Evaluating the Utility of Remote Sensing Time Series Analysis for the Identification of Grassland Conversions in Alberta, Canada

Jacob Mardian

A Thesis presented to The University of Guelph

In partial fulfillment of requirements for the degree of Master of Science in Geography

Guelph, Ontario, Canada

EVALUATING THE UTILITY OF REMOTE SENSING TIME SERIES ANALYSIS FOR THE IDENTIFICATION OF GRASSLAND CONVERSIONS IN ALBERTA, CANADA Jacob Aaron Mardian Advisor: Dr. Aaron Berg University of Guelph, 2020 Committee Member: Dr. Bahram Daneshfar

Grasslands are an important source of ecosystem services and play a critical role in climate regulation. However, this biome is threatened by agricultural expansion and intensification. Remote sensing offers a unique opportunity to monitor these grassland to cropland conversions through the collection of data at various spatial and temporal scales.

This research examines the applicability of remote sensing time series analysis as a tool for identifying grassland to cropland conversions in Alberta, Canada. The Breaks for

Additive Seasonal and Trend (BFAST) Seasonal method was the most effective model tested, identifying the correct year of change for 76% of rangeland to cropland conversions and 66% of pasture to cropland conversions. However, the results were strongly dependent on model parameterizations, the datasets used, and the subsequent crop planted. Overall, this research provides a new data-driven approach for identifying grassland to cropland conversions and can be used to improve monitoring of Canada’s grassland resources.

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ACKNOWLEDGEMENTS

I would like to thank Agriculture and Agri-Food Canada, NSERC, the University of

Guelph and the Canada First Research Excellence Fund: Food from Thought Initiative for providing funding for the research completed in my thesis. I would like to express my gratitude to my advisor, Dr. Aaron Berg, and committee member, Dr. Bahram Daneshfar, for their invaluable guidance, support and expertise over the last two years. I also appreciate the staff, faculty and students within the Department of Geography, Environment and Geomatics at the

University of Guelph who have provided assistance and encouragement throughout this process.

Finally, thank you to my friends, family and partner Emma for your continual support.

TABLE OF CONTENTS ABSTRACT ...... i ACKNOWLEDGEMENTS ...... iii LIST OF ACRONYMS ...... vi LIST OF FIGURES ...... vii LIST OF TABLES ...... viii Chapter 1.0: Introduction ...... 1 1.1 Background ...... 1 1.2 Research Aim and Objectives ...... 3 1.3 Thesis Outline ...... 3 Chapter 2.0: Literature Review ...... 5 2.1 Introduction ...... 5 2.2 Overview of Grasslands ...... 5 2.2.1 Significance of Grasslands ...... 6 2.3 Grassland Monitoring ...... 7 2.4 Remote Sensing Platforms for Grassland Monitoring ...... 8 2.4.1 Multispectral Sensors ...... 8 2.4.2 Radar Sensors ...... 10 2.4.3 Multisource Data and Image Fusion ...... 12 2.5 Vegetation Phenology and Spectral Band Indices ...... 14 2.6 Time Series Smoothing ...... 16 2.7 Change Detection ...... 18 2.7.1 Threshold-Based Change Detection ...... 18 2.7.2 Harmonic Analysis ...... 19 2.7.3 Structural Break Methods ...... 20 2.7.3.1 BFAST ...... 21 2.7.3.2 BFAST Monitor ...... 22 2.7.3.3 BEAST ...... 23 Chapter 3.0: Structural Breaks in Earth Observation Time Series Data for Grassland Change Detection in Alberta, Canada ...... 24 Abstract ...... 24 3.1 Introduction ...... 25 3.2 Methodology ...... 30

3.2.1 Study Areas ...... 30 3.2.1.1 Grassland Conversion Polygons ...... 32 3.2.2 Image Acquisition and Pre-Processing ...... 34 3.2.2.1 MODIS ...... 34 3.2.2.2 Landsat ...... 34 3.2.3 Time Series Smoothing ...... 35 3.2.4 Change Detection ...... 38 3.2.4.1 BFAST ...... 38 3.2.4.2 BEAST ...... 40 3.2.4.3 Parameter Tuning ...... 41 3.2.4.4 Influence of Data Source and Lambda ...... 42 3.2.5 Accuracy Assessment ...... 43 3.3 Results and Discussion ...... 43 3.3.1 Method Comparison ...... 44 3.3.2 BFAST Parameterization ...... 45 3.3.2.1 BFAST Seasonal ...... 45 3.3.2.2 Detrending ...... 47 3.3.2.3 Empirical Fluctuation Process ...... 48 3.3.2.4 Harmonic Order ...... 49 3.3.3 Data Source Comparison and Lambda ...... 51 3.3.4 Change Detection Timing ...... 53 3.3.5 Effect of Crop Type on Change Detection ...... 55 3.3.6 Limitations ...... 56 3.4 Conclusions ...... 56 Chapter 4.0: Conclusions ...... 58 References ...... 60

LIST OF ACRONYMS

AAFC – Agriculture and Agri-Food Canada

ACI – Annual Crop Inventory

BEAST – Bayesian Estimator of Abrupt change, Seasonal change and Trend

BFAST – Breaks For Additive Seasonal and Trend

EFP – Empirical Fluctuation Process

EVI – Enhanced Vegetation Index

LCLUC – Land Cover and Land Use Change

MODIS – Moderate Resolution Imaging Spectrometer

MSAVI – Modified Soil Adjusted Vegetation Index

NDVI – Normalized Difference Vegetation Index

SAVI – Soil Adjusted Vegetation Index

SBCPD – Structural Break Changepoint Detection

SOC – Soil Organic Carbon

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LIST OF FIGURES

Figure 2.1: Estimated time series from different smoothing techniques using discrete observations (Cai et al., 2017). Smoothing methods shown are Savitzky-Golay (SG), Locally Weighted Regression Scatterplot Smoothing (LO), Spline (SP), Asymmetric Gaussian (AG) and Double Logistic (DL)...... 17

Figure 2.2: BFAST Monitor deforestation monitoring output (DeVries et al., 2015). The black dots represent observed NDVI values, the blue line marks the fitted model, the black line indicates the end of the stable historical period and beginning of the monitoring period, and the red line denotes a breakpoint...... 23

Figure 3.1: Average MODIS NDVI seasonality of pastures, rangelands and wheat-canola rotation in the Vermilion Upland Ecodistrict, Alberta, Canada from 2010-2018...... 28

Figure 3.2: Pasture conversions identified by reference data in the Vermilion Upland Ecodistrict, Alberta, Canada from 2010-2018...... 30

Figure 3.3: Rangeland conversions identified by reference data in a subsection of the Moist Mixed Grasslands Ecodistrict, Alberta, Canada from 2010-2018...... 31

Figure 3.4: Histogram of field sizes for rangeland and pasture conversions in hectares. The red bars represent outliers, defined as fields larger than 140 hectares...... 33

Figure 3.5: The effect of the Whittaker Smoother with different values of lambda...... 36

Figure 3.6: Output from BFAST (left) and BFAST Seasonal (right) for a single pasture conversion pixel. The black dashed line shows the predicted timing of the conversion. The actual change occurred in 2014...... 46

Figure 3.7: Harmonic order comparison for a single MODIS pixel...... 50

Figure 3.8: Barplot of rangeland to cropland conversion pixels with successful change detection by month...... 54

Figure 3.9: Barplot of pasture to cropland conversion pixels with successful change detection by month...... 54

Figure 3.10: Stacked barplots of change detection accuracy by crop type. a) Results of the pasture model. b) Results of the rangeland model...... 55

Figure 3.11: Overlapping growth calendar of grasslands and winter wheat during spring (Bargiel, 2017). Note there is similar overlap between grasslands and spring wheat as spring wheat is also in its early phenological stages at this time of year...... 56

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LIST OF TABLES

Table 3.1: BFAST and BEAST Grassland to Cropland Conversion Accuracy using MODIS NDVI Data...... 44

Table 3.2: BFAST Seasonal Grassland to Cropland Conversion Accuracy using MODIS NDVI data...... 45

Table 3.3: Grassland conversion accuracy of BFAST Seasonal with and without detrending using MODIS NDVI data...... 47

Table 3.4: Grassland change detection accuracy of BFAST Seasonal with different EFPs using MODIS NDVI data...... 48

Table 3.5: Grassland change detection accuracy of BFAST Seasonal with different harmonic orders using MODIS NDVI data...... 49

Table 3.6: Pasture change detection accuracy using BFAST Seasonal and BEAST with different data sources. Lambda value that maximized the accuracy for each dataset is shown in parentheses...... 51

Table 3.7: Rangeland change detection accuracy using BFAST Seasonal and BEAST with different data sources. Lambda value that maximized the accuracy for each dataset is shown in parentheses...... 52

Chapter 1.0: Introduction

1.1 Background

The world is entering a new geological epoch called the Anthropocene, marked by the dominant and profound effect of human activity on the environment (Lewis & Maslin, 2015). A major challenge in the Anthropocene is addressing major global environmental crises such as human induced climate change (IPCC, 2013), the Earth’s sixth mass extinction event (Dirzo,

Young, Galetti, Isaac, & Collen, 2005) and the degradation of ecosystem services (Millenium

Ecosystem Assessment, 2005), while also providing enough food, fiber and fuel for a growing global population amidst scarce land resources.

Grasslands, including rangelands and pastures, are valuable land resources that produce cheap livestock feed, fiber and fuel while mitigating against climate change through carbon sequestration, supporting plant and animal biodiversity, and providing a myriad of other ecosystem services such as nutrient cycling and water filtration (Havstad et al., 2007). However, this biome is undervalued due to the higher earning potential of developing the land for crop production, leading to drastic losses of grasslands worldwide. In the United States, the estimated grassland to cropland conversion rate of 1-5% annually rivals deforestation rates seen in Brazil,

Malaysia and Indonesia (Gage, Olimb, & Nelson, 2016; Wright & Wimberly, 2013). This has serious environmental implications as grassland to cropland conversions not only result in losses of the aforementioned ecosystem services provided by grasslands, but an introduction of environmental harm from intensive agriculture including increased soil erosion, reduced soil fertility and carbon sequestration capacity (Bailey, McCartney, & Schellenberg, 2010). However, this conversion rate is not well understood in Canada and requires further attention, as grasslands

in the Canadian Prairies provide one of the world’s most important carbon pools and are necessary to sustainably meet the growing demand for meat and dairy products.

Satellite remote sensing is an effective tool for monitoring grassland to cropland land cover changes, as global imagery is frequently provided at the field scale and acts as a proxy for biophysical processes on the land surface. For example, multispectral imagery captures localized responses of vegetation to changes in soil, climate, and management practices. Given that each vegetation species has a unique spectral response, these images can be used to classify different types of vegetation and identify changes over time. Conventional remote sensing change detection methods use information from a single image to characterize the land surface for an entire growing season and identifies changes where corresponding pixels in the images differ.

While this approach can be effective with modern computing and sophisticated machine learning algorithms, it has several limitations. Certain land covers may have similar spectral signatures that can only be differentiated at a specific time of year, requiring a priori knowledge to select the optimal images (Atzberger, 2013). Additionally, the accuracy of the change maps is dependent on the accuracy of each individual image classification, as errors accumulate with each image (Yuan, Sawaya, Loeffelholz, & Bauer, 2005). This results in a negative relationship between the number of images utilized and the overall accuracy, when increased information should improve the overall accuracy.

Time series analysis methods monitor the spectral response to changes in the biophysical environment within the growing season and between growing seasons, and have shown to outperform these conventional methods in discriminating crop types (Belgiu & Csillik, 2018) and grassland types (McInnes, Smith, & McDermid, 2015). The latter is true because grasslands respond to disturbances and changes in weather quickly compared to other vegetation, making

high resolution time series monitoring of particular importance for grasslands (Zhou et al., 2019).

