Multi-Layer Perceptron – Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change: a Case Study of Stoney Creek Watershed, Bc, Canada

MULTI-LAYER PERCEPTRON – MARKOV CHAIN BASED GEOSPATIAL ANALYSIS OF LAND USE AND LAND COVER CHANGE: A CASE STUDY OF STONEY CREEK WATERSHED, BC, CANADA

Lei Shen

B.E., College of Science & Technology, North China Electric Power University, China, 2016

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 BRITHISH COLUMBIA

April 2019

ABSTRACT

This thesis study analyzed the land use and land cover (LULC) changes in Stoney Creek

Watershed, BC, Canada using the combination of remote sensing, GIS and modeling approaches. The Object-Based Image Analysis (OBIA) tool in PCI Geomatica 2017 software was applied to generate unsupervised classification LULC maps using Landsat TM and OLI images of the years 1986, 1999 and 2016. Various band ratio were computed to improve different classification results. Esri ArcMap 10.5 was used to produce all the LULC maps for subsequent modeling. A modeling method using Multi-layer perceptron (MLP) neural network and Markov Chain (MC) was performed to predict LULC changes in 2026, using hard and soft prediction results. The outcomes of this study could provide valuable information of LULC patterns and dynamics for supporting both environmental and economic development in this area.

Table of Contents

ABSTRACT ...... i Table of Contents ...... ii List of Figures ...... iv List of Tables ...... vi Acknowledgement ...... vii CHAPTER 1 INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Objectives and significance of study ...... 3 1.3 Structure of the thesis ...... 4 CHAPTER 2 LITERATURE REVIEW ...... 6 2.1 Land use and land cover (LULC) changes ...... 6 2.1.1 Rationale of land use and land cover change ...... 6 2.1.2 Remote sensing (RS) and Geographic Information Systems (GIS) ...... 7 2.1.3 Satellite image analysis ...... 9 2.1.4 Landsat image data ...... 11 2.2 Land use and land cover change prediction models ...... 14 2.3 Summary ...... 16 CHAPTER 3 METHODOLOGY ...... 18 3.1 Overview of the Stoney Creek Watershed (SCW) ...... 18 3.1.1 Location and extent ...... 18 3.1.2 Stoney Creek ...... 20 3.1.3 Sub-basins ...... 22 3.2 Reconnaissance survey ...... 23 3.3 Data selection and collection ...... 23 3.4 Identification of land use and land cover categories ...... 25 3.5 Landsat image processing and analysis ...... 31 3.5.1 Image pre-processing ...... 31

3.5.2 Image classification ...... 38 3.5.3 Image post-processing ...... 45 3.6 Land use and land cover change modeling ...... 47 3.6.1 Change analysis ...... 47 3.6.2 Transition potential modeling ...... 48 3.6.3 Change prediction ...... 53 CHAPTER 4 RESULTS AND DISCUSSIONS ...... 56 4.1 Landsat image results and discussions ...... 56 4.2 Accuracy assessment results and discussions ...... 63 4.3 LULC change analysis ...... 65 4.4 LULC analysis for sub-basins ...... 68 4.4.1 Stoney Creek (SC) sub-basin ...... 68 4.4.2 Tachick Creek (TC) sub-basin ...... 73 4.4.3 West Nulki Stream (WNS) sub-basin ...... 76 4.4.4 South Nulki Stream (SNS) sub-basin ...... 78 4.4.5 Corkscrew Creek (CC) sub-basin ...... 80 4.4.6 Mid Cork Stream (MCS) sub-basin ...... 82 4.4.7 West Cork Stream (WCS) sub-basin ...... 84 4.5 Land change modeling results and discussions ...... 86 4.5.1 Results of transition sub-models ...... 86 4.5.2 Results of Markov Chain model ...... 88 4.5.3 Results of soft prediction and hard prediction ...... 91 CHAPTER 5 CONCLUSION ...... 97 5.1 Summary of the study ...... 97 5.2 Limitations and recommendations ...... 99 Bibliography ...... 100

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List of Figures

Figure 1.1 Organization of the thesis ...... 5 Figure 2.1. The basic phenomena and processes of land change science ...... 7 Figure 2.2. Wavelength of sensors in different Landsat satellites ...... 14 Figure 3.1. Location of study area: Stoney Creek watershed ...... 19 Figure 3.2. Channel network within SCW ...... 21 Figure 3.3. Stoney Creek Watershed sub-basins ...... 22 Figure 3.4. Agricultural Area class ...... 26 Figure 3.5. Barren Land class ...... 27 Figure 3.6. Coniferous Forest class ...... 27 Figure 3.7. Cut Block class ...... 28 Figure 3.8. Deciduous Forest class ...... 28 Figure 3.9. Mixed Forest class ...... 29 Figure 3.10. Planted/Regrowth Forest class ...... 29 Figure 3.11. Urban Area class ...... 30 Figure 3.12. Water class ...... 30 Figure 3.13. Dead Pine Trees class ...... 31 Figure 3.14. Mosaiced 2016 Landsat image ...... 33 Figure 3.15. July 21, 1986 Landsat 5 TM image with 5-4-3 band combination ...... 35 Figure 3.16. July 9, 1999 Landsat 5 TM image with 5-4-3 band combination ...... 36 Figure 3.17. August 17, 2016 Landsat 8 OLI image with 6-5-4 band combination ...... 37 Figure 3.18. Segmentation results of 1986 pre-processed image ...... 39 Figure 3.19. Landsat image processing and analysis framework ...... 46 Figure 3.20. MLP neural network structure ...... 49 Figure 3.21. LULC change model framework...... 55 Figure 4.1. LULC classification map of SCW in 1986 ...... 57 Figure 4.2. Area statistics for each LULC class in 1986 ...... 58 Figure 4.3. LULC classification map of SCW in 1999 ...... 59 Figure 4.4. Area statistics for each class in 1999 ...... 60

Figure 4.5. LULC classification map of SCW in 2016 ...... 61 Figure 4.6. Area statistics for each class in 2016 ...... 62

Figure 4.7. Land gain and loss analysis for period 1986-1999 (T1) ...... 65

Figure 4.8. Land gain and loss analysis for period 1999-2016 (T2) ...... 66

Figure 4.9. Net change analysis of LULC for period 1986-1999 (T1) ...... 67

Figure 4.10. Net change analysis of LULC for period 1999-2016 (T2) ...... 67 Figure 4.11. SC sub-basin LULC map for 1986 ...... 70 Figure 4.12. SC sub-basin LULC map for 1999 ...... 71 Figure 4.13. SC sub-basin LULC map for 2016 ...... 72 Figure 4.14. LULC change of SC sub-basin...... 73 Figure 4.15. LULC change of TC sub-basin ...... 74 Figure 4.16. TC sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 75 Figure 4.17. LULC change of WNS sub-basin ...... 76 Figure 4.18. WNS sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 77 Figure 4.19. LULC change of SNS sub-basin ...... 78 Figure 4.20. SNS sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 79 Figure 4.21. LULC change of CC sub-basin ...... 80 Figure 4.22. CC sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 81 Figure 4.23. LULC change of MCS sub-basin ...... 82 Figure 4.24. MCS sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 83 Figure 4.25. LULC change of WCS sub-basin ...... 84 Figure 4.26. WCS sub-basin LULC map, (a) 1986, (b) 1999, (c) 2016 ...... 85 Figure 4.27. Transition potential map of CF to DT transition ...... 87 Figure 4.28. LULC hard prediction map for 2026...... 91 Figure 4.29. Area comparison between 2016 and 2026...... 93 Figure 4.30. Logical OR soft prediction map for 2026 ...... 94 Figure 4.31. Maximum soft prediction map for 2026 ...... 95

List of Tables

Table 2.1. Spectral features of Landsat 4-5 bands ...... 12 Table 2.2. Usage of Bands for mapping ...... 13 Table 2.3. Spectral features of Landsat 8 bands ...... 13 Table 3.1. Description of the selected satellite images ...... 25 Table 3.2. Introduction of driver variables for transition potential modeling ...... 48 Table 3.3. General model information ...... 52 Table 3.4. Forcing a single independent variable to be constant ...... 52 Table 3.5. Forcing all independent variables expect one to be constant ...... 52 Table 3.6. Backwards stepwise constant forcing ...... 53 Table 4.1. Area (km2) statistics of LULC types for each study year ...... 62 Table 4.2. Error matrix results for 2016 image classification ...... 63 Table 4.3. Accuracy statistics results for 2016 image classification ...... 64

Table 4.4. LULC change analysis results for for period 1986-1999 (T1) and 1999-2016 (T2) 68 Table 4.5. Transition probability matrix ...... 90 Table 4.6. Transition areas matrix ...... 90 Table 4.7. Area statistics of 2016 and 2026 ...... 92

Acknowledgement

First and foremost, my sincere gratitude goes to my supervisor, Dr. Jianbing Li, who has unreservedly supported me at every stage of my study and research and guided me to achieve the academic goal. I am also very grateful to my committee members Dr. Roger Wheate who generously guided and inspired me throughout the research, and Dr. Jun Yin whose support and company during the field work helped me a lot.

I would like to offer my gratitude to Scott Emmons, Senior GIS Lab Instructor, who is also the instructor of my first class at UNBC. He helped and inspired me through many ways by his kindness and profound knowledge in his field. My appreciation also goes to Ping Bai,

Lab Instructor and Anthony Jjumba, my course instructor who have provided great help to my research.

At last, I would like to give my sincere thankfulness to my parents, who raised me and gave me the opportunity to study abroad, and my wife, whose support is always on my back and accompany always on my side. My thankfulness also goes to all the friends who shared my laughs and cries throughout the time I spent outside my homeland.

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CHAPTER 1 INTRODUCTION

1.1 Background

Increasingly world landscapes have been influenced by anthropogenic activities, and human development derives heavily from natural resources. Because of this, land use and land cover (LULC) change resulting from anthropogenic activities has become an inevitable issue and leads to environmental degradation risk around the globe (Foley, et al., 2005;

Paiboonvorachat, 2008; Stabile, 2012; Islam et al, 2018b). It was reported that half of the ice- free terrestrial land in the world has been transferred to other land use and land cover types, especially to agricultural lands (Haberl et al, 2007; Turner et al, 2007). The global LULC changes have shown a tendency of transition from forests to agricultural lands and built-up areas (Munoz-Villers and Lopez-Blandco, 2008; Paiboonvorachat, 2008). Deforestation and the expansion of agricultural and built-up areas have caused various environmental issues, such as loss of biodiversity, increased water withdrawals, decreased precipitation, restricted hydrological cycle, degradation of soil nutrients, erosion of soils, increased greenhouse gas emission and climate change (Turner, et al., 2007; Paiboonvorachat and Oyana, 2011; Paul,

2013).

Remote Sensing (RS) techniques have been widely utilized for LULC change detection and dynamic observation. Geographic Information Systems (GIS) involve various functions of spatial data management, creation, and analysis. More recently, these two sub-disciplines are often applied in combination, providing a more effective and widely used technology for global observing, monitoring and analyzing (Paul et al., 2012; Srivastava et al, 2013; Nguyen et al,

2016). By using remotely sensed data and historic maps, LULC dynamics can be mapped for

LULC change analysis, and then the information resulting from the analysis can be used for

LULC change models that are important tools for LULC management, environmental impact assessment, and decision making etc. (Campbell, 2002; Turner et al., 2007; Paiboonvorachat,

2011; Paul, 2013). LULC models have been widely applied for studying the transition probability among different LULC categories, a significant criterion for LULC change prediction in modeling processes (Kamusoko, et al., 2009; Behera et al, 2012; Hua, 2017).

