferous forest comprising 39.05% of the study area, followed by deciduous forest
28.83%, mixed forest 12.87% and planted and re-grow th forest 5.53%. W etlands
coverage continued to decline with a total of 6.45% of the watershed. The H our
Glass forest fire event in this area in 2006 represents 1.17% of the w atershed area.
The fire affected forest cover is located near the south western boundary of the area.
Finally, 0.66%, 0.93%, 2.12%, 0.72% and 1.66% of the study watershed are occupied
by cropland, cut block, pasture, water, and built-up area respectively.

Forest fire
Wetland
Pasture
Wanted or regrow th forest
Deciduous forest

Arei (km2)

Cropland
0

200

400

600

800

Figure 35: Area covered by each LULC type in 2010

76

1000

1200

H i Built-up area
Coniferous forest
Cropland
H
Cut block

Hi

Hil Deciduous forest
Hi Forest fire
Hi Mixed forest
Pasture
M B Planted or regrowth forest

HI Water
Wetland

20 km

Figure 36: KRW LULC map of 2010

77

Table 4 represents a com pendium of the total area and percent of area
covered by individual LULC types.

Table 4: Surface area covered by each LULC type in a particular year

Total study area 2836 km 2
1984

1999

2010

LULC type

km2

% of total

km2

% of total

km2

% of total

Cropland (CL)

23.27

0.82

31.70

1.12

18.82

0.66

Coniferous forest (CF)

1059.06

37.35

1175.45

41.45

1107.84

39.05

Deciduous forest (DF)

796.65

28.09

660.79

23.30

815.34

28.83

Mixed forest (MF)

351.97

12.41

451.57

15.91

365.88

12.87

Planted or regrowth forest (P/RF)

59.94

2.10

140.08

4.94

157.23

5.53

Cut block (CB)

44.70

1.58

43.46

1.54

26.38

0.93

Pasture (PS)

6.53

0.23

51.63

1.82

60.30

2.12

Water (WT)

21.49

0.76

21.18

0.75

20.48

0.72

W etland (WL)

454.22

16.02

220.82

7.79

183.30

6.45

Built-up area (BA)

18.17

0.64

39.32

1.39

47.24

1.66

Forest fire (FF)

0.00

0.00

0.00

0.00

33.19

1.17

4.2 Accuracy assessment
The accuracy of satellite image classification could be constrained by the
resolution of images used and lack of fine details as well as unavoidable
generalization impacts (Oumer, 2009) and therefore, errors are always expected.
This is why, to ensure prudent utilization of the produced LULC m aps and their
associated statistical results, the errors and accuracy of the analyzed outputs should
be quantitatively explained.
78

As clarified in the 'D ata and M ethods' section, the accuracy tests for the 1984
and 1999 image classification were not possible due to unavailability of reference
data, the test was only performed for 2010 image analysis. Table 5 shows the
corresponding accuracy assessment error matrix for 2010 analysis. The random ly
generated sample points were tested w ith ground truth information as well as with
higher resolution imagery, where the point w as inaccessible. The num bers
highlighted in grey are matching samples for each LULC type, others are mis-match.

Table 5: Accuracy assessm ent error m atrix for 2010 im age classification

LULC type
Ground Truth
CL
i,

CL

CF

DF

MF

P/RF

CB

PS

WT

2

16

WL

BA

1

FF

Row Total

1

20

20

CF

20

DF

19

1

20

MF

2

18

20

P/RF

1

CB

1

PS

2

19

20

1

18

20
17

WT

20

20

WL

1

BA

2

2

1

1

20
15

20
18

1

FF
Colum n Total

1

19

20

21

21

21

21

22

20

16

19

20
19

20

20

199

CL=Cropland, CF=Coniferous forest, DF=Deciduous forest, MF=Mixed forest, P/RF=Planted or re­
growth forest, CB=Cut block, PS=Pasture, WT=Water, WL=Wetland, BA=Built-up area, FF=Forest fire

79

Based on the information from Table 5, the overall accuracy, user's accuracy,
producer's accuracy and overall kappa coefficient w ere calculated using the formula
stated in previous chapter. Table 6 recapitulates the calculated results.

Table 6: Accuracy assessm ent sum m ary

LULC type

User's Accuracy (%)

Producer's Accuracy (%)

CL

80.00

84.21

CF

1 0 0 .0 0

1 0 0 .0 0

DF

95.00

90.48

MF

90.00

85.71

P/RF

95.00

90.48

CB

90.00

85.71

PS

85.00

77.27

WT

1 0 0 .0 0

1 0 0 .0 0

WL

75.00

93.75

BA

90.00

94.74

FF

95.00

95.00

Overall Accuracy
90.45%

Overall Kappa
coefficient 0.89

The overall accuracy and kappa coefficient for 2010 image classification were
90.45% and 0.89 respectively. The producer's and user's accuracy for all classes
except cropland, pasture and w etland were higher than overall accuracy. Lower
accuracy for these classes may be partly attributed to the m edium spatial resolution
of Landsat data and general smaller size of the feature areas.

