Uav Imagery for Tree Species Classification in Hawai‘I: a Comparison of Mlc, Rf, and Cnn Supervised Classification

UAV IMAGERY FOR TREE SPECIES CLASSIFICATION IN HAWAI‘I: A COMPARISON OF MLC, RF, AND CNN SUPERVISED CLASSIFICATION

A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF ART

GEOGRAPHY

November 2020

By Derek James Ford

Thesis Committee:

Qi Chen, Chairperson Yi Qiang Clay Trauernicht

Keywords: convolutional neural network, deep learning, supervised classification, UAV, tree species, remote sensing, forest ecology

Derek James Ford

ACKNOWLEDGEMENTS

I’d like to thank everyone involved in this project including my graduate advisor Dr. Qi

Chen, whose expertise and encouragement to pursue exciting research topics was crucial for my thesis development, committee members Dr. Clay Trauernicht and Dr. Yi Qiang, Natural Area

Reserves specialist James Harmon, and the entire department of Geography faculty, staff and students. I am fortunate to have been able to experience the diversity of interests and perspectives within the department, and I feel more fortunate now than ever, to have been able to do so in-person and less than six feet away!

I’d also like to thank everyone who I’ve had the opportunity to work with during my

Master’s degree studies, including Dr. Kirsten Oleson and all the Oleson Lab members, Dr.

Brian Szuster, and Dr. David Beilman. The work opportunities you provided tremendously increased my depth and breadth of knowledge in GIS and remote sensing for a variety of applications, an invaluable addition to my formal coursework.

I’d finally like to thank everyone involved in my life in general for your direct or indirect encouragement for me to pursue my interests. I’d especially like to thank my lovely wife Tessie, my loving family and friends, and of course my dog and two cats. You all have good qualities and values which I admire and work towards, and you also remind me to enjoy every step of the way.

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ABSTRACT

Very-high resolution unmanned aerial vehicle (UAV) imagery coupled with emergent automated classification methods show great promise for fast and affordable remote sensing analysis. Tree species classification through remote sensing has traditionally been limited by spatial resolution of satellite imagery, or cost and logistics associated with aerial imagery collection. In this study, the use of red-green-blue (RGB) UAV imagery was assessed for supervised classification of multiple tree species within a tropical wet forest in Hawai‘i characterized by high species diversity and limited site accessibility. Three classifiers were tested: maximum likelihood classifier (MLC), random forest (RF), and convolutional neural network (CNN) U-Net. MLC and RF were additionally tested with the addition of texture statistics. U-Net achieved highest overall accuracy of 71.2%, compared to MLC with 48.1% and

RF with 52.1%. MLC and RF both benefited from the addition of texture statistics. This study presents a novel comparison of three important classifier types and their capabilities with an emergent remote sensing data source. Findings from this study are consistent with those of recent studies and suggest that easily-acquirable RGB UAV imagery contains the necessary information for fine-grained classification at the species level, especially when utilizing a CNN.

TABLE OF CONTENTS

Acknowledgements……………………………………………………………………………...iii

Abstract…………………………………………………………………………………………..iv

Table of Contents………………………………………………………………………………...v

List of Tables…………………………………………………………………………………...viii

List of Figures……………………………………………………………………………………ix

Chapter 1. Introduction…………………………………………………………………………1

Chapter 2. Review of Literature………………………………………………………………..6

2.1 Remote Sensing Data Sources………………………………………………………………...6

2.1.1 Satellites………………….…………………………………………………………5

2.1.2 Manned Aircraft……………………………………………………………………10

2.1.3 UAV…………………………………….…………………………………………..12

2.2 Image Classification………………………………………………………………………….24

2.3 Remote Sensing Tree Species Classification in Hawaii……..………………………………28

Chapter 3. Materials and Methods...... 30

3.1 Study Area…………………………………………………………………………………...30

3.1.1 Site Description……………………………………………………………………30

3.1.2 Description of Vegetation…………………………………………………………34

3.2 Data Collection………………………………………………………………………………43

3.3 Data Processing………………………………………………………………………………47

3.4 Class Selection……………………………………………………………………………….54

3.5 Reference Data……………………………………………………………………………….55

3.6 Texture Statistics……………………………………………………………………………..58

3.7 Classification Methods……………………………………………………………………….61

3.7.1 Maximum Likelihood Classification (MLC)……….………………………………61

3.7.2 Random Forest (RF)……………………………………………………………….62

3.7.3 Convolutional Neural Network (CNN) U-Net………………………….………….65

3.8 Accuracy Assessment………………………………………………………………………..73

Chapter 4. Results...... 76

4.1 Accuracy Assessment………………………………………………………………………..76

4.1.1 Overall Accuracy (OA) and Kappa Coefficient……………………………………76

4.1.2 Use of Texture Measures…………………………………………………………..77

4.1.3 Class Performances………………………………………………………………..79

4.1.4 Visual Inspection…………………………………………………………………..83

4.2 U-Net Optimization………………………………………………………………………….85

Chapter 5. Discussion...... 87

5.1 Texture Measures…………………………………………………………………………….87

5.2 Classifier Performances……………………………………………………………………...88

5.3 Class Considerations…………………………………………………………………………89

5.3.1 Class Selection……………………………………………………………………..89

5.3.2 Class Performances………………………………………………………………..90

5.4 U-Net Optimization………………………………………………………………………….93

Chapter 6. Conclusion...... 94

Supplementary Material……………………………………………………………………….98

References...... 105

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LIST OF TABLES

Table 1 List of satellite systems which provide near-global coverage and are commonly used for tree species classification………………………………………………………………………….9

Table 2 List of studies using UAV imagery for tree species or vegetation classification…....….18

Table 3 Training data per-class pixel count and median frequency values assigned for class weights…………………………………………………………………………………………...73

Table 4 Overall accuracy (OA) and kappa coefficient scores for the three classifiers...………...77

Table 5 UA and PA for top-performing versions of each classifier…………...………….……..81

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LIST OF FIGURES

Figure 1 Study area within the Pūpūkea-Paumalu Forest Reserve on O‘ahu, Hawai‘i………….32

Figure 2 Forest type mapping for Kaua‘i and O‘ahu (Little & Skolmen 1989)…………………33

Figure 3 Strawberry Guava………………………………………………………………………39

Figure 4 Ironwood………………………………………………………………………………..40

Figure 5 Eucalypt……………………………………………………………………..………….40

Figure 6 African Tulip……………………………………………………………….…………..41

Figure 7 Uluhe…………………………………………………………………………………...41

Figure 8 'Ōhi'a lehua………………………………………………………………….………….42

Figure 9 Trumpet Tree…………………………………………………………………..……….42

Figure 10 Bare ground………………………………………………………………..………….43

Figure 11 DJI Mavic Pro UAV used for data collection……………………………...…………45

Figure 12 Mapping flight planning in the DroneDeploy app………………………..…………..46

Figure 13 Adding images to Agisoft workspace……………….……………………………..….48

Figure 14 After image alignment in Agisoft………………….………………………………….49

Figure 15 Dense cloud production in Agisoft……………………………………………………50

Figure 16 Horizontal view of the dense cloud…………………………………………..……….51

Figure 17 Magnified view of dense cloud……………………………………………………….51

Figure 18 DSM generated in Agisoft………………………………………………...…………..52

Figure 19 Orthomosaic generated in Agisoft………………………………………………...…..53

Figure 20 Study area………………………………………………………………….………….57

Figure 21 Pixel value frequencies for each class…………………………...……………………58

Figure 22 Depiction of MLC classifier feature space (“Maximum Likelihood Classifier”,

1999).…………………………………………………………………………………………….62

Figure 23 Simplified depiction of RF classifier design………………………………………….65

Figure 24 Example U-Net architecture, adapted from Ronneberger et al. (2015)……………….68

Figure 25 Input image sizes tested……………………………………………………………….70

Figure 26 User’s Accuracy (UA) for MLC and RF without and with texture measures………...78

Figure 27 Producer’s Accuracy (PA) for MLC and RF without and with texture measures…….78

Figure 28 True versus predicted class pixel counts for each classifier…………………..………82

Figure 29 Example classification outputs from MLC and RF before and after the addition of texture data ………………………………………………………………………………..……..83

Figure 30 Example classification outputs from optimized MLC, RF and U-Net.………...……..84

Figure 31 Whole study area classification outputs from optimized MLC, RF and U-Net………85

CHAPTER 1. INTRODUCTION

Forests and natural areas across the globe provide invaluable ecosystem services which humans and many other species depend on for their survival. These services include but are not limited to: climate regulation, waste treatment, production of food and raw materials, and cultural or recreational enjoyment (Krieger 2001). Biodiversity within such areas has been identified as a major component of healthy ecosystems which have the capacity to deliver maximum ecosystem services (Gamfeldt et al. 2013). The composition of vegetation dictates the type and amount of ecosystem services which may be provided (Hooper & Vitousek 1998;

Caspersen & Pacala 2001). Many efforts are being made to understand the complex relationships between forest composition and desired ecosystem services. Due to the huge diversity of both ecosystems and management goals, management plans must be specialized for the area in question and informed by accurate and updated spatial information (Carpenter et al. 2006).

To this end, precision mapping of tree species across forested areas is highly desirable and has been pursued by many researchers (see Fassnacht et al. 2016 for a recent review).

Traditional ground-based methods for tree inventory and assessment are well-developed and can provide highly detailed information, but are time-consuming and often spatially incomplete, warranting alternative methodologies (McRoberts & Tomppo 2007). Remote sensing methods have potential for fast, affordable and spatially complete vegetation mapping. Medium to high- resolution satellite imagery has been used extensively from land cover mapping at the national level (e.g., Homer et al. 2015) to tree species classification at the individual stand scale (e.g.,

Immitzer et al. 2016).

The satellite imagery used for this goal typically has high spectral resolution (multi- and hyperspectral) which can greatly improve species separability beyond visible spectrum or red- green-blue (RGB) imagery (Fassnacht et al. 2016). Despite this advantage, the spatial resolution currently produced by most widely available satellite-borne sensors (>50 cm still presents challenges for species-level classification, especially in areas with a high diversity and dense, mixed canopies. Aerial imagery collected by manned aircraft provides much higher spatial resolution and has been utilized for more challenging plant species classification as well as plant health analysis (Vaughn et al. 2018), but similar to ground-based approaches, manned aircraft imagery can be prohibitively expensive or difficult to acquire in a timely or recurring manner.

Emergent unmanned aerial vehicle (UAV) technology has presented an additional platform of remote sensing data collection which provides unique capabilities for tree species classification (Anderson & Gaston 2013). Where satellite imagery falls short of the spatial resolution necessary for detection of tree species in complex forests, manned and unmanned aerial imagery can achieve the necessary image resolution. Furthermore, where manned aircraft imagery falls short in its availability especially within remote or under-studied areas, or in its accessibility due to costs and flight condition requirements, UAV can be deployed for site- and time-specific image collection. Paneque-Gálvez et al. (2014) present a synopsis of key advantages and disadvantages for the use of UAV in forest monitoring programs. They note high spatial and temporal resolution as major benefits over other data sources, especially within the tropical forest settings their analysis focused on.

Classification algorithms used for interpreting remote sensing imagery have progressed alongside the sensors and data being collected. Simple parametric algorithms such as Maximum

Likelihood Classifier (MLC) have been used for decades on a wide variety of remote sensing 2

data and classification goals (Moore & Bauer 1990; Wang et al. 2004; Khatami et al. 2016)

Increasingly high dimensional data such as multi- and hyperspectral imagery has called for new classifiers such as the machine learning classifier Random Forest (RF), which can better utilize such data (Immitzer et al. 2012). The most recent development in classification algorithms is deep learning with convolutional neural networks (CNN) (Zhang et al. 2016). This type of classifier presents new capabilities in automatic feature detection, and may further enhance the usefulness of very high-resolution UAV imagery.

There is a quickly growing body of research involving UAV data collection for vegetation and tree species mapping. Many of these studies follow the emphasis set by satellite and aerial imagery on high spectral resolution for species separability (Nevalainen et al. 2017;

Gini et al. 2018; Effiom et al. 2019). There are also efforts being made to leverage consumer- grade UAV equipped with standard RGB cameras for tree species mapping (Feng et al. 2015; de

Oliveira Duarte et al. 2018). This is driven by the unprecedented spatial resolution of UAV imagery, which may be further utilized by advanced classification algorithms which can process and meaningfully interpret the data.

Only a few very recently published studies have combined RGB UAV imagery with

CNN classifiers for the purpose of tree species classification (Onishi & Ise 2018; Morales et al.

2018; Kattenborn et al. 2019; Natesan et al. 2019; Santos et al. 2019), and these few studies leave many research gaps to be filled. For example, most of these studies have focused on temperate forest types, with only one considering a tropical forest setting. This is important because tropical forests typically have greater species diversity (Pianka 1966) which will likely require greater effort or alternative methods for accurate classification. Additionally, considering the subset of these studies which apply semantic segmentation, or classification of all pixels 3

within the study area, only binary classifications of two classes have been conducted. This is in contrast to many studies using more traditional classifiers and data sources, which can identify multiple species or target classes, and warrants further investigation. Finally, a comparison of parametric, non-parametric and CNN classifiers for the same UAV imagery and tree species classification goal has not been conducted to illustrate the performance differences.

In the interest of advancing this direction of UAV utilization, here is presented a novel application of data source and classification method within a complex forest setting.

Specifically, the research objective of this thesis is to assess the capabilities of three important classifier types (MLC, RF, and CNN) for interpreting RGB imagery collected from a consumer-grade UAV, for the purpose of mapping plant species in a dense sub- tropical forest.

This study presents both local and scientific significance. It is locally significant for its potential to help resource managers better understand the landscape and make informed management decisions. The methods described in this study can be adapted and widely implemented to improve resource management efforts being made in Hawaii and beyond, at a low cost. It is scientifically significant due to the novel combination of affordable UAV imagery and deep learning image classification for tree species mapping. Many studies have assessed the capabilities of different classifiers on satellite, aerial, and UAV multispectral imagery, particularly utilizing near-infrared spectral data for vegetation mapping. More recent studies have applied deep learning classifiers to similar data. There are very few which consider the use of common RGB imagery combined with advanced classifiers for tree species classification, and none of which consider a tropical forest type and multiple species.