With abundant forestry resources and agricultural lands, Vanderhoof is located near the geographical centre of British Columbia, Canada. It is a significant timber supply area in BC, with several timber mills across the town (RDBN, 2014). Moreover, this area has been known as one of the earliest agricultural settlements in BC. Due to its prolific forage production, the

Nechako River Valley and plateau have been named as the “hay basket” of the central interior- northwest of BC. However, its capacity of agricultural production has not been fully realized for planned and reserved agricultural lands (Agricultural Land Use Inventory, 2017). In the area, Nechako River is the main drainage channel and is comprised of over 30 tributaries. This study focuses on the Stoney Creek Watershed (SCW). Stoney Creek flows out of Nulki Lake then passes through the Saik'uz First Nation Reservation, and north to Tachick Lake, before finally, north-east towards Vanderhoof where it enters the Nechako River (Olin, 2013). It is one of the important water resources in Vanderhoof District. However, limited studies or information of LULC in SCW were available, posing difficulty and challenge for making informed decision regarding its development. Thus, it is essential to examine the LULC change and its dynamics within SCW.

1.2 Objectives and significance of study

This thesis study focuses on analyzing the LULC change data of SCW by using a combined RS and GIS approach to have a better understanding of the watershed. The LULC information obtained will provide accessible data for studies on hydrology, climate change and food safety in SCW. Models combined with RS and GIS are used for LULC prediction, in order to identify the scenarios of SCW in 2026. This process provides recommendations for future environmental conservation and sustainable development planning. Finally, the methods presented in this study can be applied to the whole Vanderhoof District in the future.

1.3 Structure of the thesis

This thesis is consisted of 5 chapters: Chapter1 (Introduction) introduces the background, objectives and significance of the study. The review of existing literatures is represented in

Chapter 2 (Literature Review) together with the context explanation and method evaluation.

The detailed procedures of selected method are explained in Chapter 3 (Methodology) including the overview of the study watershed, how satellite images were selected and used, image processing and models for LULC change prediction. Chapter 4 (Results and Discussions) presents the study results including processed LULC images, accuracy assessment results, current LULC change data and LULC future modeling results and their analysis. Finally, a summary of the study is described in Chapter 5, as well as the study limitations and suggestions for future work. Figure 1.1 shows the organization of the thesis.

Chapter 1: Introduction

Study Introduces the Objectives and background study significance

Chapter 2: Literature Review

Reviews existing literatures, explain context and choose suitable method for the study

Chapter 3: Methodology

Overview on Explains procedures Data selection SCW of selected method and application

Image LULC change processing modeling

Chapter 4: Results and discussions

Processed Presents and Accuracy Landsat images analyzes results assessment results

Current LULC LULC change change results modeling results

Chapter 5: Conclusion

Summarizes the study, states research limitations and provide future study recommendations

Figure 1.1 Organization of the thesis

CHAPTER 2 LITERATURE REVIEW

2.1 Land use and land cover (LULC) changes

2.1.1 Rationale of land use and land cover change

Land use and land cover are two different terms but are often used interchangeably by many researchers. Land use refers to the ways how human beings utilize the land cover for themselves and their habitat, which usually focus on the economic function of lands (e.g. residential areas and agricultural zones) (Paul, 2013; Rawat and Kumar, 2015; Islam, 2018a).

On the other hand, land cover represents the biophysical characteristics such as vegetation, soil and water distributed on earth’s surface. It also includes features created by anthropogenic activities, such as settlements.

Land use and land cover have an interactive relationship. Figure 2.1 presents the basic phenomena and processes of land change science (Turner et al., 2007). It exhibits the relationship between the human and biophysical subsystems of land systems. It is uncertain whether the land cover changes resulting from land use will lead to land degradation, but the conversion of land use in the human subsystem driven by social activities can change land cover. Moreover, these land cover changes impact natural environment and biosphere by affecting climate, biodiversity, soil, water, radiation budgets, trace gas emissions etc. (Turner et al., 2007; Paiboonvorachat, 2008; Rawat and Kumar, 2015; Liping et al, 2018).

Figure 2.1. The basic phenomena and processes of land change science (Turner et al., 2007)

All these impacts have potential to affect the long-term relationship between humans and natural environment. Recently, the process of land use and land cover (LULC) change driven by human activities and natural phenomena has accelerated and become a widespread challenge. It is thus crucial to study LULC change detection for a better understanding of landscape dynamics and future scenarios to make better land management and planning.

(Behera et al, 2012; Rawat and Kumar, 2015; Liping et al, 2018).

2.1.2 Remote sensing (RS) and Geographic Information Systems (GIS)

Remote sensing is the technique for collecting information of a surface without direct contact. It makes use of the electromagnetic signature, reflected or diffracted by the sensed

objects, for the purpose of differentiating land use and land cover categories. By using this technology, multi-spectral and multi-temporal data can be collected and converted into useful information, for studying land patterns and dynamics (United Nations, 1986; Weng, 2001; Paul,

2013). Geographic information systems (GIS) describes geographical data collecting, storing, processing, and presentation of geographically referenced information and spatial visions of locations (USGS, 2007).

The combination of remote sensing and GIS has been applied and recognized widely as a successful and effective technique in LULC change detection. Satellite remote sensing technology is especially popular as it is supported by satellite sensors which can provide image data with high spatial resolution and geometric precision, and by various accessible software

(Yeh and Li, 1997; Araya, 2009; Paul, et al., 2012; Stabile, 2012; Pervaiz et al, 2016).

Comparison of post-classification images and multi-date composite image change detection are two most effective and popular ways to study change detection. Moreover, the information of how much, where and what type of LULC change has occurred are considered as the main results from remote sensing and GIS based change detection studies (Jensen, 1996; Weng,

2001).

For examples, Weng (2002) analyzed land use change in the Zhujiang delta of China by combining satellite remote sensing, GIS and Markov chain modeling. Yang and Liu (2005) applied remote sensing and GIS for coastal LULC change mapping for the Pensacola estuarine drainage area in Mexico. By using topographic maps and multi-temporal remotely sensed data, the LULC change in Dhaka Metropolitan area of Bangladesh from 1960 to 2005 has been detected and monitored (Dewan and Yamaguchi, 2009). Multi-spectral satellite data was

applied for land use change mapping analysis in Simly watershed, Pakistan by Butt et al. (2015), using a maximum likelihood supervised classification. Rawat and Kumar (2015) used a similar method with Landsat image data to detect LULC change in the Hawalbagh block in India, while emphasizing the importance of digital change detection technologies.

2.1.3 Satellite image analysis

Different land use and land cover types on the earth surface can emit and reflect a variety of electromagnetic radiation, which can be captured by satellite sensors and then assigned digital number (DN) values for each spectral band (Oumer, 2009; Paul, 2013). Based on the

DN values of different land use and land cover classes, all pixels in multi-spectral satellite images can be categorized into respective LULC categories to perform image classification

(Lillesand, et al., 2008).

Various methods have been applied for satellite image classification. Yang and Liu (2005) applied hierarchical classification, interactive classification and iterative image interpretation for LULC change mapping. Rawat & Kumar (2015) employed supervised classification with maximum likelihood, including Normalized Difference Vegetation Index (NDVI), Normalized

Difference Water Index (NDWI) and Normalized Difference Built Index (NDBI) channels to support the classification. Chen et al (2006) applied a combination of NDVI, NDWI, NDBI and NDBal (Normalized Difference Bareness Index) on the classification of urban LULC change analysis. Most of the methods used pixel-based classification (PBC), which classifies the images based on DN values of single pixels (Dean and Smith, 2003). It is a conventional method and has been broadly applied as supervised and unsupervised classification in many

cases. However, because a pixel-by-pixel classification algorithm is applied to all available image signals considered, the pixels with similar spectral reflectance would be grouped together, regardless of their associated uses (Yang and Liu, 2005). Moreover, some spatial and contextual information of image pixels are neglected, and the pixels do not represent true geographical objects when using PBC method, thus affecting its accuracy in some cases

(Blaschke, 2010, MacLean, et al., 2013; Paul, 2013).

Compared with pixel-based classification (PBC), the object-oriented image classification

(OOIC) method classifies the satellite images by segments through creating a higher-level image objects from the segmentation of low-level pixel-based features. While PBC classifies by the characteristics of single pixels, OOIC generates segments and does the classification by characteristics such as segment size, shape, texture, etc. (Benz, et al., 2003; Hay & Castilla,

2008; MacLean, et al., 2013). Compared with PBC, OOIC produces higher classification accuracy, incorporating more information, and creating segments that are more recognizable than pixels. It also decreases local spectral variations (Shackelford and Davis, 2003; Frohn,

2011; Chen et al, 2013).

Many previous studies used OOIC method because of its great potential for LULC image analysis. For example, Zhou and Troy (2008) applied the OOIC approach to classify and analyze the urban area in the Gwynns Falls watershed, Maryland, US at the parcel level. Frohn

(2011) used OOIC to map and analyze wetlands in Florida, emphasizing the advantages of this classification method. A case study of LULC change in the Kiskatinaw River watershed in

Canada by Paul et al. (2018) also highlighted the high accuracy of the OOIC and its application potential with land change modeling.

2.1.4 Landsat image data

Landsat satellites are Earth-observing satellites that have provided an archive of space- based land remotely sensed data. The Landsat program has continuously offered the Earth’s surface record to benefit people who work in agriculture, forestry, mapping, LULC change, and global change research etc. since 1972 (USGS, 2012; USGS, 2016).

Landsat data has been widely applied for land use and land cover analysis (Weng, 2002;

Yang and Liu, 2005; Frohn, 2011; Zhao et al., 2012; Butt et al., 2015; Paul et al., 2018), and the Landsat sensors have proved sensitive enough to categorise different spectral patterns related to the LULC classes in many complex landscape conditions (Muñoz‐Villers and

López‐Blanco, 2008). In this study, Landsat 4 /5 and 8 were selected as image data sources based on extended archive accessibility. image quality, availability and cost.

2.1.4.1 Introduction of Landsat 4-5 TM

Landsat 4 and 5 instrumented the Landsat Thematic Mapper (TM) sensor onboard from

July 1982 to May 2012. Landsat 4-5 TM collected image data with a 16-day repeat cycle

(mostly Landsat 5), and data files include seven spectral bands (Table 2.1). Bands 1-5 and 7 were collected at 30 meters resolution, while band 6 (Thermal infrared) was 120 meters then resampled to 30 meters (USGS Landsat Archives, 2018).