80

4.3 D iscu ssio n on LULC changes
Forestry and agriculture have long been shaping the land use and land cover
activities in the Kiskatinaw river w atershed. But KRW's location w ithin the M ontney
shale gas play and its associated natural gas developm ent activity over the past few
years have been determining the LULC dynamics within the w atershed. The role of
these dom inant hum an interactions has been dem onstrated in the present land use
change analysis.

The LULC m aps described above w ere input into a change analysis m odule
on IDRISI 'Land Change Modeler (LCM)'. The analysis operated by LCM revealed
some significant LULC change dynamics within the watershed. The study area
which is a forested watershed m aintained its large m ature forest cover (around 80%)
within the study period from 1984 to 2010. Subtle changes have been observed for
the m ature coniferous, deciduous and mixed forest types. The noticeable change of
the planted or re-growth forest type and cut blocks indicates the impacts of the
forestry industry in this area, although cut blocks in recent images m ay be attributed
to the gas developm ent industry as well. In m any cases, cropland and pasture were
hard to differentiate during the digital image classification process of Landsat data
since these land use types are more or less similar in spectral signature. But
combinedly, cropland and pasture show a considerable change betw een 1984 and
2 0 1 0

which highlights the amplified agricultural and farming activities w ithin the
81

watershed. This analysis identified a striking change in the extent of wetlands, while
m ost of the w etland depleted betw een 1984 and 1999, estim ated as a loss of 233.22
km 2. This significant change in w etland area needs further investigation to
understand the depletion dynamics. An increase of 29 km 2 of built-up area indicates
the recent industrial booming in this area, particularly shale gas developm ent
activity. The forest fire affected 33.39 km 2 represents the Hour Glass fire event in
2006 w ithin and around the study area.

For comprehensive spatial analysis, LCM was used to account for changes
from 1984 to 1999 and from 1999 to 2010 in separate sets. Gain and loss for all land
use types as well as their contribution to net change were quantified for each time
period. Figure 37 shows the change analysis from 1984 to 1999 and Figure 38
exhibits the change analysis from 1999 to 2010. In these figures, the area gain for a
particular LULC type includes the converted land cover area which previously
belonged to another LULC type. Accordingly, the lost area of any LULC type was
changed to some other types. From 1984 to 1999, there is a striking negative change
observed for the deciduous forest and wetlands which indicates higher loss than
gain of area for these LULC types. In contrast, the sharp positive net change for
LULC features such as: cropland, coniferous forest, mixed forest, planted and re­
grow th forest, pasture and built-up area, refer to the higher gain in area than loss.
There are also some interesting changes identified from 1999 to 2010. The w etlands
82

continued the decreasing trend during this time period and total depletion
accounted for 270.92 km 2 of area from 1984 to 2010 which needs further rigorous
investigation to explore the reasons behind this w idespread depletion.

400
■ Area gained (km2)

-400

Net change (km2)

^^
CF

MF
PRF
CB
PS
WT

-300

-250

-200

-150

-100

-50

0

50

Figure 37:1984-1999 change analysis for LULC types

83

100

150

400
Area g a i n e d ( k m 2)
Area lost ( k m 2)

300

200

100

•<
fiQ
-100

-200
-3 0 0

Net change (km2)
DF

P/RF

PS

W
BA
FF
-100

-50

-25

50

100

125

150

1 "5

200

Figure 38:1999-2010 change analysis for LULC types

D uring 1999 to 2010, the deciduous forest showed a marked increase whereas
other forest types between 1984 and 1999 exhibited a sharp decrease. Forest fire also
contributed positively to the net change which was already discussed above. The
84

small but constantly increasing built-up area underscores the ongoing developm ent
activities in this area.

4.3.1 LULC change in individual sub-basin
As m entioned in the previous chapter, KRW is subdivided into five sub­
basins, namely Mainstem, Brassey, Halfmoon-Oetata, East KRW and W est KRW.
The nature and am ount of LULC change at sub-basin level depends on its LULC
type and anthropogenic signature. Therefore, it is valuable to study the LULC
changes at sub-basin scale.

4.3.1.1 Mainstem sub-basin
Mainstem sub-basin covers the north-eastern portion of the study area and
holds the main course of the Kiskatinaw River. The City of Dawson Creek's w ater
intake station, Arras is located just at the northern m ost edge of this sub-basin. This
is why Mainstem sub-basin's LULC is considered crucial for this w atershed's overall
w ater resource management.

Figures 39 - 41 show the LULC m aps of Mainstem area in 1984, 1999 and
2010. The change in LULC is depicted in Figure 42. From the m aps and graph, it is

85

evident that the major forest type in this sub-basin is deciduous which remained
more or less consistent throughout the time period from 1984 to 2010 though a slight
depletion is observed. Mixed forest covers a greater area than coniferous forest in
Mainstem. Increasing planted and re-grow th forest area is correlated w ith the
decreasing area of forest cut blocks. Also, fluctuations of the croplands and pasture
extent are interrelated. The m odest spectral resolution of the satellite data used in
this study has contributed to some confusion while classifying these two LULC
types. The shrinking of w etland area is observed in this sub-basin as well, however,
the area was depleted to its lowest in 1999 and started to regain thereafter.

86

"

I-

-

a.