The following Chapter 2 provides an understanding of the progression and state-of-the- art of remote sensing techniques for tree species classification. Chapter 3 describes the study area, data collection and preprocessing, and experimental design used for testing and assessing classifier capabilities. Chapter 4 presents the classification accuracy results and findings from the classifier parameter optimization process. Chapter 5 provides an in-depth discussion of the results and important aspects of this study in relation to similar studies conducted elsewhere.

Chapter 6 presents concluding remarks and considerations for future research.

CHAPTER 2. REVIEW OF LITERATURE

The following literature review will discuss the topics of remote sensing data sources, image classification algorithms, and previous tree species classification efforts in Hawai‘i. The purpose of this review is to provide an understanding of the development of remote sensing technology and theory, specifically in relation to tree species classification. Special attention is given to studies involving UAV imagery due to their recent proliferation and particular relevance to this study.

2.1 Remote Sensing Data Sources

2.1.1 Satellites

The demand for enhanced remote sensing capabilities can be seen by the ongoing production and installation of satellites for earth observation purposes (Belward & Skoien,

2015). The launch of Landsat-1 in 1972 marked the onset of this technological progression

(Williams et al. 2006), and subsequent space-borne systems now provide a wealth and variety of data in terms of spatial, spectral, and temporal resolution. For the purpose of tree species classification, the majority of studies make use of data collected by satellites listed in Table 1.

There are cases in which alternative satellites have been used for tree species classification, but the near-global coverage and short revisit time provided by the satellites listed here make them practical and popular choices for research applications.

The effects of spatial and spectral resolution on classification accuracy are well-studied, and play a large role in satellite data selection. Kan et al. (1975) present an early investigation which considers Landsat imagery to study the effect of spatial resolution on forest classification.

They firstly recognized that pixel size should be sufficiently smaller (>4x) than the minimum 6

spatial unit occupied by a single class in order to minimize mixed pixels. They secondly concluded that when all considered resolutions meet this first criteria, lower resolution imagery leads to higher classification accuracy due to reduced intra-class spectral variability and thus greater class separability. This second statement was echoed by Markham & Townshend (1981) and Forshaw et al. (1983).

Conversely, Moore & Bauer (1990) used Landsat data to provide early evidence against the preference for lower resolution imagery, citing the reduction of mixed boundary pixels gained through smaller pixel size. SPOT-1 began collecting high-resolution imagery in 1986, but despite its improved spatial resolution, it was initially found to be too spectrally-limited to match the accuracy of Landsat with 7 bands of 30 m resolution data (Franklin 1994; May & Kroh

1997). However, when the difference in spectral resolution was eliminated, higher spatial resolution SPOT-1 data did result in higher classification accuracy (Salajanu & Oleson 2001).

As early as 1999, sub-meter panchromatic and sub-five meter multispectral satellite imagery have been commercially available, firstly by IKONOS. Sugumaran et al. (2003) compared IKONOS and 25 cm multispectral airplane imagery for individual tree detection, and

IKONOS resulted in slightly higher accuracy, explained by the negative impact of shadow pixels within the very-high resolution aerial imagery. Katoh (2004) used IKONOS imagery for a very detailed classification of 21 temperate, broadleaf tree species in northern Japan and achieved an overall accuracy of 62%, with class accuracies ranging from 42 - 87%. Wang et al. (2004) compared IKONOS and Quickbird imagery for mangrove species classification on the Caribbean coast of Panama and found IKONOS to produce slightly higher accuracy (75%), despite

Quickbird having higher spatial resolution. This was likely attributable to the larger spectral ranges of the IKONOS image bands. Goodenough et al. (2002) supported the argument for 7

higher spectral resolution with their comparison of Landsat-7, ALI and Hyperion data for classification of seventeen temperate forest cover classes on Vancouver Island, Canada, including three tree and one shrub species.

The relative benefits of higher spatial versus spectral resolution continues to be examined today. Valderrama-Landeros et al. (2018) performed a comparison of Landsat-8, SPOT-5,

Sentinel-2 and Worldview-2 imagery for mangrove species mapping along the Pacific coast of

Mexico, in which Worldview-2 provided large accuracy improvements. The much higher spatial resolution of Worldview-2 allowed for positive identification of many small clusters which were represented by few pixels within the coarser 10 or 30 m imagery. Many subsequent studies have utilized Worldview-2 imagery due to its excellent combination of high spatial and spectral resolutions, only recently surpassed by Worldview-3 (Immitzer et al. 2012; Pu & Landry 2012).

Nevertheless, while satellite imagery has experienced significant improvements and many successful applications, species-level classification remains a challenge especially for complex forests characterized by heterogeneous stands, dense or overlapping canopy, and high species diversity (Rocchini et al. 2015).

Table 1 List of satellite systems which provide near-global coverage and are commonly used for tree species classification. Spatial resolution is reported for all relevant multispectral (MS), hyperspectral (HS), and panchromatic (PAN) bands. Revisit time is average at nadir. For Landsat 1-8 and SPOT 1-7, minimum and maximum values are listed.

System Resolution Bands Revisit Years Studies Time Landsat 1-8 80m - 30m 4 - 8 18 - 8 days 1972 - present Walsh (1980); Strahler et al. (1978); Moore & Bauer (1990); Franklin (1994); Wolter et al. (1995); Salajanu & Olson (2001); Thompson et al. (2015); Chiang et al. (2016)

EO-1 30m HS 220 16 days 2000 - 2017 Goodenough et al. (Hyperion) (not global) (2002); Christian & Krishnayya (2009); George et al. (2014)

Sentinel-2 10m - 20m 13 3 days 2015 - present Karasiak et al. (2017); MS Noi & Kappas (2017); Grabska et al. (2019)

SPOT 1-7 20m - 6m 4 - 5 26 days 1986 - present Skidmore & Turner MS; 10m - (1988); Franklin 1.5m PAN (1994); Salajanu & Olson (2001)

RapidEye 6.5m MS 5 5.5 days 2008 - present Adelabu et al. (2013); Pipkins et al. (20140; Roslani et al. (2014); Yang et al. (2014)

IKONOS 4m MS; 4 3 days 1999 - 2015 Katoh (2004); Wang et 1m PAN al (2004); Wang et al. (2008); Pu & Landry (2012)

Table 1 (Continued) List of satellite systems which provide near-global coverage and are commonly used for tree species classification.

Pleiades 2.8m MS; 5 13 days 2011 - present Ruwaimana et al. 0.7m PAN (2018); Wang et al. (2018); Effiom et al. (2019)

Quickbird 2.2m MS; 5 2 days 2001 - 2015 Wang et al. (2004); 0.5m PAN Neukermans et al. (2008); Ouyang et al. (2011)

Worldview-2 1.8m MS; 9 1.1 days 2009 - present Immitzer et al. (2012); 0.5m PAN Pu & Landry (2012); Ghosh & Joshi (2014); Adam et al. (2017); Hartling et al. (2019)

WorldView-3 1.2m MS; 29 <1 day 2014 - present Majid et al. (2016); 31cm PAN Wang et al. (2016); Ferreira et al. (2019); Hartling et al. (2019)

2.1.2 Manned aircraft

For the purposes of this discussion, the term aerial imagery will be used in reference to imagery collected by manned aircraft only. Aerial imagery has been the basis of many successful regional-scale forest mapping projects (Müllerová et al. 2005; Chapman et al. 2010; Dalponte et al. 2013). Its main advantages over satellite imagery are: (1) higher spatial resolution, (2) greater choice of spectral resolution, (3) cloud-free imagery and (4) the ability to choose and customize onboard sensors for the collection of single or multiple data types simultaneously. Due to these advantages, aerial imagery is particularly useful for analysis of forest areas which are too complex to be classified with available satellite imagery.

The customizable nature of aerial imagery, in combination with case-specific classification objectives, has led to a wealth of unique classification methodologies and case- studies which can provide valuable insights. A very early study by Rohde & Olson (1972) considered 10-band multispectral aerial imagery collected by the University of Michigan multispectral scanner for classification of 12 classes including 8 conifer and deciduous tree species classes. They identified different spectral channels which were beneficial for distinguishing among coniferous and deciduous species, respectively. Dalponte et al. (2013) conducted a comparison of hyperspectral data from two airplane-mounted sensors for classification of three boreal tree species, and found that the sensor with highest spatial resolution of 0.4 m gave highest accuracy.

NASA Jet Propulsion Laboratory’s Airborne Visible InfraRed Imaging Spectrometer

(AVIRIS) is a premier high-altitude hyperspectral sensor which has experienced significant application and improvement since its original design in the 1980’s (Harding et al. 2001). Martin et al. (1998) used 9 of AVIRIS’ 224 contiguous bands at 20 m resolution to classify 11 classes, 6 of which were tree species-specific, within the Harvard Forest in central Massachusetts. They achieved an overall accuracy of 75%, and further speculated that the remote sensing classification may actually have been more accurate than the field data classification used for their accuracy assessment, due to the challenges associated with a spatially-complete field-based assessment. In another study, Townsend & Foster (2002) compared AVIRIS and Hyperion satellite data, 20 m and 30 m resolution respectively, for classifying four tree species within a mountainous region of western Maryland. Surprisingly, the Hyperion data resulted in slightly higher accuracy, which was attributed to cloudy conditions during AVIRIS data collection, and better georectification of the Hyperion data.

Lidar data collected from manned aircraft can provide additional advantages for tree detection and species classification due to its ability to provide structural and sub-canopy information. Holmgren & Persson (2003) were able to use aerial lidar data alone for individual tree detection and classification of two conifer species. Many more studies have applied a fusion of passive multi- or hyper-spectral and active lidar data. Asner et al. (2007) introduced a sophisticated system dubbed the Carnegie Airborne Observatory (CAO) which involves a dedicated manned aircraft with the capacity for in-flight hyperspectral and lidar data fusion. A subsequent study using CAO over a forested area in Hawaii resulted in highly accurate classification of five invasive tree species, along with information on forest structural compositions in the presence and absence of these species (Asner et al. 2008). Holmgren et al.

(2008) and Heinzel et al. (2012) provide additional examples of the capabilities of hyperspectral- lidar data fusion for classification of temperate tree species. Studies such as these have resulted in impressively detailed and accurate tree species mapping, even in challenging study areas characterized by high species diversity and dense canopy. However, the current high acquisition cost for updated aerial imagery prohibits its widespread use, and warrants still another source of remote sensing data.

2.1.3 UAV

A thorough history on the early development of UAV technology is provided in

Fahlstrom and Gleason’s 2012 Introduction to UAV systems. UAV found early use in military operations as early as World War I, initially as target practice and explosive delivery platforms.

UAV for reconnaissance began during the Vietnam War era, and by the war’s conclusion in 1972

UAV operational success had dramatically increased due to improvements in platform design,

radio-link, and data collection. UAV has continued to develop for military operations, providing valuable albeit controversial reconnaissance and strike mission capacities.

The progression of UAV within the scientific community is well-described by Watts et al. (2012). NASA became an early adopter of UAV for scientific applications in the 1980’s and

90’s, but found systems capable of carrying research-grade sensors to be too large and expensive

(Watts et al. 2012). Subsequent sensor miniaturization has allowed for the development of less expensive UAV systems, spurring wide and rapid adoption of UAV for numerous scientific purposes. Today a wide variety of UAV systems are being employed for scientific remote sensing purposes such as environmental monitoring including terrestrial, aquatic and marine settings, agriculture and forestry, disaster response and monitoring infrastructure (Anderson &

Gaston 2013).

UAV imagery presents important advantages and disadvantages over both satellite and aerial imagery. It can produce maps of the highest attainable spatial resolution (cm-level), and provides great flexibility in temporal resolution. Additionally, UAV imagery can be acquired very cheaply and without the employment of specialists or costly equipment. This advantage is commonly highlighted for studies which consider remote, data-poor or economically-challenged areas (Paneque-Gálvez et al. 2014). The main disadvantages of UAV imagery are that it is spatially limited due to short flight times and small image footprint, and that payloads, in this case on-board sensors, must be significantly lighter and more compact, and thus less scientifically powerful than their satellite- or manned aircraft-based counterparts (Pajares 2015).

The limited spatial extent has restricted its use to local-scale studies, or as a means of acquiring reference data for larger-scale projects (Kato et al. 2017; Pla et al. 2017). The small payload capacity primarily manifests itself as systems composed of a single, small RGB, multi- or 13

hyperspectral sensor, as opposed to the multi-sensor systems commonly found on manned aircraft. Additionally, the use of UAV-equipped hyperspectral sensors is somewhat limited due to high cost, which is the main issue leading to the use of UAV in the first place.

A quickly growing number of studies have employed UAV-derived data for the goal of tree species classification. Table 2 lists all studies identified in this literature review, which use

UAV imagery for tree species classification. Similar to approaches using satellite and aerial imagery, many studies using UAV imagery utilize multispectral imagery. Gini et al. (2012) performed a relatively early study utilizing UAV-derived RGB and NIR imagery for classification of multiple landcover classes including 6 tree and scrub species. Unfortunately, their sub-optimal imagery (blur and occasionally off-nadir sensor angle) and unsupervised classification approach did not result in distinguishing among any of these species. Lisein et al.

(2015) similarly used RGB and NIR UAV imagery, for classification of 5 temperate deciduous tree species in southern Belgium. They additionally assessed the effect of data acquisition timing and multi-temporal analysis, in terms of seasonal tree phenology. This study was successful for species discrimination, with single-class accuracy greater than 88%. Michez et al. (2016) also utilized RGB and NIR imagery in combination with optimized data collection time for classification of six deciduous tree species dominant within two small study areas again in southern Belgium. They further split one of the tree species into two classes, either healthy or diseased, due to the known presence of a detrimental plant pathogen. Their study resulted in overall accuracy of approximately 82%, and 90% accuracy for distinguishing between healthy and diseased trees. They confirmed the benefit of collecting during late season, a well- recognized approach for deciduous forests. Additionally, the contribution of NIR data was found

to be greater within the site which presented greater variability in photosynthetic activity, by presence of both deciduous and coniferous species.

Effiom et al. (2019) chose to combine RGB UAV imagery with less detailed Pleiades

NIR satellite imagery for classifying two deciduous and one coniferous tree species in a patch forest setting in Germany. Despite the lower spatial resolution of the NIR data, this addition greatly improved results for all species. Gini et al. (2018) performed another classification study using RGB and NIR UAV imagery, highlighted by comparison of numerous variable combinations, this time focusing on a small nursery setting and eleven deciduous tree species of less dense configuration. They found the addition of texture measures calculated from the gray- level co-occurrence matrix (GLCM) to significantly increase accuracy for all classes.

Interestingly, texture measures provided greater improvement than the use of multi-temporal imagery.