Table 2.1. Spectral features of Landsat 4-5 bands (USGS Land Mission, 2015)

Wavelength Resolution Bands Spectral Respond (micrometers) (meters) 1 Blue 0.45-0.52 30 2 Green 0.52-0.60 30 3 Red 0.63-0.69 30 4 Near Infrared (NIR) 0.76-0.90 30 5 Shortwave Infrared (SWIR) 1 1.55-1.75 30 6 Thermal 10.40-12.50 120* (30) 7 Shortwave Infrared (SWIR) 2 2.08-2.35 30

2.1.4.2 Introduction of Landsat 8 OLI and TIRS

Landsat 8 satellite carries the Operational Land Imager (OLI) and Thermal Infrared

Sensor (TIRS) launched in 2013. As with Landsat 4 / 5, Landsat 8 collects image data from the earth with a 16-day repeat cycle, but data files consist of 11 spectral bands (Table 2.3). Figure

2.2 shows wavelength for different Landsat sensors. OLI Bands 2-7 have similar usage as

Bands 1-7 of Landsat 4-5 (Table 2.2). Moreover, the OLI sensor of Landsat 8 provides enhancements from prior instruments, with three new spectral bands: Band 1, an ultra blue visible channel, specifically useful for coastal and water resources studies; Band 6, a

Panchromatic channel with 15-meter resolution, useful for image sharpening; Band 9 designed for detecting cirrus clouds contamination as a new infrared channel. The TIRS sensor was designed to provide two thermal channels for thermal mapping and estimating soil moisture.

The data were captured with at least 100-meter resolution, then registered to OLI data with the resolution of 30 meters (Barsi, et al., 2014; USGS Landsat Archives, 2018).

Table 2.2. Usage of Bands for mapping (Barsi, et al., 2014)

Bands Usage for mapping 1 Discriminate soil from vegetation, and deciduous forest from coniferous forest; Bathymetric mapping 2 Useful for emphasizing peak vegetation 3 Distinguish vegetation slopes 4 Emphasize biomass content and shorelines 5 Distinguish moisture content of vegetation and soil; Useful for penetrating thin clouds 6 Thermal mapping; Assessing moisture content of soil 7 Hydrothermally altered rocks associated with mineral deposits

Table 2.3. Spectral features of Landsat 8 bands (Barsi, et al., 2014)

Wavelength Resolution Bands Spectral Respond (micrometers) (meters) 1 Ultra Blue (coastal/aerosol) 0.435 - 0.451 30 2 Blue 0.452 - 0.512 30 3 Green 0.533 - 0.590 30 4 Red 0.636 - 0.673 30 5 Near Infrared (NIR) 0.851 - 0.879 30 6 Shortwave Infrared (SWIR) 1 1.566 - 1.651 30 7 Shortwave Infrared (SWIR) 2 2.107 - 2.294 30 8 Panchromatic 0.503 - 0.676 15 9 Cirrus 1.363 - 1.384 30 10 Thermal Infrared (TIRS) 1 10.60 - 11.19 100 * (30) 11 Thermal Infrared (TIRS) 2 11.50 - 12.51 100 * (30)

Figure 2.2. Wavelength of sensors in different Landsat satellites (USGS Land Mission, 2015)

2.2 Land use and land cover change prediction models

Land use and land cover models are supportive tools for analyzing the causes and consequences of LULC changes (Verburg et al., 2004). They can be used in various fields such as earth-atmosphere interactions, biodiversity loss, vegetation cover, forest management, urban expansion and global environmental change (Weng, 2001; Echeverria, et al., 2008; Stabile,

2012; Kamusoko, et al., 2013; Paul, 2013). Modeling LULC change has six important features: level of analysis, driving forces, cross-scale dynamics, temporal dynamics, spatial interaction and neighbourhood effects, and level of integration (Verburg, et al., 2004). However, only a few models are available to solve the causes and consequences of LULC changes, which can be grouped into whole landscape models, distributional landscape and spatial landscape models

(Bark, 1989; Weng, 2002; Halmy et al, 2015). Unlike distributional models without spatial details, spatial models can be manipulated quickly by using remote sensing data with geographical referenced information (Bark, 1989; Urban and Wallin, 2017).

Many spatially explicit models such as Markov Chain (MC) model, Artificial Neural

Network (ANN) model, Cellular Automata (CA) model, etc. Mishra and Rai (2016) have been incorporated with RS and GIS technologies for more effective simulation and prediction for

LULC changes, Specifically, the CA model is one of the most commonly used models in LULC research, especially in urban development studies, in order to model complex land use change

(Verburg, et al., 2004). MC model is a stochastic model which has been adopted in many LULC change analysis studies and researches around the world. It can examine the stochastic nature of the LULC change data and forecast the stability of future land development (Weng, 2002).

ANN is a non-parametric approach which imitates the human biological nervous system. It can quantify and forecast complex non-linear patterns (Pijanowski et al., 2002; Mishra and Rai,

2016). Multi-layer perceptron (MLP) is a feedforward ANN. Typically MLP consists of one input layer, one output layer, and one or more hidden layers. It can automatically generate a large amount of parameter values but requires less data for training (Paul, 2013). MLP has the advantage of modeling several or all transitions at the same time (Eastman 2012).

In a specific study area, each individual model has its limitation to detect all essential

LULC change processes. Therefore, some studies combined two or even multiple models to reduce the uncertainty of the prediction (Verburg et al., 2008; Mishra and Rai, 2016). For single model studies, the CA model was applied by Hasbani (2008) to predict land use development in the Calgary region, Canada. The ANN method was applied with GIS techniques by

Pijanowski et al. (2002) to analyze how influential factors affect urbanization patterns and predict land use changes in Michigan’s Grand Traverse Bay Watershed. The MC model was applied for land use change analysis in Zhujiang Delta, China by Weng (2002). For studies

with combined models, Qiang and Lam (2015) applied the combination of ANN and CA approaches to model LULC changes in the Lower Mississippi River Basin region to support land planning and management of the region. Freier et al. (2011) applied Markov process aided by CA model to model rangelands considering the effects of climate change. A flexible hybrid approach consisting of CA and Markov Chain models was used to model LULC transition potentials, and then predict future LULC scenarios in the lower Hunter Valley, NSW (Stabile,

2012). A multi-scale and multi-model approach was developed by Verburg et al., (2008) to analyze future LULC changes in Europe. This is an example of solving for a multitude of changes which result from interacting processes with different operating scales and domains at the same time. Moreover, Arekhi and Jafarzadeh (2014) predicted areas vulnerable to forest conversion in the Zagros forest region using MLP neural network with MC model. This MLP-

MC modeling method was also used for land use and land cover prediction in Patna district,

India (Mishra and Rai, 2016). Similarly, modeling method with MLP neural network and MC model was utilized by Paul et al. (2018) to predicted LULC scenarios within the study watershed.

2.3 Summary

Because more and more anthropogenic and natural factors accelerate the LULC change process, it is necessary to study LULC changes to have a better understanding of land patterns and dynamics of an area. Data from Landsat satellites has been continuously provided to LULC change researchers over the world, because of its image quality and extended archive accessibility. The combined RS and GIS approach has been widely used in LULC change detection to provide high quality geospatial data and flexible data processing by using various

software. In recent years, there is a trend to use the OOIC method for LULC image classification which can produce higher accuracy with more incorporated information than conventional PBC. The integrated MLP neural network and MC analysis method is a recently developed method for quantifying and modeling spatiotemporal changes of LULC. This hybrid approach has the advantages of both MLP and MC models as MLP has excellent ability to model each LULC transition in the study area, while MC model is widely used for its capability to predict future LULC changes based on transition probability matrixes over a certain period of time. Therefore, the method using the combined RS and GIS approach to process Landsat image data through OOIC method, and with the MLP neural network and MC hybrid modeling is sufficient to perform geospatial analysis of LULC change and LULC change prediction.

CHAPTER 3 METHODOLOGY

3.1 Overview of the Stoney Creek Watershed (SCW)

3.1.1 Location and extent

The Stoney Creek Watershed is located to the south-west of Vanderhoof town, near the geographical centre of British Columbia, Canada (RDBN, 2014) (Figure 3.1). SCW lies between longitude 123°58’51” W to 124°25’12” W, and latitude 53°42’23” N to 54°01’32” N.

It covers a total drainage area of 497.02 km2. Tachick Lake, the biggest lake within the watershed, lies in the north part of the SCW. Nulki Lake lies south of Tachick Lake. The Saik'uz

First Nation Reservation lies between these two lakes. Highway 16 passes through the northeast part of the study watershed. The Nechako River flows north above the SCW then east to the city of Prince George (Figure 3.1).

Figure 3.1. Location of study area: Stoney Creek watershed

3.1.2 Stoney Creek

Stoney Creek has a total length of 20.85 km. It has a series of natural falls in the Nechako

River valley, and below these falls it flows through many different land use and land cover types such as agricultural areas, forest areas and urban areas (Olin, 2013).

Stoney Creek lies on the south bank of the Nechako River, upstream of Riverside Park in the Vanderhoof community, and is an important traditional area for Saik'uz First Nation. The lower main channel of the Stoney Creek flows 5 km within the municipal boundary of the

District of Vanderhoof. The riparian zone varies widely along most of its length within the municipal boundary (Olin, 2013). Figure 3.2 shows the main channel of Stoney Creek and its tributaries.

Figure 3.2. Channel network within SCW

3.1.3 Sub-basins

In this thesis, the SCW area was divided into seven sub-basins including Stoney Creek,

Tachick Creek, West Nulki Stream, South Nulki Stream, Corkscrew Creek, Mid Cork Stream and West Cork Stream (Figure 3.3) using the original watershed boundaries. The Stoney Creek sub-basin covers the largest area of 261.41 km2 among all sub-basins, which includes the main stream of Stoney Creek and two lakes (Tachick Lake and Nulki Lake). The other sub-basins are Tachick Creek (36.7 km2), West Nulki Stream (39.89 km2), South Nulki Stream (19.94 km2), Corkscrew Creek (71 km2), Mid Cork Stream (30.97 km2) and West Cork Stream (37.11 km2).

Figure 3.3. Stoney Creek Watershed sub-basins

3.2 Reconnaissance survey

In this study, a reconnaissance field survey aimed to collect the overall information of the land use and land cover in Vanderhoof, BC, Canada, such as historical, geographical, humanistic, biological and economic information. Several periods of field survey have been conducted for ground truth data collection and photos from 2017 to 2018. The obtained information was used for image classification accuracy assessment and identifying the types of LULC. Several sites have been chosen based on the locations where satellite images are not easy to distinguish. A deeper insight about the SCW study area was obtained from the field survey.

3.3 Data selection and collection

Data selection and collection should be considered based on the watershed boundary, purpose of the study and availability of satellite images. Based on the literature review and my prior project experiences, several rules and reasons for Landsat satellite images selection and collection were applied:

a. The objective of the study is to capture land use and land cover changes within Stoney

Creek Watershed from the early 1980s to 2016 based on the accessibility of Landsat data.

There may be land transitions before and after 1973 as the lumber mill came to production

during the time. Because Landsat data were available after 1972, this study collects

Landsat data from 1973 to 2016;

b. Landsat 4/5 and 8 were selected for the appropriate period and time span, and with

minimal or no cloud cover;

c. It is important to collect images captured at about the same time of the year. Plants

experiencing seasonal changes have different levels of ability to absorb visible light and

reflect near-infrared energy. Therefore, vegetation categories (including agricultural area,

coniferous forest, deciduous forest and mixed forest) show variable appearances

seasonally. This can impact the accuracy of classification and statistic analysis. The

period when vegetation is in good health and maturity was selected to benefit the

classification.

