To investigate and Compare the Classification

Accuracies of New Generation Sentinel 2 and Landsat 8

Optical Satellite Images in the Killarney region

Emma Sheehan

111477932

Supervisor

Dr. Fiona Cawkwell

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Abstract

This study investigates the potential of remote sensing imagery in the classification of land

cover/land use features in the Killarney region, County Kerry. Three Landsat 8 images were

acquired on the 18th

March 2015, 19th

April 2015 and the 1st June 2016. One Sentinel 2 image

was acquired on the 18th

July 2016. The images were pre-processed and then classified for

land cover/land use mapping using traditional classification such as Maximum Likelihood

Classifier (MLC) and machine learning methods such as Support Vector Machine (SVM) and

Artificial Neural Network (ANN).

The performance and accuracy of these three methods was determined using overall accuracy

and kappa coefficient parameters for land cover/land use classification of the study area.

Overall acceptable accuracies of over 85% were obtained in all the cases and the MLC

achieved the highest overall accuracies for each image with the Landsat 8 classified image

acquired on the 18th

March 2015 displaying the highest overall accuracy of 97.84% followed

by the Support Vector Machine that achieved the highest overall accuracy of 94.52% for the

same image. The Artificial Neural Network was less successful in discriminating the different

land cover types producing an overall accuracy of 88.56% for the same image.

Acknowledgments

I would like to express my sincere gratitude to my Supervisor Dr. Fiona Cawkwell for her

help, guidance and quick feedback throughout my research. I would also like to thank the

entire Geography department in particular Helen Bradley and Darius Bartlett for their support

throughout the year. Finally I would like to offer special thanks to all my classmates for their

support and encouragement throughout the process of completing my Master’s degree.

Table of Contents

CHAPTER 1: INTRODUCTION…………………………………………………………….1

1.1 Land Use and Land Cover (LULC)………………………………………………1

1.2 Land Use and Land Cover (LULC) Mapping…………………………………….2

1.3 Land Use and Land Cover (LULC) Mapping in Ireland…………………………3

1.4 Aim and Objectives……………………………………………………………….5

CHAPTER 2: LITERATURE REVIEW……………………………………………………..7

2.1 Literature review introduction…………………………………………………….7

2.2 Medium Spatial Resolution Imagery……………………………………………...8

2.3 Multi-sensors……………………………………………………………………..12

2.4 Coarse Spatial Resolution Imagery……………………………………………...14

2.5 High Spatial Resolution Imagery………………………………………………..15

2.6 Hyperspectral Imagery ………………………………………………………….17

2.7 Conclusion……………………………………………………………………….19

CHAPTER 3: STUDY AREA………………………………………………………………20

3.1 Study area………………………………………………………………………..20

3.1.1 CORINE 2012……………………………………………………………….21

3.1.2 Geology………………………………………………………………………23

3.1.3 Elevation……………………………………………………………………..24

3.1.4 Soils and Subsoils…………………………………………………………....26

CHAPTER 4: DATASETS & DATA PREPARATION……………………………………28

4.1 Datasets…………………………………………………………………………..28

4.1.1 Landsat 8 Imagery……………………………………………………………28

4.1.2 Sentinel 2 Imagery…………………………………………………………...33

4.2 Pre-processing……………………………………………………………………36

CHAPTER 5: METHODOLOGY…………………………………………………………...39

5.1 Image Classification……………………………………………………………...39

5.1.1 Maximum Likelihood Classifier (MLC)…………………………………......39

5.1.2 Support Vector Machine (SVM)……………………………………………..40

5.1.3 Artificial Neural Network (ANN)……………………………………………41

5.2 Land Cover Classes………………………………………………………………43

5.3 Converting ENVI class data to ArcGIS………………………………………….44

5.4 Accuracy Assessment…………………………………………………………….44

CHAPTER 6: RESULTS ……………………………………………………………………46

6.1 Classified Images………………………………………………………………...46

6.2 Accuracy Assessment…………………………………………………………….53

CHAPTER 7: DISCUSSION & CONCLUSION……………………………………………60

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7.1 Discussion and Conclusion……………………………………………………....60

7.1.1Limitations…………………………………………………………………....61

BIBLIOGRAPHY…………………………………………………………………………..63

Table of Figures

Figure 1: The CORINE 2012 25ha Landcover dataset for Ireland…………………………4

Figure 2: CORINE 2012 Landcover map for County Kerry……………………………….21

Figure 3: Bedrock Geology map of County Kerry…………………………………………23

Figure 4: Digital Elevation Model of County Kerry………………………………………..24

Figure 5: Hillshade of County Kerry……………………………………………………….25

Figure 6: Soils and Subsoils map…………………………………………………………..26

Figure 7: The USGS Earth Explorer Interface……………………………………………..31

Figure 8: Landsat 8 natural colour preview images………………………………………...31

Figure 9: Subset Landsat 8, 18th

March 2015………………………………………………32

Figure 10: Subset Landsat 8, 19th

April 2015………………………………………………32

Figure 11: Subset Landsat 8, 1st June 2016…………………………………………………32

Figure 12: Comparison of Landsat 7 and 8 bands with Sentinel 2………………………….33

Figure 13: Website of Sentinel 2 downloaded data…………………………………………35

Figure 14: Sentinel 2 preview image………………………………………………………...35

Figure 15: Subset of Sentinel 2 image……………………………………………………….36

Figure: 16: Masked image of water and land for Sentinel 2…………………………………38

Figure 17: Structure of SVM…………………………………………………………………41

Figure 18: ANN architecture…………………………………………………………………42

Figure 19: Landsat 8 SVM classified image 18th

March 2015………………………………46

Figure 20: Landsat 8 SVM classified image 19th

April 2015………………………………..47

Figure 21: Landsat 8 SVM classified image 1st June 2016………………………………….47

Figure 22: Sentinel 2 SVM classified image………………………………………………...48

Figure 23: Landsat 8 ANN classified image 18th

March 2015………………………………48

Figure 24: Landsat 8 ANN classified image 19th

April 2015………………………………..49

Figure 25: Landsat 8 ANN classified image 1st June 2016…………………………………..49

Figure 26: Sentinel 2 ANN classified image…………………………………………………50

Figure 27: Landsat 8 MLC classified image 18th

March 2015……………………………….51

Figure 28: Landsat 8 MLC classified image 19th

April 2015………………………………...51

Figure 29: Landsat 8 MLC classified image 1st June 2016…………………………………..52

Figure 30: Sentinel 2 MLC classified image…………………………………………………52

Figure 31: Overall accuracy of the three methods for 18th

March 2015……………………..57

Figure 32: Overall accuracy of the three methods for 19th

April 2015………………………58

Figure 33: Overall accuracy of the three methods for 1st June 2016…………………………58

Figure 34: Overall accuracy of the three methods for Sentinel 2 image……………………..59