Further advancements in sensor technology have allowed for more complex UAV systems equipped with hyperspectral and even lidar sensors. Nevalainen et al. (2017) collected hyperspectral UAV imagery consisting of 33 bands for classification of two coniferous and two deciduous tree species within multiple small test sites in southern Finland. They additionally applied several structural features calculated from UAV imagery-derived point cloud in combination with aerial lidar data available for the study sites. Optimization of spectral and structural features resulted in overall accuracy of 95%. Sankey et al. (2017) provide an unprecedented application of UAV lidar and hyperspectral data fusion for classifying five vegetation types including one pine, one shrub, and three grass species in Arizona, USA. Their approach reached 88% overall accuracy, with 100% accuracy for two of the selected species.

They also compared the use of hyperspectral and lidar, against multispectral and structure-from- 15

motion derived point cloud. Hyperspectral-lidar fusion performed better, although it is not clear whether this was due to the higher spectral or spatial resolution of the hyperspectral data.

A limited number of studies have been conducted using only RGB UAV imagery, motivated by the extremely affordable and user-friendly aspects of such data. One such study by

Santos et al. (2019) aimed to identify Cumbaru, a single tree species of importance within a semi-urban study area in Brazil. This resulted in identification accuracy of 95%. A similar study by Morales et al. (2018) classified a single palm species within a densely forested area in northern Peru, achieving 98% accuracy. In a more complex effort, Onishi & Ise (2018) considered six classes of vegetation including two at tree species level. Their semi-managed study area in Kyoto, Japan contained a dense mixture of deciduous and evergreen broadleafs and conifers. Despite the limited spectral information, they achieved high accuracy (>88%) for both species classes and 89% overall accuracy. They were able to increase accuracy by providing more training data, through augmentation of the original training data. This method greatly improved results especially for previously-sparse classes. Kattenborn et al. (2019) also used affordable RGB imagery for classification of multiple vegetation communities. They considered three different scenes, targeting an herbaceous community in either pioneer or intermediate stage of succession in southern New Zealand, a single shrub species Gorse, and a single tree species

Monterey Pine. They were able to achieve class accuracies of at least 84%. They also made use of data augmentation to increase training size and classifier robustness.

It can be seen from this review that there are two somewhat diverging approaches when collecting UAV-based imagery and data. On the one hand is the traditional approach seen in satellite and aerial imagery of collecting and analyzing as much spectral and structural information as possible, starting with RGB plus NIR imagery and progressing to hyperspectral 16

and lidar data. This approach has proven success, but comes with significant cost in equipment and the technical procedures necessary for manipulating the increasingly complex data. Many of the studies presented describe the challenges of successful collection and analysis. On the other hand is a more recent approach which make use of more simply collected RGB imagery, often gained through consumer-grade and non-customized UAV systems. This at first may seem like a step backwards in a field which traditionally seeks improvements through specialized instrumentation. However, there are two main reasons why this approach may be attractive and even advisable for those wishing to accomplish tree species classification. Firstly and quite obviously, costs are minimized when using consumer-grade systems due to the low equipment cost, and also reduced need for the fieldwork associated with procedures like hyperspectral reflectance calibration. This aspect aligns well with, for example, those who desire detailed tree species classification over relatively small areas at a very low cost, and therefore cannot turn to either satellite data due to limiting spatial resolution, or aerial data due to high acquisition costs.

Secondly, recent advances in image classification algorithms are expanding image analysis possibilities, as detailed in the next section. This improvement is largely responsible for the recent increase in successful analysis conducted on spectrally-limited RGB imagery.

Table 2 List of studies using UAV imagery for tree species or vegetation classification.

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study RGB camera Visible light 4cm Northern Tropical 4 (Prickly Acacia, Gaussian 89% Reid et al. Australia (semi-arid Parkinsonia, Euclypt, Processes (2011) low density null) vegetation) Pentax Optio Visible light; 1.33cm; Lombardy, Temperate/ 10 (unsupervised ISOCLASS NA Gini et al. A40 RGB; NIR 2.63cm Italy managed classification) (2012) Sigma DP1 park area NIR (alder, poplar, birch, etc.) River-Map Visible light 7cm Zhejiang Urban 6 (trees, shrubs, RF 90.60% Feng et al. UAV RGB Province, (residential grass, bare soil, (2015) camera China vegetation) water, impervious surfaces) Ricoh RGB; Visible light; 20cm Grand- Temperate 5 (English oak, birch, RF 61.50% Lisein et al. Ricoh NIR NIR Leez, (English oak, sycamore maple, (2015) Belgium broadleaf/ common ash, poplar) coniferous)

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study Ricoh RGB; Visible light; 10cm Wallonia, Temperate Site 1: RF Site 1: Michez et Ricoh NIR NIR Belgium (Natural/ 5 (black alder, ash, 79.5% al. (2016) managed sycamore, willow, Site 2: riparian other) 84.1% forest, black Site 2: alder 5 (black alder, dominant) spruce, sycamore, english oak, other) Canon S110 Blue, green, 8.3cm Lambaye- Tropical 4 (Alive Algarrobo, Vegetation 94.10% Baena et al. RE red-edge que, Peru (arid, low Dead Algarrobo, index (2017) density Sapote, Overo) thresholds vegetation) DJI Phantom Visible light 3.47cm Fars Temperate 2 (Wild pistachio, Nearest 90% Chenari et 4 RGB province, (semi-arid, Wild almond) neighbor al. (2017) camera West Iran low density vegetation) Sony DSC- Visible light; 3.5cm; Ontario, Temperate 4 (Cedar, Pine, RF NA (87% Franklin et WX220; 4 bands 12.9cm Canada (mature Spruce, Juniper) mean UA) al. (2017) Parrot (green, red, mixedwood) Sequoia NIR, red edge)

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study Samsung Visible light; 2.3cm; Padasjoki, Boreal 4 (Pine, Spruce, k-NN; Naïve 95.2% (RF) Nevalainen NX1000 RGB 33 bands 8.6cm Finland (pine/birch) Birch, Larch) Bayes; C 4.5 et al. (2017) camera; (11-31nm); decision tree; Fabry–Pérot Multilayer interferemoter Perceptron (FPI) (MLP); hyperspectral Random Forest (RF) Multispectral; 4 bands 12cm; Flagstaff, Temperate 6 (Ponderosa pine, Decision tree 88.0% Sankey et hyperspectral; (green, red, 15cm; 35 Arizona, (pine/grassla Rabbitbrush, Pine al. (2017) lidar red edge, points/m2 USA nd) dropseed, Arizona near fescue, Blue grama, infrared); Bareground/shadow/ 272 bands unclassified) (400- 1000nm) Samsung Visible light; 3cm; 8cm; Kouvola, Boreal (high 26 (conifer and k-NN; RF 82.3% (k- Tuominem NX300 RGB 36 bands 20cm Finland diversity broad-leaf tree NN) et al. (2017) camera; FPI (VNIR 409- coniferous/ species) hyperspectral 973nm); 32 broad-leaf (2 total) bands managed (SWIR 110- forest) 1600nm)

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study Sony NEX- Visible light, 5cm Beskids Temperate 4 (Spruce, Fir, Nearest 78.4% Brovkina et 5R RGB; NIR Mts, Czech (coniferous/ Beech, dead tree) neighbor al. (2018) Sony NEX- Republic deciduous) 5R NIR UHD 185 125 bands 15cm Guangdong Managed 10 (6 mangrove KNN; SVM 89.6% Cao et al. Firefly (454 - Province, mangrove species, common (SVM) (2018) hyperspectral 950nm) China forest reed, water, boardwalk, shadow) Sony ILCE Visible light 5cm Minas Urban 5 (Forest, agriculture, Parallelpiped; NA de Oliveira RGB camera Gerais, (university soil, urbanization, MLC; Duarte et al. Brazil campus with water) Minimum (2018) temperate Distance; forest Artificial remnants, Neural Network agriculture, buildings) Sony DSC- Visible light; 3.5cm; Ontario, Temperate 9 (Sugar maple, Red ISODATA; 69% (RF) Franklin et WX220; green, red, 12cm; 11cm Canada (mature maple, Birch, Ash, MLC; RF al. (2018) Parrot NIR; 5 bands mixedwood) Aspen, Basswood, Sequoia; (RGB, NIR1, Spruce, Pine, Cedar) Tetracam NIR2) MiniCam MCA6

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study Nikon 1 J1 Visible light, 1.3cm; Como, Italy Plant nursery 11 (broadleaf tree MLC 87.2% Gini et al. RGB; NIR 1.6cm species) (2018) Tetracam ADC Lite RGNIR

RGB camera Visible light 5cm Kyoto, Temperate 7 (Deciduous broad- GoogLeNet 83.1% Onishi & Japan (cypress/ leaf, deciduous Ise (2018) broadleaf) conifer, Chamaecyparis obtuse, Pinus spp., Pinus strobus, other) RGB camera Visible light 1.4 - 2.5cm Tropical 1 (Aguajal (Mauritia Deeplab v3+; 98% Morales et (rainforest/ flexuosa) U-Net (Deeplab al. (2018) swamp) v3+) GR_GRLEN Visible light, 30cm; 50cm Amstelveen Temperate 5 (Scots pine, Beech, MLC, SVM, 84.4% (RF) Effiom et S_18.3 RGB RGB+NIR Village, (coniferous/ Birch, Water, RF al. (2019) camera; Germany deciduous Shadow) Pleiades forest satellite fragments) multispectral Canon 100D Visible light 3cm Central Temperate 3 (Monterey pine, U-Net 85.5% (site Kattenborn RGB camera Chile; (successional Gorse, other) (site 1); 1); 90.5% et al. (2019) (site 1); DJI Mount shrublands) 2 (pioneer vegetation (site 2) Phantom 4 Cook 22

Highest Overall Spectral Spatial Accuracy Sensors Resolution Resolution Location Forest Type Species/Classes Classifiers (OA) Study Pro+ RGB National community, other) camera (site Park, New (site 2) 2) Zealand RGB camera Visible light 1-4cm Ontario, Temperate 3 (Red pine, White ResNet 80% Natesan et Canada (mature pine, non-pine) al. (2019) mixedwood)

RGB camera Visible light 0.82cm Mato Urban 1 (Cumbaru Faster-RCNN; 92.6% Santos et al. Grosso do (Dipteryx alata)) YOLOv3; (RetinaNet) (2019) Sul, Brazil RetinaNet GoPro RGB Visible light; 11cm; 1 Curitibanos, Tropical 12 (tropical tree SVM 72.40% Sothe et al. camera; 25 bands point/m2 Santa (successional species) (2019) Fabry-Perot (500 - Catarina, forest stages) interferometer 900nm) Brazil hyperspectral; Optech Model 3033 lidar

2.2 Image Classification

For many purposes, remote sensing data requires significant interpretation to convert raw data into useful information. Particular research purposes and data types will often present unique interpretation approaches. This section will focus on interpretation techniques for high- resolution imagery including RGB, multi- and hyperspectral data, for the purpose of vegetation mapping and especially tree species classification.

The most straightforward approach for imagery interpretation is visual interpretation by an informed individual or team who can recognize the desired classes within the imagery. This is an important process which has been the basis of many important map products and analyses

(Ulbricht & Heckendorff, 1998; Bey et al. 2016; Fiorucci et al. 2018). It is a very time- consuming process however, and presents a large potential for human error. Due to this, significant effort has been made and continues to be made for the development of statistical learning procedures which employ computers to analyze the data and perform classification.

Three great benefits of this approach are, firstly, that large areas can be classified with minimal human input, reducing both cost and time for analysis. Secondly, computer-based classification is conducted in a consistent manner which eliminates the potential of natural human error or inconsistencies when more than one human image interpreter is employed. Thirdly, computers can utilize data outside of the visible light spectrum which allows for analysis of the multi- and hyperspectral data, or data fusion methods previously discussed. Visual interpretation still holds tremendous value for the purposes of reference data production and accuracy assessment, but computer-based classification should be employed to the greatest extent possible. This holds true for the classification of tree species, and the following paragraphs will summarize the progression and state-of-art of such pursuits. 24

A summary on the progression of classification algorithms used in tree species identification is provided by Fassnacht et al. (2016). Maximum likelihood classifier (MLC) is a standard choice in many early studies, and has maintained its presence into even the most recent classification studies. This is due to its simplicity, relatively high accuracy and availability across computer platforms, and it serves as a standard measure for evaluating newer algorithms. Many of the studies previously discussed have utilized MLC either solely or in comparison with other classifiers. A major shortcoming of MLC is its assumption of normal distribution of intra-class pixel values, which is not always the case in remote sensing imagery (Belgiu & Dragut, 2016).

To overcome this limitation, non-parametric, decision tree-based classifiers in the category of machine learning have been developed such as support vector machine (SVM) (Cortes & Vapnik

1995) and random trees, also called random forest (RF) (Breiman 2001). These classifiers have been particularly useful for studies using multiple data types because of their capabilities with high-dimensionality and scarce training data (Mountrakis et al. 2011; Belgiu & Dragut, 2016).

Most recently, there has been significant interest in the use of artificial intelligence (AI) for remote sensing data analysis. AI is an advanced form of machine learning, and refers generally to computer systems which are able to perform tasks which normally require human intelligence such as visual perception, speech recognition, and decision-making. AI is distinct from previously mentioned forms of machine learning due to its ability to identify important patterns and features without human input. Progress in AI is largely attributed to causes outside of remote sensing, and includes factors such as improved computers, big data, and a demand for image recognition capabilities from various data analytics industries (Chen & Lin, 2014). A sub- category of AI termed deep learning has proven to be particularly successful, and there is now a

growing body of work using deep learning for remote sensing of the environment (Brodrick et al.

2019).

Deep learning is characterized by neural networks, multiple layers of non-linear functions which consider input data and learn to extract patterns and features without any human input.

There are many deep learning algorithms, which have been developed and tested over the past few years. Pertaining to vegetation mapping, de Oliveira Duarte et al. (2017) compared an artificial neural network to several traditional classifiers including MLC, for mapping five land cover types within UAV imagery. They found the artificial neural network most accurate (93%) with MLC in close second (87%), however significant time and effort went into training the neural network to exceed MLC. Nevalainen et al. (2017) also used UAV imagery and compared classification results for four evergreen tree species using multiple non-parametric classifiers

(RF, K-nearest neighbor, C4.5) and one eighteen-layer deep learning classifier (multilayer perceptron (MLP)), achieving similarly high accuracy (>95%) with both RF and MLP.