Based on these considerations, the Landsat satellite images in the following three years were chosen for analysis:

1) 1986 Landsat 5 TM imagery: this represents the earliest year with available spectral image

data of 30-meter resolution. The lumber mill came into production in 1973, and 1986 is

the closest date to represent the original data before LULC affected by forestry industries.

2) 1999 Landsat 5 TM imagery: the mountain pine beetle infestation occurred in the central

interior region in the late 1990s. Since 1999, significant actions have been made by the

BC government such as province-wide aerial overview surveys (Province of BC, 2016;

Ebata, 2003). Mountain pine beetle infestation has had a significant impact on the

coniferous forest and mixed forest categories.

3) 2016 Landsat 8 OLI and TIRS imagery: this can represent current status within SCW.

Landsat satellite imageries were collected from Earthexplorer and Remotepixel websites.

By using dates from late July to early August can effectively keep the analysis free from seasonal variability impact. The Stoney Creek Watershed boundary I used for my research was downloaded from iMapBC website. The whole boundary of the study area was merged with the main Stoney Creek basin and the sub-basins around it. Those sub-basins were chosen based on the stream sources.

Table 3.1. Description of the selected satellite images

Year Date Landsat Bands Resolution(meters)

1986 July 21th 5 TM Level 1 1 – 5 30

1999 July 9th 5 TM Level 1 1 – 5 30

2016 August 17th 8 OLI & TIRS 2 – 6 30

For Landsat 5 image data, bands 6 and 7 were excluded. For Landsat 8 data, bands 2-6 were selected for this project.

3.4 Identification of land use and land cover categories

Information and characteristics of different land use and land cover categories were identified based on land survey data and information gained from locals. In total 10 LULC classes were created and named for the subsequent satellite image analysis: Agricultural Area,

Barren Land, Coniferous Forest, Cut Block, Deciduous Forest, Mixed Forest,

Planted/Regrowth Forest, Urban Area, Water and Dead Pine Trees. A modified version of the

Anderson scheme has been applied as a reference for describing and identifying land use and land cover classes. Both Level I and Level II categories have been used at the same time

(Anderson, 1976). Because of the image resolution and areas of the study area, these classes were chosen based on their areas and the importance for the watershed.

Figure 3.4. Agricultural Area class

Agricultural Area: This class is mainly for food and fiber production. It mostly consists of flat areas with more regular shape such as rectangles. Figure 3.4 shows cropland, which is part of Agricultural Area class, within the study area.

Figure 3.5. Barren Land class

Barren Land: This class represents areas with less than one-third area vegetated or any other type of cover, and its skill to support life is limited (Anderson, 1976). It includes some structures such as roads, open pits etc. Figure 3.5 shows an abandoned quarry pit within the study area.

Figure 3.6. Coniferous Forest class

Coniferous Forest (Figure 3.6): This class represents the forest areas covered mainly by evergreen trees.

Figure 3.7. Cut Block class

Cut Block: This class represents the areas with forest removed after wild fire or pine beetle infestation, or tree cutting for wood supply and industrial purposes. Figure 3.7 shows a cut block area with grass and dead trees resulting from wild fire.

Figure 3.8. Deciduous Forest class

Deciduous Forest (Figure 3.8): This class represents the forest areas covered predominately by trees which lose or gain leaves seasonally, and especially they lose leaves at the end of the forest-free season or the beginning of a dry season (Anderson, 1976).

Figure 3.9. Mixed Forest class

Mixed Forest (Figure 3.9): This class consists of forest lands with both coniferous and deciduous forest and neither predominates.

Figure 3.10. Planted/Regrowth Forest class

Planted/Regrowth Forest: This class represents the areas covered by young coniferous plants, deciduous plants or mixed young plants which have naturally regrown or been planted by human on cut block or barren land. Figure 3.10 is a typical Planted Forest area grown with circa 4-year-old pine trees.

Figure 3.11. Urban Area class

Urban Area: This class represents the areas have much of the land covered by structures.

For example, cities, towns, villages, strip development along highways, transportations, etc.

(Anderson, 1976). Figure 3.11 shows a lumber mill located within Vanderhoof city area next to highway 16.

Figure 3.12. Water class

Water: This class consists the areas mainly covered by a water body such as lakes, rivers and creeks. Figure 3.12 is part of the main channel of Stoney Creek.

Figure 3.13. Dead Pine Trees class

Dead pine trees: This class represents the areas with pine trees killed due to the infestation of mountain pine beetles. Those pine trees only show great changes since the second stage of infestation, so only the satellite image of 2016 includes this class. Figure 3.13 shows the area with most pine trees killed by mountain pine beetles.

3.5 Landsat image processing and analysis

Before land use and land cover modeling, processing and analysis for three satellite images were applied by using two main software packages: PCI Geomatica 2017 and Esri

ArcGIS 10.5.1. PCI Geomatica 2017 was mostly used to conduct image pre-processing, classification, accuracy assessment etc. ArcGIS 10.5.1 was used mainly for image post- processing, such as format converting, mapping, statistic analysis etc.

3.5.1 Image pre-processing

On the USGS Earthexplorer website, before searching proper Landsat images, some parameters need to be set up. In this thesis, the date of all images was selected for July - August.

Comparison for data quality was applied to result in ideal Landsat images for later image classification. Landsat image data files downloaded from USGS datasets contain all bands in

TIFF format files, and an MLT text file. These bands were opened in Focus, the main platform on PCI, through the MLT file and using Multi Spectral (MS) input option.

While choosing Landsat images, one single image in 2016 does not cover the entire study area. The Mosaic tool in Geomatica software therefore was utilized to combine all bands of the two adjacent images into one respectively. Figure 16 is a mosaiced Landsat image for 2016 showing false color with 6-5-4 combination bands and Adaptive enhancement. For the purpose of generating a more accurate classification result, a larger rectangle area was used, but clipped by the SCW boundary for later analysis. The yellow boundary is the SCW study area surrounded by the rectangle study area (red boundary) (Figure 3.14).

Path 49 & Row 22

Path 49 & Row 23

Figure 3.14. Mosaiced 2016 Landsat image

Using the Clipping tool on PCI, the original Landsat images were clipped by the rectangle boundary (shapefile). The SWIR-NIR-Red ‘false color’ composite can provide the user with a great amount of information and color contrast, which is the 5-4-3 band combination for

Landsat 4-5 images (1986 and 1999) and 6-5-4 band combination for Landsat 8 image (2016).

[TM Band 5 has the most agricultural information.] This combination is not only widely used

for vegetation studies, but also useful for timber management, pest infestation etc. (James,

2001). Heathy vegetation shows in a green color and soil is mauve in this combination.

Waterbodies appear in blue color with varying depth and turbidity of water. The Adaptive enhancement, which shows the best enhancement result, was applied for all three Landsat images. Figure 3.15 and Figure 3.16 are the Landsat images for 1986 and 1999 with 5-4-3 band combination and clipped to the SCW extent. Figure 3.17 shows the 2016 image with 6-5-4 band combination clipped by the SCW boundary.

Figure 3.15. July 21, 1986 Landsat 5 TM image with 5-4-3 band combination

Figure 3.16. July 9, 1999 Landsat 5 TM image with 5-4-3 band combination

Figure 3.17. August 17, 2016 Landsat 8 OLI image with 6-5-4 band combination

3.5.2 Image classification

In remote sensing, digital image classification, also known as spectral pattern recognition, detects the spectral information for each pixel in a satellite image to cluster pixels into the same spectral categories. Different classes contain a mosaic of pixels to composite the thematic maps which is the classified image (Geomatica, 2017; Paul, 2013).

In this study, Object Analyst on PCI 2017 has been utilized to generate the object-oriented image classification. It is called the object-based image analysis (OBIA) method in PCI. There are six steps for performing OBIA by using the pre-processed images from the last step:

Segmentation, Feature extraction & calculating features, Unsupervised classification, Rule- based classification and manual editing.

3.5.2.1 Segmentation

A successful classification starts with good segmentation, which means in general most of the segments should correspond to a single land use and land cover class. Also, the shapes of objects should align with the boundaries of different classes in the image (Geomatica, 2017).

Moreover, in Object Analyst, combinations of different parameters including scale (SC), shape

(SP) and compactness (CP) can greatly influence the results of segmentation. Figure 3.18 shows segmentation results on the same area of the 1986 image, with various combinations of these three parameters.

Figure 3.18. Segmentation results of 1986 pre-processed image

Multiple tests show that the combination (SC: 25, SP: 0.25, CP: 0.5) has the most satisfying result for the image, as it segments most of the objects needed for later classification and analysis. Other combinations that provide few segments or massive segments may result

in lower classification accuracy. The compactness was constant because it was barely changing the segmentation results, and 0.5 has been found to be the best value for this case.

After running the Segmentation process, three vector layers were generated for each year in .pix file format. All later processes were applied on these vector layers.

3.5.2.2 Feature extraction & calculating features

Performing feature extraction provides attributes of all segments, which are the reference data for image object classification. In Object Analyst, two types of features were computed.

Statistical features were computed by the pixels within an object. These features include minimum, maximum, mean and standard deviation for each of the selected image bands.

Geometrical features were computed using the geometric characteristics of objects based on the segment boundary created during segmentation. This is the advantage of object-based image analysis compared with the conventional pixel-based analysis. Compactness, elongation, circularity and rectangularity were computed for geometrical features (Geomatica, 2017). All these features were added to the attribute table of each vector layers.

The features above were used as basic bands attributes. Moreover, some extra feature calculations were made for a better classification result by using the statistical features. In the attribute manager of each segmentation layers, several new fields were added and new feature layers below were computed:

1) NDVI (Normalized difference vegetation index), determines the density of green on an

area, which helps to classify live vegetation or not. It is calculated by the formula:

NDVI = (NIR — Red) / (NIR + Red) (NASA, 2000).

2) TM 2/3 Ratio, sharply differentiates croplands and barren lands, but not for separating

croplands, forests and water bodies;

3) TM 3/2 Ratio, for separating forests and croplands, and it is useful to distinguish broad

classes of vegetation;

4) TM 3/4 Ratio, classifies barren lands and urban area sharply, but not for defining

waterbodies, forests and croplands;

The last three ratios were applied based on the evaluation of Quinn (2001).

These features were computed for minimum, maximum, mean and standard deviation values respectively, e.g. NDVI_Min, NDVI_Max, NDVI_Mean and NDVI_StdV.

3.5.2.3 Unsupervised classification

Before running unsupervised classification, fields of minimum, maximum, mean and standard deviation features for band 3, 4 and 5 (4, 5 and 6 for image of year 2016) were selected.

The geometrical features and new calculated features were also included for the classification.

There were nine LULC classes for images of years 1986 and 1999, and ten LULC classes for year 2016. Clusters were generated with twice as many as the selected LULC classes, so 20 clusters were set for the classifier. These 20 clusters could provide all the LULC classes with better unsupervised classification results than any other numbers of cluster. Within the 20 clusters, several clusters could be classified as the selected LULC classes, because most of the segments they covered belong to specific classes, while the rest covered segments for different

LULC classes averagely.