Table of Tables

Table 1: Landcover types of County Kerry…………………………………………………22

Table 2: Description of bedrock geology……………………………………………………24

Table 3: Description of soils and subsolis…………………………………………………...27

Table 4: Spectral and spatial detail about Landsat 8………………………………………...29

Table 5: Landsat 8 observatory capabilities…………………………………………………30

Table 6: Landsat 8 imagery acquired……………………………………………………….30

Table 7: Spectral and spatial detail about Sentinel 2……………………………………….34

Table 8: Sentinel 2 image details……………………………………………………………34

Table 9: Land cover classes description…………………………………………………….44

Table 10: Confusion matrix for Landsat 8 image, 18th

March 2015………………………...53

Table 11: Confusion matrix for Landsat 8 image, 19th

April 2015………………………….54

Table 12: Confusion matrix for Landsat 8 image, 1st June 2016…………………………….55

Table 13: Confusion matrix for Sentinel 2 image……………………………………………56

Table 14: Kappa coefficient values of all three methods…………………………………….57

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CHAPTER ONE: INTRODUCTION

1.1 Land Cover/Land Use (LCLU)

Land is a valuable natural resource to human beings and remote sensing has become an

effective tool to obtain land resources information (Zhang et al, 2015). Land cover and land

use have often been confused and used interchangeably in the literature and also in daily

practice. The definition of these terms are essential so they can be used correctly,

meaningfully and to the best advantage (Giri, 2016). Land cover is the type of physical

surface present at a given point on the earth, such as forest and water, while land use on the

other hand denotes the type of human activity taking place at that point such as agriculture

(Lillesand and Kiefer, 2004). Meyer et al. (1995) stated that land use characterizes the human

use of a land cover type. Land cover and land use are distinct but closely linked

characteristics of the Earth’s surface and an area of land can have only one land cover but can

have more than one land cover type (Giri, 2016).

Land cover is identified as one of the Essential Climate Variables (ECVs) by the Global

Climate Observing System (GCOS, 2013) (Giri et al., 2013). The GCOS developed a set of

50 ECV’s for the detection and quantification of climate related changes and these were

identified in 2010. In recent years land cover/land use is found to be closely associated with

our ecosystems and environments such as global and local climate, hydrologic circle,

pollutions, biodiversity and soil erosion (Zhou et al, 2008). The spatial pattern of land cover

is essential for determining the capacity of biodiversity to adapt to climate change. Land

cover/land use force climate by modifying greenhouse gases and changing energy exchanges

in the atmosphere as land cover/land use contribute ~20% of carbon dioxide emissions to the

atmosphere (IPCC, 2007). As a result land cover was identified as one of the five highest

priorities ECVs (Giri et al., 2013). This indicates the importance of land cover and the need

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for land cover monitoring on a global scale with the use of both in situ stations and satellites.

Information on land cover/land use derived from observations of the Earth from space

enhances the knowledge of human utilization of the landscape (Horvat, 2013).

1.2 Land Cover/Land Use (LCLU) Mapping

Remote sensing is an important data source to extract land cover/land use information.

Remote sensing techniques are often viewed as a viable alternative to expensive and time

consuming ground methods. Due to the developments of remote sensing technology, remote

sensing data have been widely adopted to classify land cover/land use, which ensure the

updating of maps more frequently and on a near real-time basis (Taati et al, 2015).

Land cover/land use mapping has become one of the most important applications of remote

sensing (Lo and Choi, 2004). They argue that land use was mapped more frequently than land

cover up until the 1970s, however the launch of the Landsat-1 satellite in 1972 altered this.

Accurate information on land cover/land use is essential for environmental change studies,

land management and planning (Yu et al., 2016). Babamaajii and Lee (2014) showed that

information on land cover type, land use, fragmentation, burning and environmental events

can be derived from the imagery.

Land cover classification is one of the main applications of remote sensing imagery and land

cover maps are crucial for a variety of analyses such as; biomass estimation, species habitat

monitoring, analysis of change and conservation land suitability to name just a few (Ledoux,

2015). The need for accurate, reliable and timely information on land cover globally at a

finer scale is stated by many national and international programmes (Giri et al, 2013). Land

cover maps also provide the basis for many applications such as estimation of crop yields,

management of forests and modelling of carbon budgets (Zhu and Woodcock, 2014).

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Understanding the distribution and dynamics of the world’s land cover is essential to better

understand the Earth’s fundamental characteristics and processes, including biogeochemical

cycles, hydrological cycles and biodiversity (DeFries, 2008).

1.3 Land Cover/Land Use (LULC) Mapping in Ireland

Our understanding of land cover/land use in Ireland has been limited due to a number of

factors; the unavailability of cloud free data, the sheer volume of data and the high

computational resources needed. However recent developments such as free and open access

to a global archive such as Landsat and the ESA Sentinels have created an opportunity for

high resolution land cover mapping. The launch of the Landsat 8 and Sentinel 2 satellites has

led to increased imaging over Ireland leading to more cloud free images and these images are

freely downloadable, making them available to a wide range of users.

The CORINE land cover map is by far the most widely used land cover map for Europe with

a total of 44 classes identified. The European Environmental Agency (EEA) determined the

classes and approach. The main advantage of CORINE is the creation of a standardized

nomenclature and mapping protocol across all European countries making it a valuable

information source at the European level (Jakovels et al, 2016). However it is designed on

Central and Southern Europe environments and therefore has unsuitable class descriptions for

Ireland (Nitze et al, 2015). Ireland only has 26 of these and these classes can be very generic

and not as accurate, as a result many countries do their own land cover classes. The low

spatial resolution and accuracy, infrequent updates and costly manual production levels has

limited its use at the national level (Jakovels et al, 2016).

The Environmental Protection Agency produce land cover maps for Ireland. A lot of Ireland

is pasture (73%), which is only one class therefore it can be a much generalised product but

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gives us a good overview of the different land cover classes in Ireland. According to the latest

CORINE dataset CORINE 2012, Ireland is composed of 2.49% artificial surfaces, 68.13%

agricultural areas, 11.49% forest & semi-natural areas, 15.75% wetlands and 2.14% water.

The limitations of CORINE are the inadequate coarse spatial and temporal resolution and the

inappropriate thematic classes. The combination of SPOT-4 and IRS P6 satellite imagery are

applied and these satellite sensors have similar spatial coverage as Landsat 7. The CORINE

dataset delivers information at the 1/100 000 scale which not suitable for local studies that

need more detailed information (www.epa.ie)

Figure 1: The CORINE 2012 25ha landcover dataset for Ireland

Source: Environmental Protection Agency (EPA)

Teagasc produced the first land cover and habitat map of Ireland for the year 1995 using the

Landsat TM. The two maps produced were ‘Teagasc Land Cover (1995)’ and ‘Teagasc

Habitat Indicator Map’ (1995). These projects were funded by the Department of the

Environment, Heritage and Local Government and are referred to as the Teagasc/EPA Soils

and Subsoils project. Teagasc researcher Stuart Green stated that “this project shows the

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enormous potential for the use of satellites for monitoring land use and agriculture and the

wider environment” (www.teagasc.ie).