Using this approach, analysts are able to perform statistical analysis on a pixel-by-pixel basis to identify changes in vegetation through the identification of long-term trends, abrupt changes in reflectance, and changes in seasonality that correspond to the timing of key phenological events such as the start of season, maturity, peak biomass, senescence and dormancy (Zhang et al.,

2003), as well as management practices such as tilling, mowing and harvesting (Griffiths,

Nendel, Pickert, & Hostert, 2020).

1.2 Research Aim and Objectives

The research aim is to develop remote sensing time series methodologies to detect where and when grasslands have converted to croplands in Alberta from 2011 to 2017. This will be achieved through the following research objectives:

1) Assess the effectiveness of time series algorithms in identifying grassland to cropland

conversions

a. Determine which models and parameterizations are most effective

2) Evaluate the effectiveness of different input datasets

a. Determine the spatial resolution sufficient for change detection (e.g. MODIS

or Landsat)

b. Determine which spectral band indices perform the best

3) Compare the results for both pasture-cropland conversions and rangeland-cropland

conversions

1.3 Thesis Outline

Chapter 2.0 reviews the current literature on the importance of grasslands and remote sensing time series techniques for grassland change detection. Chapter 3.0 is a manuscript

describing the problem domain, methods, results and discussion of the research implications.

Chapter 4.0 broadly contextualizes the research implications and offers recommendations for further research.

Chapter 2.0: Literature Review

2.1 Introduction

The aim of this chapter is to summarize the current scientific literature on remote sensing methods for grassland monitoring and change detection. The review first highlights the significance of grasslands to the environment followed by a brief overview of satellite remote sensing for grassland monitoring. Next, satellite images sources and band indices are investigated in the context for grassland monitoring, followed by an examination of time series smoothing methods. Finally, time series change detection methodologies for identifying land cover and land use changes (LCLUCs) are discussed.

2.2 Overview of Grasslands

Modern grassland ecosystems in Canada are diverse, ranging from native and semi-native to non-native vegetation composition with a variety of management practices and land use intensities. For the purposes of clarification, three different categories of grasslands are recognized in this paper (altered from Ali et al., 2016). First, seeded forage is defined as non- native grasslands that are intensively managed like croplands to maximize production through activities such as seeding, irrigation and fertilizer application. Examples include alfalfa and clover grown individually or as mixtures to produce fodder for livestock. Second, pastures are defined as natural grasslands that are modified and managed to support intensive grazing and/or periodic cultivation. These are often referred to as semi-native grasslands and improved pastures.

Third, rangelands are defined as grasslands generally composed of native grasses and shrubs with the purpose of livestock grazing, although they may also provide feed for wild herbivores.

These grasslands are considered unmanaged, with the exception of controlling the number of

grazers and length of the grazing season. They are also commonly referred to as natural grasslands.

2.2.1 Significance of Grasslands

Grasslands contribute to the livelihoods of over one billion people through the production of food, fiber and fuel (Beer et al., 2010; Steinfeld, 2006), but are often undervalued due to the higher earning potential of improving the land for crop production. However, the value of grasslands cannot be determined by their market value alone as people rely on their ecosystem services including air and water regulation, nutrient cycling, biodiversity, soil erosion prevention, waste treatment, pollination and recreational space. In fact, the value of ecosystem services and natural capital in grasslands has been valued at US$906 billion annually, or over

US$1.4 trillion in 2019 after adjusting for inflation (Costanza et al., 1997). This number has the potential to be much higher as many of the world’s grasslands are mismanaged and overgrazed, reducing the ability of the land to provide these ecological services (Petz et al., 2014). More importantly, the aforementioned figure excludes the value of carbon sequestration.

Grasslands represent about 30% of the world’s ice-free land area (McDermot & Elavarthi,

2014) and an equivalent amount of terrestrial carbon stores, almost equal to the carbon mass found in the entire atmospheric reservoir (Ciais et al., 2013; Lal, 2004; White, Murray, &

Rohweder, 2000). In addition, grazed grasslands contain higher soil carbon than the equivalent non-grazed lands (Schuman, Janzen, & Herrick, 2002). Consequently, they are an important link in the global carbon cycle with a substantial capacity to mitigate climate change. In fact, improving management practices on grasslands can increase carbon sequestration enough to offset one-third of the expected increase in carbon emissions over the next 50 years (McDermot

& Elavarthi, 2014), as the current widespread overgrazing, mismanagement and degradation reduces their sequestration capacity (Petz et al., 2014; Yusuf, Treydte, & Sauerborn, 2015).

While terrestrial ecosystems are a net carbon sink, land cover changes accounted for about

50% of the amount carbon emitted by fossil fuels and cement production combined between

1850 and 1995 (Lal, 2002), and are currently responsible for 10% of global carbon emissions as deforestation, urbanization and agricultural expansion replaces natural ecosystems (Ciais et al.,

2013). Almost 80% of all these emissions are attributable to croplands (Houghton & Nassikas,

2017), as the majority of soil organic carbon (SOC) is lost after conversion from natural to agricultural ecosystems (Lal, 2002; Reid et al., 2004). For example, the Canadian Prairies originally contained 61 million hectares of grasslands, but has since dwindled to just 20% of its original extent and become highly fragmented to accommodate crop production (Bailey et al.,

2010). This has depleted the SOC pool and reduced the national carbon sequestration capacity

(X. Wang, Vandenbygaart, & McConkey, 2014).

Therefore, these grassland resources are critical links in the global carbon cycle due to their carbon storage and sequestration capacities. They should be preserved, restored and effectively managed to mitigate against climate change.

2.3 Grassland Monitoring

Gathering in-situ land cover and land use data on a large scale is expensive and timely, requiring extensive travel, equipment and expertise. This method of data collection is limited in geographical extent as it must be sampled at discrete locations and requires repeated visits as agriculture land cover is constantly changing. Yet, understanding these spatial and temporal patterns is critical to grassland monitoring.

As a result, remote sensing is an effective monitoring tool by frequently gathering information on a large geographic scale. The data is more accessible than ever, with an increasing number and diversity of earth observation satellites. In particular, open access to the

Landsat, MODIS and Sentinel archives provide users with dense temporal datasets, enabling research into multitemporal image analysis to overcome the limitations of conventional remote sensing methods. For example, state-of-the-art techniques are being developed in time series analysis for grassland monitoring (Griffiths et al., 2020; Jones et al., 2018) and the identification of agricultural LCLUCs (Y. Chen et al., 2018; Zhong, Hu, & Zhou, 2019). The potential to upscale these data-driven workflows of high-resolution satellite datasets is critical for accurate regional, national or global scale characterization of the agricultural sector. Sections 2.4 to 2.6 will outline remote sensing techniques for grassland LCLUC identification.

2.4 Remote Sensing Platforms for Grassland Monitoring

There are many different sensors that can be used for satellite vegetation monitoring, each with their own strengths and weaknesses. The different types of sensors and their characteristics will be discussed in the context of agricultural remote sensing.

2.4.1 Multispectral Sensors

Multispectral sensors are commonly used for agricultural monitoring, as the marked difference in reflectance between the RGB and infrared wavelengths in green vegetation can be leveraged to estimate biomass (Powell et al., 2010) and Leaf Area Index (Liu, Pattey, & Jégo,

2012; Richter, Hank, Vuolo, Mauser, & D’Urso, 2012), differentiate crops (Kussul et al., 2016) and detect land cover changes (Hansen & Loveland, 2012). Landsat is the most studied sensor due to its long historical archive, but its 16-day revisit cycle, further compounded by cloud

contamination, limit its ability to characterize crop phenology and detect changes (Gao, Masek,

Schwaller, & Hall, 2006).

Alternatively, MODIS provides global daily surface reflectance products and cloud free

VI composites with 8-day temporal resolution when combining Aqua and Terra. This allows a more precise estimation of intra-annual dynamics, but the trade-off of lower spatial resolution means that MODIS is better suited for more general vegetation mapping and not for crop or species classification (Shao, Lunetta, Wheeler, Iiames, & Campbell, 2016). For example, the temporal coverage of MODIS is sufficient for differentiating grasslands and croplands, but the spatial resolution is often too coarse to discriminate between grasslands (Müller et al., 2015).

Nevertheless, MODIS-based operational monitoring of grasslands in Alberta is more feasible than most agricultural regions as the landscape is organized into 800 square meter quarter sections and are therefore comprised of several MODIS pixels (Bédard, Crump, & Gaudreau,

2006).

While Landsat and MODIS are the most well-studied and readily available image sources, there are advantages of using other satellites for grassland monitoring. Most notably, more recently launched satellites include spectral bands that may improve grassland monitoring.

For example, the yellow band on Worldview-2 is designed to detect ripening or dying plants, which has potential in monitoring semiarid grasslands with senescent vegetation (Marshall,

Lewis, & Ostendorf, 2012). The red-edge band has also shown to be effective in grassland monitoring as its high sensitivity to grass chlorophyll content improves differentiation of grassland types (Ali, Cawkwell, Dwyer, Barrett, & Green, 2016). This band is now included in most new multispectral launches, including Worldview-2 and Sentinel-2. The latter is a particularly interesting new option for grassland monitoring, being an open archive multispectral

sensor with 20m spatial resolution and a revisit cycle of 2-5 days, depending on the latitude

(ESA, n.d.). While studies have shown the sensor is suitable for grassland monitoring (Shoko,

Mutanga, Dube, & Slotow, 2018) and multitemporal crop classification (Vuolo, Neuwirth,

Immitzer, Atzberger, & Ng, 2018), Sentinel-2B was only launched in 2017 and therefore data is not available for historical studies.

2.4.2 Radar Sensors

Synthetic Aperture Radar (SAR) sensors are beneficial for grassland monitoring as they can be used regardless of meteorological conditions and time of day – a severe limitation of multispectral sensors. The development of vegetation over time is known to cause changes in backscatter received by SAR sensors, and these changes are unique to each species due to the influence of plant morphology on scattering mechanisms. This includes direct backscatter from bare soil, direct backscatter from the plant itself, double bounce backscatter from the soil and canopy and, less commonly, multiple scattering mechanisms. The nature and magnitude of backscatter received is also strongly related to frequency, polarization and incidence angle of the wave.

First, the frequency of the emitted electromagnetic wave largely determines how far the wave will penetrate into the vegetation canopy, and concomitantly which part of the vegetation the SAR sensor is sensitive to. Higher frequency sensors respond more to vegetation canopy, while lower frequency sensors are able to penetrate the canopy and return signals related to plant structure or even ground structure. Determining exactly which part of the vegetation a SAR sensor responds to will vary greatly for each use case and requires intimate knowledge of the physical landscape. For example, it is generally understood that C-Band sensors have high potential for crop classification and change detection as it is sensitive to plant structure, while L-

band is less sensitive due to increased canopy penetration and more surface scattering (Skriver,

Svendsen, & Thomsen, 1999). That being said, the structure of grasslands is much different than croplands so this must be taken into consideration when choosing a SAR sensor for change detection between these two vegetation types. In this regard, it has been shown that X-band imagery may be more effective than C-band imagery (Voormansik, Jagdhuber, Zalite, Noorma,

& Hajnsek, 2016) and that C-band imagery alone is inadequate for grassland monitoring (Hill,

Smith, & Foster, 2000). Like multispectral data, the most promising results are achieved when multiple frequencies are used in conjunction with another for SAR analysis as each frequency provides unique insight into vegetation characteristics (Barrett, Nitze, Green, & Cawkwell,

2014).