3.5.2.4 Rule-based classification

By using this rule-based classification, custom rules were created to assign class membership to segments. Membership of a sample in a class could be determined by the criteria selected based on the understanding of domain, data, or both (Geomatica, 2017). To edit and improve classification results, custom classification rules were performed on classified or unclassified segments by assigning or removing them. These two steps could simply split a class into subclasses or combine two classes into a new one. An extra step was used to support classification by selecting specific range of a feature. The discrimination between specific

LULC classes were performed by choosing effective features as range field. Based on some experiment and ground truth experiences, a good range was picked by adjusting the minimum and maximum values. For example, the cluster with both segments of barren lands and segments of unvegetated croplands could be split by using ‘Mean_2/3 Ratio’ field as the range field, as this feature is unique for separating barren lands and croplands. ‘Mean_3/4 Ratio’ can improve the classification between barren lands and urban area for the cluster with both segments of these two classes. Or by using the ‘Rectangle’ feature because of the artificial shape of most cropland. Then adjusted both the minimum and maximum value till most of the required segments were included, and the rule-based classification result could be satisfied for later analysis.

3.5.2.5 Accuracy assessment

Accuracy assessment is an important step before generating a final classified image.

Usually it could be performed by using a reference map which has been field-checked or

contains ground truth data collected during the field survey (Weng, 2002). In this study, because of the lack of reference map for all three years and the ground truth data was only collected from 2017 to 2018, a training field was created for image 2016 through the tool for editing training sites on the Object Analysis. This training field was then used in the accuracy assessment section on the Object Analyst. By comparing unsupervised classified image and training field created based on field survey, three accuracy reports were generated. As they were almost the same procedures, features and rules were applied to classify the images for all three years, the accuracy assessment of 2016 could represent the accuracy of 1986 and 1999.

To avoid the subjectivity of picking manual points, a random points layer was created from ArcGIS software by using the points creating tool which could generate random point layers for accuracy assessment using stratified random sampling method. In this study, the

‘Area’ field was selected as the target field, and the number of random points was set as 200 because of the extent of the study area. Therefore, those random points were created to distribute proportionally in relative classes, which means the classes with bigger areas such as

‘Coniferous Forest’ and ‘Deciduous Forest’ covered more points than classes with smaller areas. This method could significantly improve the objectivity of assigning segments while creating the training field.

The random points shapefile layer was transferred from ArcGIS to Geomatica and overlaid on the pre-processed satellite image of 2016. Segments which covered random points were distinguished based on ground truth data and assigned to relative classes. Finally, three accuracy assessment reports were generated:

a. Accuracy Statistics Report: this report shows overall accuracy, 95% confidence

intervals, kappa coefficient and producer’s and user’s accuracy for all classes.

b. Error (Confusion) Matrix Report: this report shows the matrix reference data and

classified data of all classes.

c. Sampling List Report: this report displays all assessed samples, described with

georeferenced position, classified value/name, and reference value/name.

In the accuracy assessment report, Kappa Khat method could test the data in an error matrix better than the random, as the K coefficient estimates the agreement between a modeled

LULC contribution and ground truth (Lillesand, et al., 2008). For an error matrix with n number of LULC categories, the equation to compute the Kappa coefficient is:

� −� �� = 0 � (1) 1−��

where,

p0 = Observed agreement,

pe = Expected agreement.

Overall accuracy is the ratio between the total number of correct classifications and the total number of classifications. User’s accuracy defines the ratio between the number of correct classifications in a category and the total number of classifications in that category, and

Producer’s accuracy is the ratio between the number correctly identified in reference plots of a given category and the actual number in that reference category.

3.5.3 Image post-processing

In this study, all the images and layers were projected to UTM zone 10 with North

America Datum (NAD) 1983. Classified images, modeling results and some maps for study area introduction were generated as final maps using ArcGIS. Those maps were exported as jpg image files with suitable resolution, which were helpful for result display and analysis.

Many images and tables were created for later results display and discussion, such as areas of each class for both whole study area extent and sub-basins extent, etc.. The clipping tool in

ArcGIS software was applied to generate classified images with the extent of each sub-basin.

By working on attribute tables, new fields like ‘Areas’ could be computed. All these images and tables have made the results discussion more visual and persuasive.

In summary, Figure 3.19 shows the flow chart of all the steps for Landsat image processing and analysis:

Image pre-processing Download Landsat images

Mosaic and clip images to study area extent

Bands composite (5-4-3 for 1986 and 1999; 6-5-4 for 2016)

Image classification Image segmentation (OBIA) Feature extraction

Unsupervised classification

Rule-based classification

Training sites editing

Accuracy assessment

Image post-processing Manual editing and checking

Generate final maps for 1986, 1999 and 2016

Convert image format

Statistic analysis

Figure 3.19. Landsat image processing and analysis framework

3.6 Land use and land cover change modeling

To model LULC change, an integrated application on IDRISI Selva software (Land

Change Modeler (LCM)) is used. The LCM is used for historical LULC change analysis, transitions potential modeling, LULC change scenarios prediction, assessing the implications for biodiversity from specific change, and generating the evaluation for possible interventions for maintaining ecological sustainability. It includes a set of tools for the changing assessment, generating evaluations of gains and losses between two images, net change, persistence and specific transitions with one-click in both maps and forms (Eastman, 2012).

3.6.1 Change analysis

The analysis for historical LULC data was performed, and graphs and maps of LULC change were generated. The change analysis would be applied for three sets of time periods:

T1, T2 and T3. T1 represents the period from 1986 to 1999, and T2 from 1999 to 2016. These two ranges were used for assessing the changes between different years, and creating graphs and maps of LULC gains and losses, net change, etc.. T3 represents the time range between the earliest (1986) and the latest time (2016). The transition potential modeling and change

2 prediction would be performed by using T3. During T3, the transitions with less than 5 km have been ignored. Some of these small changed areas were generated from irrational transitions, e.g. transition from water to agriculture area, while some may result from classification errors. Both types of transitions would cause negative impacts on modeling, so those transitions were filtered and manually picked for later use. In addition, LCM is equipped with the function of special modification to the output, so that it could mask out the transitions

that do not match any specific transition cases (Eastman, 2012).

3.6.2 Transition potential modeling

In the first section of the transition potential modeling tab, selected transitions from change analysis were listed. All the transitions would be modeled one by one as sub-models.

To group these sub-models, a common name, ‘CHANGE8616’, was applied. Then in the third part of this tab, all the driver variables were added into the model through the evaluating and testing function. After testing it was found that some driver variable layers have different parameters such as resolution, number of rows and columns. Therefore, these driver layers were projected and output using image data of 1986 or 2016. In total, six driver variables were added and listed in the table (table 3.2).

Table 3.2. Introduction of driver variables for transition potential modeling

Driver variable layers Role

Hazard rating of mountain pine beetle This driver comprises extent and hazard rating of infestation pine beetle infestation. This driver controls the general hydrology in this Distance to major channel network watershed. This driver responsible for road development, Distance to recommended permanent built-up area development, forest clear cutting roads etc. This driver determines the hydrological flow Digital elevation model (DEM) path i.e. overall hydrological process within a watershed (Paul, 2013). TWI defined as Ln(A/tanB) (Sörensen, et al., 2006) where, Topographic wetness index (TWI) A = local upslope area draining through a certain point per unit contour length, tanB = the local slope. Settlement reserved area and Agriculture These two can influence future built-up area and development area croplands development, forest harvesting etc.

Finally, the multi-layer perceptron (MLP) neural network technique was utilized to perform the transition potential modeling. Non-linear neural networks can be conceived as a complex mathematical function that is capable of converting input data to a required output.

Unlike other statistical techniques, the MLP has no pre-assumption to constrain data contribution. It uses a back-propagation (BP) learning algorithm to model highly non-linear functions. A typical MLP neural network consists of one input layer, one or more hidden layers and one output layer, but generally one hidden layer is adequate for most studies. Figure 3.21 shows the MLP neural network structure (Eastman, 2012; Gardner, 1998; Lin et al., 2011)

Figure 3.20. MLP neural network structure (Eastman, 2012)

Nodes (or neurons) contained by each layer are connected to each other with unequal connecting weights. The hidden layer nodes are significant for MLP to learn and utilize

interaction effects (Eastman, 2012; Paul, 2013).

For running transition sub-models, the training process used samples taken from pixels that went through the transition being modeled, or pixels from persistence classes. 50% of the samples were used for training and the remaining 50% were used for validation of the transition potential model. MLP in LCM has been launched with an automatic training mode, which is capable of monitoring and modifying the start and end learning rates by using the sample training data (Eastman, 2012). All the parameters were assigned default values, except the number of the hidden layer nodes. At the first time of running a sub-model, the default hidden layer nodes value was used, then more running tests were performed. If the total accuracy and skill score increase then the value will be doubled, otherwise, the last value is selected. Running

MLP provided the report with the aggregate accuracy and the skill measure score. The following formula is the measure of model skill (Eastman, 2012):

S = (A – E(A)) / (1 – E(A)) (2)

where,

E(A) = excepted accuracy,

A = measured accuracy.

Moreover, the expected accuracy (E(A)) was expressed as:

E(A) = 1 / (T + P) (3)

where,

T = the number of transitions in the sub-model,

P = the number of persistence classes in the sub-model.

Each sub-model was modeled with different driver variable groups. For the purpose of getting higher modeling accuracy and skill score, it is necessary to remove the variables without power. The backward stepwise analysis has been applied for model development

(Eastman, 2012). When running a sub-model with all the variables listed in table 3.3, each variable was held constant one after another to figure out the one with the least effect on modeling. If the model skill of holding a variable constant was similar with the skill of modeling with all variables, the variable doesn’t affect the model much and it can be removed.

Using the transition from ‘Planted/Regrowth Forest’ to ‘Mixed Forest’ as an example, Tables

3.4, 3.5 and 3.6 are the sensitivity results of this transition potential sub-model by forcing independent variables to be constant.

Table 3.3. General model information

Var. 1 DEM Var. 2 Distance to major channel Var. 3 Hazard rating of mountain pine beetle infestation Var. 4 Distance to recommended permanent roads Var. 5 TWI Input layer neurons 5 Hidden layer neurons 3 Output layer neurons 2

Table 3.4. Forcing a single independent variable to be constant

Model Accuracy Skill measure Influence order (%) With all variables 85.93 0.7185 N/A Var. 1 constant 63.05 0.261 1 (most influential) Var. 2 constant 81.13 0.6226 3 Var. 3 constant 82.52 0.6504 4 Var. 4 constant 78.27 0.5653 2 Var. 5 constant 85.89 0.7177 5 (least influential)

Table 3.5. Forcing all independent variables except one to be constant

Model Accuracy (%) Skill measure With all variables 85.93 0.7185 All constant but var. 1 71.58 0.4316 All constant but var. 2 61.15 0.223 All constant but var. 3 49.91 -0.0018 All constant but var. 4 52.51 0.0503 All constant but var. 5 49.91 -0.0018

Table 3.6. Backwards stepwise constant forcing

Model Variables included Accuracy (%) Skill measure With all variables All variables 85.93 0.7185 Step 1: var. [5] constant [1,2,3,4] 85.89 0.7177 Step 2: var. [5,3] constant [1,2,4] 82.36 0.6472 Step 3: var. [5,3,2] constant [1,4] 78 0.5599 Step 4: var. [5,3,2,4] constant [1] 71.58 0.4316

In this example, table 3.4 shows that variable 5 is the least influential. Then in table 3.6, after running the first step of the backwards stepwise analysis, it was found that the skill measure doesn’t change much. This means variable 5 which is the TWI layer is powerless for the model, and it could be removed. This method was repeated till there are no such variables.