There is a need for accurate land cover maps in Ireland that can help a variety of sectors such

as agriculture and planning. The recent Irish Land Mapping Observatory (ILMO) and the

Toward Landcover Accounting and Monitoring (TaLAM) projects did not produce national

scale maps but were looking at alternative datasets and methods to CORINE. The Irish Land

Mapping Observatory project involved University College Cork, Ordnance Survey Ireland

(OSI), Teagasc and Forest Environmental Research and Services Ltd (FERS). The ILMO

uses a combination of optical and radar satellite data to discriminate land cover/land use and

sets out a methodology of a land cover classification scheme comparing the benefits and

limitations of optical remote sensing data against radar. Another project funded by the

Environmental Protection Agency, the TaLAM (2014-2016) project involved the University

College Cork and Teagasc. The overall aim of this project was to provide land cover mapping

that is comparable with CORINE but more detailed spatially and of direct relevance to Irish

land cover and habitats that could then be used to inform any future national mapping

operations.

1.4 Aim and Objectives

Aim

The main purpose of this study is to investigate and Compare the Classification Accuracies of

New Generation Sentinel 2 and Landsat 8 Optical Satellite Images in the Killarney region.

Objectives

The preparation and classification of Landsat-8 and Sentinel-2 imagery

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Investigate and compare machine learning algorithms such as SVM and ANN and

traditional classifications such as MLC

Compare the performance and accuracy of these three methods using overall

accuracy and kappa coefficient parameters

Determine the optimal classifier for accurate land cover/land use classification of the

study area

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CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

An increasing amount of remotely sensed data is becoming available and the remote sensing

of landscapes is classified into three main groups; employing imagery data with coarse spatial

resolution such as MODIS with a spatial resolution of >250m, medium spatial resolution

imagery such as Landsat with a resolution of 30m and finally fine spatial resolution imagery

such as Quickbird and IKONOS that acquire imagery at a spatial resolution of less than 5m

(Asgarian et al., 2016). Spectral resolution ranges from the limited number of multi-spectral

bands of the Landsat Thematic Mapper (TM) to the over 200 bands of the hyperspectral

Hyperion (Lu et al, 2011).

Low spatial resolution data have a short revisit cycle and a large coverage area. This type of

resolution is more suited to large scale research of land cover/land use. However low spatial

resolution can lead to mixed pixels and as a result have difficulty dividing different features

of land. Medium resolution data and high resolution data are widely adopted to establish land

cover/land use as the accuracy in determining land cover/land use types is greatly enhanced

by these resolutions and medium resolution data is generally applied at national scale studies

of land cover/land use. However higher spatial resolution data can have a longer visit cycle

and require larger storage space (Liu et al, 2007).

Since the early 2000’s there has been a shift from low spatial resolution data (>100m) to

medium resolution sensors (10-100m) to generate more detailed land cover (Wulder et al.,

2008). In previous years (early 2000’s) coarse spatial resolution such as AVHRR data was

used for global land cover mapping and Landsat data was used for results (Giri et al., 2013).

As a result of the change in the data policy in 2008 all new and archived Landsat data was

made freely available to all users leading to the open data policy of Landsat with many

researchers utilising the free availability of the Landsat archive. The Sentinel series which

8

began in 2014 is also committed to free and open access to all users (Wulder et al., 2012).

The new developments of Landsat-8 in the Landsat series and Sentinel-2 in the Sentinel

series are now being explored by researchers to investigate their potential for land cover

mapping in particular (Zhu &Woodcock, 2014). However due to the limited temporal

coverage of medium spatial resolution satellite data such as Landsat, research efforts are now

exploring the possibility of combining satellites with similar characteristics such as Landsat-8

and Sentinel-2 (Vanhellemont & Ruddick, 2014). High spatial resolution satellite

imagery(<10m) is also being utilised by researchers for land cover classification but

acquiring imagery from these satellites is more expensive and these sensors tend to have a

much narrower swath width and longer repeat time meaning fewer images are available

(Alphan et al., 2009).

2.2 Medium Resolution Imagery

Zhu & Woodcock (2014) utilise the Landsat series for land cover classification and change

detection. The authors’ decision to use all available Landsat data was influenced by the long

continuous record, spatial resolution and near nadir observations of this satellite series. The

authors address the main drawback of Landsat, the low temporal frequency which can be an

issue in areas that have prevalent cloud conditions as repeat observations are essential to

allow for increased cloud free images. To mitigate for this many researchers (Zhu &

Woodcock., 2014; Babamaajii & Lee., 2014; Zlatko., 2013), choose to use a select few cloud

free Landsat images acquired during the growing season for accurate land cover analysis.

Clouds can greatly reduce the amount of data that is usable, especially in Ireland where cloud

cover is prevalent so there is a need for more frequent observations to mitigate atmospheric

effects (Roy et al., 2014).

A solution that could improve the temporal frequency of Landsat is combining it with data

from other remote sensing satellites with similar characteristics complementing the Landsat

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series and an example of this is the Sentinel-2 sensor (Roy et al., 2014). The design of the

Sentinel-2 mission contains similar spectral bands to Landsat excluding the thermal bands

aiming to acquire multispectral imagery at a global scale with a high revisit frequency that

complements the Landsat series, ultimately improving data availability for users (Drusch et

al., 2012). The swath width of Sentinel-2 is wider than Landsat-8, increasing temporal

coverage, therefore the combination of the two data sources will lead to increased frequency

of observations (Vanhellemont & Ruddick, 2014).

The long term continuity of measurements, global and generally frequent coverage and

careful calibration of the satellite sensors make the Sentinel-2 suitable for land cover

classification (Malenovský et al., 2012). Similarly to the Landsat series the Sentinel-2 data

policy gives free and open access to users, increasing the amount of end users and as a result

increases the use of Landsat and Sentinel-2 for land cover detection (Zhu & Woodcock,

2014).

Topalog͂lu et al. (2016) evaluate and compare the classification accuracies of new generation

Sentinel-2 and Landsat-8 optical satellite images in Istanbul. Support Vector Machine (SVM)

and Maximum Likelihood Classifier (MLC) were applied to both images in this study to

create comparable land cover maps of the area. These datasets were employed to accurately

identify land cover/land use classes of the study area. The Landsat-8 image was acquired on

the 22nd

February 2016 and the Sentinel-2 image was acquired on the 8th

February 2016. A

total of 8 land cover classes were identified and the COINE nomenclature was used as a

reference. The results of the SVM and MLC classifications were compared for both images.

SVM produced better results than MLC with the producer’s and user’s accuracy values of the

SVM higher than 70% for all classes whereas the MLC approach range from 50 – 100%.

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Poursanidis et al. (2015) compare the use of Landsat 8 against Landsat 5 for land cover

mapping. The authors outline several aspects that account for the accuracy of the classified

map, these include; the spatial and spectral resolution, the seasonal variability in vegetation

cover types and soil moisture conditions. Landsat 5 TM has 7 spectral bands and 1 thermal

band while the Landsat 8 OLI contains additional bands with 9 spectral bands with 2 thermal

bands. Data generated by Landsat 8, the most recent satellite of the series has an increased

radiometric resolution, increasing the dynamic range of the data the sensor can retrieve and

the authors showed that Landsat 8 proved to be the most accurate for land cover classification

in this study. The land cover maps generated from the Landsat-8 image were more spatially

homogenous then the land cover maps produced by the Landsat-5 image. The machine

learning algorithm Support Vector Machine proved to be the most accurate with an overall

accuracy of over 80% for both Landsat images.