V

Built-up area
Coniferous fore6t
Cropland
Cut bloc*

Deciduous forest
Mixed forest
Pasture

m
m

Water
Wetland

Planted or regrowth forest

Figure 39: LULC map of Mainstem sub-basin in 1984

87

k
N
10 km

1999

Buit-up area
Coniferous forest
Cropland
Cut block

Deciduous forest
Mixed forest
Pasture

m
m

Water
Wetland

Planted or regrowtn forest

Figure 40: LULC map of Mainstem sub-basin in 1999

88

10km

I Built-up area
I Coniferous forest
Cropland
I Cut block

Deciduous forest
Mixed forest
Pasture

m
m

Water
Wetland

Ranted or regrowth forest

Figure 41: LULC map of Mainstem sub-basin in 2010

89

I
10 km

300

Mainstem LULC Change

250
200
11984
1999
2010

150
100
50
0
CL

CF

DF

MF

Figure 42: Change in LULC w ithin M ainstem sub-basin

A slow, but steady increase in the built-up area is captured from this analysis
which m ay be attributed to various infrastructure developments in this sub-basin.
Among all the sub-watersheds, Mainstem is the closest to the City of Dawson Creek
and contains high am ounts of agricultural and farming activities which defend the
enhanced infrastructure developm ent within this sub-basin.

4.3.1.2 Brassey sub-basin
Brassey sub-basin is located in the north-w estern corner of the study
watershed. Figures 43 - 46 describe the LULC changes w ithin Brassey. Similar to
Mainstem, this sub-w atershed's forest area is also dom inated by deciduous forest,
90

followed by mixed and coniferous forest. The significant and dynam ic extent of
planted and re-grow th forest during the study period refers to the forest industry
interest w ithin Brassey.

| Built-up area
| Coniferous forest
Cropland
I Cut block

Deciduous forest
Mixed forest
Pasture

Water
Wetland

Planted or regrowth forest

Figure 43: LULC m ap of Brassey sub-basin in 1984

91

5 km

Built-up area
Coniferous forest
Cropland
Cut block

Deciduous forest
Mixed forest
Pasture

m
m

Water
Wetland

Planted or regrowth forest

Figure 44: LULC m ap of Brassey sub-basin in 1999

92

t

5 km

| Built-up area
I Coniferous forest
Cropland
I Cut block

| Deciduous forest
Mixed forest
Pasture

Water
Wetland

I

5 km

Planted or regrowth forest

Figure 45: LULC m ap of Brassey sub-basin in 2010

Brassey LULC Change

1984
1999
2010

n

CL

i

CF

i

DF

i

i

MF P/RF CB

i

i

PS

i

WT WL

r

BA

Figure 46: Change in LULC w ithin Brassey sub-basin

93

W etlands comprise a notable portion of Brassey. Interestingly, the w etland
area has increased slightly between 1984 and 2010 in this sub-w atershed even
though the w etlands faced massive depletion on the whole. Consistent increase of
the built-up area underlines the anthropogenic modification in this sub-basin.

4.3.1.3 Halfmoon-Oetata sub-basin
Halfmoon-Oetata is the smallest sub-basin within this study watershed. This
is another deciduous forest dom inant sub-basin. But during the study period,
deciduous forest area was highest in 1984, then dropped in 1999 and started
increasing thereafter. On the other hand, unlike the two sub-basins explained above,
coniferous forest area in Halfmoon-Oetata was constantly higher than mixed forest
during these three study years. Planted and re-growth forest elevated continuously
which signifies the planned forest harvesting w ithin this sub-basin. Cropland and
pasture have a m inor share in this sub-watershed. Figures 47 - 50 show the LULC
changes w ithin Halfmoon-Oetata.

94

Built-up area
Coniferous forest
Cropland
Cut block

Deciduous forest
Mixed forest
Pasture

_

Water
Wetland

i

5 km

Planted or regrowth forest

Figure 47: LULC map of Halfmoon-Oetata sub-basin in 1984

95

Built-up area
Coniferous forest
Cropland
Cut block

Deciduous forest
Mixed forest
Pasture

m

Water
Wetland

t

5 km

Planted or regrowth forest

Figure 48: LULC map of Halfmoon-Oetata sub-basin in 1999

96

Built-up area
Coniferous forest
Cropland
I Cut block

I

Deciduous forest
Water
Mixed forest
Wetland
Pasture
Planted or regrowth forest

5 km

Figure 49: LULC map of Halfmoon-Oetata sub-basin in 2010

120

Halfmoon-Oetata LULC Change

100
80

-\

1984
1999

60

P

40
I

20
I

0 T

I

1----------1
----------r

CL

CF

DF

2010

r

1----------1----------1----------r

MF P/RF CB

PS

WT WL

BA

Figure 50: Change in LULC within Halfmoon-Oetata sub-basin

97

W etland extent was m inim um in 1999, however, it was similar in 1984 and
2010. Thus, w etlands were depleted to a larger am ount in between 1984 and 1999,
but increased between 1999 and 2010. The consistent increase in the built-up area is
significant in this sub-basin as it is mostly due to the recent shale gas developm ent
infrastructures.