Further, studies have shown that single-polarized SAR images cannot discriminate grassland types as backscatter from different sources cannot be differentiated (Hill et al., 2000;

Smith & Buckley, 2011), and perform worse than multispectral images in single-image crop classification because of differences in spectral resolution (Brisco & Brown, 1995; Smith &

Buckley, 2011). Dual polarimetry has been shown to increase this accuracy (Abdikan, Sanli,

Ustuner, & Calo, 2016), likely because co-polarized and cross-polarized images have a strong relationship with LAI throughout the entire growing season (Cable, Kovacs, Jiao, & Shang,

2014). Multitemporal classification approaches using variables derived from dual-polarized SAR images (e.g. Cloude-Pottier decomposition) can even outperform multispectral data in discriminating grasslands from croplands (Dusseux, Corpetti, Hubert-Moy, & Corgne, 2014).

Quad-polarized imagery, while understudied, has even higher potential as different polarization bases (circular, linear rotated 45˚) have shown to improve grassland discrimination (Larrañaga &

Álvarez-Mozos, 2016).

Finally, incidence angles have an inverse relationship with backscatter, as higher incidence angles (i.e. more shallow angles) results in more specular reflection (Cable et al.,

2014). However, higher frequency sensors such as the C-Band sensor on Sentinel-1 and

RADARSAT-2 and X-band sensor on TerraSAR-X, are less sensitive to changes in incidence angles due to higher canopy-volume scattering (Skriver et al., 1999).

Therefore the SAR data best suited to grassland-cropland discrimination is likely to be time series data to capture vegetation responses at different phenological stages, dual-polarized

(or potentially quad-polarized) imagery to separate scattering mechanisms, and higher frequency sensors such C-band or X-band with low incidence angles to increase sensitivity. Optimally, multiple frequencies would be combined for synergistic effects. Unfortunately, like Sentinel-2, the lack of long-term continuity in SAR open data archives makes it a challenge for longitudinal studies, although the launch of Sentinel-1 and the opening of the RADARSAT data archives will mark a fundamental shift in how these datasets are utilized in the future. Additional challenges include the inability to separate the signal associated with vegetation cover from moisture content and acquisition conditions, as well as a lack of knowledge on grassland backscatter behaviour during different phenological stages (Ali et al., 2016).

2.4.3 Multisource Data and Image Fusion

The most promising results are achieved when multiple data sources are used in tandem to overcome their individual limitations and obtain synergistic effects. For example, when the spatial resolution of MODIS is too coarse and the repeat pass of Landsat is too long, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) can be used to blend the two sources and create synthetic daily surface reflectance products at Landsat resolution (Gao et al.,

2006). Although this method does not perform well in heterogeneous regions (Gao et al., 2006;

Senf, Leitão, Pflugmacher, van der Linden, & Hostert, 2015) and is therefore unsuitable for agricultural areas, the Enhanced STARFM (Knauer, Gessner, Fensholt, & Kuenzer, 2016) and

Unmixing-based STARFM (Xie et al., 2016) have shown drastic improvements over the original algorithm and may be suitable for agricultural monitoring. More recently, the NASA

Harmonized Landsat Sentinel-2 (HLS) surface reflectance product was created to create an analysis ready dataset with an improved revisit cycle at Sentinel resolution (Claverie et al.,

2018). This dataset has shown potential for accurate agricultural land cover mapping (Griffiths,

Nendel, & Hostert, 2019) and improved tracking of within-season grassland dynamics (Zhou et al., 2019). This improved intra-annual characterization suggests that HLS could improve interannual change detection of grassland-cropland conversions as well, given that seasonal time series models evaluate changes in within-season dynamics from year to year.

Multispectral satellites typically monitor the presence and health of vegetation through the knowledge that the visible and near-infrared wavelengths are sensitive to leaf pigments (e.g. chlorophyll and mesophyll structure while the shortwave infrared (SWIR) wavelengths are sensitive to the presence of water, nitrogen and other non-photosynthetic components (Castro &

Sanchez-Azofeifa, 2008; Peña-Barragán, Ngugi, Plant, & Six, 2011). On the other hand, SAR sensors are more sensitive to plant morphology and orientation, dielectric properties of the target, and physical properties of the wave itself. Blending these different products is even more promising due to the additional biophysical information that can be retrieved and combined.

Optical-SAR fusion has been used to improve crop classification (Salehi, Daneshfar, &

Davidson, 2017), grassland discrimination between rangelands, pastures and seeded forage

(Lindsay, King, Davidson, & Daneshfar, 2019) and differentiating grasslands from alfalfa (Hong,

Zhang, Zhou, & Brisco, 2014). This improved classification accuracy between grassland types

suggests that optical-SAR fusion improves their separability, but further research is needed to determine whether this can improve interannual change detection between grassland types, and between grasslands and croplands.

2.5 Vegetation Phenology and Spectral Band Indices

It can be challenging to detect LCLUCs using any given spectral band (i.e. green, near infrared) because of similarities in reflectance measurements between different land cover classes. However, spectral band indices, or combinations of reflectance from two or more spectral bands, are often used to improve monitoring tasks by utilizing prior knowledge of spectroscopy to produce new remote sensing derived variables. Vegetation indices (VIs) are commonly used as the relationship between electromagnetic radiation and green vegetation is well understood, enabling the detection, quantification and estimation of vegetation health and vitality in a satellite image. Monitoring how VIs vary over time provides insight into the biological lifecycle of vegetation and enables change detection if and when key events change from year to year. This includes the timing and magnitude of phenological events such as greenup, maturity, senescence and dormancy, as well as management practices such as seeding, mowing and harvesting (Griffiths et al., 2020; Zhang et al., 2003). This is necessary in differentiating natural grasslands, improved pastures and croplands, where spectral information alone is insufficient but temporal information uncovers notable differences in both phenology and management (Müller et al., 2015).

For example, the most common VI is the Normalized Difference Vegetation Index

(NDVI), given by:

�� = �� – �� [2.1] ��

where NIR is the reflectance in the near infrared band and RED is the reflectance in the red band.

This VI leverages the physical properties that chlorophyll absorbs optical wavelengths and spongy mesophyll reflects radiation in NIR spectrum, creating a marked difference between red and NIR reflectance values received by multispectral sensors (Castro & Sanchez-Azofeifa, 2008;

Pinto et al., 2017; C. Wang et al., 2017). Therefore, higher values (closer to 1) indicate the presence of green vegetation, while lower values (closer to zero) indicate the absence of green vegetation. NDVI is useful in identifying LCLUCs due to the different values of vegetated and non-vegetated areas, and has shown to be effective in discriminating between agricultural land use and land cover classes (Atzberger, 2013; Jafari, Bashari, & Tarkesh, 2017). However, there is some evidence that this VI may not be suitable for semiarid or unmanaged grassland monitoring where vegetation is scarce (Bédard et al., 2006).

There are many other spectral band indices relating to vegetation, soil and moisture that may be effective in grassland monitoring. The most effective index is contextual, dependent on the target and sources of noise that may cause interference with the signal. For example, the

Enhanced Vegetation Index (EVI) is another common VI, given by:

�� – �� = � × [2.2] �� (��) – ��(��) � where BLUE is the blue spectral band, G is a gain (scaling) factor, L is the canopy background adjustment, and C1 and C2 are coefficients to account for aerosol influences.

There is evidence to suggest that EVI performs similar or even better than NDVI in an agricultural setting (Kouadio, Newlands, Davidson, Zhang, & Chipanshi, 2014; Liu et al., 2012) as it is more resistant to changes in background reflection than NDVI, including soil, atmosphere and leaf chlorophyll perturbations (Hatfield & Prueger, 2010; Liu, Pattey, & Jégo, 2012; Viña,

Gitelson, Nguy-Robertson, & Peng, 2011).

Furthermore, the Soil Adjusted Total Vegetation Index (SATVI) has been shown to outperform both NDVI and EVI in predicting fractional cover of total vegetation in rangelands

(Hagen et al., 2012). This is because the formula replaces the NIR band with two Shortwave

Infrared (SWIR) bands, increasing sensitivity to altered water usage in dry and senescent vegetation cover, while reducing sensitivity to soil background noise (Hagen et al., 2012;

Mundava et al., 2014):

�� = �� (� + �) − �� [2.3] �� where L is a soil adjustment factor. This is important because grasslands are often situated in arid to semi-arid regions where vegetation is dry, they may contain senescent vegetation at certain times of year if the land is unmanaged, and they often have exposed soils due to low vegetation density. Consequently, NDVI, EVI and other indices that rely on the NIR band alone are, in theory, poor indicators of vegetation cover on grasslands.

In addition, wetness related indices such as the Normalized Difference Moisture Index

(NDMI) and Tasseled Cap Wetness (TCW) have outperformed vegetation indices in forested landscapes (Schultz et al., 2016). Although their effectiveness in grassland monitoring is unclear, it is likely that these indices would be complementary alongside VIs in differentiating grasslands from crops due to the differences in irrigation. In fact, fusing multiple indices has generally shown to be superior to relying on individual indices (Hatfield & Prueger, 2010; Liu et al., 2012;

Schultz et al., 2016), with a combination of vegetation and wetness indices improving performance in the detection of grassland to cropland conversions (Klouček, Moravec, Komárek,

Lagner, & Štych, 2018).

2.6 Time Series Smoothing

A time series of remotely sensed variables contains a significant amount of noise, even

after pre-processing steps, as a result of undetected sub-pixel clouds, atmospheric turbulence,

coregistration errors, resampling errors, anisotropic errors, soil background noise and system

noise (Atzberger, 2013; Cai, Jönsson, Jin, & Eklundh, 2017). In time series data, this is

manifested as high frequency fluctuations. Time series smoothing methods fit a continuous curve

to discrete observations with the goal of reducing these high frequency oscillations and unveiling

the lower frequency patterns in the data (Figure 2.1) In other words, this technique reduces the

influence of the aforementioned sources of noise to uncover patterns of vegetation density and

phenology.

series data, including Fourier and wavelet transformations (X. Lu, Liu, Liu, & Liang, 2013;

Shao, Lunetta, Ediriwickrema, & Iiames, 2013), the Best Index Slope Extraction (Viovy, Arino,

& Belward, 1992), Asymmetric Gaussian (Jonsson & Eklundh, 2002), Double Logistic (Beck,

Atzberger, Høgda, Johansen, & Skidmore, 2006), Savitzky-Golay (Jin Chen et al., 2004), and the

Whittaker Smoother (Atzberger & Eilers, 2011; Eilers, 2003). Research shows that the most

successful algorithms for noise removal varies depending on crop type, location, noise level,

parameterization and statistical assumptions (Hird & McDermid, 2009). For example, Hird &

McDermid (2009) and Cai et al. (2017) found that the Asymmetric Gaussian and Double

Logistic function fitting methods were generally most effective, while studies that included the

Whittaker Smoother generally found it superior when exposed to a wide variety of satellites, vegetation types and noise levels (Geng et al., 2014; Shao et al., 2016).

2.7 Change Detection

Changes on the Earth’s surface can be identified by differences in satellite images over time, and these changes can be used to improve ecological understanding. The simplest approach to identify LCLUCs is to categorize image pixels taken at different times into land cover classes

(i.e. agriculture, forest, urban) based on their reflectance values. These images can then be compared with another to observe if, and how, the land had been altered between the image acquisition dates. However, this approach suffers from an accumulation of misclassification errors that causes a decrease in accuracy as more images are analyzed (C. Wu, Du, Cui, &

Zhang, 2017), while an increase in information should result in higher accuracies. As a result, there are many different types of change detection algorithms based on radiometric changes that have been developed. These techniques, along with their potential use in monitoring grassland conversions, will be discussed.