Among deep learning algorithms, convolutional neural network (CNN) has shown to be particularly successful for object recognition problems (Krizhevsky et al. 2012), and has already been applied to classification tasks involving satellite, aerial, and UAV imagery (Rebetez et al.

2016; Iglovikov et al. 2017; Iglovikov & Shvets 2018;Santos et al. 2019). CNN takes advantage of convolutional and max pooling layers which effectively extract patterns from an image, starting with very abstract and generalized features and working towards specific output classes

(Zhang et al. 2016). CNN can be very successful for a variety of image recognition problems, and is most commonly applied for identifying objects within an image. For mapping purposes however, semantic segmentation, or classification of all pixels within an image, is desirable because it preserves the spatial arrangement of objects being classified. U-Net, the winning CNN 26

of the ISBI cell tracking challenge 2015, accomplishes this through its unique architecture of data down-sampling or encoding, followed by up-sampling to result in pixel-wise classification of the original image (Ronneberger et al. 2015).

While originally created for use on microscopy images, a modified U-Net was subsequently applied towards the DSTL Satellite Imagery Feature Detection Challenge in 2017, winning 3rd place out of 5,543 entries for classification of 10 classes such as structures, trees, and vehicles (Iglovikov et al. 2017). Progressing from this work, Iglovikov & Shvetz 2018 were able to improve U-Net accuracy when mapping buildings within aerial imagery, by pre-training the network using one of two large labeled datasets, ImageNet (Russakovsky et al. 2015) and

Carvana (Kaggle). Dong et al. (2019) also applied a modified U-Net for classification of high- resolution aerial imagery into six classes including buildings, trees, and cars, and achieved

85.6% overall accuracy.

Most recently, U-Net has proven successful for classifying and mapping tree species.

Kattenborn et al. 2019 conducted four binary classifications using U-Net, the first two identifying pioneer versus established vegetation communities in two different settings in New

Zealand, thirdly identifying a shrub species versus all other vegetation in central Chile, and fourthly identifying Pinus radiata versus other temperate tree species, also in central Chile.

Accuracies of 89%, 90%, 84%, and 87% were achieved within these four classifications, respectively. Wagner et al. 2019 conducted two binary classifications using U-Net for study areas in the Atlantic forest biome of Brazil, firstly classifying natural forest versus eucalyptus plantations, and secondly identifying Cecropia hololeuca versus all other vegetation. These two classifications achieved 95.4% and 97.1% accuracy respectively. U-Net has also been used for

classifying functional classes such as vegetated versus non-vegetated areas (Flood et al. 2019), and rice crop disease detection(Zhao et al. 2019).

These most recent studies show great promise for U-Net as a high-performing remote sensing tool, and furthermore demonstrate a potential shift in data requirements. All of these studies were conducted using low-cost RGB imagery, and one case (Zhao et al. 2019) even showed higher accuracy with RGB over multispectral (R-G-NIR) data, with speculation that the higher spatial resolution collected by the RGB camera contributed to higher classification accuracy. The ability for CNN’s to consider contextual information within the imagery explains why very-high spatial, but relatively-low spectral resolution data such as that produced by low- cost UAV can be used to match or even surpass results from traditional pixel-based classifiers informed by multispectral data.

2.3 Tree Species Classification Efforts in Hawai‘i

Previous remote sensing efforts for tree species mapping in Hawai‘i include the use of satellite and aerial imagery. Harman (2006) conducted a study comparing classification results from two high-resolution multispectral satellite images acquired respectively by QuickBird and

IKONOS, in Mākaha Valley on O‘ahu. This study resulted in an improvement over existing vegetation mapping for the area of interest by providing species-level classification for native species 'Ōhi'a (Metrosideros polymorpha) and non-native species Kukui (Aleurites moluccana).

Additional classes could not be refined to species-level. Also, accuracy assessment proved to be challenging due to accessibility issues for field verification in terms of challenging terrain and

vegetation in the study area. This study provides valuable information on some biophysical challenges faced such as the presence of shadow and clouds in the imagery.

Asner et al. (2008) performed a detailed study encompassing multiple forested areas on

Hawai‘i Island, focusing on the detection, mapping, and three-dimensional structure analysis of five invasive plant species. Remote sensing data consisted of aerial hyperspectral and lidar data.

Previously acquired species-specific spectral characteristics were applied towards classification, and lidar-derived structural data was used for subsequent analysis. The species distribution maps resulting from this study are impressive in accuracy and spatial extent, but the methodology is prohibitively expensive. Similarly, Balzotti & Asner (2017) utilized aerial hyperspectral and lidar data, with SVM classification to accurately map 16 tree species in a dry forest region of

Hawai‘i Island. The wealth of data and sophisticated analysis allowed for a relatively high level of species separability compared to many other species-level classification studies which are typically confined to only a few target species.

While some progress has been made in describing tree species distributions in Hawai‘i through remote sensing, the methodologies previously employed for this have been either marginally successful due to site accessibility, data quality and classifier capabilities, or uneasily reproducible due to costs for data acquisition and analysis. A robust and easily-deployable remote sensing procedure has yet to be developed which can be effectively applied for real-time research and management purposes at the species level.

CHAPTER 3. MATERIALS & METHODS

The following chapter contains a detailed explanation of the methodology employed in this study. Firstly a description of the study area including recent land cover changes (early

1900’s to present) and current vegetation composition is provided. Secondly, UAV system, data collection and processing steps are discussed. Thirdly, sample plot selection and reference data production are explained. Fourthly, texture measures and feature reduction methods are discussed. Fifthly is an explanation of the three classifiers to be used and their particular input requirements. This chapter concludes with an explanation of the accuracy assessment which will be used to assess classifier performances.

3.1 Study Area

3.1.1 Site Description

The study area is located in Hawai‘i on the island of O‘ahu, and encompasses roughly 17- hectares situated within the Pūpūkea-Paumalu portion of the Hawai‘i State Forest Reserve

System (Figure 1). It is centered at approximately 158°1'25.767"W, 21°38'46.163"N, with elevation ranging from 150-300 meters above sea level. The study area is situated in an ecological zone characterized by a transition from lowland, invasive-dominated forest, to upland native-dominated forest. Transitional areas such as this are ecologically important because they present a potential barrier for further progression of invasive plant populations into native forests if managed correctly (Cordell et al. 2009). This site has also been identified as an area of interest by local resource managers because of the remnant native species which have managed to exist within the increasingly invasive-dominated landscape.

The presence and distribution of plant species within the study area is largely a result of direct human actions, namely deforestation by cattle grazing and subsequent intensive reforestation efforts beginning in the early 1900’s. Significant denuding of the landscape in many parts of Hawai‘i was recognized as a problem with implications of excessive erosion and inadequate groundwater recharge. To combat this, large out-planting efforts were taken by both public and private entities. Within the study area, significant out-planting was performed beginning in 1920 in exchange for use of forest reserve land for pineapple cultivation (The

Hawaiian Forester and Agriculturist: A Quarterly Magazine of Forestry, Entomology, Plant

Inspection and Animal Industry, Volumes 17-18). Early records show thirteen tree species which were outplanted within the “extreme western tip of the Pupukea Forest Reserve” (Skolmen

1980). Little and Skolmen (1989) describe the forest type within the area as Eucalyptus and

Mixed exotic hardwoods (Figure 2), and further note remaining patches of native forest where reforestation efforts were not necessary.

Figure 1 Study area within the Pūpūkea-Paumalu Forest Reserve on O‘ahu, Hawai‘i.

Figure 2 Forest type mapping for Kaua‘i and O‘ahu (Little & Skolmen 1989). General location of the study area is located within the red box. This area is described primarily as Eucalyptus and mixed exotic hardwoods, with 'Ōhi'a-hāpu'u and Guava bordering on the southern and northern edges, respectively. 33

3.1.2 Description of Vegetation

The study area is densely forested and is characterized by a mixture of native and non- native tree and shrub species, including four dominant non-native trees Strawberry Guava

(Psidium cattleianum), Ironwood (Casuarina sp.), Eucalypts (Eucalyptus spp. and Melaleuca quinquenervia), and African Tulip (Spathodea campanulata), and dominant native fern Uluhe

(Dicranopteris linearis) (Figures 3 – 7). These four species or closely related groups of species occur in largely monotypic stands of varying shape and size throughout the study area. Mixed within the dominant species are several other less-common species such as native trees 'Ōhi'a lehua (Metrosideros polymorpha) (Figure 8), Ahakea and Ahakea Lau Nui (Bobea sandwicensis,

B. elatior), and Iliahi (Santalum freycinetianum), and the non-native Trumpet Tree (Cecropia obtusifolia) (Figure 9). There are also small areas of bare ground present due to excessive erosion, sparse canopy, and otherwise unknown processes (Figure 10).

Strawberry Guava is a highly invasive tree species in Hawai‘i, native to Brazil and likely introduced in 1825 (Motooka et al. 2003). It is easily spread through bird and feral pig dispersal, and is now found forming dense monotypic stands in many mesic to high-rainfall areas across the Hawaiian Islands. Its negative impacts on the native ecosystem are well-documented, namely out-competition of native species through exclusion (Wikler et al. 2000), also its capacity to alter soil chemistry through leaf litter accumulation (Enoki & Drake 2017), and potential alteration of groundwater recharge and stream flow (Strauch et al. 2016). Within the study area, Strawberry

Guava is widespread and can be found predominantly growing in dense monotypic stands in flat to gradually-sloping areas. These dense stands contain little understory growth and the soil can remain muddy long after rainfall events.

Ironwood is a non-native, tall nitrogen-fixing tree which is represented by multiple similar-appearing species in Hawai‘i (Skolmen 1980). It is native to Australia and was introduced in the late 1800’s, experiencing significant out-planting mostly for the purposes of coastal windbreak and reforestation (Motooka et al. 2003). It is now widespread throughout the

Hawaiian Islands and many other areas of the Pacific. Its wood is extremely dense and useful for tools, and its bark can be processed for medicinal purposes. It does not exhibit particularly invasive behavior in Hawai‘i, but has been recognized as problematic in coastal areas of Florida where it disrupts important animal habitat and reduces sand retention (Wheeler et al. 2011).

Within the study area, Ironwood can be found growing primarily on steep slopes in semi-dense, monotypic stands and also mixed with Strawberry Guava and Eucalypt. The location of these trees is likely due to out-planting done here to prevent soil erosion. Within the Ironwood stands there is an open understory which can contain some lower-growing plants such as Pukiawe

(Styphelia tameiameiae) in very low density. The ground in these areas contains significant

Ironwood leaf litter, under which the soil remains relatively dry and crumbly.

Eucalypt is a grouping of closely related tree species within the family Myrtaceae.

Species from this group are mostly native to Australia. At least ninety species from the Eucalypt group have been planted in Hawai‘i (Skolmen 1980). Eucalyptus (Eucalyptus spp.) plantations are among the most successful and plentiful type of out-plantings in the state. Members of this genus also includes the tallest tree species (E. saligna) currently found in Hawai‘i (Little &

Skolmen 1989). Similar to Ironwood and despite its very widespread distribution, Eucalyptus does not exhibit invasive behavior in Hawai‘i. It is found, however, that Eucalyptus stands in wetter environments commonly contain a dense understory of Strawberry Guava, and in this sense do contribute to the proliferation of invasive species. Paperbark (Melaleuca

quinquenervia), another species within the Eucalypt group, has also been extensively outplanted in Hawaii. Unlike species from the Eucalyptus genus, Paperbark is considered highly invasive and has escaped cultivation in many locations across the world (U.S. Fish and Wildlife Service

2019). Within the study area, Eucalypt trees are found in areas similar to those of Ironwood characterized by steep slopes and crumbly soil. They have particularly been seen occupying areas directly adjacent to or slightly mixed with Ironwood and Strawberrry Guava.

Uluhe is a fern species native to and widely distributed across Hawai‘i, the tropical and sub-tropical Pacific and southeast Asia (Zhao et al. 2012). The genus Dicranopteris also includes several other species which occupy comparable ecological niches to Uluhe in other parts of the world. Uluhe is an early-stage colonizer capable of establishing on bare rock and nutrient-poor soils, and can establish dense monotypic thickets through clonal or vegetative growth (Russell

1996). These thickets can remain indefinitely given continued high rainfall and sunlight availability, but will eventually diminish under increasing overstory canopy. An important characteristic of Uluhe in the presence of increasing invasive species in Hawaii is its ability to suppress the establishment of other plants through both physical (sunlight exclusion) and chemical (allelopathic) processes (Tet-Vun & Ismail 2006). Within the study area, Uluhe is found growing in dense thickets on moderately sloped terrain. These thickets are largely monotypic, but also punctuated by individual native and invasive trees which have managed to establish and reach overstory height.

African Tulip is a tree species native to west and central Africa, and was introduced to

Oahu in 1915 as an ornamental and reforestation tree (Little & Skolmen 1989; Staples & Herbst

2005). It exhibits invasive behavior, and can now be found in low- to mid-elevation forests throughout Hawai‘i, many other sub-tropical and tropical Pacific and Caribbean islands, and 36

continental locations such as Australia and India (Larrue et al. 2016). It can grow to a height of approximately 24 meters and produces large quantities of wind-dispersed seeds. Seedlings are found to be very shade-tolerant allowing it to establish widely within intact forested areas, although it does require nearly full sunlight for reproduction (Larrue et al. 2014). Flowers of the

African Tulip tree are large and strikingly orange, and can occur year-round. Within the study area African Tulip is found growing along the main drainage which runs from south to north, as either an individual tree or small to medium monotypic or mixed stands with dense closed canopies. As African Tulip blooms year-round, it can be found within the study area in various stages of flowering or vegetative growth.

Small to medium sized patches of bare ground are present within the study area. These patches are partially explained by erosional events and the combination of somewhat open canopy and sparse understory within the Ironwood stands. Some other areas of bare ground have less obvious causes, but seem to occur at the margins between Uluhe and Strawberry Guava stands. It is possible that the remaining areas of bare ground are remnants from the widespread deforestation previously described, which were not treated by subsequent reforestation efforts and are slow to be vegetated naturally.

The remaining vegetation within the study area is composed of a variety of tree species which are in much lower abundances and found in more sparse and sporadic distributions.