4.3.1.4 East KRW sub-basin
East KRW sub-basin covers the south-eastern portion of the w atershed and is
home to the east confluence of the Kiskatinaw River. Figures 51 - 54 display the
LULC changes w ithin East KRW. Over the last few years, this sub-w atershed
remains very busy for gas developm ent activities which have been reflected by the
continuous increase of the built-up area.

98

Built-up area
Coniferous forest
Cropland
Cut block

Deciduous forest
Mixed forest
Pasture

m

Water
Wetland

Planted or regrowth forest

Figure 51: LULC map of East KRW sub-basin in 1984

99

I
10 km

Built-up area
I Coniferous forest
Cropland
I Cut block

Deciduous forest
m
Water
Mixed forest
Wetland
Pasture
Planted or regrowth forest

Figure 52: LULC map of East KRW sub-basin in 1999

100

I
10 km

Built-up area
I Coniferous forest
Cropland
Cut block

| Deciduous forest
Mixed forest
Pasture

m

Water
Wetland

Planted or regrowth forest

Figure 53: LULC map of East KRW sub-basin in 2010

101

t
10 km

600

East KRW LULC Change

500
400
11984
300

1999
2010

200
100

0
CL

CF

DF

MF P/RF CB

PS

WT WL

BA

Figure 54: Change in LULC within East KRW sub-basin

Unlike the other three northern m ost sub-basins, the landscape of East KRW
is significantly coniferous forest dom inant with a gradual increase in extent.
Deciduous and mixed forest shared mostly the same am ount of land area in 1984,
1999 and 2010 although m inor changes are observed. A smaller am ount of planted
and re-growth forest and cut block is also noteworthy. W etlands exhibited a
continuous and striking dim inution during the study period w ithin this sub­
watershed. The area covered by cropland and pasture is minimal.

102

4.3.1.5 West KRW sub-basin
West KRW occupies the south-western portion of the study area and holds
the western confluence of the Kiskatinaw River. The most conspicuous LULC
feature of this sub-basin is the fire affected forest area in the central-western border.
Similar to East KRW, this is another coniferous forest dom inant sub-watershed
while the coniferous area reached its maximum in 1999. Other m ature forest types,
e.g. deciduous and mixed forest showed m inor changes during the study period.
Gradual increment of the planted and re-growth forest area m ay be supported by
the declining forest cut block area. Figures 55 - 58 show the LULC changes within
East KRW.

103

Built-up area
Coniferous forest
Cropland
Cut block

Forest fire
Mixed forest
Pasture

M

Water
Wetland

Planted or regrowth forest

I Deciduous forest
Figure 55: LULC map of West KRW sub-basin in 1984

104

t
10 km

1999

Built-up area
Coniferous forest
Cropland
Cut block

H I Water
H I Wetland

Forest fire
! Mixed forest
Pasture

Planted or regrowth forest

I Deciduous forest
Figure 56: LULC map of West KRW sub-basin in 1999

105

I
10 km

2010

I Built-up area
i Coniferous forest
Cropland
Cut block

Water
■ ■ Wetland

Forest fire
Mixed forest
Pasture

Planted or regrowth forest

I Deciduous forest
Figure 57: LULC map of West KRW sub-basin in 2010

106

t
10 km

W etland areas dropped to their lowest coverage in 1999 and then show ed a
very m inor increase in 2010. Built-up area is also escalating, but rem ained mostly the
same from 1999 to 2010. Cropland and pasture covers insignificant land area in this
sub-basin indicating minimal agricultural and farming activities.

600

West KRW LULC Change

500
400

OJ
11984

300

1999
2010

200
100

0
Ar

&

Figure 58: Change in LULC w ithin W est KRW sub-basin

107

4.3.2 Wetland depletion
A striking depletion of w etlands in KRW has been captured in this satellite
image analysis. W etlands are a vital com ponent for regional ecosystems. W etlands
play a dom inant role in the global carbon cycle, containing about 12% of the global
carbon pool (Sahagian & Melack, 1998). Thus, this prevalent depletion of w etlands
has become a vital concern, given the current state of global w arm ing and climate
change as well as ongoing high dem and of w ater use (groundwater-surface w ater
interaction). Most of this extensive w etland depletion occurred betw een 1984 and
1999. Figure 59 shows contribution of wetlands to the net land use change w ithin the
w atershed and clearly exhibits that m ost of the lost w etland area has been converted
to various forest categories. Contribution to pasture land, built-up area and forest
fire also need to be noted as these conversions m ay have im portant implications to
the ecosystem of the watershed.

108

FF
BA
WT
PS
CB
P/RF
MF
DF
CF
CL
0

20

40

60

80

100

120

Figure 59: W etlands converted to other land-use type (1984-2010)

Figure 60 shows gained, lost and persistent wetland area w ithin the
watershed from 1984 to 2010. It is clear that a very m inor part of w etlands remains
unchanged within the w atershed during this period. The major change has occurred
in the southern portion which is dom inated by coniferous forest. The depletion in
w etland extent may cause m ulti-dim ensional impacts on the watershed, such as:
change in carbon storage, loss of biodiversity, increase in flooding, decrease in w ater
quality etc. Therefore, further rigorous research should be designed and perform ed
to investigate the reason and effects of this extensive depletion of wetlands.