Asgarian et al. (2016) utilise the Landsat 8 satellite for their study to map the crop types in a

highly fragmented agricultural landscape in Iran and discuss the enhancement of the new

Landsat satellite in terms of a radiometric resolution of 12 bits and spatial resolution of 30m

as the main reasons for this choice. The Landsat series has proven successful in the study of

agricultural landscapes and according to the authors provides the most popular and applicable

imagery. The authors refer to Roy et al. (2014) for the enhanced abilities of the Landsat 8

satellite sensor.

Clevers et al. (2004) investigate the use of the medium resolution imaging spectrometer

(MERIS) for land cover classification over a region in the Netherlands. This satellite sensor

acquires imagery in the visible (VIS) and near-infrared (NIR) part of the electromagnetic

spectrum. MERIS has a spatial resolution of 300m at full resolution and 1200m, as a result

land cover can be monitored at regional to global scales. One image was used for this study

and this image was acquired on the 16th

June 2003 at a spatial resolution of 300m. This study

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produced only moderate classification results with an overall accuracy of 49.7%. The authors

stated that a multitemporal analysis will produce much better classification results.

Babamaaji et al. (2014) utilise Landsat data as well as Nigeria Sat-1 to classify land use/land

cover of the vicinity of Lake Chad. The authors identified the need for a suitable classifier to

enhance the overall accuracy of the classification results. They reveal the four main factors

that influence the classification results and these are; ground truth data, the complexity of the

landscape as well as the analyst’s knowledge of the study area, image band selection and

processing and the classification algorithm chosen. In this study the authors investigated

statistical methods and the maximum likelihood classifier was applied as it takes into account

the variability of classes which other statistical classifiers may not consider. It was the most

appropriate selection for this study area due to the variability and diversification of classes of

the study area and unsupervised methods were also applied. Similarly to the study carried out

by Alphan et al. (2008), the authors’ utilised Landsat imagery for the classification of land

cover and a combination of both unsupervised and supervised classification methods were

applied. The accuracy of the LULC maps generated was calculated for the producers, users

and overall accuracy in both studies to assess the potential of the satellite sensors and the

chosen classifiers for LULC applications with a high overall accuracy of 95.24%.

Srivastava et al. (2012) also utilise Landsat imagery for the selection of classification

techniques for land use/land cover change investigation and state that Landsat data is a major

data source for land cover/land use mapping at a regional and global level. The authors

evaluate the use of three commonly used classifiers, similarly to Babamaajii et al. (2014),

they investigate the use of a common parametric classifier called the maximum likelihood

classifier but also explore the use of two non-parametric classifiers, support vector machine

(SVM) and artificial neural network (ANN), two machine learning algorithms. The maximum

likelihood classifier assumes the data for individual classes are distributed normally however

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the two machine learning algorithms make no assumptions. For this study the ANN classifier

showed a greater accuracy then the kernel based SVM and maximum likelihood classifier

with a producer’s accuracy of nearly 98% however the authors suggested that this may not

always be the case as the SVM is one of the most common algorithms used in the processing

of remote sensing multispectral imagery hence the need for comparison of a variety of

classification tools to determine the best option.

Zlatko (2013) demonstrates the potential of multi temporal Landsat satellite imagery to

produce accurate land use/land cover maps for an area in Croatia. Three Landsat images were

acquired for this study area in June and August in 1978, 1992 and 2007. The author shows

how the Landsat imagery is economically beneficial, with their free and open access.

However he poses the question of the possibility of a combination of high resolution satellite

images such as Quickbird and IKONOS with medium resolution satellite imagery such as

Landsat to improve the accuracy and achieve more detailed land use/land cover maps in the

future.

2.3 Multi-Sensors

There is now a reliance on multiple data sources for mapping and monitoring LULC,

therefore the ability to merge multiple sources of remote sensed data is essential for

consistent and accurate land cover classification (Wulder et al., 2008). A multi-sensor

approach can improve spatial and temporal coverage leading to more frequent images of

greater quality as repeat observations over the same location can increase the chance of cloud

free images even in areas such as Ireland where cloud cover is persistent (Gómez et al.,

2016).

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Jia et al. (2014) address the issue of infrequent temporal resolution of finer resolution

satellites by fusing observation from multiple sensors with different characteristics. Coarse

spatial resolution imagery such as MODIS can have issues accurately identifying land cover

change and medium spatial resolution satellite sensors such as Landsat can have problems

with infrequent coverage. Integrating temporal features from coarser resolution data with

finer resolution satellite data are investigated to improve land cover classification. MODIS

NDVI data was fused with Landsat NDVI using the Spatial and Temporal Adaptive

Reflectance Fusion Model (STARFM). This study aimed to classify land cover of finer

resolution remote sensing data integrating temporal features from time series coarse

resolution data. Temporal features were extracted from MODIS time series from October

2009 to September 2010 and October 2012 to September 2013 to improve the overall

classification accuracy by approximately 4%, compared to that of using a single date Landsat

image.

Lu et al. (2011) Investigate the integration of different satellite sensors for improved land

cover classification in a region of the Brazilian Amazon. Integration of optical and radar data

is often applied to enhance the performace and improve the overall visual interpretation of

images. Data fusion involves two processes: the geometric co-registration of two datasets and

the mixture of spatial and spectral information. Data fusion can extract valuable information

from both datasets and can either apply the same sensor data with different spatial resolutions

or data from different sensors. For this study, Landsat Thematic Mapper (TM), PALSAR L-

band and RADARSAT-2 C-band images were used. The authors viewed a variety of data

fusion methods but Wavelet multisensor fusion method achieved the best results with an

improved overall accuracy of 3.3-5.7%. Data fusion if often used to improve spatial

resolution but may reduce the mixed class pixels.

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The ability to combine information from multiple sensors with similar characteristics aims to

reduce the possibility of data gaps and improve the frequency of observations, ultimately

enhancing the reliability of medium resolution remote sensing (Wulder et al., 2012).

2.4 Coarse Resolution

Since the early 2000’s researchers have looked at MODIS for land cover classification due to

its higher temporal frequency as it images the Earth every 1-2 days, however the coarse

spatial resolution of MODIS has limited its ability for identifying small changes in land cover

(Zhu & Woodcock, 2014). Carrão et al. (2008) examine the contribution of multispectral and

multitemporal information from MODIS images to land cover classification. They evaluate

the high spectral and temporal resolution of MODIS imagery for land cover detection. The

authors address the infrequent temporal coverage and cloud contamination of Landsat TM

data as the reason for opting for MODIS as an alternative. They also address the use of

AVHRR (Advanced Very High Resolution Radiometer) for land cover detection but state that

even though AVHRR has been widely used for land cover mapping, only two of the five

broad spectral bands sensed by this instrument are useable for land observation. AVHRR can

be insufficient in distinguishing small differences in vegetation types in particular (Carrão et

al., 2008). Even though the coarse spatial resolution of MODIS can make it difficult for

differentiating certain land cover classes and the authors believe that MODIS provides the

best available imagery for their research using 12 MODIS 8-days composited images

monthly acquired in 2000 primarily due to its frequent temporal coverage. In order to ensure

more accurate results, the authors used previous classifications of land such as CORINE to

ensure that the land cover classes identified are compatible with the 9 land cover classes

identified in this study.