Among them are several native species of interest to forest reserve managers for both their preservation and seed collection potential. 'Ōhi'a lehua is the most iconic of these species, as a tree of very high practical and spiritual significance in Hawaiian culture (Abbott 1992). It is a long-lived tree which can thrive in an impressive range of environmental conditions, and in native forests of Hawai‘i is often the dominant canopy species (Cordell et al. 1997). It is however 37

a relatively slow-growing tree, which can be easily outpaced by more aggressive introduced species (Schulten et al. 2014). 'Ōhi'a lehua can be found within the study area as individual trees punctuating Uluhe patches and adjacent in the margins of other species stands.

The other three native tree species listed are very rare within the study area. Some can be found at canopy level, while others may only reach mid- or sub-canopy height. Ahakea and

Ahakea Lau Nui are closely related species from the genus Bobea, a genus native only to

Hawai‘i (Little & Skolmen 1989). They are medium-sized trees and are typically found scattered in dry and wet forest areas. Iliahi is a species of historical socioeconomic significance in Hawai‘i due to its exhaustive harvesting for the valuable but short-lived sandalwood trade, which began in Hawai‘i around 1800 and concluded in 1830 with the significant diminishing of the wild populations(St. John 1947). While the original extent and amount of Iliahi within the natural landscape is unknown, Iliahi can still be found in low densities within many low to mid-elevation forests.

The last notable tree species within the study area is a less common non-native, but one which has the potential to be highly problematic. Trumpet Tree is native to Central America and has been recognized as a moderate to highly invasive tree species in many parts of the sub- tropical and tropical Pacific (Sherley 2000). It is a medium-sized, fast-growing tree with high- reaching branches of up to 15 meters topped by large fan-like leaves. Additionally, Trumpet Tree is a well-known for its symbiosis with ant colonies which are harbored within its hollow branches (Wetterer & Dutra 2011). Ants are not native to Hawai’i, and their increasing presence has been linked to biodiversity loss of native arthropods, vertebrates and plants (Krushelnycky et al. 2005). Several ant species have been observed residing on Trumpet Trees in Hawai‘i

(Wetterer 1997). While symbiotic relationships are not currently evident, this tree species’ 38

proven potential for supporting ant communities increases its threat to the native environment.

Unfortunately, this species has been used for reforestation purposes in Hawai‘i, although somewhat sparingly (Little & Skolmen 1989). It is now commonly found in lowland forests where it can easily reach canopy height and is also capable of establishing in some Uluhe thickets.

Figure 3 Strawberry Guava. This species is commonly found growing in very dense monotypic patches such as this. The leaves are small, waxy and grow very densely, creating a highly textured appearance.

Figure 4 Ironwood. This genus of tall trees dominate the upper canopy in the areas which they occupy, with the exception of some trees from genus Eucalyptus which can grow to similar height. Eucalypt species can be seen mixed in with the Ironwood canopy within this image.

Figure 5 Eucalypt. Trees from this group found within the study area are of very similar appearance. They are commonly found growing adjacent or mixed with Ironwood trees, with which they share similar growth characteristics and likely history within the study area. 40

Figure 6 African Tulip. This tree species is characterized by bright green to yellow-green leaves, and also striking red or orange flowers when in bloom. It can bloom year-round and can be found at various flowering stages within the study area.

Figure 7 Uluhe. This common fern species typically forms dense monotypic patches such as this. It is composed of many small, detailed dark to bright green leaves, which makes this species easily recognizable within the imagery. 41

Figure 8 'Ōhi'a lehua. This native tree species is found only as individual trees sparsely occurring within the study area, most commonly within otherwise-monotypic Uluhe stands.

Figure 9 Trumpet Tree. This highly invasive tree species is not yet common within the study area, but can be seen in some locations where it surpasses the surrounding canopy height with its high-reaching branches and recognizable fan-like leaves. 42

Figure 10 Bare ground. The bare ground within the study area is characterized by red, highly oxidized soil. The bare ground within this image is likely the result of erosional processes, while other areas with bare ground are more likely due to the absence of understory growth within Ironwood and Eucalyptus stands.

3.2 Data Collection

UAV imagery of the study area was collected using a DJI Mavic Pro (Figure 11) with standard-equipped digital camera, which has a 1/2.3-inch CMOS sensor and 3,000 x 4,000 pixel image size. Image collection was conducted on March 28, 2019 between the hours of 11:30 and

13:30 Hawaii Standard Time (UTC-10:00), slightly before the solar noon at 14:06. Image collection for the entire study area was accomplished with three autonomous mapping flights under full sunlight and light wind conditions.

Flight planning was accomplished with DroneDeploy (www.dronedeploy.com, Infatics

Inc., San Francisco, Unites States), a web and mobile app-based UAV flight planning platform.

A polygon shapefile of the study area was created on ArcGIS 10.7 (ESRI, Inc., Redlands, United

States) and imported to DroneDeploy to identify the specific mapping location, and flight parameters were then defined (Figure 12). To ensure complete capture of the desired study area, a larger area was identified for UAV image collection. This decision was based on previous experience and the unique topography of the study area, namely the increasing surface elevation on both east and west sides of the area which may result in distortion or inadequate overlap towards the edges. Front and side overlap between adjacent images was set to 80% and 75% respectively to ensure an adequate area of overlap within the images as recommended for quality orthomosaic production (Dandois et al. 2015). Total mapping area was 26.7 hectares, and flight time across the three mapping flights was thirty-six minutes. The UAV acquired a total of 640 images in JPEG format. Flight altitude was held constant at an average of 75 meters above ground level to achieve 4-cm average ground sample distance (GSD).

Figure 11 DJI Mavic Pro UAV used for data collection.

Figure 12 Mapping flight planning in the DroneDeploy app. After identifying the mapping area, DroneDeploy provides estimates of flight time, size of the mapping area, and total images collected. It also estimates the required image spacing to achieve the desired amount of image overlap.

3.3 Data Processing

The UAV images were processed using Agisoft Metashape Professional Edition v1.5.1 software (www.agisoft.com, Agisoft LLC, St. Petersburg, Russia) to produce an orthomosaic of the study area. Agisoft utilizes photogrammetry processing techniques to combine many individual images and also incorporate global positioning system (GPS) coordinates for orthomosaic production along with additional 3D data products. This software has been tested and found comparable or superior to other industry-standard photogrammetry software options

(Gini et al. 2013; Sona et al. 2014).

Orthomosaic production in Agisoft consists of four main processes as outlined in the

Agisoft Metashape user manual (Agisoft LLC 2019) (Figures 13 – 19). The first step is adding all images to the workspace. At this point Agisoft used image metadata stored in exchangeable image file format (EXIF) to determine camera focal length, focal plane X and Y resolution, and

GPS latitude, longitude and altitude. The second step involves image alignment, which utilizes aerial triangulation to identify key points, or matching features, located in multiple images.

Bundle block adjustment is then performed, in which the camera focal plane is estimated and the image geometry is reconstructed to minimize errors due to distortion and image orientation. The image alignment step results in a sparse 3D point cloud of key points, which can then be refined and processed to a dense cloud of much higher detail. The third step is the creation of a digital surface model (DSM) based on the dense cloud. The fourth step is creation of the orthomosaic.

This is accomplished by projecting the aligned images onto the DSM surface. The resultant orthomosaic from this process can be exported for analysis in software such as ArcGIS and

MATLAB (www.mathworks.com, Mathworks, Inc., Natick, United States).

Figure 13 Adding images to Agisoft workspace. Images are initially placed using EXIF data containing image center coordinates. Blue dots within the figure represent image capture locations. At this stage, erroneously placed images can be corrected or removed as necessary.

Figure 14 After image alignment in Agisoft. Successfully aligned images change from blue dots to blue rectangles, which represent the image plane in its correct orientation. Unsuccessfully aligned images remain as blue dots, and areas which lacked adequate imagery can be seen through the blue rectangles.

Figure 15 Dense cloud production in Agisoft.

Figure 16 Horizontal view of the dense cloud. The dense cloud can be used to explore the 3D structure of the study area.

Figure 17 Magnified view of dense cloud.

Figure 18 DSM generated in Agisoft.

Figure 19 Orthomosaic generated in Agisoft.

3.4 Class Selection

Classes for this study were selected based on field observations and analysis of preliminary UAV imagery to identify dominant canopy or land cover within the area. Through this process five classes were selected:

1. Uluhe (Dicranopteris linearis) - native fern species

2. Ironwood (Casuarina spp.) and Eucalypt (Eucalyptus spp. and Melaleuca quinquenervia)

- three tall non-native tree genera which contain one or more species.

3. Strawberry Guava (Psidium cattleianum) - non-native, highly invasive small tree species

4. African Tulip (Spathodea campanulata) - non-native, invasive medium-sized tree species

5. Other - bare ground, dead trees, less common plant species within study area

Due to the natural complexity of the vegetation, not all classes are single-species or genera. It was decided to combine Ironwood and Eucalypt into one class due to the similarity of these two tree types in both their physical appearance within the UAV imagery and distribution across the study area. Additionally, these trees were likely introduced to the study area at similar times and locations, being planted in areas needing reforestation.

Bare ground, dead trees, and all plant species which did not appear as dominant during site visits and within the imagery were grouped into “Other”. This class was designated to contain all land cover outside of the four classes already listed. The plant species within this class were small and difficult to confidently identify within the UAV imagery, or so few that an adequate amount of sample data could not be provided.

3.5 Reference Data

A random sampling procedure was used to select 10% of the entire study area for manual classification and use as reference data (Figure 20). Random sampling is an equal probability sampling design which ensures that all regions of the study area are available for sample selection, and eliminates potential bias (Stehman & Czaplewski 1998). For supervised classification, it is advantageous to use as much reference data as possible. Nonetheless, reference data production is a time-consuming process, and image classification studies should consider the practical limitations of this process. For this reason, reference data was limited to

10% of the entire study area. Twenty-seven sample plots, each encompassing 566 m2, were selected and manually labeled through visual interpretation of the orthomosaic imagery, supplemented with additional close-up imagery. Specifically, all areas within the sample plots were delineated and identified to the five selected classes. Manual labeling was accomplished in

ArcGIS.

Visual interpretation of the orthomosaic for sample data production is advantageous because it avoids the issue of mis-registration (Foody 2002). Given the absence of centimeter accurate control points to align the orthomosaic precisely within the real world, any attempt to match in-field ground-truth data to the orthomosaic would very likely result in misclassified sample data. Unlike many previous tree species classification studies which consider coarser resolution imagery, the selected classes for this study could be confidently identified within the orthomosaic itself. Additionally, the easily deployable UAV system allowed for follow-up image collection from very low altitudes, specifically targeting areas within the sample plots which were challenging to visually identify from the orthomosaic. The usefulness of UAV for this purpose alone has already been identified (Pla et al. 2017; Chen et al. 2018). 55

Following visual interpretation of the reference data, the sample data was assessed for pixel value frequency and distribution (Figure 21). All classes showed highly correlated bands, with the red and green bands maintaining very close alignment across all pixel values. The blue band had lower values, along with many zero values within the imagery, indicating that this color was not as strongly reflected and may not contain as much useful information for classification.

The sample images and corresponding labels were randomly split approximately 70/30, resulting in 19 training images and 8 testing images. The 70/30 reference data split is a commonly applied method, alotting the majority of reference data for use in classifier training which typically benefits from increased sample size (Fassnacht et al. 2016).

Figure 20 Study area. 27 randomly selected sample plots outlined in red.

Figure 21 Pixel value frequencies for each class. Calculated for all three bands of the UAV imagery using all available reference data. Graph y-axes are scaled by the amount of reference data available for each class.

3.6 Texture Statistics

Texture statistics can provide valuable information on tonal pattern and variation within an image (Haralick et al. 1973). This is especially applicable for very-high resolution imagery, in which individual features are often represented by numerous adjacent pixels and finer levels of detail can be observed (Kelcey & Lucieer 2013). Within this study, texture statistics derived from the gray-level co-occurrence matrix (GLCM) were supplied as additional data for MLC and

RF classification. Note that CNN automatically extracted texture information by itself and does not require hand-crafted features from GLCM. GLCM is the most common algorithm applied for 58

texture measurement, and has been identified as particularly successful when considering optical imagery (Carr & De Miranda 1998). GLCM measures the co-occurrence of image pixel gray levels within a moving window of user-defined size. From GLCM, a number of second-order statistics can be extracted as measures of texture (Dian et al. 2015). The degree to which texture statistics can improve classification results is largely dependent on the amount of textural difference there is among the selected classes. Tropical and sub-tropical forests commonly consist of diverse tree species which can exhibit a variety of distinct physical characteristics such as canopy roughness, shape, branch and leaf arrangement, which can contribute to differences in texture especially within very high-resolution imagery (Ferreira et al. 2019). As such, texture statistics are highly applicable for this study.

Many different texture measures are available for consideration. Haralick et al. (1973) identified fourteen measures which may be applied to GLCM. Due to computing limitations inherent with some classification algorithms, a limited amount of data in the form of spectral and texture bands must be selected for analysis. The selection of texture measures can be accomplished through two processes of feature reduction, namely feature selection or feature extraction.

Feature selection involves user-define selection of features to use for analysis and is typically guided by recommendations of previous studies. This is a common strategy within the reviewed studies (Franklin et al. 2001; Wang et al. 2016; Dian et al. 2015; Ferreira et al. 2019), although each of these studies identify different important texture measures through their various sources of recommendation. The disadvantage of feature selection is that no information can be gained by those features which are eliminated. The selection process relies on user expertise or

inference from previous studies, which may deal with different data types or study area characteristics. This creates an opportunity for useful information to be disregarded.

Alternatively, feature extraction involves the application of principal component analysis

(PCA) to consider all available texture features, and derive new independent features composed of the most important aspects of the original features. From this, a subset of these new features can be selected for use in classification. The use of PCA for texture feature reduction in tree species classification is less common, but has been successfully applied in at least one case

(Rampun et al. 2013), which dealt with 32 texture measures before feature reduction. This strategy does not rely on expertise or recommendations from previous studies, and allows all available data to be considered. The disadvantage of PCA is that the resultant features are not easily interpretable, although this is typically not of importance for the goal of image classification.