109

Wetland area
Gains

20 km

Figure 60: Gains and losses in wetland area (1984-2010)

110

4.3.3 Natural gas development infrastructure
The unconventional shale gas developm ent w ithin Kiskatinaw w atershed has
gained trem endous interest over the past few years, particularly in the late 1990s. A
great deal of oil and gas activity is under intense operation within this w atershed for
shale gas development. As a result, oil and gas infrastructures, such as: drilling
wells, drilling pads, petroleum developm ent roads etc. are becoming widespread
features in this watershed, influencing the land use change dynam ics w ithin this
area.

r\,

1984

1999

2010

Built-up are a

2
0K
m

Cut block

Figure 61: Changes in built-up area and cut block (1984-2010)

111

Figure 61 explains the conditions in 1984,1999 and 2010 for built-up area and
cut block features which were changed mainly due to gas developm ent industry. In
the figure, the built-up area comprises perm anent major roads, petroleum access
roads and petroleum developm ent roads; conversely, cut block feature includes
forest clear cut for installing drilling pads and wells as well as forest harvesting by
the forestry industry. Thus, both these contain footprints of natural gas developm ent
in this watershed. It is evident that there was almost no or very little gas activity in
1984, but it intensified by 1999. The road netw ork was mostly com partm entalized in
East KRW sub-basin in 1999 which underscores amplified developm ent activity
w ithin this sub-basin. There is some activity observed in the Brassey and HalfmoonOetata as well. The larger cut blocks are mostly due to forest industry harvesting,
but the relatively small clear cuts are a gas developm ent signature.

In 2010, the East KRW gas developm ent continued at a high pace.
Additionally, Brassey, Halfmoon-Oetata and north-western part of Mainstem drew
significant industry interest; however, West KRW and the southern m ost edge of
East KRW rem ained unaffected even in 2010. This intensified gas developm ent
activity has m ulti-dimensional impacts on KRW's natural landscape as it is
attracting a trem endous inflow of population from outside and their concomitant
multi-faceted demands. Therefore, a proper planning on this regard needs to be
strictly framed and implemented.
112

4.4 Land u se m o d elin g
A series of transition sub-models were identified by the IDRISI Land Change
Modeler (LCM) based on the change analysis between two input land use m aps of
1984 and 2010. All of these empirically evaluated sub-models w ere considered
individually for m odeling except for those w ith forest fire class since prediction on
forest fire is out of the scope of this study.

4.4.1 Results of modeling analysis
At the initial stage of modeling, driver variables as described in Table 3 were
evaluated for their potential explanatory pow er in LULC change projection. The
selection and processing of driver variables entailed careful consideration of KRW's
ongoing land use activities and data availability. The variables played both static
and dynamic role during the m odeling process.

The m ulti-layer perception (MLP) neural netw ork tool on Land Change
M odeler (LCM) has a strong evaluation procedure for m easuring the efficiency of
driver variables. Input variables of each transition sub-models w ere evaluated under
MLP. For example, modeling efficiency of transition from wetlands to pasture was
evaluated as shown in Table 7. Four governing driving variables w ere identified for
113

the sub-model of w etland transition to pasture, including "distance to gas
developm ent infrastructure", "distance to major channel network", "topographic
wetness index (TWI)", and "digital elevation m odel (DEM)". A num ber of scenarios
were considered during the evaluation process considering one or m ultiple variables
to be constant each time. This way, the im pact of the constant variables in the
modeling process w as determined. In the example, the variable (V4) digital
elevation m odel (DEM) w as identified as the m ost influential variable for m odeling
the transition from w etlands to pasture. The modeling accuracy and skill m easure of
this sub-model w ere found to be 78.55% and 0.5709, respectively.

The MLP neural netw ork modeling process generated transition potential
m aps for each evaluated transition sub-models. For example, Figure 62 exhibits the
transition potential m ap for the conversion from planted or re-grow th forest to
deciduous forest. The maximum possibility of this transition is observed as 50%
within the watershed, particularly in the northern half of the watershed. The
transition potential m aps generated from MLP m odeling were used in Markov
Chain (MC) m odel for calculating the am ount of change to be expected for each
transition and predicting future scenarios. MC process recorded the m odeled
transition probabilities in a matrix which contains information on exactly how much
land would be expected to alter from the later date (2010) to the prediction date
(2020).
114

Table 7: Sensitivity of transition model for forcing independent variables

Independent V ariables

VI

Distance to gas development infrastructure

V2

Distance to major channel network

V3

Topographic wetness index (TWI)

V4

Digital elevation model (DEM)

Forcing a Single Inde jen d en t V ariable to be Constant

Sensitivity of m odel

Accuracy (%)

Skill measure

Influence order

With all variables

78.55

0.5709

N/A

V 1 constant

78.55

0.5709

4 (least influential)

V 2 constant

78.37

0.5674

2

V 3 constant

78.45

0.569

3

V 4 constant

49.81

-0.0038

1 (most influential)

Forcing All Independent V ariables Except O ne to be Constant

Sensitivity of m odel

Accuracy (%)