3.6.3 Change prediction

After generating all transition potential sub-models, they were applied in the Markov chain model by using the tool for LULC change prediction on LCM. The future LULC scenarios have been generated with the prediction date as 2026. In this procedure, the Markov

Chain determined the amount of LULC change using land cover images from the earlier date to later date. It measured how much land is possible to transition from the later date to the prediction date (Eastman, 2012). In the Markov Chain method, different categories were applied as the states of a chain. The value at the previous time t+1, Xt+1, only depends on the value at time t, Xt, and not on the process passing through Xt. It can be expressed as (Weng,

2002):

��+1 = �(��) (4)

If the transition probabilities are tabulated in a transition matrix, P, Xt-1 can be expressed

(Benito, et al., 2010):

��+1 = �� ∙ � (5)

The Markov Chain analysis can create a transition probabilities file, which describes the probability of land cover change from one period to another by developing a transition probability matrix between the earlier date and the later date. (Araya & Cabral, 2010; Houet &

Hubert-Moy, 2006).

Finally, two types of soft predictions were generated based on the present state of transition potentials for each transition. The first one is maximum soft output, which shows a maximum transition probability aggregation for all transition potentials. Then the ‘Logical OR’ type is calculated as (a+b – ab) for all transitions included, which can characterize a location wanted by different transitions. The ‘a’ and ‘b’ represent the transition potential of each sub- model. Along with the comprehensive soft output maps of transition probability, a hard prediction LULC map was also created. Running the Markov Chain process provides the quantity data. Then a multi-objective land allocation algorithm was applied to look through all transitions and generate a list of classes both gain and lose land. While running this allocation procedure, all the changed land of a class was allocated and then overlay to generate the output

(Eastman, 2012).

Figure 3.22 below, shows the flowchart of the LULC change model structure:

LCM Project LCM Project LCM Project Driver T1 (1986-1999) T2 (1999-2016) T3 (1986-2016) Variables

Change Analysis

Transition Sub-models

LULC Net change gain & loss MLP analysis for analysis for T1 and T2 T1 and T2 Transition Potentials

Markov Chain

3 3

of T of Maximum soft soft Maximum Logical OR soft soft OR Logical prediction map of T of map prediction prediction map of T of map prediction map prediction Hard

Figure 3.21. LULC change model framework

CHAPTER 4 RESULTS AND DISCUSSIONS

4.1 Landsat image results and discussions

The Stoney Creek watershed study area is 496.963 km2. In 1986, more than 75% of the area was covered by different types of forest land, including 38.61% coniferous forest, 22.40% mixed forest, 10.92% deciduous forest and 4.08% planted/regrowth forest. The other LULC types include 4.85% agriculture area, 9.08% barren land, 2.00% cut block, 0.41% urban area and 7.65% water (Figures 4.1 and 4.2).

Figure 4.1. LULC classification map of SCW in 1986

Water Urban Area Planted/Regrowth Forest Mixed Forest Deciduous Forest Dead Pine Trees Cut Block Coniferous Forest Barren Land Agriculture Area

0 20 40 60 80 100 120 140 160 180 200 220 AREA (KM2)

Figure 4.2. Area statistics for each LULC class in 1986

Figures 4.3 and 4.4 present the image and graph results from the Landsat image analysis of 1999. As with 1986, forest land comprised the most area of the study watershed, with coniferous forest covering the maximum (30.83% of total area), followed by mixed forest

(22.73%), deciduous forest (14.18%). In 1999, the forest land including planted/regrowth forest (7.10%) occupied almost 75% of the study area. Other LULC classes including agriculture area, barren land, cut block, urban area and water covered 6.11%, 8.24%, 2.38%,

0.64% and 7.78% of the total watershed, respectively.

Figure 4.3. LULC classification map of SCW in 1999

Water Urban Area Planted/Regrowth Forest Mixed Forest Deciduous Forest Dead Pine Trees Cut Block Coniferous Forest Barren Land Agriculture Area

0 20 40 60 80 100 120 140 160 180 AREA (KM2)

Figure 4.4. Area statistics for each class in 1999

Figures 4.5 and 4.6 exhibit image analysis output of 2016. In this period, forest land also covered the majority of the watershed. However, a new LULC class of dead pine trees was added in 2016 that covered 14.96% of the total area. This LULC class was mostly transferred from coniferous forest and slightly from mixed forest. In 2016, the mixed forest occupied 21.43% of the total area, followed by coniferous forest (18.97%), deciduous forest (13.54%), and planted/regrowth forest (5.95%). Although it was affected by dead pine trees, forest land comprised almost 60% of the whole study watershed. The remaining LULC features, including agriculture area, barren land, cut block, urban area and water composed 9.62%, 6.45%, 0.83%,

0.57% and 7.69% of the SCW study area respectively. Table 4.1 shows the area statistic results of LULC categories for all the three study years.

Figure 4.5. LULC classification map of SCW in 2016

Water Urban Area Planted/Regrowth Forest Mixed Forest Deciduous Forest Dead Pine Trees Cut Block Coniferous Forest Barren Land Agriculture Area

0 20 40 60 80 100 120 AREA (KM2)

Figure 4.6. Area statistics for each class in 2016

Table 4.1. Area (km2) statistics of LULC types for each study year

1986 1999 2016 LULC Class Area Percentage Area Percentage Area Percentage Agriculture Area 24.113 4.85% 30.352 6.11% 47.786 9.62% Barren Land 45.108 9.08% 40.962 8.24% 32.072 6.45% Coniferous Forest 191.898 38.61% 153.207 30.83% 94.282 18.97% Cut Block 9.920 2.00% 11.852 2.38% 4.109 0.83% Dead Pine Trees 0.000 0.00% 0.000 0.00% 74.339 14.96% Deciduous Forest 54.290 10.92% 70.465 14.18% 67.306 13.54% Mixed Forest 111.333 22.40% 112.984 22.73% 106.444 21.42% Planted/Regrowth Forest 20.257 4.08% 35.288 7.10% 29.585 5.95% Urban Area 2.037 0.41% 3.188 0.64% 2.835 0.57% Water 38.006 7.65% 38.664 7.78% 38.205 7.69%

4.2 Accuracy assessment results and discussions

Errors of Landsat image classification can result from the lack of detailed information, the application of medium-resolution images and other general influential factors (Paul, 2013).

For a more comprehensive discussion, results of accuracy statistics and error matrix were used to explain and evaluate image classification output, as shown in Tables 4.2 and 4.3. The related

LULC types listed in the tables include Agriculture Area (AA), Barren Land (BL), Coniferous

Forest (CF), Cut Block (CB), Dead Pine Trees (DT), Deciduous Forest (DF), Mixed Forest

(MF), Planted/Regrowth Forest (PT), Urban Area (UA) and Water (WT).

Table 4.2 was generated by comparing the reference data (i.e. the training layer classified by random sampling points) with the classified image data. Cells highlighted with grey shade represent the segments that have been correctly classified, while the others, except blank cells, represent the number of the mis-classified segments

Table 4.2. Error matrix results for 2016 image classification

Reference Data WT CF MF DF PF AA BA UA CB DT Row Totals Classified Data WT 10 10 CF 47 2 49 MF 31 1 1 1 34 DF 4 2 17 23 PF 11 11 AA 1 18 2 21 BL 1 15 16 UA 5 5 CB 8 1 9 DT 1 21 22 Column Totals 10 51 35 19 12 18 18 5 8 24 200

By using the error matrix results, producer’s accuracy, user’s accuracy, overall accuracy,

95% confidence interval and overall Kappa value were calculated, as presented in Table 4.3.

Table 4.3. Accuracy statistics results for 2016 image classification

Overall Accuracy: 91.50% 95% Confidence Interval (87.39% 95.62%) Overall Kappa Statistic: 0.90 Producer's User's Class Accuracy 95% Confidence Accuracy 95% Confidence Kappa Name Interval Interval Statistic （%）（%） AA 100 (97.22% 100.00%) 85.71 (68.37% 100.00%) 0.84 BL 83.33 (63.34% 100.00%) 93.75 (78.76% 100.00%) 0.93 CB 100 (93.75% 100.00%) 88.89 (62.80% 100.00%) 0.88 CF 92.16 (83.80% 100.00%) 95.92 (89.36% 100.00%) 0.95 DF 89.47 (73.04% 100.00%) 73.91 (53.79% 94.03%) 0.71 DT 87.5 (72.19% 100.00%) 95.46 (84.48% 100.00%) 0.95 MF 88.57 (76.60% 100.00%) 91.18 (80.17% 100.00%) 0.89 PF 91.67 (71.86% 100.00%) 100 (95.46% 100.00%) 1 UA 100 (90.00% 100.00%) 100 (90.00% 100.00%) 1 WT 100 (95.00% 100.00%) 100 (95.00% 100.00%) 1 Quantity Disagreement: 4.00% Allocation Disagreement: 4.50%

In this study, the overall accuracy for image classification of 2016 is 91.50%, while the overall Kappa statistic is 0.90. Most of the LULC classes have a larger producer’s accuracy than overall accuracy except the classes of barren land, deciduous forest, dead pine trees and mixed forest. The lower accuracy for these four classes can be attributed to the low Landsat image resolution and the not very comprehensive ground truth data. However, the classification with ≥ 90% overall accuracy is adequate for this study.

4.3 LULC change analysis

Based on the method stated in the previous chapter, the data of land gain and loss as well as the net change for each LULC type have been generated through a change analysis procedure in LCM on IDRISI.

Figures 4.7 and 4.8 represent the land gain and land loss of different LULC types for the period of 1986-1999 (T1) and 1999-2016 (T2), respectively. Land gain results from the transition from other classes to this class, and the opposite transition is land loss. It was observed from Figures 4.7 and 4.8 that the mature forest land (including coniferous forest, deciduous forest and mixed forest) occupied most of the study watershed.

Gains and losses between 1986-1999 60

0 AA BL CF CB DF MF PF UA WT -20

-40

-60

Area Losses (km²) Area Gains (km²)

Figure 4.7. Land gain and loss analysis for period 1986-1999 (T1)

Gains and losses between 1999-2016 100 80 60 40 20 0 -20 AA BL CF CB DF MF PF UA WT DT -40 -60 -80 -100 -120

Area Losses (km²) Area Gains (km²)

Figure 4.8. Land gain and loss analysis for period 1999-2016 (T2)

From 1986 to 1999, coniferous forest experienced a great negative net change (i.e. greater loss than gain) of 38.68 km2, while there is also a negative change for barren land. In contrast, other LULC classes had more gain than loss, especially for deciduous forest and planted/regrowth forest (Figure 4.9). The obvious changes of forest land may indicate the forestry activities applied during this period. From 1999 to 2016, the class of dead pine trees experienced a sharp increase in this area due to the infestation of mountain pine beetles causing coniferous forest to lose 58.92 km2 of area. The other classes except agricultural land, also had negative changes. An increasing trend for agricultural area since 1986 was observed, which may be due to the changing policies and activities for agricultural development (Figure 4.10).