2.7.1 Threshold-Based Change Detection

Image differencing is a common method of change detection due to its simplicity. This method only requires calculating the difference in corresponding pixel values between two images, and can be used for object tracking, deforestation, crop monitoring and more.

Calculating the image difference between the two maps yields a difference map. Unfortunately, this difference map is not necessarily indicative of any changes on the land surface as it does not

control for other factors such as solar illumination, sensor calibration and atmospheric conditions

(Xu, He, Zhang, & Guo, 2008)

Similarly, image ratioing calculates changes between two images through division instead of subtraction. The added benefit over image differencing is that multiplicative noise is removed, including slope and aspect, solar illumination and seasonal changes (Berberoglu &

Akin, 2009). The main disadvantage with this method is that is cannot determine the nature of the change, only the magnitude.

A more complex approach to threshold-based radiometric change detection is Change

Vector Analysis (CVA), which identifies the magnitude and direction of changes in the input datasets. CVA has the added benefits that it can utilize all input bands, avoid compounding spatial-spectral errors inherent in multi-date change detection, identify both abrupt and gradual changes and facilitates change interpretation by retaining multidimensional change vector components (Johnson & Kasischke, 1998). This method has shown to be more accurate than band differencing and ratioing (Berberoglu & Akin, 2009), and could be useful for grassland monitoring as it is capable of identifying both abrupt changes and gradual changes.

For each of these methods, a threshold value is then chosen to separate pixels that have changed from those that have not. This threshold should always be chosen based on image content, for example by histogram interpretation or modelling the signal and noise content of the image spatially or radiometrically (Rosin, 1998). Regardless, this method inevitably faces challenges as binary interpretations of image changes oversimplifies the complexity of real- world changes and forces users to make decisions that cannot be generalized to other study areas, datasets or time periods.

2.7.2 Harmonic Analysis

Detecting long-term phenological changes amidst interannual variation is a difficult problem in remote sensing as most change detection methods do not explicitly account for the variability related to seasonality. Additionally, many time series methodologies lose temporal detail when deriving temporal metrics from the full dataset (Verbesselt, Hyndman, Newnham, &

Culvenor, 2010; Verbesselt, Hyndman, Zeileis, & Culvenor, 2010).

Harmonic analysis can be used for cyclical data by breaking a time series, or a complex waveform, into the sum of a series of sinusoids. This can be used to detect changes in vegetation from satellite images by measuring stability of the phase and amplitude of these sinusoids over time (Jung & Chang, 2015). Alternatively, these harmonic terms have been used within a discriminant analysis framework for crop type identification (Jakubauskas, Legates, & Kastens,

2003). This would be useful for grassland monitoring, where the vegetation is known to have markedly different phenological cycles than other types of vegetation, including crops.

2.7.3 Structural Break Methods

Structural break changepoint detection (SBCPD) methods aim to identify the number and time of structural changes in a time series. In other words, it tests the constancy of regression model parameters and estimates where and when changes occur (Bai & Perron, 2003). Consider the standardized linear regression model for time series data:

�� = � + �� + �� [2.4] where Y is the dependent variable at time �, � is the independent variable, � is the intercept, � is the slope and � is the residual or error term. The model coefficients (i.e. � and �) are determined by the values that minimize the residual sum of squares (RSS). For example, the OLS estimation of � is determined by:

� � � � = �� ∑�� = �� ∑��(�� − � − ��) [2.5]

However, it should be noted that an assumption of linear regression for time series data is that the regression relationship is constant over time; this assumption is often not satisfied when analyzing environmental data as structural changes may occur during the sample period as a result of climate change and/or human intervention. This assumption can be tested by examining the constancy of the model coefficients over time through residual analysis. A mean residual value of 0 is expected, and a substantial shift from this average may indicate structural change is present and the data may be better fitted by a piecewise linear model. More specifically, empirical fluctuation processes (EFPs) are used to test model parameter constancy with the null hypothesis that regression parameters are constant over time. If rejected, at least one structural change exists within a linear regression model.

2.7.3.1 BFAST

The BFAST algorithm is a promising SBCPD method because of its ability to accurately detect and differentiate changes in seasonality (Verbesselt, Hyndman, Zeileis, et al., 2010; Xue,

Du, & Feng, 2014) from abrupt trend changes (M. Lu, Pebesma, Sanchez, & Verbesselt, 2016;

Reiche, Verbesselt, Hoekman, & Herold, 2015; Watts & Laffan, 2014) and gradual trend changes (Schultz et al., 2016; Watts & Laffan, 2014). Basically, BFAST works in the following way:

a) The data is deseasonalized using the Seasonal Trend decomposition using Loess

(STL) method (Cleveland, Cleveland, McRae, & Terpenning, 1990) and the trend

model is initialized by fitting a linear regression model to the deseasonalized data

b) The seasonal component is initialized by fitting a multiple linear harmonic regression

model to the detrended data (Walker, 1971)

c) The presence of a structural change in both the trend and seasonal components is

determined using an EFP (Brown, Durbin, & Evans, 1975; Chu, Hornik, & Kaun,

1995)

d) If a structural change is present, the number of breakpoints is estimated by

minimizing an information criterion (Bai & Perron, 2003)

e) The location of each breakpoint is determined by minimizing the residual sum of

squares (Bai & Perron, 2003)

f) The trend and seasonal models are recalculated as piecewise linear models; the model

coefficients are allowed to differ before and after structural breaks but are fixed

between breaks

2.7.3.2 BFAST Monitor

BFAST is an offline change detection method, meaning that the entire time series is processed and examined for changes simultaneously. The alternative approach is called online change detection, whereby live streaming data is fed into the algorithm with the main purpose of ongoing anomaly detection. An online extension to BFAST, called BFAST Monitor, has been used to detect changes in near real-time (DeVries, Verbesselt, Kooistra, & Herold, 2015;

Hutchinson, Jacquin, Hutchinson, & Verbesselt, 2015; Schultz et al., 2016; Verbesselt, Zeileis, &

Herold, 2012) based on a stable historical record. This method can be useful for grassland monitoring if prior knowledge exists that a field has been a grassland historically and there is a desire for operational monitoring of grassland to cropland conversions.

For example, this would operate as follows. Data is acquired that indicates a particular pixel in a satellite image was a grassland from 2000-2009, and ongoing monitoring of that pixel is desirable. Historical NDVI values are obtained and a model is fit to the observations. That model

is extrapolated into 2010 and NDVI values are collected as they become available. A MOSUM

process is applied similar to BFAST. If three or more consecutive residuals are larger than

expected based on historical model variance, a change is identified (Figure 2.2).

2.7.3.3 BEAST

The Bayesian Estimator of Abrupt change, Seasonal change and Trend (BEAST) method

is a SBCPD method similar to BFAST. BEAST decomposes a time series into additive seasonal

and trend components and estimates the position of breakpoints based on an OLS-MOSUM or

OLS-CUSUM test.

However, BFAST somewhat arbitrarily decomposes the time series using a defined

harmonic term and piecewise linear regression, although there are numerous ways of

decomposing a complex time series (i.e. fitting a series of line segments and harmonics). The

most effective method may differ for each use case, so choosing the optimal model amidst a

large number of conflicting models may be a difficult choice. As a result, BEAST uses a

Bayesian general linear regression model that randomly traverses the model space to decompose

the time series and estimate breakpoints, eliminating the need for individual model selection

(Zhao et al., 2019). Using a Bayesian approach to SBCPD introduces two main benefits. First,

BEAST not only estimates the number and timing of breakpoints but quantifies how likely these

detected changes are. Additionally, it is able to identify non-linear trends, rather than just

piecewise linear trends.

Chapter 3.0: Structural Breaks in Earth Observation Time Series Data for Grassland Change Detection in Alberta, Canada

Abstract

Grasslands are an important and often undervalued link in the global carbon cycle, and their large extent, diversity and climate resiliency make them valuable carbon sinks in the effort to mitigate climate change. However, they are not well protected and are consequently being replaced by agricultural systems worldwide. Current monitoring efforts using remote sensing and ground-based methods are insufficient, and accordingly the mapping of grassland to cropland conversions must be improved to better document these changes in the Canadian Prairies. The purpose of this study is to compare the applicability of two different structural break change detection algorithms with MODIS and Landsat data for grassland change detection. The earth observation-based Breaks For Additive Seasonal and Trend (BFAST) and Bayesian Estimator of

Abrupt change, Seasonality and Trend (BEAST) methods were applied to evaluate their sensitivity to rangeland and pasture conversions in two Alberta study areas from 2011 to 2017.

The predicted year of change by the optimal BFAST model was correct for 76% of rangelands and 66% of pastures, which was notably higher than the BEAST model accuracy of 63% and

59%, respectively. In general, MODIS data outperformed Landsat, outlining the importance of high temporal resolution remote sensing data to successful change detection, even at the expense of higher spatial resolution. Significant improvements were made using our BFAST Seasonal model instead of the conventional seasonal-trend model, suggesting that only seasonal changes are useful for detecting grassland conversions. An analysis of breakpoint timings revealed that changes from rangelands to crops are detectable throughout the majority of the growing season, while changes from pasture to crops are detectable during the spring. This has important implications for future change detection work, particularly for research where careful image

selection is necessary. Finally, change detection was effective for most grassland to cropland changes, but the accuracy was significantly reduced when grasslands were subsequently replaced by wheat. This highlights the need for further research in differentiating grasses from specific crops. Overall, this study demonstrates that structural break methods are effective in identifying grassland to agriculture transitions and may be useful for the operational monitoring of grassland inventories in the future.

3.1 Introduction

Grasslands play an integral role in the global carbon cycle, storing 10-30% of the world’s soil organic carbon (Schuman et al., 2002). Grasslands have the potential sequester one-third of the expected increase in carbon emissions over the next half century (McDermot & Elavarthi,

2014) and may be more reliable carbon sinks than forests in some regions due to their climate resiliency (Dass, Houlton, Wang, & Warlind, 2018). However, this biome is threatened by agricultural expansion and the subsequent depletion of carbon stocks as a considerable portion of carbon is released back into the atmosphere through oxidation (Lal, 2002). In the United States, the estimated grassland to cropland conversion rate is 1-5% annually (Gage et al., 2016). In the

Canadian Prairies, only 20% of grasslands remain (Bailey et al., 2010) but the current conversion rate is not well understood.

Monitoring grassland conversions is also critical to Agriculture and Agri-Food Canada

(AAFC)’s monitoring and reporting programs. The National Agri-Environmental Health

Analysis and Reporting Program (NAHARP) is used to predict the interactions between agriculture and the environment and requires biophysical and land use data as part of their models (Clearwater, Martin & Hoppe, 2016). The Agricultural Greenhouse Gases Program

(AGGP) requires specific knowledge on land cover changes to improve carbon accounting

accuracy (AAFC, 2014; IPCC, 2014). Finally, high accuracy, high resolution land cover data is needed to meet Canada’s international reporting commitments to the United Nations Framework

Convention on Climate Change (UNFCCC), the Organization for Economic Co-operation and

Development (OECD) and the Food and Agriculture Organization (FAO; AAFC, 2015).

Yet, identifying these grasslands conversions remains a significant challenge. Modern grasslands vary from native and semi-native to completely non-native compositions and are often interspersed with agriculture and other land covers, resulting in fragmented and heterogenous landscapes that are more difficult to characterize from satellite images.