Skill m easure

With all variables

78.55

0.5709

All constant but V 1

49.79

-0.0042

All constant but V 2

49.79

-0.0042

All constant but V 3

49.8

-0.004

All constant but V 4

78.27

0.5654

Backwards Stepw ise C onstant Forcing

Sensitivity of m odel

Variables included

Accuracy (%)

Skill measure

With all variables

All variables

78.55

0.5709

Step 1: V [1] constant,

[2,3,4]

78.55

0.5709

Step 2: V [1,3] constant

[2,4]

78.45

0.5690

Step 3: V [1,3,2] constant

[4]

78.27

0.5654

115

ir
'■ //

.. j i i V
v

* 'V ^

,

0.48 - 0.49
0 - 0.46

0.49 - 0.50

20 km

0.46 - 0.48

Figure 62: T ransition probabilities from planted/re-grow th forest to deciduous forest

116

4.4.2 Discussion on modeled outcomes
The transition probability matrix (Table 8) elucidates that coniferous (0.92)
and deciduous (0.82) forest have high probability of maintaining most of their
current extent in 2020; however, this probability is 54% for mixed forest. Chance of
mixed to coniferous forest transition is 0.19. The likelihood of converting planted or
re-growth forest to deciduous is 0.33 while there is a 40% chance that the planted or
re-growth forest will remain the same. These outcomes represent the planned and
healthy forestry practice in this area since there is no major depleting trend for any
forest type observed. The matrix exhibits that transition from cropland to pasture
and vice-versa will be continuing during the model period. There is a 92% chance
that there will be very minor change in open water extent.

Table 8: Transition probability matrix

Given •
LULC 1
type
CL
CF
DF
MF
P/RF
CB
PS
WT
WL
BA

l ii : j__~ j C-1_____
_
riU D d D liliy Ul t l l d l l g l l t g IU

CL
0.16
0.00
0.00
0.00
0.04
0.04
0.14
0.00
0.00
0.05

CF
0.00
0.92
0.00
0.19
0.00
0.04
0.00
0.00
0.22
0.01

DF
MF
0.05
0.00
0.01
0.02
0.82
0.06
0.14
0.54
0.33
0.00
0.38
0.05
0.03
0.00
0.00
0.01
0.14 r 0.30
0.02
0.00

P/RF
0.10
0.01
0.10
0.03
0.40
0.06
0.03
0.00
0.00
0.00

117

CB
0.01
0.01
0.00
0.01
0.00
0.02
0.05
0.00
0.00
0.04

PS
0.51
0.00
0.00
0.01
0.10
0.14
0.59
0.04
0.02
0.03

WT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.92
0.00
0.00

WL
0.13
0.03
0.01
0.06
0.13
0.26
0.15
0.03
0.31
0.03

BA
0.04
0.02
0.01
0.01
0.00
0.03
0.01
0.00
0.00
0.84

Similarly, there is a 84% chance that the built-up area will rem ain unaltered.
At the same time, probabilities of gain in built-up area extent from other LULC types
are also insignificant; but this projection dem ands further investigation since it
w ould make m ore sense if the chances of built-up area gaining w ould be higher
given the current situation of dense industrial developm ent in this watershed.
Changing the driver variables or modifying other m odeling param eters m ay result
differently and could produce m ore reasonable o utput for built-up area.

D uring the m odeled period, wetland to forest conversion will rem ain active
w ith probabilities of 0.22 for coniferous, 0.14 for deciduous and 0.30 for mixed
forest. There is only a 31% chance is that wetland extent will be unaffected, yet there
is notable likelihood of alteration of other classes to wetland, such as: chance of
change from cropland to w etland is 0.13; planted or re-growth forest to w etlands is
0.13 and for cut block and pasture, chance is 0.25 and 0.15 respectively.

Summarizing all the transition probabilities explained above, the MC m odel
was used to create another matrix of expected transition of pixels for enhanced
understanding (Table 9). This matrix exhibits how m any pixels of a LULC type are
expected to transition to other LULC types in 2020. In this table, the highlighted
pixels will rem ain unchanged for each LULC class. Based on this latest matrix, LCM
produced a hard prediction using multi-objective land allocation algorithm. Also, a

118

soft prediction is crafted using the principle that a location is m ore vulnerable to
change if it is desired by several transitions at the same time than if it is desired by a
single transition.

Table 9: Expected transition of pixels

Pixels in
1

1
CL
CF
DF
MF
P/RF
CB
PS
WT
WL
BA

CL
20913
0
0
0
0
0
0
0
0
0

CF
DF
MF
0
0
0
1192282 4501
10791
0
831158 28544
38905 29027 319499
0
29036
0
0
0
0
0
0
0
0
0
0
22838
14210 30223
0
0
0

P/RF
0
5488
43795
6464
145759
0
0
0
0
0

CB
PS
0
0
7338
0
0
0
0
0
0
0
29312
0
0
66995
0
0
0
2068
0
0

WT
0
0
0
0
0
0
0
15051
0
0

WL
BA
0
0
9558
3453
3141
3960
12602
0
0
0
0
0
0
0
0
0
0
134392
0
52499

A hard prediction result w as two land use-land cover m aps for 2015
(intermediate stage) and 2020 (final stage) w ith all the classes that exist in 2010
LULC m ap while forest fire affected area remained the same as it w as excluded from
the transition modeling process. Figure 63 and Figure 64 exhibit the m aps generated
from hard prediction.