The slight net change of urban area indicates that there was no significant urban expansion and industrial development. The town of Vanderhoof and the Saik'uz First Nation Reservation area remain stable from 1986 to 2016 based on the results.

Net change between 1986-1999 (km²) 20

0 AA BL CF CB DF MF PF UA WT -10

-20

-30

-40

-50

Figure 4.9. Net change analysis of LULC for period 1986-1999 (T1)

Net change between 1999-2016 (km²) 100

0 AA BL CF CB DF MF PF UA WT DT -20

-40

-60

-80

Figure 4.10. Net change analysis of LULC for period 1999-2016 (T2)

Table 4.4. LULC change analysis results for period 1986-1999 (T1) and 1999-2016 (T2)

T1 T2 LULC Area Loss Area Gain Net Change Area Loss Area Gain Net Change Class (km2) (km2) (km2) (km2) (km2) (km2) AA -14.62 20.86 6.24 -10.43 27.88 17.44 BL -33.31 29.15 -4.16 -30.80 21.92 -8.88 CF -52.80 14.13 -38.68 -92.30 33.38 -58.92 CB -9.85 11.78 1.93 -11.81 4.07 -7.74 DF -28.21 44.40 16.19 -54.16 50.97 -3.19 MF -48.69 50.32 1.63 -75.19 68.67 -6.52 PF -3.01 18.05 15.03 -18.86 13.16 -5.70 UA -0.19 1.34 1.15 -1.02 0.67 -0.35 WT -0.82 1.48 0.66 -1.15 0.69 -0.46 DT 0.00 0.00 0.00 0.00 74.33 74.33

4.4 LULC analysis for sub-basins

Discussions on sub-basins level based on the LULC types within individual sub-basins can provide more detailed information to have a better understanding of the LULC patterns and dynamics of the area. It also may provide specific data for the research of certain sub-basin in the future.

4.4.1 Stoney Creek (SC) sub-basin

SC sub-basin is located in the north part of SCW with the largest land area. It covers the major water bodies of Stoney Creek, Tachick Lake and Nulki Lake. It also includes the Saik'uz

First Nation Reservation area and part of the town of Vanderhoof.

Figures 4.11, 4.12 and 4.13 show the LULC maps of the SC sub-basin area for 1986, 1999 and 2016. Based on the maps and graph (Figure 4.14), it is clear that the major LULC type in this area was forest land where mixed forest occupied the largest land area. Coniferous forest decreased sharply from 1986 to 2016, partly due to the infestation of mountain pine beetles, generating the dead pine trees class covering 25.59 km2. Agriculture area increased from 20.24 in 1986 to 38.41 km2 in 2016, indicating the successful agriculture development over 30 years.

The change of deciduous forest in this area was fluctuated, and further research is needed to explain this. The remaining features were mostly unchanged, such as the urban area.

Figure 4.11. SC sub-basin LULC map for 1986

Figure 4.12. SC sub-basin LULC map for 1999

Figure 4.13. SC sub-basin LULC map for 2016

STONRY CREEK LULC CHANGE

1986 1999 2016

80 73.55 66.06 70 65.75 61.81

60 55.18

AREA (KM2) AREA 50 42.59 38.41 37.81 37.45 37.40 37.20 40 35.53 29.11 27.76 26.77 25.96 25.59

30 23.24 20.24 20 3.21 10 3.19 2.83 2.83 2.45 2.04 0.19 0.00 0.00 0.00 0.00 0 AA BL CF CB DT DF MF PF UA WT LULC CLASS

Figure 4.14. LULC change of SC sub-basin

4.4.2 Tachick Creek (TC) sub-basin

The TC sub-basin is located in the west corner of the study area. Streams in this sub-basin flow into Tachick Lake. Figure 4.15 shows LULC change in the RC sub-basin. Figure 4.16 shows the LULC map of 1986, 1999 and 2016.

TACHICK CREEK LULC CHANGE

1986 1999 2016

16 15.01 14 11.08 12 10.94 10.56 AREA (KM2) AREA

10 8.75 8.21 8.18 8 7.63 5.92 5.91

6 5.26 4.29

4 2.87 2.14 1.74 2 1.56 0.00 0.00 0.00 0.00 0.00 0 AA BL CF DT DF MF WT LULC CLASS

Figure 4.15. LULC change of TC sub-basin

The forest land experienced some changes from 1986 to 2016 (Figure 4.15). As with the

SC sub-basin, coniferous forest decreased mainly because of the mountain pine beetle infestation. The transitions from coniferous forest to the other LULC types also contributed to this decrease, especially to the agriculture area. Agriculture area class had a 6.07 km2 growth to 2016, due to agricultural development in this area. There are only a minimum number of small water bodies within TC sub-basin and the area of water class showed as ‘0.00’.

4.4.3 West Nulki Stream (WNS) sub-basin

This sub-basin covered almost half the land of the dead pine trees class with coniferous forest representing the major land type in both 1986 and 1999. However, after suffering from the mountain pine beetle infestation, the land area of coniferous forest class was down to only

3.96 km2 in 2016 (Figure 4.17). The land with dead pine trees is distributed on more than 60% area of this sub-basin, which may cause negative effects on ecological and hydrological aspects.

WEST NULKI STREAM LULC CHANGE

1986 1999 2016

30 28.88 24.79 25 23.49 AREA (KM2) AREA 20

10 6.71 5.87 4.66 3.96 2.80 2.55 2.53 2.41 5 2.31 1.65 1.15 1.01 0.95 0.95 0.70 0.57 0.37 0.36 0.27 0.25 0.25 0.21 0.00 0.00 0 AA BL CF CB DT DF MF PF WT LULC CLASS

Figure 4.17. LULC change of WNS sub-basin

Figures 4.18 exhibits LULC maps of all three years with the WNS sub-basin extent.

4.4.4 South Nulki Stream (SNS) sub-basin

This sub-basin contains the stream which flows north and enters Nulki Lake at the south part of the lake. Approximately 70% of this area was covered by mixed forest in both 1986 and

1999 (Figure 4.19). However, in 2016 most of the mixed forest land had transferred to deciduous forest, which may indicate the forestry activities such as coniferous tree cutting; or coniferous tress killed by mountain pine beetles in this area. Deciduous forest reached 8.57 km2 in 2016, while there was a slight increasing trend for coniferous forest.

SOUTH NULKI STREAM LULC CHANGE

1986 1999 2016

16 13.76

14 12.70 12

AREA (KM2) AREA 10 8.57 8

6 5.61 4 2.54 2.52 2.26 2.09 1.65 1.63 1.19 1.10 1.05 2 1.05 0.62 0.47 0.29 0.29 0.26 0.10 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0 AA BL CF CB DT DF MF PF WT LULC CLASS

Figure 4.19. LULC change of SNS sub-basin

Figures 4.20 shows the LULC maps for the SNS sub-basin; the transition from mixed forest to deciduous forest can be seen in the maps for 1999 and 2016.

4.4.5 Corkscrew Creek (CC) sub-basin

The CC sub-basin is in the south part of the study watershed. The stream within it flows from the farthest south of SCW to the north and enters Nulki Lake. Coniferous forest is the major LULC type, but it had a decreasing trend since 1986. Some of the coniferous land had been cut or transferred to built-up areas and roads (Figures 4.21 and 4.22). This may indicate the development of the forest industry during that time. Moreover, the coniferous forest had decreased in 2016 because of the extent of dead pine trees. Most of the cut block area transferred to planted or regrowth forest in 1999 and part of the forest had grown into mature forest later in 2016. Therefore, there was a slight decrease for planted/regrowth forest, while the mixed forest and deciduous forest increased.

CORKSCREW CREEK LULC CHANGE

1986 1999 2016

50 46.87 45 40.21 40 35 AREA (KM2) AREA 29.40 30 25 20 15.48

15 11.63 9.93 9.73 9.67 8.82

10 6.18 4.46 4.33 3.70 3.66 2.95 1.42 1.39 1.21

5 0.88 0.50 0.34 0.09 0.07 0.07 0.00 0.00 0.00 0 AA BL CF CB DT DF MF PF WT LULC CLASS

Figure 4.21. LULC change of CC sub-basin

4.4.6 Mid Cork Stream (MCS) sub-basin

The MCS sub-basin is surrounded by the CC sub-basin and WCS sub-basin. The sub- basin area had most forestry activities for all three years. Cut block increased from 1986 to

1999. In 2016, most of the cut block area transferred to planted/regrowth forest, while some new cut block appeared. Similar to other sub-basins, coniferous forest had decreased sharply because of the transitions to other LULC classes such as dead pine trees, cut block and barren land. The cut block experienced a decrease from 1999 to 2016 as part of it had been planted or regrown with young trees (Figures 4.23 and 4.24).

MID CORK STREAM LULC CHANGE

1986 1999 2016

25 20.52 20 16.46 AREA (KM2) AREA 15 9.92 8.62 10 8.33 7.42 6.61

5 3.40 2.71 2.18 1.63 1.53 1.18 0.64 0.39 0.39 0.38 0.22 0.16 0.11 0.10 0.01 0.01 0.00 0.00 0.00 0.00 0 AA BL CF CB DT DF MF PF WT LULC CLASS

Figure 4.23. LULC change of MCS sub-basin

4.4.7 West Cork Stream (WCS) sub-basin

The WCS sub-basin is located in the south-western part of the SCW study area. Its stream flows south and reaches the stream of the CC sub-basin. Coniferous forest occupied the maximum land area in this sub-basin. As it is close to the MCS sub-basin, similar effects occurred from 1986 to 2016 within this area, e.g. the coniferous forest had a decrease trend while the area of cut block, barren land and dead pine trees increased. The transition from cut block to planted/regrowth forest occurred after 1999, and then part of this young forest grew and transferred into other mature forest land (Figures 4.25 and 4.26).

WEST CORK STREAM LULC CHANGE

1986 1999 2016

35 30.06 30 25.69 25 AREA (KM2) AREA

20 17.22

10 7.18 5.68 4.85 4.59 3.22 5 3.16 1.64 1.19 1.18 1.18 1.09 0.98 0.90 0.81 0.36 0.13 0.08 0.07 0.00 0.00 0.00 0 BL CF CB DT DF MF PF WT LULC CLASS

Figure 4.25. LULC change of WCS sub-basin

In summary, changes such as the decrease of coniferous forest and the increase of dead pine trees from 1986 to 2016 are similar for the sub-basins because they were all affected by the mountain pine beetle infestation before 2016. The Stoney Creek sub-basin and Tachick

Creek sub-basin which located in the north region of the SCW have rough terrain so agricultural activities remain unchanged; however, agricultural policies such as reservation areas were made during the period so agricultural areas increased from 1986 to 2016. Urban areas did not have very clear boundaries as vegetation grows in and around the area so errors may happen when separating urban areas from forest lands. The slight decrease of urban area from 1999 to

2016 may have resulted from this.