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Nitze et al. (2015) utilise MODIS imagery for land cover classification over Ireland, and state

that for areas such as Ireland that experience prevalent cloud conditions, MODIS can be very

useful due to its frequent temporal coverage as repeat coverages over the same area can

greatly reduce the effects of atmospheric conditions on the imagery. The main restrictions of

multitemporal classifications are cost and useable data, MODIS addresses both these

limitations due to free MODIS data and repeat coverages over Ireland. However due to the

coarse spatial resolution of 250m just four land cover classes identified; improved and semi-

improved grassland, forest, settlement, peatland and water. The authors examined the use of a

machine learning method called Random Forest and successfully showed the accuracy of this

algorithm for land cover classification for future use in Ireland in particular. Although coarse

spatial resolution satellite sensors such as MODIS and AVHRR are widely used for global

land cover classification as they are useful at the global scale where only a few classes are

being defined and a broad swath low resolution has to be used to give coverage however their

quality at local scale is questionable (Chen et al., 2015).

2.5 High Resolution

SPOT (Satellite Pour l’Observation de la Terre) has the potential to image very large areas

but unlike Landsat data, SPOT imagery is not freely accessible as a standard license is

required to purchase the imagery (Wulder et al., 2008). Therefore in many cases it may not be

feasible to use high resolution imagery like SPOT to monitor large regions due to the high

costs, effort and data unavailability associated with the imagery as it is not made freely

available to all users. SPOT imagery is widely used but for smaller areas with less of a time

series component to the study.

16

Ikiel et al. (2012) use one SPOT-5 image acquired on 7th

December 2010 with 10m resolution

and 4 spectral bands for land cover classification in Turkey. The authors use this high

resolution satellite imagery to allow for more accurate identification of land cover classes.

The results identified that agricultural areas are the dominant land cover class with 78.4%

followed by forests with 14.8%. High accuracy land use/land cover map was obtained with

both the producers and users accuracy over 80% and the overall accuracy of 94%. Similarly

to many other studies that utilise satellite imagery for land cover and land use classification,

the authors chose the CORINE land cover nomenclature/classification system to refer to for

this study. This classification system has proven essential in many other studies for

comparison to ensure accurate results (Carrão et al, 2008).

Alphan et al. (2009) outline the major role cost plays in data selection and the authors address

the issue of cost when acquiring imagery from high resolution satellite sensors such as SPOT

as the acquisition costs of SPOT in particular can be relatively high compared to the Landsat

archive which is freely available. As a result the authors opted for ASTER imagery for their

study due to its spatial resolution of 15m and reasonable cost as the price of images can range

from $10/km2 to $20/km2. This study also acquired Landsat imagery to compare to the

ASTER data to show the success of using satellite images with different spatial resolutions

for land cover classification and change analysis. Crop fields, sparsely vegetated areas and

grasslands were identified as the dominants land cover classes in the area. The authors

believe that the use of very high resolution satellite sensors such as Quickbird with ASTER

imagery could be a possibility in the future for land cover classification with a more detailed

analysis.

Giri et al. (2013) also discuss the potential of very high resolution sensors such as Quickbird,

IKONOS, RapidEye and WorldView2 for the next generation of global land cover mapping.

The aim of utilising these very high resolution images is to develop accurate global land

17

cover maps that are consistent. The authors outline the financial issue of acquiring very high

resolution satellite imagery, many researchers believe this is the main limitation of very high

resolution data. Acquiring the very high resolution data can be costly and their acquisition is

sporadic which can cause problems for researchers as very high resolution satellite data differ

in their acquisition footprints, native resolutions and formats.

2.6 Hyperspectral Imagery

Petropoulos et al. (2012) utilise Hyperion, which is a satellite hyperspectral sensor on board

the Earth Observer-1 (EO-1) platform for their study to use Hyperion hyperspectral imagery

analysis combined with machine learning classifiers for land cover/land use mapping. The

authors investigate the potential of hyperspectral data for land cover management and

monitoring in an area located 50km north of the centre of Athens. The Hyperion acquires

images at 30m spatial resolution and 242 spectral bands. Similarly to previous studies where

multi-temporal data were utilised for land cover mapping, the researchers acquired the

CORINE land cover map for comparison and the land cover maps generated by the authors

proved to be more accurate. Many studies have evaluated the performance of Support Vector

Machines (SVMs) and Artificial Neural Networks (ANNs) for land cover mapping, but this

study is one of the first to utilise these classifiers on hyperspectral imagery. The results

showed that the SVM proved to be more accurate than the ANN with an overall accuracy of

89.26%, proving to be the better option for identifying the cover density of each land cover.

The Hyperion collects data over 220 spectral bands and this allows for the detection of fine

spectral bands. However one of the main limitations of using the Hyperion is timing, the

18

authors believe that if the imagery can be made available at regular time intervals the use of

Hyperion hyperspectral imagery would be extremely useful for accurate land cover mapping.

Many land cover maps have been produced by multi-spectral and multi-temporal satellite

imagery, Clark & Kilham (2016) investigate the potential of hyperspectral imagery and

discussed the benefit of hyperspectral sensors to map a wide range of land covers. According

to the authors, hyperspectral sensors have shown their ability in land cover mapping but so

far hyperspectral sensors have focused on small areas. A popular classifier called Random

Forest was evaluated by the authors and proved to be successful as research with random

forest and hyperspectral data is somewhat limited. Random forest classifier has proved useful

in land cover mapping with multi-spectral, hyperspectral and multi-temporal imagery. The

speed at training and mapping classes and the ability to accommodate the diversity of

different classes over large areas, are the main benefits of the classifier that the authors

believe achieves high accuracy of land cover distinction.

They believe that hyperspectral imagery can strengthen our ability to map land covers over

large areas. Unfortunately the Hyperion satellite is nearing the end of its mission but there are

a next generation of hyperspectral satellites such as the United States Hyperspectral Infrared

Imager and the German Environmental Mapping and Analysis Program (EnMap) that will

have a revisit time of 16-27 days and a spatial resolution of 30m to further enhance the ability

of these satellites to provide information on the land cover of large areas. However Vijayan et

al. 2014 outline the main limitations of hyperspectral data for accurate land cover mapping,

these drawbacks include; the lack of data for temporal studies and the difficulty in handling

the data especially the pre-processing of the huge data.

19

2.7 Conclusion

Satellite imagery has become an integral tool for land cover/land use mapping and the

development in spatial and spectral resolution has led to an increase in the use of remote

sensing imagery for land cover classification. Land cover is classified at a range of scales

from local to regional and global. Landsat imagery is by far the most widely used data for

land cover classification due to its free available and easily accessible data archive. Advanced

classifiers such as Support Vector Machines (SVM’s), Random Forest (FR) and Artificial

Neural Networks (ANN) are widely being used (Petropoulos et al., 2012; Nitze et al., 2015 &

Srivastava et al., 2012) to ensure a high level of accuracy is achieved. The option of multi-

sensors is increasingly being explored by researchers to overcome the drawbacks of some

satellite sensors to ensure the highest level of accuracy is achieved (Wulder et al., 2008).