As a result, national-scale crop classification products often achieve lower than desired accuracies in distinguishing different grasslands types or grasslands from croplands on any given year, let alone the more difficult task of identifying the nature of changes from one year to the next. For example, the United States Department of Agriculture Cropland Data Layer typically classifies different grassland types with < 50% accuracy (Olimb et al., 2018; USDA, 2019). In

Canada, the AAFC’s Annual Crop Inventory (ACI) is also limited in its ability to differentiate its rangeland and pasture/seeded forage categories. AAFC’s internal accuracy assessment shows the average classification accuracy of natural grasslands in Alberta from 2015-2018 to be 58% and

80% for user’s and producer’s accuracy, respectively (L. Campbell, personal communication,

March 3, 2020). This suggests that a) natural grasslands may not be as widespread as the ACI might indicate and b) further research is necessary to improve mapping of Canada’s natural grasslands. The former is alarming as these datasets are the most reliable source of annual land cover characterization at the national level and may represent an inaccurate inventory of grassland resources. The latter is further reinforced when examining the 2018 ACI, the most recent and accurate year to date. Over 13% of the natural grasslands in AAFC’s internal accuracy

assessment were incorrectly classified as agriculture. Consequently, spatial overlays of national land cover datasets are not a reliable method to quantify and characterize grassland land cover changes. Further research is required to understand how Canada’s grassland resources are shifting.

Given that the most accurate land cover classification products are insufficient for monitoring grassland conversions, a different approach is necessary. Earth observation satellites such as the Moderate Resolution Imaging Spectrometer (MODIS) and Landsat provide land surface data at the field-scale over vast areas, enabling the nationwide detection and characterization of land cover changes. Time series analysis approaches utilize reflectance measurements or other derived variables from many images to track vegetation within and across growing seasons. This has shown to be effective in identifying land cover changes and crop rotation patterns (Y. Chen et al., 2018; Wardlow, Egbert, & Kastens, 2007) and differentiating grassland types based on their land use intensity (Griffiths et al., 2020; Hong et al., 2014).

To illustrate this, the average NDVI time signature for rangelands, pastures and the common wheat-canola crop rotation is presented to elucidate the how their phenology differs and how time series algorithms may identify these agricultural transitions (Figure 3.1). It is evident that the start of season and development stages are nearly identical for rangelands and pastures and occur earlier than the average field with a wheat-canola rotation. This time of the year before

June appears to be the optimal time to differentiate grasslands from croplands due to high separability, although it would be difficult to separate grassland types at this time of year. As

June approaches, the rangeland curve begins to flatten, while the pasture curve continues to rise as more intensive management practices improve growth. This results in a higher peak for pastures that occurs a couple weeks later than rangelands, on average. At the same time, the

wheat-canola curve very rapidly develops, resulting in a peak much higher than rangelands, but only slightly higher than pastures. This time of year (i.e. July) is likely optimal for differentiating rangelands from croplands. Finally, the wheat-canola curve quickly drops while the slopes of the grassland curves are gentler, resulting in higher grassland NDVI values after the crops are harvested. While the rangelands and croplands are still marginally separable at this time of year, pastures are significantly greener than croplands, making early autumn another potentially optimal time for pasture-cropland differentiation. This varies, however, depending on the harvest timing of each specific crop.

Figure 3.1: Average MODIS NDVI seasonality of pastures, rangelands and wheat-canola rotation in the Vermilion Upland Ecodistrict, Alberta, Canada from 2010-2018.

Structural Break Changepoint Detection (SBCPD) methods are a prime candidate to detect these changes, as they are a class of methods that aim to retrospectively identify key changepoints in a univariate time series. These methods have a long history in economics to identify fundamental changes in market behaviour (Hackl & Westlund, 1991), but have only more recently been applied to remote sensing with the goal of environmental change detection

(Verbesselt, Hyndman, Newnham, et al., 2010; Zhao et al., 2019). For example, these methods

have been applied to multispectral imagery to monitor forest fires (Darmawan & Sofan, 2012;

Fang et al., 2018), deforestation (DeVries et al., 2015; M. Lu et al., 2016; Schultz et al., 2016), long-term trends in vegetation health (Geng, Che, Wang, & Wang, 2019) and grassland dynamics (Browning, Maynard, Karl, & Peters, 2017), but have not yet been used to detect grassland to agricultural land cover changes.

This experiment explores the capacity of SBCPD methods to identify grassland to agriculture conversions. More specifically, the Breaks For Additive Seasonal and Trend

(BFAST) and Bayesian Estimator of Abrupt change, Seasonal change and Trend (BEAST) algorithms will be used to detect pasture-cropland and rangeland-cropland conversions in

Alberta, Canada. These algorithms decompose a time series into its trend, seasonal and remainder components, enabling the detection of three distinct change types. Abrupt changes are identified by breakpoints on the trend component of the model and typically represent environmental disturbances. Gradual changes can be extracted through the slope of the trend component, typically corresponding to interannual greening or browning of vegetation. Finally, changes in phenology can be identified by breakpoints on the seasonal component.

The algorithms will be tested on both MODIS and Landsat datasets to investigate the sensitivity of these models to multispectral remote sensing datasets with different resolutions and vegetation indices (VIs). The main difference between BFAST and BEAST is that BFAST uses a single defined set of parameters to decompose the time series, while BEAST uses a Bayesian model averaging approach to decomposition and allows for non-linear trend analysis. As a result, various parameterizations of BFAST will be used to compare the results of an appropriately tuned model with the Bayesian model averaging approach of BEAST.

3.2 Methodology

Ancillary datasets were used to identify pasture-cropland conversions and rangeland- cropland conversions in two different Alberta study areas. Time series analysis of MODIS and

Landsat imagery using SBCPD methods were applied to these change areas to estimate the year each grassland conversion occurred. These predictions were validated against observed changes from existing datasets alongside manual inspection of high-resolution imagery. All data analysis was completed in the statistical computing software R (R Core Team, 2019).

3.2.1 Study Areas

Two study areas were chosen for analysis. The first study area is the Vermilion Uplands

Ecodistrict, located in central Alberta along the Saskatchewan border near Lloydminster (Figure

3.2). This study area was chosen as its location within the Aspen Parkland Ecoregion acts as a

Kilometers Alberta

Legend

Conversions Wetland Water Rangeland Exposed Land/Barren Cropland Urban Pasture Shrubland Forest

Figure 3.2: Pasture conversions identified by reference data in the Vermilion Upland Ecodistrict, Alberta, Canada from 2010-2018.

transition zone between the Boreal Forest to the north and grasslands to the south. It was hypothesized that this would be an ideal location for agricultural intensification from grassland to cropland as its fertile Black Chernozemic soils (Alberta, 2020a) and less arid climate

(compared to grasslands to the south) make for some of the most productive agricultural land in the Canadian Prairies. AAFC’s Annual Crop Inventory (ACI) confirmed a very high density of both pastures and croplands and a lower density of rangelands in this Ecodistrict compared the rest of the province (AAFC, n.d.), increasing the probability of identifying pasture to cropland conversions. The second study area chosen is formed by a northern subsection of the Moist

Mixed Grasslands Ecoregion on the southern border of the aforementioned study area. (Figure

3.3). The annual precipitation in this area is about 350-450mm (Alberta, 2020b) acting as a transition zone between the drier mixed grasslands to the south and the parkland to the north,

Alberta

Legend

Conversions Wetland Water Rangeland Exposed Land/Barren Pasture

50 Urban Cropland

Kilometers Shrubland Forest Figure 3.3: Rangeland conversions identified by reference data in a subsection of the Moist Mixed Grasslands Ecodistrict, Alberta, Canada from 2010-2018.

making its semiarid climate suitable for both natural grasslands and intensive agriculture. It was hypothesized that rangeland-cropland conversions seldom occurred over this time period, as the majority of remaining rangelands in Alberta occupy lands unsuitable for crop production due to hills, poor soils and susceptibility to drought (Bailey et al., 2010). Therefore, a larger study area was chosen to increase the number of rangeland-cropland conversions that can be identified. The

ACI confirmed a high density of rangelands and croplands and a lower density of pastures in this area compared the rest of the province, making it an ideal study area (AAFC, n.d.).

3.2.1.1 Grassland Conversion Polygons

Crop insurance data was spatially analyzed to identify fields that were pastures in 2010 and croplands in 2018 within the Vermilion Uplands Ecodistrict, yielding a map of pasture- cropland conversions (n = 55). However, the exact year of the change remains unknown. To remedy this issue, the ACI was analyzed from 2010-2018 to identify the first year in which a crop replaced the grassland. The overall accuracy of the ACI for agricultural land cover in

Alberta is 88% or higher each year, as validated using crop insurance data and manual ground observations from AAFC staff (AAFC, n.d.). While this is sufficient for many studies, the accuracy needed to be improved for verification purposes. As a result, manual inspection of high-resolution satellite images was used to confirm ACI results. For this task, DigitalGlobe images available through Google Earth Pro historical imagery were used wherever possible

(Google, 2020). Otherwise, Landsat and Sentinel images were accessed through Google Earth

Engine (Gorelick et al., 2017). During this process, there were 11 fields in which the year could not be accurately determined from high resolution imagery, and an additional 3 fields with changes occurring in 2018 which had to be removed due to the inability of SBCPD methods to

detect changes close to the edges of a time series. This reduced the sample size to 41 with a median field size is 95 Ha (Figure 3.4).

For the purposes of this research, the crop insurance data did not contain a sufficient number of rangeland-cropland conversions, as landowners are not as likely to insure a rangeland compared to a pasture. As a result, the process of identifying land cover conversions was altered.

Instead of using crop insurance data, ACI data covering the study area was spatially analyzed to identify potential rangeland-cropland conversions. In this case, pixels were identified by overlaying the 2010 and 2018 ACI rasters to find areas identified as grassland in 2010 and cropland in 2018. A spatial mode filter with a 5x5 kernel was applied to the binary output image to reduce image noise. Finally, these pixels were converted to polygons and were further filtered to exclude small areas deemed as noise based on natural breaks in the dataset and knowledge of field sizes in the Alberta. These polygons were overlaid with the ACI and high-resolution images to identify entire fields that changed and the year of change. In most cases, entirely new

Figure 3.4: Histogram of field sizes for rangeland and pasture conversions in hectares. The red bars represent outliers, defined as fields larger than 140 hectares.

polygons were drawn using the high-resolution images to properly coincide with field boundaries. Of the hundreds of fields identified, the 41 largest were chosen to match the sample size of the first study area with a median field size of 65 Ha (Figure 3.4).

3.2.2 Image Acquisition and Pre-Processing

3.2.2.1 MODIS

All MODIS VI 16-day Level 3 Global 250m HDF files intersecting the study areas for both the Terra (Didan, 2015a) and Aqua (Didan, 2015b) platforms were downloaded from 2010 to 2018 from NASA’s Land Processes Distributed Active Archive Center (LP DAAC). When combined, the Aqua and Terra platforms provide MODIS VI composites at a 250m spatial resolution and a temporal resolution of 8 days. These Level 3 Normalized Difference Vegetation

Index (NDVI) and Enhanced Vegetation Index (EVI) composite products are derived from the highest quality daily, atmospherically corrected surface reflectance images over each 16-day period, based on an algorithm that chooses one of the highest values with the lowest viewing angle to minimize anisotropic errors (Didan, Munoz, Solano & Huete, 2015). The corresponding

VI quality images were bit decoded and masked such that only the highest quality pixels were included in the analysis.

Given that the median field size is 95 Ha for rangelands and 65 Ha for pastures, it is likely that the 6.25 Ha pixels in MODIS imagery are sufficient to capture field sized changes in vegetation composition. However, the minimum field sizes are < 10 Ha and may be too small for

MODIS image analysis.