4.5 Land change modeling results and discussions

By using Land Change Modeler (LCM) in IDRISI software, LULC change analysis between 1986 and 2016 was performed. The results of transition sub-models, Markov Chain matrix, hard prediction and soft predictions were generated.

4.5.1 Results of transition sub-models

Based on the method mentioned in the previous chapter, effective driver variable groups for each individual transition sub-model were selected based on the reports which generated from running sub-models. This was accomplished by utilizing the MLP neural network on

LCM, as mentioned above. Every driver variable that had been selected for modeling process should be positive to influence the overall accuracy and skill measure. Otherwise, the drivers with no power were excluded from the group.

Running sub-models with ideal driver variable groups generated transition potential maps for individual transitions. Using the transition from coniferous forest to dead pine trees as an example, Figure 4.27 shows the change probability from 1986 to 2016 within the SCW study extent.

Figure 4.27. Transition potential map of CF to DT transition

The map shows that most areas with more than 0.55 transition probabilities are distributed in the middle part of the study area. In the 2016 image, the west part of the study watershed was mostly covered by dead pine trees. Therefore, the area of dead pine trees may expand from the west to east. The areas near dead pine trees are more likely to be affected by mountain pine beetle infestation. Moreover, the more the position moves south, the less potential for the transition.

4.5.2 Results of Markov Chain model

Markov Chain model determined the amount of LULC change using land cover images from the earlier date to later date, and two matrixes were generated. Table 4.5 shows the probability of LULC changing and persistence and Table 4.6 shows how much land is possible to transition from the later date to the prediction date. Blank cells in both tables represent 0 value, and cells with gray shade means probability or pixel numbers of persistent area for each class.

In Table 4.5, classes of urban area and water have very high probability with 0.917 and

0.991 respectively, meaning most of these areas can be persistent in 2026. Planted/regrowth forest (0.765) and agriculture area (0.740) have high probability to maintain more than 70% of their previous extent till the final period. The table tells that planted/regrowth forest is mostly possible to transfer to mixed forest with 0.103. Besides the high probability of agriculture area to maintain its area, barren land has 0.203 and deciduous forest has 0.167 probabilities to transfer to agriculture area. These all represent the suitable agricultural management in this area.

Around 50% of cut block will change to other LULC types in 2026. This can happen because cut block area has been planted or regrowth to become other forest lands after decade.

Based on the probability matrix, barren land, deciduous forest and mixed forest have about 0.3

- 0.4 probabilities to maintain their extent. The probability is only 0.408 for mixed forest (Table

4.6), as it has the maximum pixels (areas) transferred from other classes through the modeling period.

Coniferous forest has no probability to maintain its extent, as it will change mainly to mixed forest and planted/regrowth forest with 0.433 and 0.446 probabilities respectively.

However, further research and more information about mountain pine beetle infestation areas are needed to explain the transitions from coniferous forest to others. The 0.121 probability to change to dead pine trees is also important because it is the most influential aspect for coniferous forest decrease in 2016.

As mentioned before, there were no dead pine trees LULC type in the earlier date (1986).

Therefore, the LULC change determined by Markov Chain model by using the earlier date image and later date image is not available for change prediction. Because of the lack of dead pine trees data in the earlier image, the Markov Chain model generated the transition probability value and the transition areas to other classes with equally divided values, which are useless for prediction analysis. Therefore, the rows for dead pine trees in both tables are filled as not available (N/A). Dead pine trees will not only remain its previous extent in the final stage (2026), but also gain land which results from other transitions.

Table 4.5. Transition probability matrix from 2016 to 2026

Probability of changing to Given AA BL CB CF DF MF PF UA WT DT Class AA 0.740 0.178 0.056 0.027 BL 0.203 0.399 0.002 0.088 0.232 0.015 0.003 0.001 0.058 CB 0.013 0.543 0.014 0.044 0.057 0.024 0.001 0.306 CF 0.000 0.433 0.446 0.121 DF 0.167 0.055 0.011 0.303 0.459 0.004 MF 0.023 0.019 0.247 0.293 0.408 0.006 0.002 0.002 PF 0.011 0.053 0.030 0.012 0.103 0.765 0.027 UA 0.000 0.083 0.000 0.917 WT 0.002 0.002 0.002 0.004 0.000 0.991 DT NA N/A N/A N/A N/A N/A N/A N/A N/A N/A

Table 4.6. Transition areas matrix from 2016 to 2026

Expected to transition to Given AA BL CB CF DF MF PF UA WT DT Class AA 353658 84949 26531 12859 BL 65008 127963 706 28108 74346 4941 931 353 18514 CB 12067 511509 13104 41007 53545 23002 566 287995 CF 0 17784 18322 4984 DF 112539 37289 7539 203944 308810 2894 MF 24277 20018 262893 312085 434428 6282 2236 2555 PF 3225 15740 8787 3610 30326 226309 7870 UA 9 2365 26091 WT 611 573 649 1414 38 378724 DT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

4.5.3 Results of soft prediction and hard prediction

By running a multi-objective land allocation algorithm procedure, all the changed land of a class was allocated and overlain, then a hard prediction map was generated for 2026 (Figure

4.28).

Figure 4.28. LULC hard prediction map for 2026

Table 4.7 and Figure 4.29 list the area statistics for both stages (2016 and 2026). Dead pine trees will gain areas from other classes, but it is not sure if it will face an increasing trend because of the lack of data for hard prediction. Based on the comparison between 2016 and

2026, agricultural area shows an increasing trend in the next decade which refers to a stable and planned agricultural development in this area. Forest land will experience a slight decrease according to the hard prediction data, while the remaining classes have no significant change.

Table 4.7. Area statistics of 2016 and 2026

2016 2026 LULC Class Area (km2) Percentage Area (km2) Percentage Agriculture Area 47.786 9.62% 59.492 11.97% Barren Land 32.072 6.45% 30.773 6.19% Coniferous Forest 94.282 18.97% 77.487 15.59% Cut Block 4.109 0.83% 1.809 0.36% Deciduous Forest 67.306 13.54% 59.565 11.98% Mixed Forest 106.444 21.42% 93.031 18.72% Planted/Regrowth Forest 29.585 5.95% 30.687 6.17% Urban Area 2.835 0.57% 2.846 0.57% Water 38.205 7.69% 38.205 7.69% Dead Pine Trees 74.339 14.96% 103.129 20.75%

) 120 2

100 Area (km Area 80

0 AA BL CF CB DF MF PT UA WT DT LULC Class

2016 2026

Figure 4.29. Area comparison between 2016 and 2026

Similar to the hard prediction, soft prediction is also short of earlier data of dead pine trees class. There was no sub-model for the transition from dead pine trees to other LULC types.

Therefore, in the soft prediction map, the area of dead pine trees in 2016 will be persistent and impossible to change. Following are two types of soft prediction maps. Figure 4.30 shows the logical OR soft prediction map, which is calculated as (a+b – ab) for all transitions. This kind of soft prediction can describe areas with multiple transitions. The other is maximum soft prediction that represents a maximum transition probability aggregation for all transition potentials (Figure 4.31).

Figure 4.30. Logical OR soft prediction map for 2026

Figure 4.31. Maximum soft prediction map for 2026

Referring to both soft prediction maps, the middle and north region of the study area have the highest potential to change. These transitions may be attributed to the agricultural development and forestry activities in this area. In the central north area surrounding two lakes, the transition probability is variable. Further south, the potential shows a decreasing trend.

However, the planted/regrowth forest extent has low transition potential, which means a high probability to be persistent. In both soft prediction maps, water and urban area show no probability to change.

CHAPTER 5 CONCLUSION

5.1 Summary of the study

This study generated the LULC change analysis and prediction in SCW by using remote sensing, GIS and modeling methods. LULC maps with composite bands and classified maps were generated and analyzed to provide a better understanding of LULC patterns and dynamics.

Transition potentials were then modeled as sub-models, and several LULC scenarios were finally predicted.

Several periods of field survey were made for collecting overall information and ground truth data in the beginning of the study. Landsat image data were collected for July – August,

1986, 1999 and 2016 and clipped to the SCW extent. The OBIA technique was utilized to classify pre-processed LULC maps and resulted in an overall classification accuracy at 91.50%.

These three maps were then applied in LULC change analysis for two study periods, from

1986 to 1999 and from 1999 to 2016. Forest lands had been the major land cover within the watershed from 1986 to 2016, but they had a decreasing trend especially for coniferous forest.

The LULC class of coniferous forest experienced a sharp land loss because of the infestation of mountain pine beetles, which led to the transition from coniferous forest to dead pine trees in map 2016. Agriculture area had a stable increasing trend (23.68 km2 land gain) for both study periods, which may refer to the successful agricultural development activities in this watershed.

Moreover, more detailed change analysis was performed at sub-basin level, as LULC patterns and dynamics vary from one sub-basin to another. It has been found that the dead pine trees

were mainly distributed in the south-western area and expanded from west to east over time.

Based on the LULC change analysis data from 1986 to 2016, transition potentials were modeled by MLP neural network. Each transition sub-model was performed with a group of suitable driver variables, which can improve the accuracy and skill measure. Markov Chain model was utilized to generate future transition matrixes of 2026 by using the data resulted for

LULC change analysis from the earlier date to later date. Matrixes data were later applied for hard prediction result generation, which represents a predicted LULC map. Transition potentials were computed together and transferred into soft prediction results, which show the transition probabilities calculated by Logic OR and Maximum methods. Based on the results of soft prediction, forestry lands with higher elevations had lower transition probabilities in the future. Areas with the highest potential to change were mainly distributed in the middle and north part of watershed, which may refer to agricultural and forestry activities in these areas.

To summarize, this thesis study utilized one of the most widely applied geospatial analytical method (combined RS and GIS approach) with a more recently developed image classification method (OOIC) to generate final maps. It also performed a powerful modeling approach (Multi-layer perceptron and Markov Chain model) for fine LULC scenarios. Results of this study can provide accessible information for planners and researchers from LULC or other fields, which can be supportive for both environmental and economic development in this area.

5.2 Limitations and recommendations

There are several limitations for this study:

a. Free-accessed Landsat images with 30-meter spatial resolutions is impossible to classify

some features such as the main channel of Stoney Creek.

b. The lack of comprehensive historical and current ground truth data, due to the limited

accessibility of part of the watershed.

c. Restricted official information about land management for SCW limited the analysis on

LULC potential transitions.

d. The lack of ‘Dead Pine Trees’ class in earlier images meant the Markov Chain modeling

method cannot model the transitions from dead pine trees to other classes. This problem

may cause negative effects on LULC change prediction process.

For the recommendations for further study in the future, firstly, satellite images and aerial photographs with higher resolution or larger study extent may result in more ideal LULC maps and more detailed study. Secondly, if more comprehensive ground truth data could be applied, more accurate and strict classification results will be created. The third recommendation is to collect more satellite images during the period when mountain pine beetle infestation is active, so that a better LULC change modeling can be performed. Moreover, it is useful for a further study on when and how mountain pine beetle infestation expended in this area. Finally, more detailed climate change studies such as climate modeling and the relationship between LULC changes and climate changes are meaningful for SCW or larger regions.

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