In this study, 8 standard Haralick measures (Haralick et al. 1973; Dorigo et al. 2012; Gini et al. 2018) were calculated from GLCM for the red image band. The red band was selected because it showed the greatest entropy across the study area, indicating a higher potential for inter-class variability (Dorigo et al. 2012; Gini et al. 2018). Five window sizes (15x15, 35x35,

55x55, 75x75, and 95x95 pixels) or approximately 0.4 to 15.5m2, were selected for texture calculations. These windows sizes were chosen for their ability to capture relevant contextual information from multiple important spatial extents, from the individual leaf or branch to tree crown width. This resulted in 40 texture features, to which PCA was applied for feature reduction. The resultant texture features from PCA were evaluated and the three most important were applied in classification for both MLC and RF. This process was also conducted using only the smallest window size of 15x15 pixels for comparison. 60

Texture was of interest for this study as a comparison of user-defined features for MLC and RF, versus algorithm-defined features identified using CNN. Texture features were not supplied to CNN because this classifier defines its own features to consider and may derive its own set of important texture features, instead of relying on those created and supplied by the user.

3.7 Classification Methods

All classifiers were trained on the same data, in this case the nineteen training images.

3.7.1 Maximum Likelihood Classification (MLC)

ArcGIS was used to perform MLC classification. MLC uses a probability density function to predict the class of an image pixel (Figure 22). MLC is a well-known classifier which is commonly used as a benchmark in classifier performance comparisons. MLC relies on the assumption of normally-distributed data, which is not always the case in remote sensing classification problems. Despite this, MLC is an attractive choice due to its ease of use with no user-defined parameters, and the wealth of studies that have employed it.

Figure 22 Depiction of MLC classifier feature space (“Maximum Likelihood Classifier”, 1999).

3.7.2 Random Forest (RF)

ArcGIS was also used to perform RF classification. RF is a more computationally advanced classifier within the general category of ensemble classifier. In this method, decisions made by multiple classifiers are considered in determining the final classification of a pixel or object. RF is more specifically an ensemble of tree classifiers, referring to the multiple classifier

“trees” consisting of many nodes or “branches” which identify differentiating factors among the classes in a yes-or-no fashion (Pal 2005) (Figure 23). Each classifier tree is supplied with a random subset of training data generated through bagging (Breiman 1996), which is intended to improve the robustness of the classifier. The decision of each tree is counted as a unit vote, and the most popular voted class becomes the final prediction.

RF presents three user-defined parameters: number of trees, depth of trees, and maximum samples per class. There is currently no general guideline for parameter settings, and studies either employ an iterative testing procedure to optimize the parameters (Lawrence et al. 2006;

Immitzer et al. 2012), or defer to settings used in previous publications (Ghosh & Joshi 2014;

Millard & Richardson 2016; Hartling et al. 2019). Some studies have suggested that specific parameter settings do not have a large effect on classifier performance (Chapman et al. 2010).

Additionally, many studies which do not primarily focus on RF, do not specify parameter settings.

Number of trees can be interpreted as the number of individual classifiers which constitute the ensemble. The default value is 500, which is commonly employed within the literature. Immitzer et al. (2012) found that above a certain number of trees, 250 in their case, the addition of more trees did not negatively impact results. Millard & Richardson (2015) used 1000 trees, acknowledging that this was likely an excessive number, but apparently unconcerned with the extra computation time required for such a large number of trees.

Depth of trees refers to the number of predictors which are considered at each node or branch within the classifier trees. These predictors are the spectral bands or texture measures which constitute the image data. Similar to bagging for training data subset selection, each

classification tree uses a random subset of predictors to increase the robustness of the RF classifier. For the subset procedure to be effective, it is beneficial to set depth of trees to a number less than the total number of predictors. Immitzer et al. (2012) tested depth of trees and found that for their 8-band multispectral imagery, classification results degraded at depths greater than two. Other studies state that depth of trees optimization was performed, but final parameter values are not provided (Lawrence et al. 2006; Hartling et al. 2019).

Maximum samples per class determines how many randomly selected training data pixels will be selected for each class and applied to a given classifier tree. This parameter is not commonly discussed within the existing literature. ArcGIS provides a default setting of 1000

(“Train Random Tree Classifier” 2020), which was applied by Felton et al. (2019) without further consideration. Millard & Richardson (2015) likewise accepted the default setting provided within the R Statistics (R Core Team 2019) randomForest package (Liaw & Wiener

2002), which provides a sample of 63.2% the size of the entire training data. It can be seen from surveying the existing literature that the samples per class parameter is not commonly optimized, with much more effort being committed to the other two parameters. It is likely that given enough classifier trees, the samples per class parameter becomes less significant due to the overall redundancy gained by many trees.

Within this study, an iterative process was used to optimize these three parameters. For each parameter, a list of settings was selected which would demonstrate the effects of a wide range of settings, and all combinations of these settings were tested. Number of tree was tested at

10, 100 and 1000, depth of trees was tested at 1, 2, 5, and 10, and samples per class was tested at

100, 1000, 10,000, 100,000, and 212,456, which was the maximum available sample pixels for

the smallest class, African Tulip. Through this optimization process RF parameter settings were selected as follows: 100 trees, depth of 2, and 212,456 samples per class.

Figure 23 Simplified depiction of RF classifier design.

3.7.3 Convolutional Neural Network (CNN) U-Net

CNN is the newest type of classifier to be used for remote sensing data analysis, and significant work is being done on testing and improving or developing new network designs for the types of data and classification objectives typical of this field of study. An example of this is

Iglovikov & Shvets (2018) who compared the performance of a U-Net with standard architecture against another standard U-Net pre-trained on a large dataset, and a modified U-Net with a pre- trained encoder section based on a VGG-11 network architecture. They found both pre-trained networks to perform equally well, and better than the standard U-Net with no pre-training. This

study and others of this type are important because many of the existing CNN’s were not designed for specific remote sensing applications, and new or improved network designs may greatly improve classifier performance.

It is equally important for studies to be conducted using more well-established CNN’s, for the purpose of developing best practices in terms of data collection, network hyper- parameters, pre- and post-processing and production of actionable classification products. Along this line of study, Iglovikov et al. (2017) conducted an early and novel application of U-Net for classification of satellite imagery into 10 landcover and object classes and achieved third highest accuracy against 418 other classifiers. Kattenborn et al. (2019) used a standard U-Net architecture with seven hyper-parameter selections (input image size, final activation layer, optimizer, initial learning rate, loss function, data augmentation, batch size) made based on a consideration of study area and data characteristics. This proved to be very successful with accuracies of 84% and above for binary classification of vegetation types, and it showed that

RGB data is suitable for detailed vegetation classification. Wagner et al. (2019) also used a standard U-Net and made selections for the same seven hyper-parameters based on considerations of study area and objectives. They trained two networks to perform two different binary classifications, one of natural versus plantation forests, and one of Cecropia, a tree species of interest, versus all other vegetation. By only changing the input image size to suit the spatial characteristics of the classes, U-Net was able to conduct either classification with over

95% accuracy which demonstrates U-Net’s wide applicability with minimal hyper-parameter tuning. Similarly, the goal of this study was to compare the performance of an existing and well- suited CNN against other commonly used classifiers. For this purpose a standard U-Net was used

with no architecture modifications, and hyper-parameters selections based partially on those used in other studies, and partially from testing for optimal values.

MATLAB was used to perform U-Net classification. The U-Net employed for this study

(Figure 24) consisted of 58 layers involving one image input layer, twenty-three convolutional layers (nineteen convolution and four transposed or up-convolution layers), twenty-two rectified linear units (RELU) (eighteen RELU and four up-RELU layers), four max-pooling layers, two dropout layers, four depth concatenation layers, one softmax and one pixel classification layer.

The input image is an image patch of specified size which is randomly selected from the training data. In the case of RGB imagery used in this study, the input image contained three channels, one for each spectral band. Additionally, the input patches were subjected to random data augmentation. This can be an effective way of improving training data by increasing both its quantity and variability (Yu et al. 2017). This is especially useful when training data is limited or there is significant intra-class spectral and spatial variability, and it is a common feature in CNN classifications (Iglovikov et al. 2017; Kattenborn et al. 2019; Wagner et al. 2019). Data augmentation was applied within U-Net training in the form of random horizontal and vertical reflection and ninety-degree rotation to the input patches.

U-Net analyzes each input patch through a series of feature extraction or encoder stages which consist of two sets of convolutions each followed by a RELU, and a single max pooling operation (Ronneberger et al. 2015). Convolutions involve the use of a filter or kernel to consider

3x3 pixel patches across the entire input image, and calculate the dot product of each patch. Each convolution is succeeded by a RELU, an activation function which introduces non-linearity to the network by maintaining positive values, and fixing all negative values to zero. This non-

linearity is a critical aspect of CNN’s as explained by Kuo (2016). Many convolutions are applied at each feature extraction stage resulting in many feature channels. Max pooling is then applied to each of the feature channels, which considers a sliding and non-overlapping 2x2 pixel window to extract the maximum values. This results in reduced dimensionality by halving the feature channel width and height, while preserving the feature channel depth or number of channels.

U-Net performs this three-step process at five encoder stages, resulting in 1024 feature channels which are one-sixteenth of the input image width and height. At this point U-Net utilizes its novel architecture for up-sampling and decoding the feature channels through four stages, each involving transposed convolutions to reduce the channel depth, depth concatenations to map features back to their locations within the input image, and convolutions with associated

RELU’s to improve mapping precision. A final convolutional layer is applied to reduce the

Figure 24 Example U-Net architecture, adapted from Ronneberger et al. (2015). 68

feature channels to a depth of five, and per-pixel class predictions are made using commonly- applied softmax and cross-entropy loss functions.

Three hyper-parameter settings were tested and optimized within this study: (1) input image size, (2) initial learning rate, and (3) class weights. Each of these hyper-parameters will be discussed in the following paragraphs.

(1) Input image size:

Input image size should be appropriately selected to ensure that entire class objects or important features can be captured within a single image patch (Flood et al. 2019; Wagner et al.

2019). During network training, U-Net utilizes random image patch extraction in order to greatly increase the variety of training data. For this process, random patches of user-defined pixel size are selected from within each training image and used as input images. In this way, many unique input images can be extracted from each training image. It can be beneficial to provide input images which are large enough to capture contextual information such as size and spatial arrangement of the different classes. However there is a trade-off between input image size and network training time due to increasing computation costs for larger images. Iglovikov et al.

(2017) tested two input image sizes (112 and 224) and used the smaller size due to the computational limitation. Wagner et al. (2019) selected input image sizes for their two binary U-

Net classifiers based on class sizes within the imagery. A size of 128 was used for the smaller class, and 256 was used for the larger class. Input image sizes were tested in this study by training U-Net using three different input image sizes (128, 256 and 512) (Figure 25).

Figure 25 Input image sizes tested.

(2) Initial learning rate:

Learning rate determines the step size, or amount to which network weights are adjusted during training. This can have large effects on CNN training time and accuracy, with high rates training quickly but potentially reaching sub-optimal results, and low rates training very slowly and possibly becoming stuck. There is no one correct learning rate, and therefore it is important to optimize this hyper-parameter (Bengio 2012). A common method for learning rate optimization is testing of values on a logarithmic scale (Goodfellow et al. 2016). Additionally, it 70

is common to use multiple learning rates throughout the network training, typically by reducing the learning rate as training progresses to allow for network fine-tuning (Zhao et al. 2018;

Wagner et al. 2019). In this study, the initial learning rate was tested on a logarithmic scale from

1e-1 to 1e-7, and learning rate was incrementally reduced by 1e-1 during training.

(3) Class weights:

Imbalanced training data can negatively affect classifier performance by presenting bias in favor of more common classes (Badrinarayanan et al. 2017). The random sampling procedure used in this study produced significantly imbalanced training data (Table 3). For example the most common class, Ironwood/Eucalypt, represented almost half of all training data, and contained twenty-three times as many pixels as the least common class, African Tulip.

Iglovikov et al. (2017) avoided this issue by creating separate binary U-Net classifiers for each of their six target classes. In fact, all of the reviewed studies using U-Net for remote sensing classification have done so in a binary classification fashion, either by focusing on a binary classification goal (Zhao et al. 2018; Iglovikov & Shvets 2018; Kattenborn et al. 2019; Kim et al.

2019; Wagner et al. 2019; Zhao et al. 2019), or by separating multi-class problems into binary forms as discussed above (Iglovikov et al. 2017). However this method can result in multiple class predictions for the same pixel, and so was not used in the current study. Instead, this issue was assessed by testing the use of class weights in the final classification layer of U-Net as a means of class balancing. This method has already been applied in other studies using CNN.

Kampffmeyer et al. (2017) tested the use of median frequency class weights for classifying six land cover classes using a CNN modified for semantic segmentation and found this to improve classification of their smallest target class by 34%. Dong et al. (2019) also applied median

frequency class weights to a modified U-Net, and similarly improved classification of the smallest target class.

In this study, median frequency class weights (푓) were assigned through calculating the image frequency (푓푖푚푎푔푒) for each class:

푐푙푎푠푠 푝푖푥푒푙 푐표푢푛푡푛 푓푖푚푎푔푒,푛 = 푖푚푎푔푒 푝푖푥푒푙 푐표푢푛푡푛

Image frequency for a given class (n) is calculated as the class pixel count divided by the image pixel count. Class pixel count is the total number of training data pixels for a given class, and image pixel count is the total number of image pixels across all training images which contain at least one pixel from a given class. Image frequency describes how dense or sparse a class is within the imagery, with higher values going to classes which have high class pixel count, or do not occur in sparse distribution. Median frequency is calculated from image frequency as follows:

푚푒푑푖푎푛(푓푖푚푎푔푒,푛..푎푙푙) 푓푛 = 푓푖푚푎푔푒,푛

Median frequency for a given class (n) is calculated as the median value across all class image frequencies divided by the image frequency for n. This results in a weight of 1 for the class with median image frequency (Uluhe). Weights of less than one go to classes which have an image frequency above the median (Ironwood/Eucalypt and Guava), and weights greater than one go to classes with image frequencies below the median (Other and African Tulip). Other receives a larger class weight despite this class having more training pixels than African Tulip, because Other also has a much larger image pixel count. This means that Other is sparsely distributed within the images where it is present, while African Tulip has greater representation 72

in the images where it is present and is not as widely distributed throughout the training images or study area.

These hyper-parameters were tuned using early stopping during U-Net training, in which expedites network training by concluding once validation loss does not decrease for a specified number of training iterations. The final U-Net with optimized settings was trained for 6,500 epochs.

Table 3 Training data per-class pixel count and median frequency values assigned for class weights.