119

2015

Built-up area

Deciduous forest

Planted or regrowth forest

Coniferous forest

Forest fire

Water

Cropland

Mixed forest

Wetland

Cut block

Pasture
Figure 63: Interm ediate stage LULC m ap from hard prediction

120

N

25 km

2020

Built-up area

Deciduous forest

Planted or regrowth forest

Coniferous forest

Forest fire

Water

Cropland

M xed forest

Wetland

Cut block

Pasture
Figure 64: Final stage LULC map from hard prediction

121

k
rjj
25 km

Table 10 lists the area calculated for both predicted LULC maps. Though
m inor changes are projected for few classes, no significant change is observed. This
is further realized in Figure 65 which dem onstrates the difference in area for each
LULC type betw een the predicted LULC m aps and existing (2010) LULC map.
Forest cover will remain almost the same w ith m inor shifting from one to another; a
45 km2 increase in total forest cover is predicted. The depleting trend of w etland area
appears to be continuing; depletion of another 67.89 km2 of w etland w as estimated
by the model. It also forecasted an increase of 11.57 km2 of built-up area and 7.74
km2 of cut block area from which it m ay be inferred that the ongoing industrial
activity will keep escalating during the prediction period. The depletion of w etlands
needs further examination to identify the probable cause of change. Some possible
reasons may be climate change, enhanced anthropogenic activities including natural
gas development, logging, agriculture etc. as explained in the preceding section.

122

Table 10: Area calculated for predicted LULC maps

2010

LULC
type

km2

CL
CF
DF
MF
P/RF
CB
PS
WT
WL
BA
FF

18.82
1107.84
815.34
365.88
157.23
26.38
60.30
20.48
183.30
47.24
33.19

%
0.66
39.06
28.75
12.90
5.55
0.93
2.13
0.72
6.46
1.67
1.17

Intermediate Stage (2015)
km2
%

Final Stage (2020)
km2
%

0.66
39.72
28.81
12.28
6.32
1.15
2.19
0.73
5.09
1.88
1.17

18.82
1126.51
817.06
348.19
179.37
32.59
62.16
20.67
144.26
53.18
33.19

0.66
41.08
28.21
11.44
7.12
1.21
2.24
0.73
4.07
2.07
1.17

18.82
1164.88
800.01
324.51
201.88
34.13
63.57
20.79
115.41
58.81
33.19

1200.00

1000 00
2010
Intermediate Stage (201S)

800 00

Final Stage (2020)

x,

600.00

400 00

200.00

0 00
CL

CF

DF

MF

PR F

CB

PS

WT

WL

BA

FF

Figure 65: Difference in area for each LULC type between existing and predicted maps

123

The soft prediction output consists of m aps representing the probability of
change for a given set of transitions. Soft prediction w as also perform ed in two
stages. Figure 66 exhibits the soft prediction output from LCM for 2020. The soft
output was a continuous m apping of vulnerability to change for selected set of
transitions. This prediction identified the extent to which the land area has the
propensity and right criteria to be altered. While the hard prediction created only a
single realization of the future LULC status, the soft prediction w as a comprehensive
assessment of change potential. This is w hy the soft o utput detected the areas with
varying degree of vulnerability instead of identifying w hat and how m uch of LULC
area w ould be changed. From the m odeled output, it is evident that m ost of the
southern portion of the w atershed is highly vulnerable to transition under the
current set of driver variables and identified individual transitions from one type to
another. This is reasonable as this part of the watershed has large area of wetlands
which has exhibited the m ost significant depletion during the study period.
Considerable vulnerability is observed in the northern portion as well where
Halfmoon-Oetata, Brassey and Mainstem sub-basins are located. Reasons for this
vulnerability m ay be attributed to the recent intensified gas development activity in
this area along w ith land use change derived by agricultural and farming activities.
The middle portion of the w atershed is characterized by mixed probability of change
while most of the open w ater body showed almost no vulnerability to change.
124

2020
Vulnerability to change
0
0 .0 1 - 0.20

0.20-0.30
0.30 0 40
0.40-0.50
0.50 - 0.60
O.SO- 0.70
0.70-0.80
0.80-0.90
0.90 100

n

'

20 km

Figure 66: Soft prediction output for 2020

125

t

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Summary
Remote sensing, GIS and m odeling techniques were em ployed in this
research to study the land use-land cover dynamics in Kiskatinaw River W atershed
in north-eastern BC. The study provided an avenue for comprehensive LULC
analysis in KRW. LULC m aps w ere created to understand the characteristics of
LULC change which were later used for transitional potential m odeling and
projecting future LULC pattern.

Landsat satellite images were successfully exploited in the present research
for producing LULC information for the study area. Three satellite images of the
years 1984, 1999 and 2010 were studied. An object oriented image classification
technique was executed for analyzing the satellite images w ith a high degree of
accuracy (90.45%). The analysis of the images generated three LULC m aps for the
study area. The produced m aps identified that the major share of the LULC within
the study area is occupied by different types of forest which was estim ated as 80% in
1984, 85.60% in 1999 and 86.28% in 2010. Forest harvesting has been com pensated by
planting or allowing re-growth of the forests according to forestry practice in this
watershed.