Satellite sensors with a wide range of spectral bands and frequent revisit time are essential for

accurate land cover/land use mapping.

20

CHAPTER 3: STUDY AREA

3.1 Study Area

The area of study is located in Killarney Co. Kerry which is situated in the South-west of

Ireland. Killarney has a population of approximately 15,000 (census 2011) and climatically

the study area enjoys a warm and temperate climate and the Köppen-Geiger climate

classification is Cfb (maritime temperature climate). On average the study area receives about

1305mm of rainfall annually and the average annual temperature is 10.2ºC (www.climate-

data.org).

Geologically this area is made up of sandstone/shale and limestone with reference to the

bedrock geology map of Ireland (Figure 1.2). The main land cover types consist of urban,

pastures, broad leaved forest, mixed forest, moors and heathland, estuaries and land

principally occupied by agriculture according to CORINE 2012 land cover map (Figure 1.1).

The soils and subsoils are dominated by sandstone till (TDSs), bedrock at surface (Rck) and

sandstone, sands and gravel (CDSs), according to the soils and subsoils map (Figure 3).

One of the main reasons for choosing this study area is the cloud free coverage. Cloud

contamination can severely affect the images so an area with as low amounts of cloud in the

images is essential. Imagery acquired over Ireland is significantly affected by cloud cover and

the best time to acquire imagery over Ireland is first thing in the morning where there is less

cloud present.

21

3.1.1 CORINE 2012

Study Area

Figure 2: CORINE 2012 Land Cover Map of County Kerry and the study area

Code Landcover Type

111 Continuous urban fabric

112 Discontinuous urban fabric

121 Industrial and commercial sites

131 Mineral extraction sites

142 Sports and leisure facilities

231 Pasture

243 Land principally occupied by

agriculture

311 Broad leaved forest

312 Coniferous forest

22

313 Mixed forest

321 Natural grassland

322 Moors and heathland

324 Transitional Woodland

333 Sparsley Vegetated areas

412 Peat bogs

Table 1: Landcover types of County Kerry according to CORINE 2012

CORINE 2012 data was downloaded from the Environmental Protection Agency website

(www.epa.ie). The study area is primarily composed of water bodies,

continuous/discontinuous urban fabric, pasture, broad leaved forests, coniferous forests,

mixed forests, land principally occupied by agriculture, peat bogs and transitional woodland

scrub (CORINE 2012). Visually pasture, water bodies and peat bogs appear to be the most

dominant land cover classes for this area. The eight land cover classes chosen were be based

on this CORINE 2012 dataset.

23

3.1.2 Geology

Study Area

Figure 3: Bedrock Geology Map of County Kerry and the study area

Bedrock geology data was downloaded from the Geological Survey of Ireland. Bedrock

geology can have a significant influence on the soil type and land cover/land use of an area.

The study area is dominated by 5 main types of bedrock geology, these include; Continental

redbed facies, Marine shelf & ramp facies, Waulsortian mudbank, Marine shelf facies and

Fluvio-deltaic & basinal marine (Turbidtic) shown in detail in the table below.

Code Group Type

53 Continental redbed facies Sandstone, siltstone and mudstone

54 Continental redbed facies Sandstone, conglomerate and siltstone

61 Marine shelf & Ramp facies Argillaceous dark grey bioclastic

limestone subsidiary shale

62 Waulsortian Mudbank Pale grey massive limestone

24

64 Marine shelf facies Limestone and calcareous shale

71 Fluvio-deltaic & Basinal

marine

Shale, sandstone, siltstone and coal

Table 2: Description of bedrock geology of study area

3.1.3 Elevation

Study Area

Figure 4: Digital Elevation Model (DEM) of County Kerry and the study area

A digital elevation model (DEM) represents bare earth terrain and this DEM is represented as

a raster. In a DEM each cell has a value corresponding to its elevation. A DEM can be

represented as a raster or a TIN, this DEM model is a raster DEM. DEMs are used to derive a

wide range of information about the morphology of a land surface (Jensen, 1988). The DEM

in County Kerry ranges from 0-1033.37.

25

Study Area

Figure 5: Hillshade of County Kerry and the study area

The hillshade is a 3D representation of the surface, with the suns relative position taken into

account. Latitude and azimuth properties are used to specify the suns position. County Kerry

is home to Ireland highest mountain, Carrauntoohil and this can be seen by the high level of

hill shade cross the County. As shown in the top right image displayed above the study area is

composed of relatively high elevation just below Lough Leane Lake and this area is

dominated by peat bogs, suggesting the topography of the area can have a direct effect on the

type of land cover.

26

3.1.4 Soils and Subsoils

Study Area

Figure 6: Soils and Subsoils Map of County Kerry and the study area

Soils and subsoils data were downloaded from Teagasc. Teagasc created the first national

subsoils map to a standardized methodology in 2009. This map classifies the subsoils of

Ireland into 16 themes. The main factors that influence soil information and development are;

parent material, climate, vegetation, activities of man and topography (influence of slope and

slope shape on water and soil movement). 8 main types of sediment are recognised and 6 of

these are within the study area, these include; Glaciolacustrine deposits, Alluvium, Peat,

Diamictons (mostly tills), Glaciofluvial sands and gravels and others. Glaciolacustrine

deposits are deposited into a large number of meltwater-fed lakes during and after

degradation and these deposits consist of gravel, sand, silt and clay. Alluvium is post glacial

deposits and consists of gravel and sand with a minor fraction of silt and clay. Alluvium

usually consists of fairly high percentage of organic carbon (10-30%). Peat is a post glacial

27

deposit consisting mostly of vegetation which has only partially decomposed in an

ombrotrophic environment. Diamictons consists of mostly tills and till is a sediment

deposited by or from glacier ice. Glaciofluvial sands and gravels are different from tills as

they are deposited by running water only.

Map Code Group Sediment Type

A Alluvium Alluvium undifferentiated

BktPt Peat Blanket peat

Cut Peat Cutover peat

GDSs Glaciofluvial Sands & Gravels Sandstone, sands & gravels

KaRck Other Karstified limestone bedrock at

surface

L Glaciolacustrine deposits Lake sediments

Made Other Made ground

Rck Other Bedrock at surface

TDSs Till Sandstone till

TLs Till Limestone till

TNSSs Till Shale & sandstone till

Table 3: Description of subsoils of study area

28

CHAPTER 4: DATASETS AND DATA PREPARATION

4.1 Datasets

Selecting appropriate satellite imagery for the study area given the cloudy conditions in

Ireland is essential. Four satellite images were acquired for this research from the Landsat 8

and Sentinel 2 sensors. Three images were acquired by Landsat-8 in March 2015, April 2015

and June 2016 (displayed in Table 1 and Figures 1.1, 1.2 and 1.3). One image was acquired

by Sentinel-2 in July 2016 (shown in Table 2 and Figure 1.4). The images were carefully

selected to ensure minimal cloud contamination. The 2012 CORINE (Coordination of

Information on the Environment) imagery was used in this study to verify and compare

classification results. The CORINE data was feely downloaded from the Environmental

Protection Agency website: (http://www.eea.europa.eu/pubications/CORO-landcover).