Class Pixel Count (x 1 million) Median Frequency Value Uluhe 1.04 1.00 Ironwood/Euc 2.91 0.39 Other 0.59 2.43 Strawberry Guava 1.59 0.85 African Tulip 0.13 1.27

3.8 Accuracy Assessment

Accuracy assessment is a critical aspect of image classification as it provides information on how reliable and suitable a classification is for a specific purpose, and allows for comparison of multiple classifiers (Congalton 2001). Congalton (1994) and Foody (2002) both describe the historical progression of accuracy assessment methods, starting with visual inspection, then progressing from non-spatial to spatial assessments, and finally being able to derive more detailed data on classifier performance. The confusion matrix, also known as the error matrix, is the most common means for accomplishing this final level of accuracy assessment. It is a cross-

tabulation of the predicted class pixel counts against the testing data class pixel counts, and it allows for calculation of multiple measures of classifier accuracy across multiple classes (Foody

2002).

In this study, classification accuracy was assessed statistically using a confusion matrix for each classifier output, with calculated overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA)and kappa coefficient (Congalton & Green 1999). The eight testing images were used for this assessment. The confusion matrix was also used to examine class-specific performance differences.

OA is a percentage calculated as the sum of correctly classified pixels from all classes divided by the total number of pixels within the image, and as its name implies, it provides a measure of the image’s accuracy across all classes. This is the single most commonly reported measure of accuracy within the reviewed literature. PA is calculated for each class as correctly classified pixels divided by total true pixels for that class. This accuracy helps to understand how well a classifier can identify positive instances of a class within the imagery. A class with low

PA has been under-predicted, and will have omission error. UA is also calculated for each class, as correctly classified pixels divided by total predicted pixels for that class. This accuracy helps to understand how reliable the classification is for a given class. A class with low UA has been over-predicted and will have high commission error. Kappa coefficient is a measure of statistical agreement which describes how much of a classifier’s successful prediction is due to random chance.

Classification results were also assessed through visual inspection to consider more qualitative strengths and weaknesses of each classifier. Specifically, observations were made on

what types of patterns were being recognized, and how realistic the composition of the classification output was overall. This is an important consideration for determining practical applicability of a classification product.

CHAPTER 4. RESULTS

The following section contains the findings from this study. Firstly, overall accuracy and kappa coefficient are presented for all classifiers and data types tested. Secondly, key findings on the use of texture measures for MLC and RF are described. Thirdly, class-specific performance differences are explained through the use of user’s and producer’s accuracies. Fourthly, evaluation through visual inspection is described using sample images and corresponding classifications. Finally, findings from the U-Net optimization process are described.

4.1 Accuracy Assessment

4.1.1 Overall Accuracy (OA) and Kappa Coefficient

A confusion matrix was constructed for each classifier output (see supplementary material) for comparison of OA and kappa coefficient. U-Net achieved the highest OA at 71.2% and showed a 19.1% and 23.1% improvement over RF and MLC, respectively (Table 4). U-Net also had the highest kappa coefficient at 0.61.

Table 4 Overall accuracy (OA) and kappa coefficient scores for the three classifiers. Scores for MLC and RF provided before and after the addition of texture data. RGB+T8 is classification using the RGB imagery and PCA features produced by 8 texture measures calculated with the 15x15 window size. RGB+T40 is classification using the RGB imagery and PCA features produced by texture measures calculated at all window sizes (40 texture measures total).

Classifier Data Overall Accuracy (%) Kappa Coefficient MLC RGB 40.8 0.20

MLC RGB+T8 44.6 0.27

MLC RGB+T40 48.1 0.30

RF RGB 38.3 0.20

RF RGB+T8 52.1 0.36

RF RGB+T40 46.1 0.26

U-Net RGB 71.2 0.61

4.1.2 Use of Texture Measures

Texture measures were provided as additional data for classification with MLC and RF.

Both classifiers benefited from the additional data, which increased OA respectively by 7.3% and 13.8%, and increased kappa coefficient respectively by 0.1 and 0.16. MLC gained an additional 3.5% OA when all texture window sizes were included in PCA. Conversely, RF lost

6% OA when all texture window sizes were included, compared to using only the 15x15 window size. For both classifiers, texture improved UA for all classes, and greatly improved PA for some classes (Figures 26 and 27). Overall, RF benefitted more than MLC from the addition of texture data in terms of OA, kappa coefficient, and most UA and PA.

User's Accuracy 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Uluhe Ironwood/Euc. Other Strawberry African Tulip Guava MLC RGB MLC RGB+T40 RF RGB RF RGB+T8

Figure 26 User’s accuracy (UA) for MLC and RF without and with texture measures. Results shown for each classifier and the best-performing texture measures.

Producer's Accuracy 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Uluhe Ironwood/Euc. Other Strawberry African Tulip Guava MLC RGB MLC RGB+T40 RF RGB RF RGB+T8

Figure 27 Producer’s accuracy (PA) for MLC and RF without and with texture measures. Results shown for each classifier and the best-performing texture measures. 78

4.1.3 Class Performances

UA and PA scores are presented for all three classifiers in their optimal forms MLC with texture data from five window sizes, RF with texture data from one window size, and U-Net with optimized hyper-parameters (Table 5). In order to explain specific class and inter-class performance differences, UA and PA are examined here for each class and classifier.

Additionally, true versus predicted pixel counts for each class are presented for the three optimized classifiers (Figure 28).

(1) Uluhe

All classifiers performed relatively well for Uluhe, with above-average UA and PA for all classifiers. All classifiers predicted Uluhe very close to the true total amount, but sources of lower UA and corresponding commission error for Uluhe varied across classifiers. MLC most commonly misclassified either African Tulip or Strawberry Guava as Uluhe, while RF predominantly misclassified African Tulip as Uluhe. U-Net achieved the highest UA for Uluhe, but for this classifier Strawberry Guava was the class most commonly misclassified as Uluhe.

Sources of lower PA and corresponding high omission error for Uluhe showed more similarity between MLC and RF, while U-Net differed. Both MLC and RF most commonly misclassified

Uluhe as African Tulip, while U-Net most commonly misclassified Uluhe as Other.

(2) Ironwood/Eucalypt

All classifiers had relatively low UA for Ironwood/Eucalypt, meaning that many pixels were incorrectly predicted to be this class. Indeed, all classifiers over-predicted the presence of the Ironwood/Eucalypt class, most significantly by misclassifying Strawberry Guava as

Ironwood/Eucalypt. Conversely, Ironwood/Eucalypt median to high class PA for all classifiers,

with most omission error being due to the misclassification of Ironwood/Eucalypt as Strawberry

Guava. For U-Net, Ironwood/Eucalypt gained the highest PA of all classes, and predicted closest to the correct total amount of pixels for this class.

(3) Other

Other showed similar low to median UA and PA across classifiers, and consistent sources of both commission and omission error. Other had low to median UA for both MLC and RF, while this class had the lowest UA for U-Net. Commission error for this class was due to misclassification of Strawberry Guava as Other for all classifiers, and U-Net also committed the greatest over-prediction of Other. PA for Other showed the greatest range across classifiers with

U-Net highest, RF slightly lower and MLC very low, but in all cases omission error was mainly due to misclassification of Other as either Strawberry Guava or Ironwood/Eucalypt. Other also showed the greatest range in total predicted pixels across the three classifiers.

(4) Strawberry Guava

Strawberry Guava had high UA for all classifiers and also high PA for MLC and RF, but low-median PA for U-Net. Commission and omission error were due to the same two classes,

Ironwood/Eucalypt and Other. Strawberry Guava was the only class to be under-predicted by all classifiers, with RF committing the greatest under-prediction for this class.

(5) African Tulip

African Tulip showed very different levels of predictability across classifiers, and also different sources of error. This class had median UA for MLC, the lowest UA for RF, and highest class UA (0.96) for U-Net. Commission error for African Tulip was largely due to misclassification of African Tulip as either Ironwood/Eucalypt or Uluhe for MLC, and either 80

Uluhe or Other for RF.U-Net had very low commission error for this class, and but mainly misclassified African Tulip as Other. U-Net committed the greatest under-prediction of African

Tulip, but was very accurate in its positive predictions. PA for African Tulip was more similar across classifiers, and this was the only class for which U-Net did not have the highest PA compared to MLC and RF. MLC gained the highest PA for this class, and most omission error was due to misclassification of Ironwood/Eucalypt as African Tulip. U-Net had the second highest PA for African Tulip, with most omission error due to misclassification of Other as

African Tulip. RF had the lowest PA for African Tulip, with omission error due almost equally to Uluhe and Other being misclassified as African Tulip.

Table 5 UA and PA for top-performing versions of each classifier. Highest score for each class in bold.

User's Accuracy

Classifier Uluhe Ironwood/Euc Other Strawberry Guava African Tulip

MLC 0.70 0.22 0.33 0.69 0.38

RF 0.69 0.32 0.36 0.78 0.30

U-Net 0.83 0.66 0.42 0.89 0.95

Producer's Accuracy

Classifier Uluhe Ironwood/Euc Other Strawberry Guava African Tulip

MLC 0.66 0.50 0.20 0.52 0.44

RF 0.56 0.62 0.51 0.53 0.24

U-Net 0.83 0.86 0.74 0.68 0.36

14 12 10 8 6

Pixel Count (x100k)PixelCount 4 2 0 Uluhe Ironwood/Euc. Other S. Guava African Tulip MLC RF U-Net TRUE

Figure 28 True versus predicted class pixel counts for each classifier. Results for MLC and RF are shown for their highest scoring classifications with the addition of texture measures.

4.1.4 Visual Inspection

This section describes findings gained by visual inspection of the classified imagery.

Three sets of classification results have been provided to assist in explaining the observations made by this method of assessment.

The first set depicts sample results from MLC and RF before and after the addition of texture data (Figure 29). The addition of texture data improved OA for both of these classifiers.

MLC showed slight improvement in discerning between Uluhe and African Tulip, and also between Strawberry Guava and Other. RF showed greater improvements for recognizing both

African Tulip and Other.

Figure 29 Example classification outputs from MLC and RF before and after the addition of texture data, with corresponding reference label.

The second set depicts sample results from MLC, RF, and U-Net at their optimal settings

(Figure 30). Within this image, MLC outperforms RF in accurate prediction of African Tulip, but underperforms with its distinction between Guava and Other. U-Net most accurately predicted all three of these classes. All three classifiers were able to predict some of the Ironwood/Eucalypt within the image, but MLC and RF both showed over-classification in the form of very small patches in other areas of the image.

Figure 30 Example classification outputs from optimized MLC, RF and U-Net with corresponding reference label and original UAV imagery.

The final set depicts classification outputs for each of the optimized classifiers for the entire study area (Figure 33). It is useful to consider the classification results across larger areas to observe how realistically each classifier predicts the arrangement of classes across the landscape. MLC and RF both display some salt-and-pepper effect with classes appearing in single-pixel arrangements. U-Net maintains larger single-class patches.

Figure __ Example classification outputs from optimized MLC, RF and U-Net with corresponding reference label and original UAV imagery. Figure 33 Whole study area classification outputs from optimized MLC, RF and U-Net, with corresponding reference label and original UAV imagery.

4.2 U-Net optimization

Optimization of U-Net hyper-parameters was performed as previously described.

Through this process, it was discovered that hyper-parameter tuning was less significant than originally anticipated to achieve optimal classifier performance. Many different hyper-parameter settings performed similarly well, although small improvements were realized by the optimized settings provided below.

(1)Input image size was optimized at 128 x 128 pixels. Larger input image sizes required significantly more training time and did not result in increased classifier accuracy.

(2)Learning rate was optimal within the range of 1e-3 to 1e-5. Above or below these values, U-Net could not learn well. In final optimization, the initial learning rate was set to 1e-3 and was multiplied by 1e-1 every 2000 training epochs. This resulted in a learning rate drop from

1e-3 to 1e-5 throughout the course of training.

(3)The use of median frequency class weights did improve U-Net classification by 7.9% when all other hyper-parameters were optimized.

CHAPTER 5. DISCUSSION

The following section contains a discussion and concluding remarks about the results from this study. The topics of texture measures, classifier performances, target class considerations, and U-Net optimization are each examined and suggestions are made for future methodologies.

5.1 Texture Measures

Texture data was important for both MLC and RF classification, improving OA by 7.3% and 13.8%, respectively. This suggests that the classes were characterized by at least somewhat unique texture patterns. This suggestion is supported by other studies, which realize varying amounts of improvement with the use of texture data dependent on class similarity. For example,

Dian et al. (2015) realized OA improvement of less than 5% with the addition of texture data.

They speculated that the tree species in question had high textural similarity, thus reducing the importance of this type of data. Conversely, Gini et al. (2018) realized greater than 10% improvement in OA with the addition of texture data. This was likely due to the greater variety of tree species which is similarly encountered in tropical forest settings such as Hawai‘i, and demonstrates the particular importance of texture data for such forest types.

Additionally, RF benefitted more than MLC from the addition of texture data. This is likely due to RF’s well-known capabilities in utilizing high-dimensional data, allowing it to further capitalize on the additional information while MLC experienced less improvement. No other studies were found which compare MLC and RF performance specifically in regards to texture data, however Dalponte et al. (2013) found RF to outperform MLC for tree species classification using high-dimensional hyperspectral data, citing the Hughes phenomenon as

MLC’s shortcoming. For this reason, it is likely that as more features are included in classification, RF will benefit increasingly more than MLC.

MLC and RF both require the user to define second-order data inputs such as texture, while U-Net is able to consider this aspect of the imagery without explicit user input. The limitation of user-defined data inputs has been considered by Franklin (2018). He noted that one tree species, white pine, exhibited a distinct texture within the imagery which allowed for easy visual interpretation. By contrast, this species was relatively poorly classified by either MLC or

RF when using RGB imagery and GLCM-derived texture measures, suggesting that the texture measures did not fully capture the useful characteristics. It is likely that U-Net can recognize texture as well as contextual patterns which may not be considered or easily calculated by the user, thus reducing the manual effort needed to achieve good results.

5.2 Classifier Performances

In this study CNN U-Net was shown to be a more accurate classifier than either parametric classifier MLC, or non-parametric machine learning type classifier RF, when considering very high-resolution UAV visible light imagery for tree species mapping in a dense mixed forest setting. This agrees with many other studies which have found CNN’s to be the most accurate classifier type to date for image classification tasks (Hu et al. 2015; Yoo et al.

2019). U-Net achieved the highest OA (71.2%), kappa coefficient (0.61), and most classes’ UA and PA. OA from this study was 16.3% and 25.9% lower than that gained by recent studies using

U-Net for tree species classification in RGB UAV imagery (Kattenborn et al. 2019; Wagner et al. 2019). However as discussed previously, these studies both employed binary classifications which greatly simplified the classification task. Considering this difference, U-Net performed

comparably in the study presented here, with UA for one class, African Tulip, even surpassing

UA scores gained in either of the previously mentioned studies.