3.2.2.2 Landsat

Accordingly, Level 2 surface reflectance Landsat images were obtained to evaluate the benefits of using higher resolution imagery. All Landsat surface reflectance images intersecting the study areas with less than 80% cloud cover from 2010 to 2018 were identified using USGS

EarthExplorer and exported to a CSV file. This file was then used to place a bulk order through the USGS EROS Science Processing Architecture (ESPA) On-Demand Interface, where surface reflectance products and their corresponding NDVI, EVI, Soil-Adjusted Vegetation Index

(SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI) and pixel quality assurance

GEOTIFF files were ordered in AAFC’s custom Albers Equal Area projection and downloaded using the ESPA bulk downloader (USGS, 2018a). The quality assurance images were bit decoded and reclassified to create a mask that includes only cloud-free pixels.

Once all MODIS and Landsat Data were downloaded and assessed for quality, all pixel values from November to February were set to 0.1 to represent the dormancy of vegetation during the midlatitude winters (Hird & McDermid, 2009) and to discourage false change detection during the agricultural offseason. The fields that were identified as a grassland conversion between 2010 and 2018 were then used to clip the downloaded images.

3.2.3 Time Series Smoothing

Although many steps were previously taken to reduce noise and improve data quality, undetected clouds, system noise, anisotropic errors and resampling errors remain (Atzberger,

2013; Cai et al., 2017). To remove the high frequency fluctuations introduced by this noise, time series smoothing is employed to uncover the lower frequency patterns related to vegetation phenology. The Whittaker Smoother is a common noise reduction technique for time series data originally published in 1923 (Whittaker, 1923), and revived in 2003 (Eilers, 2003). The algorithm, based on penalized least squares, aims to fit a smooth time series, z, to a noisy time series, y, by minimizing the sum of the smoothness of the curve and magnitude of the residuals:

� = � + �� [3.1] where S is the sum of squared differences between the observations and residuals:

� � � = ∑� (��– ��) [3.2]

R is a measure of smoothness of the curve, typically expressed as second order differences for remote sensing applications (Atzberger & Eilers, 2011):

� � � � = ∑� ((�� − ��) − (�� − ��)) [3.3] and l is the smoothing parameter. A larger l creates a smoother time series with larger residuals, while a smaller l creates a noisier time series with smaller residuals (Figure 3.5). This parameter can be optimized using leave-one-out cross-validation (Shao et al., 2016). In this process, each value of y is left out, the data is smoothed to obtain a prediction for yi, and the error is calculated.

This is repeated for each value of l and the model with the lowest error is selected.

Figure 3.5: The effect of the Whittaker Smoother with different values of lambda.

Atzberger & Eilers (2011) suggest a modified interpretation of the Whittaker smoother for remotely sensed data based on the assumption that atmospheric signal attenuation introduces a negative bias. This modified interpretation is as follows:

1) Apply the original Whittaker algorithm to the observed data (Equation 3.1)

2) All observed values below the smoothed time series are replaced with their fitted value

3) All missing or low-quality values are replaced with their fitted value

4) The Whittaker smoother is applied to the new time series

It is hypothesized that the modified Whittaker smoother reduces this negatively biased noise to more closely approximate the true surface reflectance values.

The modified Whittaker Smoother was chosen in this experiment for its speed, simplicity, ability to smooth unevenly spaced time series and demonstrated accuracy (Geng et al., 2014;

Shao et al., 2016). The algorithm was applied to each pixel in both the MODIS and Landsat image stacks. When applied to the MODIS data, the composite day of the year images were used to extract the exact date of the corresponding pixel value to improve smoothing accuracy.

However, given that the MODIS VI datasets have 8-day temporal resolution with no gaps, the algorithm was primarily used to reduce high frequency noise rather than an interpolation tool. On the other hand, Landsat data has uneven and inconsistent temporal resolution. Each sensor has a

16-day temporal resolution, but combinations of different Landsat sensors and cloud cover means that temporal resolution ranged widely. In this case, temporal smoothing is critical to not only reduce noise, but to interpolate large gaps in the data. For example, gaps of 32 days without an observation were observed in some circumstances. This could result in errors during change detection if critical phenological stages are absent due to missing data. To overcome this limitation, the Whittaker smoother was applied similar to the MODIS data, but with a caveat.

Instead of smoothing each pixel time series and sampling on the image acquisition dates, the smoothed data was sampled on the same dates the MODIS images are acquired (i.e. every 8 days) to create an artificially dense image stack with interpolated values. This is because the poor temporal resolution of Landsat has been shown to reduce the accuracy of vegetation monitoring

(Baumann, Ozdogan, Richardson, & Radeloff, 2017; Gao et al., 2006).

3.2.4 Change Detection

To detect changes in the MODIS and Landsat time series, both BFAST and BEAST were applied to the smoothed time series for each pixel in the rangeland and pasture conversion polygons. The models were constrained to allow a maximum of one breakpoint, with the assumption that this change corresponds to the known grassland conversion and the timing deduces the year in which the change occurred. After change detection is performed, the result is a raster image with the estimated date of the land cover change for each pixel. To estimate the year of change for the entire fields (i.e. object-based analysis), these images were reclassified into YYYY format and zonal statistics with a majority filter were calculated to find the growing year identified by the largest number of pixels.

3.2.4.1 BFAST

BFAST iteratively decomposes a time series into a piecewise trend and seasonal model

(Verbesselt, Hyndman, Newnham, et al., 2010):

�� = �� + �� + �� [3.4] where Y is the observed data at time t (t = 1,…,n), T is the trend component, S is the seasonal component and e is the remainder component. The trend component is assumed to be a piecewise linear model with m breakpoints and m + 1 linear models, where abrupt changes are found at the breakpoints ti, i = 1,…m and gradual changes are given by the linear model:

�� = �� + �� [3.5] for ti-1 ≤ t < ti and where � is the intercept and � is the slope of the linear segment. The seasonal component is fit with a harmonic model with p breakpoints and p + 1 harmonic models, where seasonal changes are found at the breakpoints xj, j = 1,…p. Harmonic models account for periodicity using K number of harmonic terms (i.e. sinusoids with different frequencies):

� = ∑� � , �� (�� + � , ) [3.6] � �� for xj-1 ≤ t < xj and where a is the amplitude, f is the frequency (number of observations per year) and d is the phase of the sine wave. Equation 3.6 can be rewritten into a multiple linear harmonic regression model using the trigonometric sine sum identity:

� � = ∑ � � , �� + � , �� [3.7] � �� where the linear coefficients of the regression model are gj,k = aj,kcosdj,k and qj,k = aj,ksindj,k and

th Kj is the harmonic term for the j segment. Note that K is a user-defined constant by default (it does not vary across segments). The remainder component is estimated from the difference between the observational data, the estimated trend component and estimated seasonal component:

�� = �� − (�� + ��) [3.8]

Finally, breakpoints in the time series are estimated through the following steps (Bai &

Perron, 2003; Zeileis, Kleiber, Walter, & Hornik, 2003). An Empirical Fluctuation Process (EFP) is used to test the presence of breakpoints in the time series by examining the constancy of the model coefficients through residual analysis. By default, BFAST uses the Ordinary Least

Squares Moving Sum (OLS-MOSUM) test, which evaluates a moving window of OLS residuals to evaluate if the distribution of residuals changes. This uses the sum of a fixed number of residuals in a data window, with the window size being determine by the parameter h. In other words, if we have a sample size (n = 100) and a window size (h = 0.05), this means that a window of 5 samples is moved over the dataset sequentially to determine the sum of residuals for each observation. For a more elegant explanation, see the summary paper on MOSUM tests (Chu et al., 1995). An EFP can also use a Cumulative Sum (CUSUM) test which uses all residual values from the start of the time series until the current observation instead of a moving window.

Both the MOSUM and CUSUM tests can be used with OLS residuals, calculated from a simple linear regression model following the usual formula:

�� = �� − � − �� [3.9]

However, these residuals have some drawbacks. Namely, they are not independent and in general are not heteroscedastic (Galpin & Hawkins, 1984). Instead, recursive residuals are commonly used for testing structural change as they satisfy these criteria (Brown et al., 1975). Recursive residuals are obtained by a) calculating the predicted value for the �th observation, �, by using regression parameter estimates obtained only from �-1 observations, b) calculating the residual and c) scaling the difference (Kianifard & Swallow, 1996). This results in standardized residuals from the regression of each observation and ensures no value is used to predict itself and has a zero mean under the null hypothesis (Brown et al., 1975).

If the null hypothesis for the EFP is rejected (p < a), the number of breakpoints is determined by minimizing an information criterion (although the algorithm was forced to have a maximum of one breakpoint in this experiment) and the position of the breakpoints is determined by minimizing the residual sum of squares (Bai & Perron, 2003). It is assumed that the number of breakpoints corresponds to the number of land cover conversions, and that the position of these breakpoints is indicative of the date the changes occurred.

Verbesselt et al. (2012) found that changes of greater than 0.1 in amplitude are needed to identify changes in MODIS NDVI datasets due to background noise, and this has been confirmed by successfully characterizing vegetative changes with amplitudes close to 0.1 amidst high inter-annual variability (Browning et al., 2017). However, it is unclear how this will vary with different data sources, indices and smoothing methods.

3.2.4.2 BEAST

Similar to BFAST, BEAST decomposes the time series into its trend, seasonal and remainder components. However, the model structure, M, or the number and timing of changepoints and seasonal harmonic orders, is estimated through Bayesian modeling rather than frequentist statistics with user specified values (in the case of the harmonic order term) with the purpose of reducing uncertainty, overfitting and model misspecification. The model structure is defined as (Zhao et al., 2019):

� = {�} ∪ {� } ∪ {�} ∪ {� } ∪ � [3.10] � ��,…� � ��,…� � ��,…�

The model is expressed as a simple linear regression form to solve for the optimal M:

�� = �� + �� [3.11] where XMt is the design matrix and bM are the associated model coefficients. Both depend on M as the column vectors in each dependent variable are associated with each individual segment between breakpoints in the linear and harmonic model.

Traversing the entire model structure space (i.e. every possible combination of m, ti, p, xj, and Kj) to find the optimal M amidst an infinite number of possibilities would be computationally inefficient, so BEAST estimates M through Bayesian inference to estimate the optimal model parameters and their posterior probability distribution given the observed data. Each sampled M is a possible scenario of abrupt changes, gradual changes and phenological changes on the land surface, so model averaging is used for a final estimate and uncertainty. Model averaging also allows the estimation of nonlinear dynamics through the combination of many different linear models (Zhao et al., 2019).

3.2.4.3 Parameter Tuning

The initial BFAST model in this experiment uses the following parameters. The h parameter was set to 0.15 as the time series includes data from 2010 to 2018, but only evaluates

changes from 2011 to 2017. Therefore, a value of 0.15 corresponds to just over one year in this experiment, avoiding change detection during the first and last year of the dataset. The EFP used is the OLS-MOSUM test (with a = 0.10), the harmonic order is 3 and the maximum number of breakpoints is 1. However, various parameterizations were tested to examine their effect on both pasture and rangeland conversions:

1) BFAST Seasonal: The number of trend breakpoints, m, was set to zero to constrain

breakpoints to the seasonal component.

2) Detrending: The practice of detrending the model before identifying seasonal breakpoints

is removed.

3) EFP: Four different types of EFPs are tested here, based on either Ordinary Least Squares

(OLS) or Recursive residuals (Rec) and either a cumulative sum of residuals or a moving

sum of residuals (herein OLS-CUSUM, OLS-MOSUM, Rec-CUSUM and Rec-

MOSUM).

4) Harmonic order: The number of sines and cosines used in the harmonic model were

tested from values of 1 to 4.

For a fair comparison, the parameters of BEAST were constrained to match the BFAST parameter space. The harmonic order was limited from a range of 1 to 4, the trend was either a constant or a linear term and the maximum number of breakpoints was set to 1.