There are five important characteristics of satellite sensors that affect the data and their

applications, these include; spectral, spatial temporal and radiometric resolution as well as the

spatial extent. Temporal resolution in particular is dependent upon; the image swath width,

the number of satellites in the family, the orbital characteristics of the instruments and the off

nadir viewing capacities (Lillesand et al, 2015).

4.1.1 Landsat 8 Imagery

The Landsat 8 Operational Land Imager and Thermal Infrared Sensor (TIRS) was launched

on the 11th

February 2013 and has a design life of 5 years. The overall objectives of this

satellite are to provide data continuity with Landsat 4, 5 and 7, offer 16 day repetitive Earth

coverage, build and periodically refresh a global archive of sun lit, substantially cloud free

land images. It is part of a global research program known as National Aeronautics and Space

Administrations (NASA’s) Science Mission Directorate (SMD). This satellite is the future of

29

Landsat satellites which has provided over 40 years of imagery of the Earth’s surface and the

Landsat series is one of the most important sources for identifying different land cover/land

use due to the long continuous record, spatial resolution and near nadir observations (Zhu and

Woodcock, 2014). The Landsat data have been used since the start of the program to monitor

the status and changes of the Earth’s land cover and condition (Roy et al, 2014).

Landsat 8 was developed through the partnership of NASA and the United States Geological

Survey (USGS). The mission objective is “to provide timely, high quality visible and infrared

images of all landmasses and near-coastal areas on the Earth” (USGS Landsat 8 Data Users

Handbook, 2016). The Landsat 8 imagery used for this study was acquired at 30m resolution

from the bands 1-7 and 9 and at 15m resolution for band 8, the panchromatic band as

illustrated in (Table 1.1). In addition to these bands the data generated by Landsat 8 ae

delivered at an increased radiometric resolution in comparison to that of previous Landsat

sensors therefore increasing the dynamic range of the data the sensor can retrieve

(Vanhellemont and Ruddick, 2014).

Band

Band

Spatial

resolutionctral

resolution

Name gi

Name given to this part of EM

spectrum

part of EM spectrum

N

Spatial resolution

Band 1 0.433-0.453µm Coastal aerosol 30m

Band 2 0.450-0.515µm Blue 30m

Band 3 0.525-0.600µm Green 30m

Band 4 0.630-0.680µm Red 30m

Band 5 0.845-0.885µm NIR 30m

Band 6 1.560-1.660µm SWIR 30m

Band 7 2.100-2.300µm SWIR 30m

Band 8 0.500-0.680µm Panchromatic 15m

Band 9 1.360-1.390µm Cirrus 30m

Band 10 10.6-11.2µm TIR 30m

Band 11 11.5-12.5µm TIR 30m

Table 4: Spectral and spatial detail about Landsat 8 images

30

Scenes/Day ~650

SSR Size 3.14 Terafit, file-based

Sensor Type Pushbroom (both OLI & TIRS)

Compression ~ 2.1 Variable Rice Compression

Image D/L X-band Earth Coverage

Date Rate 384 M bits/sec, CC SDS Virtual Channels

Encoding CCSDS, LDPC, FEC

Ranging GPS

Orbit 705km Sun-Sync 98.2º inclination (WRS-2)

Crossing Time ~ 10:11 AM

Table 5: Landsat-8 Observatory Capabilities

Source: http://landsat.gsfc.nasa.gov/

The Landsat 8 imagery was acquired on the 18th

March 2015, 19th

April 2015 and the 1st June

2016 (Table 3.1). These images were acquired at a medium spatial resolution of 30m.

Date Time Cloud

Cover

Sensor Type Source

18th

March

2015

11:35:30 16.23 Landsat-8 Level 1

GeoTIFF

USGS

19th

April

2015

11:35:17 0.47 Landsat-8 Level 1

GeoTIFF

USGS

1st June 2016 11:29:24 0.06 Landsat-8 Level 1

GeoTIFF

USGS

Table 6: Landsat-8 Imagery acquired over the Killarney region

The Worldwide Reference System (WRS) is a global notation system for Landsat data. It

allows the user to inquire about any area over the world by specifying a nominal scene centre

designated by path and row numbers. The combination of a path and row number uniquely

identifies a nominal scene centre, the row is the latitudinal centre line of a frame of imagery

and the path number is always given first (http://landsat.gsfc.nasa.gov/). The path/row of the

31

Landsat-8 image acquired on 18th

March 2015 is 208/24 with a size of 814.6MB, the

path/row on 19th

April 2015 is also 208/24 with a size of 759.0MB while the path/row on 1st

June 2016 is 207/24 with a size of 811.7MB. These images were freely downloaded from the

United States Geological Survey (USGS) Earth Explorer website:

(http://earthexplorer.usgs.gov/) which allows users to download the most up to date Landsat

imagery free of charge.

Figure 7: The USGS Earth Explorer Interface

Source: (http://earthexplorer.usgs.gov/)

Figure 8: Landsat 8 ‘natural colour’ preview images for the 18th

March 2015, 19th

April 2015

and the 1st June 2016

Source: (http://earthexplorer.usgs.gov/)

32

Figure 9: Subset of Landsat-8 natural colour composite, bands 432 (RGB) acquired 18th

March 2015

Figure 10: Subset of Landsat-8 natural colour composite, bands 432 (RGB) acquired 19th

April 2015

Figure 11: Subset of Landsat-8 natural colour composite, bands 432 (RGB) acquired 1st June

2016

33

4.1.2 Sentinel 2 Imagery

Sentinel-2 is the second satellite to be launched in Europe’s Copernicus Environmental

Monitoring Program. Sentinel-1A and 1B were launched on April 3, 2014 and April 25, 2014

while Sentinel-2A was launched on June 23, 2015 and Sentinel-2B is set to follow with an

expected launch planned for mid-2016. USGS and ESA worked together to ensure that the

Sentinel data would complement the Landsat data by cross-calibrating the sensors, figure 1.1

below shows the comparison of Landsat 7 and 8 bands with Sentinel-2. This satellite is a

polar-orbiting multispectral high resolution sensor with 13 spectral bands in the visible and

near-infrared (VNIR) and short wavelength infrared (SWIR) spectrum and contains similar

spectral bands as Landsat excluding the thermal bands of Landsat-8. These spectral bands

allow for land cover/change detection, atmospheric correction and cloud/snow separation

with the ESA stating “as well as monitoring plant growth Sentinel-2 will be used to map

changes in land cover and to monitor the world’s forests”. (www.esa.int/ESA)

Figure 12: Comparison of Landsat 7 and 8 bands with Sentinel 2

This sentinel has a high revisit time of 10 days at the equator with one satellite and 5 days

with two under cloud free conditions which results in 2-3 days at mid-latitudes. This satellite

34

has a swath width of 290km and 10m, 20m and 60m spatial resolution, an operational life

span of 7.25 years and an orbit height of 786km, table 1.2 gives details of the spectral and

spatial resolution of Sentinel-2. The overall mission objective of this wide swath high

resolution satellite is to provide global acquisitions of high-resolution multispectral images.