Additionally, visual inspection of the three classifier outputs reveals that U-Net interpreted the imagery in a manner most similar to the human interpretation employed during training data production. While MLC and RF outputs both exhibited significant salt-and-pepper class distribution, U-Net output contained larger single-class areas and much less salt-and-pepper effect. This is a notable advantage of CNN’s, which has been further explored and improved upon by Sun et al. (2018).

5.3 Class Considerations

5.3.1 Class Selection

The aim of this study was primarily to assess species-specific classification capabilities.

In practice however, there were two cases in which multiple species were grouped together due to their relative scarcity within the study area. Ironwood/Eucalypt is a class which contains at least three species, one or more from the Ironwood genus, and two or more from the Eucalypt group. As explained earlier, these species were grouped into a single class due to their close proximity and appearance throughout the study area. In diverse forests it can be difficult or impossible to collect enough reference data to accurately classify relatively rare species. It is also impossible using an optical sensor such as the one employed in this study, to classify trees or vegetation which are significantly obscured by overhead canopy. Considering these two limitations, it may be beneficial to approach class selection based on vegetation functional groups (Körner 1994) or other relevant groupings in cases were individual species cannot be accurately classified. Such an approach was used by Kattenborn et al. (2019), which classified

pioneer versus climax vegetation to address an ecological pattern without the need for species- level classification.

Other is a class which contains an unknown number of species as well as bare ground, a non-vegetated land cover. This class was not defined by objects with similar characteristics, but instead consisted of all areas which did not contain the target species. As such, Other has significant intra-class variability in both spectral and spatial terms. This variability likely made this a more challenging class to accurately predict, to the detriment of all other classes. While a catch-all type of class such as this may be necessary when dealing with diverse forests, it could also be improved through careful consideration of the different features which are being included. Further class divisions which reduce intra-class variability may improve overall classifier performance and may also lead to more informative classification outputs.

5.3.2 Class Performances

This section will discuss classifier performance for each target class individually.

(1) Uluhe

Uluhe was the most accurately predicted class within this study, being the only class to gain both UA and PA above 0.8. This is not particularly surprising, as Uluhe is the most easily discernable class when performing visual interpretation due to its unique bright green color and monotypic habit. Despite this class’s relative simplicity, U-Net still outperformed MLC and RF, which shows that even the most easily discernable class within this study is not an inherently easy classification goal, and the use of an advanced classifier is important for maximum accuracy.

(2) Ironwood/Eucalypt

Ironwood/Eucalypt was the only class to be over-predicted by all three classifiers. The over-prediction of this class is not surprising, as Ironwood/Eucalypt contained far more training data than any other class, thus skewing class predictions towards this most common class.

Despite this, U-Net performed comparatively well with Ironwood/Eucalypt, with very high PA.

Due to U-Net’s use of random patch extraction during network training, it is likely that some training iterations contained more balanced class samples, reducing the negative impact of the original, very imbalanced training data.

(3) Other

Other was a challenging class, with low UA for all three classifiers. MLC heavily under- predicted this class and it had MLC’s lowest PA. Both RF and U-Net over-predicted Other, but had much higher PA for this class than MLC. As discussed earlier, Other was used as a “catch all” class for any vegetation or land cover which was not a species of interest. As such, this class presented a high amount of variability in tone, texture and spatial arrangement. MLC assumes a normal distribution for the training data from each class. This was not the case for any class, with

Other’s distribution being particularly non-normal. RF and U-Net were both more successful with this class, but it was still a comparatively difficult class for U-Net. Further division of Other into two or more classes with more similar characteristics would likely result in improvements across all target classes.

(4) Strawberry Guava

Strawberry Guava was classified relatively well by all three classifiers. This class had the second-highest UA for MLC and U-Net and had the highest UA for RF. Although Strawberry

Guava had the second-largest training data set, it was under-predicted by all three classifiers.

This class was most commonly confused for Ironwood/Eucalypt, and vice versa. Within the study area, these two classes are commonly found adjacent, mixed or overlapping throughout the study area. In fact, there are many areas which contain an overstory of Ironwood/Eucalypt and an understory of Strawberry Guava. Due to their spatial arrangement, these two classes are difficult to exclusively identify during visual interpretation. This can lead to sample data for either class which may actually contain both classes, and this can negatively affect both classifier performance and subsequent accuracy assessment. This problem may be addressed by considering areas with both classes present, as a unique third class characterized by mixed vegetation.

(5) African Tulip

African Tulip was a particularly interesting class to consider within this study. This class had the smallest amount of training data due to both its relative scarcity and limited distribution throughout the study area. Despite this, African Tulip was actually over-predicted by MLC, with

Uluhe most commonly being misclassified as African Tulip. RF more expectedly under- predicted this rare class, and also struggled with discerning between African Tulip and Uluhe.

U-Net performed very differently than the other two classifiers in regards to African

Tulip. U-Net achieved its highest UA with this class, and an end user could feel very confident about any areas classified as African Tulip. Unfortunately PA was not nearly as high. There was a lot of African Tulip within the study area which was not successfully identified by U-Net, mostly being mistaken as Other. Overall however, through visual inspection it can be seen that

U-Net performed relatively well with African Tulip, predicting its presence only within the main drainage which it is known to occupy within the study area.

5.4 U-Net Optimization

U-Net was not very sensitive to parameter tuning. While this is a necessary step to ensure optimal performance, it is also reassuring to know that the classifier will perform relatively well without employing an exhaustive tuning procedure. Adaptation of this network for different tree species with similar imagery may be expedited by beginning parameter tuning at the optimal settings from this study. This suggestion is also supported by the similar hyper-parameter settings used in the other U-Net studies previously discussed.

The use of class weights did improve U-Net performance, specifically by reducing the over-prediction of the most common class Ironwood/Eucalypt. This indicates that the sample data used in this study was problematically un-balanced. In practice, it may be unfeasible to collect balanced sample data due to either study area inaccessibility, or scarcity and uneven distribution of some target species or classes. In fact, classification goals often involve detection of both common and rare classes within a study area. Therefore, when balanced sample data is unachievable, the use of class weights can minimize this negative impact.

CHAPTER 6. CONCLUSION

In this study the capabilities of three important classifier types were assessed for interpreting consumer-grade UAV imagery, for the purpose of mapping tree species in a dense mixed forest setting. It was found that U-Net, a CNN-type classifier, greatly outperformed either

MLC or RF for this task. Based on this finding, it is recommended that future classification tasks be approached with the use of a CNN classifier. The current high pace of experimentation with

CNN’s, coupled with powerful computing technology, will likely lead to further improvements for these classifiers.

It was also found that the texture data contained within the very high-resolution imagery is beneficial for class discrimination. This was only an important consideration during the use of

MLC and RF, but it does identify one way in which U-Net was able to out-perform the other two classifiers. U-Net can automatically look for textural and contextual patterns within the imagery, and UAV imagery is particularly rich in this type of information due its resolution. Therefore, U-

Net and other CNN’s are well-suited for classifying UAV imagery.

This classification goal is an on-going challenge which has been approached with a variety of methods including testing of different platforms, sensors and classification algorithms.

This study incorporated the most recent trends in remote sensing image classification for this purpose, along with some novel elements which can provide insight for future classification efforts.

The UAV platform used for the data collection in this study is a very recent introduction to the remote sensing community, with a quickly growing body of work around it. The use of

UAV in this study allowed for maximum control over the study area data in terms of coverage,

image resolution and timing of acquisition. Unlike many remote sensing classification efforts which must source pre-existing satellite or aerial imagery, all the spatial data used in this study was collected specifically for the purpose of species classification at the study site. While the study area presented here has not experienced any major changes in land cover within recent history, it is possible that study areas in other locations may undergo significant disturbances which will not be represented if very recent imagery is not available. Additionally, if there is vegetation which exhibits seasonal or even diurnal characteristics, the UAV platform will allow for optimized timing of data collection.

The RGB camera deployed in this study is not an advanced type of sensor, but the data collected was of a much higher spatial resolution than the vast majority of remote sensing imagery that currently exists. As explained in the literature review, previous limitations in spatial resolution have driven research in the application of more advanced sensor types, namely hyperspectral and lidar. The recent development of low-cost, sophisticated UAV platforms now allows for very high-resolution image collection in many areas which otherwise lack such data.

There is now a large amount of very high-resolution RGB UAV imagery being collected, and this calls for further development of classification methods which capitalize on the unique aspects of UAV imagery. It is equally important that the use of hyperspectral, lidar and other data types continue to be improved upon, but low-cost RGB UAV imagery can allow for data collection and successful image classification for areas and objectives which until now lacked funding or access to the necessary spatial data.

The deep learning classification algorithm used in this study represents the cutting edge of remote sensing image analysis techniques. U-Net and other such CNN’s present new capabilities for image analysis, and when applied correctly will likely surpass other classifier 95

types as demonstrated in this study. CNN’s are able to consider many aspects of the spatial data which are not recognized by more traditional classifiers unless explicitly provided, as was the case with the addition of texture data for MLC and RF in this study. Furthermore, CNN’s can consider contextual data such as the size, shape and arrangement of features within the imagery.

The wealth of textural and contextual data within the very high-resolution imagery used in this study was more fully utilized by U-Net, because it was not limited to the texture data that was designed and provided for MLC and RF.

The classification goal for this study presented novelty among classification studies using

U-Net. All previously discussed studies applied this classifier in a binary classification for the separation of one or two target classes. This study, by contrast, assessed U-Net’s capability with distinguishing among five mutually exclusive classes. This is a more complex classification objective, and admittedly the OA achieved in this study was lower than those obtained in the previously reviewed U-Net studies. Nevertheless, this study has shown that U-Net outperforms both MLC and RF, even for the novel objective of multi-class vegetation classification.

Furthermore, this study showed the benefit of using class weights in U-Net when class sample data is heavily imbalanced.

It is anticipated that findings from this study will help both researchers and natural resource managers progress towards their vegetation classification goals. Both of these communities have already recognized the utility of UAV for spatial data collection, but there are still challenges associated with collecting optimal imagery and extracting the desired information. The methods presented in this study can be used to streamline future UAV operations from study area selection, to UAV flight planning, through data collection and processing, and finally image classification and analysis. Additionally, this study provides 96

significant insight into what level of vegetation classification is currently achievable in this type of setting using low-cost UAV and advanced classification methods. With this example in mind, realistic classification goals can be identified and accomplished with minimal need for experimentation.

SUPPLEMENTARY MATERIAL

Confusion matrix for MLC RGB classification

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 202851 29983 15508 24213 105521 378076 53.7%

Ironwood/ 9474 170587 21594 141711 8491 351857 48.5% Eucalypt

Other 13141 134455 66561 178624 25473 418254 15.9%

Strawberry 32848 559876 87789 566979 33337 1280829 44.3% Guava

African 49911 52426 16136 23640 70343 212456 33.1% Tulip

Total 308225 947327 207588 935167 243165 2641472 Predicted

UA 65.8% 18.0% 32.1% 60.6% 28.9%

Confusion matrix for MLC RGB+T8 classification

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 225422 29154 13965 20402 89133 378076 59.6%

Ironwood/ 55440 180024 16556 89553 10284 351857 51.2% Eucalypt

Other 15919 142471 57114 166127 36623 418254 13.7%

Strawberry 22184 527508 54406 608386 68345 1280829 47.5% Guava

African 29497 49977 8449 29066 95467 212456 44.9% Tulip

Total 348462 929134 150490 913534 299852 2641472 Predicted

UA 64.7% 19.4% 38.0% 66.6% 31.8%

Confusion matrix for MLC RGB+T40 classification

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 249490 21775 19969 13094 73748 378076 66.0%

Ironwood/ 9574 177027 31253 128425 5578 351857 50.3% Eucalypt

Other 27577 128776 82155 149685 30061 418254 19.6%

Strawberry 30496 442545 94737 669128 43923 1280829 52.2% Guava

African 39984 41800 21244 16045 93383 212456 44.0% Tulip

Total 357121 811923 249358 976377 246693 2641472 Predicted

UA 69.9% 21.8% 32.9% 68.5% 37.9%

100

Confusion matrix for RF RGB classification

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 260485 36742 12823 62912 5114 378076 68.9%

Ironwood/ 6397 165253 15688 163392 1127 351857 47.0% Eucalypt

Other 29745 141862 82060 162531 2056 418254 19.6%

Strawberry 18288 371542 100056 787632 3311 1280829 61.5% Guava

African 94311 41159 16902 58099 1985 212456 0.9% Tulip

Total 409226 756558 227529 1234566 13593 2641472 Predicted

UA 63.7% 21.8% 36.1% 63.8% 14.6%

101

Confusion matrix for RF RGB+T8 classification

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 211289 29383 35809 17784 83811 378076 55.9%

Ironwood/ 8571 217089 30019 92111 4067 351857 61.7% Eucalypt

Other 15781 102064 214391 70966 15052 418254 51.3%

Strawberry 7872 313170 260578 680714 18495 1280829 53.1% Guava

African 64356 17509 63092 15944 51555 212456 24.3% Tulip

Total 307869 679215 603889 877519 172980 2641472 Predicted

UA 68.6% 32.0% 35.5% 77.6% 29.8%

102

Confusion matrix for RF RGB+T40 classification.

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 244633 32797 22440 46074 32132 378076 64.7%

Ironwood/ 5586 151991 58755 131924 3601 351857 43.2% Eucalypt

Other 13491 133807 134441 126430 10085 418254 32.1%

Strawberry 8140 500162 110410 650984 11133 1280829 50.8% Guava

African 59152 63406 23376 32052 34470 212456 16.2% Tulip

Total 331002 882163 349422 987464 91421 2641472 Predicted

UA 73.9% 17.2% 38.5% 65.9% 37.7%

103

Confusion matrix for U-Net classification.

Ironwood/ Strawberry African Total Class Uluhe Other PA Eucalypt Guava Tulip True

Uluhe 314501 12474 36368 14576 157 378076 83.2%

Ironwood/ 8463 304151 7395 31848 0 351857 86.4% Eucalypt

Other 16436 39242 309629 49811 3136 418254 74.0%

Strawberry 20377 100753 284202 875118 379 1280829 68.3% Guava

African 18528 4232 105913 6655 77128 212456 36.3% Tulip

Total 378305 460852 743507 978008 80800 2641472 Predicted

UA 83.1% 66.0% 41.6% 89.5% 95.5%

104

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