3.2.4.4 Influence of Data Source and Lambda

Once the aforementioned parameters are compared for accuracy, the best BFAST model parameterization was selected. This model was then used to find the most effective MODIS and

Landsat-derived variables. The MODIS data used in this study only utilized the 250m NDVI and

EVI composites. On the other hand, Landsat surface reflectance images were used to derive

NDVI and EVI, as well as the SAVI, MSAVI.

Additionally, the optimal value of the Whittaker smoothing parameter, l, is tested using a grid search. To do this, the time series of each pixel was smoothed using different values of l ranging from 0 (raw data with noise present) to 750 (only low frequency oscillations are present) before change detection is performed to find the region of the parameter space with the lowest error for each data source. The accuracy of change detection with different values are compared to examine the effect of smoothing on the detection of grassland conversions, ceteris paribus.

3.2.5 Accuracy Assessment

Each of the estimated change dates for both pastures and rangelands were evaluated against the validation data by assigning each observation to one of three categories. As long as the estimated and validated changes occurred in the same calendar year (and thus agricultural season), the observation is labelled as “Correct Year”. This is the metric for overall accuracy in this experiment. If the estimated and observed changes occurred in different years, it is labelled as a “Temporal Mismatch”, and if the algorithm did not detect a change it is labelled as “No

Detection”. It is important to note that the overall accuracy only refers to the algorithm’s ability to detect the change on the correct year, and not its general ability to detect the change. Also included are the categories “One Year Early” and “One Year Late”, indicating the model predicted the conversion one year early or late, respectively. This was included to evaluate if the model is frequently predicting the changes slightly early or late due to a statistical bias. For example, if a model is frequently predicting late, it is likely the model sensitivity needs to be increased.

3.3 Results and Discussion

3.3.1 Method Comparison

BFAST predicted the correct year of agricultural conversion for 61% (kappa = 0.47) of rangelands and 27% (kappa = 0.15) of pastures. The algorithm did not detect the change at all in just 2% of rangeland conversions and 5% of pasture conversions. The remainder of grassland conversions were predicted on the incorrect year. 17% of pasture conversions were predicted one year late and another 17% were predicted one year early, while 15% of rangeland conversions were predicted one year late and 5% were predicted one year early (Table 3.1).

Table 3.1: BFAST and BEAST Grassland to Cropland Conversion Accuracy using MODIS NDVI Data.

Method Correct Temporal No One Year One Year Year Mismatch Detection Late Early BFAST - Rangelands 61% 37% 2% 15% 5% BEAST- Rangelands 59% 41% 0% 12% 12% BFAST - Pastures 27% 68% 5% 17% 17% BEAST - Pastures 46% 54% 0% 7% 20%

These results indicate that the two methods are about equally effective in identifying rangeland conversions, while BEAST is substantially more accurate in identifying pasture conversions. One explanation is that BFAST predicted breakpoints on the trend component over

80% of the time, while BEAST did not have this bias. A change from the natural vegetation of a rangeland to the intensively managed vegetation of a cropland is large in magnitude, allowing the trend component to capture this breakpoint from a sudden change in the mean and variability of NDVI values. For this reason, the trend component is also preferred for detecting deforestation (Darmawan & Sofan, 2012; Verbesselt, Hyndman, Newnham, et al., 2010).

However, the change in NDVI values for a pasture to crop transition is generally lower in magnitude, so trend analysis is not effective in detecting agricultural land cover changes. In fact, this oversensitivity to the trend component is a known behaviour of BFAST and should be

excluded from land cover change analyses to focus solely on phenological changes (Tsutsumida,

Saizen, Matsuoka, & Ishii, 2013).

3.3.2 BFAST Parameterization

3.3.2.1 BFAST Seasonal

As a result, BFAST was constrained to only allow breakpoints in the seasonal component

(i.e. BFAST Seasonal). The result was an improvement in accuracy over BFAST, with an increase from 61% to 76% (kappa = 0.72) for rangeland conversions and 27% to 44% (kappa =

0.35) for pasture conversions. The change detection accuracy +/- 1 year was 93% (kappa = 0.88) for rangelands and 76% (kappa = 0.58) for pastures. In both cases, BFAST Seasonal was unable to detect the grassland conversion in 2% of the fields, or 1 out of 41 fields in the study area

(Table 3.2). BFAST Seasonal outperforms BFAST for both types of grassland conversions. It outperforms BEAST for rangeland conversions and has roughly the same accuracy for pastures.

Table 3.2: BFAST Seasonal Grassland to Cropland Conversion Accuracy using MODIS NDVI data.

Method Correct Temporal No One Year One Year Year Mismatch Detection Late Early BFAST Seasonal - 76% 22% 2% 12% 5% Rangelands BFAST Seasonal - 44% 54% 2% 5% 27% Pastures

This substantial improvement in accuracy suggests that grassland to cropland conversions of all types are more easily detected through harmonic analysis than trend analysis. This is because there are marked differences in the phenology of grasslands and croplands, even if the land covers are not differentiable during certain periods in the season. For example, it is understood that grasslands have an earlier start to the growing season and a longer growing season length compared to crops, and the length of the development and senescence phases are roughly equal compared to the more asymmetrical phenology of croplands (Du, Liu, Li, Xu, &

Diloksumpun, 2019). All of these phenological characteristics can be captured by harmonic analysis and not trend analysis. The use of a BFAST Seasonal should be evaluated for other studies involving land cover changes, such as deforestation and urbanization.

It is also promising that the harmonic models captured 93% of rangeland conversions and

76% of pasture conversions +/- 1 year. The nature of these changes can be complex, making the exact year of change ambiguous in some cases. For example, some of the observed grassland conversions were preceded by a year of bare soil as the land was prepared for cultivation the following season. In other cases, only parts of the field were seeded the first year as farmers test the growing conditions before cultivating the entire field. These scenarios may cause the model to incorrectly predict a change one year late or early while still detecting the underlying grassland to cropland change signal.

A single pixel was chosen to show the output from both BFAST and BFAST Seasonal

(Figure 3.6). The actual year of the pasture conversion is 2014, and this is evident through a visual inspection of the seasonality of the original time series, Yt. Yet, BFAST predicts a structural break on the trend component in 2017 while BFAST Seasonal correctly predicts the pasture conversion that occurred in 2014.

3.3.2.2 Detrending

It is evident that detrending has a large effect on the detection of grassland conversions, but the directionality of the effect is different for rangeland and pasture conversions. The removal of detrending (i.e. fitting the harmonic model to raw data) resulted in a decrease in accuracy from 76% to 59% for rangelands. Conversely, the removal of detrending substantially improved the ability of the algorithm to detect pasture conversions, increasing the accuracy from

44% to 63% (Table 3.3).

Table 3.3: Grassland conversion accuracy of BFAST Seasonal with and without detrending using MODIS NDVI data.

Detrended Rangelands Pastures Yes 76% 44% No 59% 63%

We define detrending as the common practice of fitting and subtracting a linear model from a dataset with the purpose of removing a change in mean of a time series.

This practice is often employed to decouple interannual variation in phenology from the long- term climate change signal (Iler, Inouye, Schmidt, & Høye, 2017). In this experiment with a shorter time scale and the aim of change detection, detrending serves the purpose of decoupling changes in phenology (due to land cover changes) from gradual changes introduced by interannual variability of climate conditions, soil conditions and land management. Given that native rangelands respond relatively quickly to changes in climate and soil conditions (Hagen et al., 2012; Havstad et al., 2007), it becomes clear that detrending improves the accuracy of rangeland change detection by removing the strong effects of interannual variability to reveal the underlying phenological change signal.

However, a linear trend does not necessarily represent any real biophysical process; therefore it can be presumptuous and even counterproductive to detrend the data before change

detection due to possible model overfitting (Z. Wu, Huang, Long, & Peng, 2007). This is strongly dependent on the underlying mechanisms at play and the time scale of interest. Pasture change detection decreased in accuracy when the time series was first detrended, presumably due to lower interannual variability. Inappropriately detrending the data in this scenario may reduce the magnitude of the change and influence the presence or timing of breakpoints. This would be particularly true if the change in seasonality simultaneously results in change in the mean NDVI value, as part of this seasonal change would be removed by the detrending process.

3.3.2.3 Empirical Fluctuation Process

The accuracy of BFAST Seasonal grassland change detection using MODIS NDVI data varies depending on the EFP used. Both grassland change types responded best to the OLS-

MOSUM and Rec-MOSUM processes with a rangeland conversion accuracy of 76% and a pasture conversion accuracy of 59%. Conversely, the OLS-CUSUM and Rec-CUSUM processes resulted in a decrease in overall accuracy, with rangeland conversion accuracies of 66% and 61% and pasture conversion accuracies of 54% and 51%, respectively (Table 3.4).

Table 3.4: Grassland change detection accuracy of BFAST Seasonal with different EFPs using MODIS NDVI data.

OLS- OLS- Rec- Rec- MOSUM CUSUM MOSUM CUSUM BFAST Seasonal - 76% 66% 76% 61% Rangelands BFAST Seasonal - 63% 54% 63% 51% Pastures

Overall, it is evident that MOSUM processes are more sensitive to grassland conversions than CUSUM processes. The likely culprit is that the CUSUM processes are based on maximal fluctuation of accumulated sums since the beginning of the time series, and therefore becomes less sensitive as the length of the time series increases. On the contrary, MOSUM processes only use recent information and remain equally sensitive over the entire time series. This is in

agreement with the finding that CUSUM processes are less sensitive to deforestation than their

MOSUM counterparts (M. Lu et al., 2016). However, Chu et al. (1995) reported that this change in sensitivity between MOSUM and CUSUM processes would only occur in the presence of more than one breakpoint, while we find that CUSUM processes inhibit the ability to detect a single grassland to cropland conversion in a MODIS NDVI time series.

It appears that the EFP is indifferent to the use of recursive residuals or OLS residuals when MOSUM processes are used, although OLS residuals have higher performance with

CUSUM processes. This supports the findings in the economic literature that state the OLS residuals have more power in detecting parameter shifts late in the time series when CUSUM processes are used, while this is a known deficiency in recursive residuals (Hackl & Westlund,

1991). In theory, recursive residuals are generally understood to be better suited for time series regression than OLS residuals as future observations are not used to predict the past, satisfying the independence assumption (Brown et al., 1975; Galpin & Hawkins, 1984; Kianifard &

Swallow, 1996). However, in practice we find that this is not the case.

3.3.2.4 Harmonic Order

The accuracy of the BFAST seasonal model ranges widely for different harmonic orders.

The rangeland conversion accuracies are 27%, 34%, 76% and 71% for harmonic orders 1-4, respectively. The corresponding pasture conversion accuracies are 20%, 56%, 63% and 56%

(Table 3.5).

Table 3.5: Grassland change detection accuracy of BFAST Seasonal with different harmonic orders using MODIS NDVI data.

1 2 3 4 BFAST Seasonal - 27% 34% 76% 71% Rangelands BFAST Seasonal - 20% 56% 63% 56% Pastures

It is evident that a low harmonic order is incapable of capturing the complexity of vegetation phenology. A harmonic order of 1 indicates a single sinusoid is fit to the entire growing season; any asymmetry in the temporal trajectory between the first half of the growing season (i.e. development) and the second half (i.e. senescence) will not be captured. There is an overall improvement in the ability of BFAST Seasonal to detect changes when using a harmonic order of 2, as biannual seasonal patterns are now captured through two additive sinusoids. While many studies have found the majority of variability in vegetation phenology can be attributed to the first one or two harmonic orders (Hermance, 2007), this experiment supports the conclusion of

Chen, Chen, Liu, & Peng (2018) that first and second order harmonics are insufficient to capture interannual changes in phenology for cropland change detection. Higher orders are necessary to capture subtle differences between different types of vegetation as low pass filtering results in information loss. As the order is increased to 3, more nuanced fluctuations are captured, allowing a closer fit to the data and the highest accuracy. However, a harmonic order of 4 decreases the accuracy to 56%, as using too many sinusoids can result in overfitting the model to spurious oscillations. The differences for each harmonic order are shown for a randomly selected pixel (Figure 3.7).