126

Land use practices, such as agriculture, farming etc. are m ostly limited to the
northern part of the watershed. O ther than these, the m ost striking land use activity
during the past few years is natural gas development. The study area falls w ithin the
M ontney shale gas play which is one of the large underground shale gas reservoirs
of the world. The extraction of this natural gas is heavily impacting the study
w atershed's land use dynamics. Until now, the signature of this intensified gas
developm ent activity is narrow in the LULC maps; however, the im pact could be
significant if not properly planned and managed. East KRW sub-basin is facing the
m ost extensive natural gas development. Activities are also prom inent in HalfmoonOetata, Brassey as well as in Mainstem sub-basins.

Another distinguishing feature of this LULC change analysis is the extensive
depletion of the w etland area. A total of 270.78 km 2 of w etlands have disappeared
during the study period from 1984 to 2010 while most of the depletion occurred
between 1984 and 1999 estim ated as 233.22 km 2. This wetland depletion dem ands
further investigation to satisfy the queries of 'w hat are the reasons of this depletion'
and 'w hat is its impact on the watershed system'. The northern location of the
w atershed m ade the w etland depletion m ore serious due to the implications of
climate change.

127

LULC changes were also realized at the sub-watershed scale. The dynamics
of change vary from sub-basin to sub-basin since the land use activity is different in
each area. The cropland and pasture land use type exist mainly in the M ainstem and
Brassey sub-basins and thus, these two sub-basins experienced associated alteration
in the extent of cropland and pasture. Mainstem was identified as m ore dynamic
than Brassey for agriculture and farming. The change in forest cover rem ains active
for all the sub-watershed during the study period. However, shrinking of wetland
area varied for different sub-basins w ith higher am ount of depletion observed in the
southernm ost areas. The increase in built-up area occurred across the watershed,
although east KRW sub-basin featured the m ost rigorous gas developm ent activity.

Based on the change analysis perform ed for KRW, a modeling approach was
applied in this study to forecast the future LULC dynamics of the watershed. Multi­
layer perception (MLP) neural netw ork was utilized to m odel the transition
potential of each identified LULC transition during the study period while Markov
Chain (MC) model was used to generate future LULC scenario. Both models were
sim ulated up to 2020. Several driving variables were identified for the modeling
analysis. These variables are expected to alter LULC in KRW and were tested for
their effectiveness before final m odel was run. The transition probability matrix and
expected transition area matrix were produced from the first phase of modeling.
These matrices exhibited the likelihood of change for each LULC class. Different
128

forest types and open w ater body showed the highest probability that they will keep
their current extent unaltered after the modeled period in 2020. W etlands will
continue to deplete during this period though at a slower rate. Dynamics for other
LULC types will remain m ore or less the same. Based on the transition probability
estimation, two types of output w ere produced from MC modeling: hard prediction
and soft prediction. Soft prediction generated m aps show overall vulnerability of
change for the whole watershed, w hereas hard prediction generated LULC m aps for
2015 and 2020.

In summary, the study combined contemporary satellite image classification
m ethods w ith a robust m odeling environm ent and proved effective for the land-use
and land cover analysis in Kiskatinaw River W atershed. The research produced a
LULC inventory for the study area which will benefit the land use planner and
stakeholders of the w atershed to formulate and implement an efficient w ater
resources management. Being the first comprehensive LULC inventory of the
watershed, its leverage could be multi-faceted for this fast economically growing
watershed. The LULC information could be realized in every sector related to
natural resources extraction and developm ent within Kiskatinaw River W atershed.

129

5.2 Limitations and recommendations for future work
In this study, the application of RS, GIS and modeling has proven as an
effective m eans for land use and land cover change analysis and produced some
meaningful information about the overall land use system of Kiskatinaw watershed.
But it involves some inherent limitations, such as:

1. Challenges in acquiring cloud free useable satellite imagery;

2. With the spatial resolution (30 m) of the Landsat satellite data, it w as hard
to differentiate the signature of some LULC features, e.g. cropland and pasture etc.;

3. Inaccessibility of the study w atershed for which some ground truth survey
locations could not be sampled;

4. The time frame and scope of the study did not allow furthering the
investigation of the w etland depletion findings.

The

experiences

obtained

from

this

study

put

forw ard

some

recommendations for future research. Aerial photograph or higher resolution
satellite imagery of dry and w et season for each year m ay produce m ore robust
results, particularly to justify the change of the wetlands w hose remarkable
depletion poses a serious urgency. Also, incorporation of a soil m oisture study with

130

LULC change analysis w ould be effective as soil m oisture varies w ith land-use types
and have the potential to m ake the detection process m ore robust.

For more comprehensive study, RS and GIS analysis can also be aligned w ith
hydrological m odeling to investigate if there is any connection betw een the land-use
changes, alteration of hydrologic regime and anthropogenic activity which m ay also
be able to tell us about the causes of the w etlands decrease.

131

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