Figure 3.7: Harmonic order comparison for a single MODIS pixel.

3.3.3 Data Source Comparison and Lambda

The change detection accuracy of BFAST Seasonal and BEAST for each dataset are presented for rangeland conversions (Table 3.6) and pasture conversions (Table 3.7). The values listed are the maximum accuracy of change detection after testing different values of lambda.

The highest accuracy for pasture conversions was 66%, obtained using MODIS EVI with a lambda value of 500. The highest accuracy for rangeland conversions was 76%, obtained by both

MODIS NDVI and MODIS EVI datasets with different values of lambda.

Overall, the most effective datasets evaluated were MODIS NDVI and EVI using BFAST

Seasonal. That MODIS was more effective in identifying grassland conversions than Landsat using BFAST Seasonal suggests that temporal resolution is generally more important than spatial resolution for identifying these changes. The low temporal resolution of Landsat, particularly in the presence of cloud cover, severely limits the ability of harmonic models to accurately map changes in vegetation phenology. Therefore, in the absence of earth observation data with both high temporal and high spatial resolution such as the NASA Harmonized Landsat and Sentinel-2

Table 3.6: Pasture change detection accuracy using BFAST Seasonal and BEAST with different data sources. Lambda value that maximized the accuracy for each dataset is shown in parentheses.

BFAST Seasonal BEAST MODIS NDVI 63% 49% (l = 50) (l = 250) MODIS EVI 66% 49% (l = 500) (l = 250, 750) Landsat NDVI 54% 59% (l = 250) (l = 500) Landsat EVI 56% 51% (l = 50) (l = 250, 500) Landsat SAVI 56% 46% (l = 250, 500, 750) (l = 250) Landsat MSAVI 59% 54% (l = 750) (l = 250)

Table 3.7: Rangeland change detection accuracy using BFAST Seasonal and BEAST with different data sources. Lambda value that maximized the accuracy for each dataset is shown in parentheses.

BFAST Seasonal BEAST MODIS NDVI 76% 63% (l = 50, 250, 750) (l = 750) MODIS EVI 76% 59% (l = 250, 500) (l = 250) Landsat NDVI 54% 63% (l = 750) (l = 500) Landsat EVI 56% 59% (l = 750) (l = 250, 750) Landsat SAVI 63% 54% (l = 500) (l = 500) Landsat MSAVI 56% 49% (l = 750) (l = 50) product and the RADARSAT Constellation Mission (RCM), MODIS should be used instead of

Landsat or Sentinel to map grassland to cropland conversions. However, the accuracy of BEAST using Landsat data is equal to or better than BFAST Seasonal for both types of grassland conversions. This suggests that the higher spatial resolution (and therefore larger number of pixels comprising each field) is as important as the temporal resolution when using Bayesian time series methods. In cases where MODIS is not plausible, for example when the spatial resolution is too coarse for a study area, BEAST should be considered. This is particularly true for pasture conversions, suggesting that Bayesian time series methods perform well in cases of high uncertainty and should be investigated further for its utility in identifying crop rotations.

The performance of the two MODIS VIs were nearly identical, with the same overall accuracy for rangeland change detection and a slight edge to EVI for pasture change detection.

This is in agreement with the consensus in agricultural remote sensing that NDVI and EVI are equally effective in most cases, but that EVI occasionally performs better than NDVI due to the atmospheric and background corrections in its formula and higher sensitivity over high biomass regions (Huete et al., 2002; Wardlow & Egbert, 2010).

MSAVI outperforms SAVI in rangeland change detection, while the opposite is true in pasture change detection. The difference between the two VIs is that MSAVI tunes the soil brightness factor, L, empirically by identifying the soil line in a NIR-RED feature space plot of the Landsat image. SAVI uses a default value of 0.5. This empirical estimation in MSAVI is useful if the land cover of interest is the majority land cover in the image as the image statistics will be representative. This may explain the difference between pastures and rangelands, as pastures and croplands, both with relatively high vegetation cover, are the dominant land cover in the study areas. Therefore, the L parameter is tuned accordingly. Conversely, the vegetation cover in rangelands is sparser than pastures or croplands, causing the L parameter to be inappropriately tuned for rangeland vegetation estimation. The use of these VIs should be investigated further based on their high accuracy with Landsat data.

Higher values of lambda tended to increase the model accuracy as the low frequency filtering reduced noise and made the model more sensitive to changes. The raw data contains too many high frequency fluctuations meaning structural changes in the residuals need to be larger in magnitude for changes to be detected; smoothing these fluctuations dramatically improves characterization of the phenology.

3.3.4 Change Detection Timing

Breakpoint statistics were calculated to determine the most sensitive times of the year for grassland to cropland change detection. The results show that spring is the optimal time of year for pasture-cropland differentiation as the majority of correct pixels identify the pasture to cropland change in April or May (Figure 3.8). Grasses are known to have an earlier start of season than crops, so the large difference in NDVI between pastures and croplands at this time of year is detectable by BFAST Seasonal, while the difference in NDVI is negligible for the rest of

the season (Figure 3.1). Conversely, the breakpoint statistics for rangeland conversions show the entire growing season is sensitive to rangeland conversions (Figure 3.9).

Figure 3.9: Barplot of pasture to cropland conversion pixels with successful change detection by month.

Figure 3.8: Barplot of rangeland to cropland conversion pixels with successful change detection by month.

June is the month most sensitive to the rangeland-cropland changes followed by August and

May, but the differences are minor. September is the only month during the growing season that

appears incapable of differentiating rangelands and croplands. This suggests that the harvesting

of crops reduces their NDVI values to levels close to rangelands, inhibiting separability.

3.3.5 Effect of Crop Type on Change Detection

Given that the phenology of croplands varies by species, an examination of the change

detection accuracy is presented by crop type, as indicated by the ACI. The results suggest that

the correct year of pasture to cropland conversion can be identified the majority of the time when

the crop subsequently seeded is barley, canola or oats, while the change detection accuracy drops

for wheat (Figure 3.10a). The remaining crops were only represented by one field so conclusions

cannot be made. Similarly, rangeland change detection is consistently accurate for most crops,

with the exception of wheat (Figure 3.10b).

a b

Figure 3.10: Stacked barplots of change detection accuracy by crop type. a) Results of the pasture model. b) Results of the rangeland model. The overlapping growth stages between grasslands and wheat in the spring make

classification and change detection a difficult task (Sonobe et al., 2017; Figure 3.11). Recent

research suggests that multispectral based Leaf Area Index (LAI) proxies, VIs utilizing the

SWIR wavelengths and SAR derived HH/VV ratio values improved separability significantly

compared to traditional VIs (Dusseux et al., 2014; Peña-Barragán et al., 2011).

The higher accuracy of rangelands compared to pastures is likely the result of phenological

differences. However, the size of the rangelands in this study are substantially larger than

pastures which may have introduced a statistical bias. The rangelands are larger for two reasons.

These natural landscapes tend to be larger in size than pastures as they are not constrained by

field boundaries. In addition, the process of choosing rangelands was more subjective in this

study. Pastures were chosen based on crop insurance data and therefore tended to follow quarter

section boundaries, hence the mode at about 65 Ha (Figure 3.4). On the other hand, rangelands

were chosen based on visual inspection of high resolution imagery, so larger fields were easier to

identify. The analysis on the effect of crop type on change detection was limited by a small

sample size, particularly for beans, peas and triticale. While it is notable that wheat transitions

were less accurate than the total accuracy for both rangelands and pastures, further research is

required to elucidate the effect of these crop types on grassland-cropland change detection.

3.4 Conclusions

This study evaluated the ability of structural break methods to detect grassland to

cropland conversions in remote sensing time series data for the first time. The study was

conducted in two Alberta study areas between 2011 and 2017 using MODIS and Landsat data.

The results were compared against ancillary datasets obtained from crop insurance, high resolution imagery and the ACI. The results indicate that these methods are suitable for accurately identifying the timing of land cover changes from natural rangelands to croplands.

This is also possible for pasture to cropland changes, albeit with a lower accuracy. The BFAST seasonal model substantially outperformed the seasonal-trend models traditionally used by

BFAST, suggesting that trend analysis is not useful for detecting these changes. This method was also more effective than BEAST in predicting the correct year of these land cover changes.

MODIS datasets were more effective than Landsat datasets, suggesting that high temporal resolution data is critical for time series change detection, and that spatial resolution is not as important. This experiment revealed that rangelands and croplands are generally separable throughout the entire growing season, while the spring months of April and May are critical time periods for differentiating pastures and croplands. Finally, the type of crop replacing the grassland has a large influence of change detection. Change detection was overall successful for most of the crops studied, although grassland to wheat transitions proved to be more difficult.

This research is promising to improve the mapping of grassland changes in an operational context.

Chapter 4.0: Conclusions

In summary, grasslands are an important economic and environmental resource, providing a source of livestock feed, fiber and fuel as well as ecosystem services ranging from water filtration to carbon sequestration. The latter is particularly important to meet international climate change mitigation targets, as grasslands are a reliable carbon sink that should be valued and protected to prevent a catastrophic rise in global surface temperatures. However, grasslands are being converted to agricultural systems at an alarming rate. In Canada, this rate is unknown and further research is required to improve remote sensing techniques for grassland monitoring.

Accordingly, the aim of this thesis was to develop remote sensing time series methodologies to detect where and when grasslands have converted to croplands in Alberta from 2011 to 2017.

Structural break change detection methods were applied to MODIS and Landsat time series data to predict the timing of these changes, and the results were validated using in-situ data, high resolution imagery and the ACI. The BFAST algorithm was altered to only allow breakpoints on the seasonal component (i.e. BFAST Seasonal) to identify phenological changes. The results showed a huge improvement and outperformed both BFAST and BEAST in predicting rangeland to cropland conversions (76%) and pasture to cropland conversions (66%). The superior accuracy of MODIS over Landsat data indicates that high temporal resolution is necessary to utilize these time series methodologies, and that medium spatial resolution data is sufficient to detect these changes in the Canadian Prairies. This research showed that rangeland to cropland conversions can be detected throughout the entire agricultural growing season, while pasture to cropland conversions can only be detected in from April to May. Finally, the spectral similarities of grasslands and wheat made change detection a difficult task, lowering the overall accuracy.

This thesis provides a structure for detecting grassland to cropland conversions over large areas and may be useful for the operational grassland monitoring in a Canadian context, particularly as the algorithms used in this research do not require training data to initialize the model. It also provides insight into the mechanisms used to successfully detect these changes.

For example, it showed that phenological change detection focused on harmonic analysis was significantly more successful than approaches using trend analysis or a combination of the two.

It identified the remote sensing data characteristics necessary for change detection on this scale.

Finally, it revealed the optimal time of year for separation between grasslands and croplands, which may be useful to remote sensing scientists who may need to carefully select a smaller number of images for this purpose.

However, this research revealed some of the challenges of this approach that require further research. The types of grasslands and croplands involved in the land cover changes significantly influence change detection accuracy due to large in-class variability. Most notably, changes involving pastures and crops, or grasslands and wheat are more difficult to detect. In addition, the algorithms were useful for detecting when these changes occur but need to be combined with other methods to detect where they are occurring, as the statistical approach used in this research cannot characterize the land cover classes involved. Finally, the algorithms used rely on a univariate time series, which required using image transformations to derive vegetation indices.

Further research should focus on phenological change detection utilizing all spectral information to negate the effects of information loss.

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