Band

Resolution

Cent

ral

wavel

ength

(nm)

Ban

dwid

th

(nm)

Purpose

Band 1 60m 443nm 20nm Aerosol detection

Band 2 10m 490nm 65nm Blue

Band 3 10m 560nm 35nm Green

Band 4 10m 665nm 30nm Red

Band 5 20m 705nm 15nm Vegetation

classification

Band 6 20m 740nm 15nm Vegetation

classification

Band 7 20m 783nm 20nm Vegetation

classification

Band 8 10m 842nm 115nm Near infrared

Band 8A 20m 865nm 20nm Vegetation

classification

Band 9 60m 945nm 20nm Water vapour

Band 10 60m 1375nm 30nm Cirrus

Band 11 20m 1610nm 90nm SWIR

Band 12 20m 2190nm 180nm SWIR

Table 7: Spectral and spatial detail about Sentinel-2 images

Date Time Cloud

Cover

Product

Type

Processing

Level

Source

18th

July

2016

11:54:28Z 6.6% S2MSI1C Level1C www.mapshup.com

Table 8: Sentinel 2 image acquired over the Killarney region

One relatively cloud free image was acquired by Sentinel-2 over the Killarney region as this

satellite sensor has only been acquiring imagery over Ireland since November 2015 and

unfortunately passes over in the evening, when the best time to acquire imagery over Ireland

is first thing in the morning when there is less clouds. The Sentinel-2 Image was acquired at a

Band Resolution Central

wavelength

Band

width Purpose

35

spatial resolution of 10m, by the MSI instrument, the sensor mode is INS-NOBS and the orbit

number is 5597. The Sentinel 2 image was freely downloaded from the

(http://mapshup.com/projects/rocket/#/home) website, this image was then imported into

ENVI for processing.

Figure 13: Website of Sentinel 2 downloaded data

Source: (http://mapshup.com/projects/rocket/#/home)

Figure 14: Sentinel-2 preview image for 18th

July 2016

Source: (http://mapshup.com/projects/rocket/#/home)

36

Figure 15: Subset of Sentinel-2 natural colour composite, bands 432 (RGB) acquired 18th

July

2016

4.2 Pre-processing

Each remotely sensed image is usually not ready for use directly and therefore need to

undergo a series of pre-processing steps to get imagery in the most useable format.

Fortunately some pre-processing steps on satellite images are already undertaken by the

ground receiving stations, this allows the users to then focus primarily on the processing and

image interpretation. Ground receiving stations of Landsat images (Level 1T, Gt, IG) employ

a variety of ground control points for systematic radiometric and geometric corrections

(Asgarian et al., 2016). The Landsat imagery was obtained at level 1 meaning it had already

been geometrically corrected and orthorectified.

Sub-Setting

The spatial subsets were created from the original Landsat 8 and Sentinel 2 images to

represent the study area for which the image will be used to classify (Figures 1.1-1.4). The

reduction of data is known as sub-setting and aims to reduce the size of the image to include

only the area of interest. A spatial subset was created from the original Landsat-8 and

37

Sentinel-2 scenes to represent the study area for which the image will be used to classify. To

ensure consistency among all satellite images were subset. This reduction eliminates the

extraneous data in the file and speeds up the processing due to a smaller amount of data to

process (Horvat, 2013). The subset image were then displayed as true colour composites

using the red, green and blue bands which is bands 4,3,2 for Landsat 8 and Sentinel 2.

Atmospheric Correction

A large amount of low quality data caused by cloud cover is inherent in daily datasets

acquired over Ireland (O’Connor et al, 2012). Weather cannot be controlled but it can be

accounted for and its effects on the data lessened (Lillesand et al, 2015). Atmospheric

correction is often a primary concern, however sometimes it is not always necessary (Song et

al, 2001). The quality of the Landsat-8 and Sentinel-2 data were good and cloud was nearly

absent in some of the acquired data. However the Landsat 8 image acquired on the 18th

March 2015 had significantly more visible cloud then the other images. Atmospheric

correction was performed on this image using the red (Band 4), green (Band 3), blue (Band 2)

and panchromatic (Band 8) bands and the following band math formula was used:

((B1 Lt _ Eq 1.0) AND (B1 GE _ Eq 0.0)) * B1

((B Lt _ Eq 1.0) AND (B1 GE _ Eq 0.0)) * B2

This band math formula was applied to the band math under the band algebra tool in ENVI.

When to apply atmospheric correction to overcome the atmospheric effects depend on the

remote sensing and atmospheric data available, the information desired, and the analytical

methods used to extract the information. The development of cloud free images requires the

identification of cloud features (Song et al, 2001). It will always be difficult to obtain cloud

free image in cloud prone environments like Ireland but applying this technique of

38

atmospheric correction can greatly reduce the effects of cloud on these images and make

these images more effective for monitoring and classification.

Figure 16: Masked Image of Water and Land for Sentinel 2 18/07/2016

Masks were created for cloud and water to distinguish these from the land cover/land use

classes. This image shows the mask created for water and land in the study area. This image

accurately identifies the large body of lake (Lough Leane) present in the study area and

clearly distinguishes the water bodies present from the land.

39

CHAPTER 5: METHODOLOGY

5.1 Image Classification

The image classification is carried out in ENVI 5.3 (64 bit) software. The classification of the

remotely sensed images is an important phase in the determination of land cover/land use

information of the study area. Classification is a method by which labels are attached to

pixels in view of their character and can be applied in two stages; training of the classifier

and testing the performance of the trained classifier on unknown pixels (Yuan et al, 2009).

These categorized data can then be used to produce thematic maps of the land cover/land use

contained in an image (Ojaghi et al, 2015). Classification techniques fall into two broad

categories: parametric and non-parametric classifiers. Parametric assume that the data for

individual classes are distributed normally and the most widely used is the MLC whereas

non-parametric make no assumption about the statistical nature of the data and SVM and

ANN are an example of non-parametric classifiers (Taati et al, 2015).

A number of pixel based classification algorithms have been developed over the past years of

the analysis of remotely sensed data (Otukei et al, 2010). The choice of classification

algorithms are usually based upon the availability of software, the ease of use and

performance (Pal and Matler, 2003). Image classification was carried out by using Maximum

Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network

(ANN) algorithms. In the following subsections a brief explanation of the three algorithms is

produced.

5.1.1 Maximum Likelihood Classifier (MLC)

MLC is a statistical image classification technique based on nearest neighbour; a pixel based

statistical classification method that assumes that spectral classes can be described by a

statistical distribution in multi spectral space (Pradhan et al, 2010). It creates decision

surfaces based on mean and covariance of each class (Taati et al, 2015). MLC is one of the

40

most widely used supervised classification methods and assumes the image data for each

class in each band is normally distributed (Ojaghi et al, 2015). However this classifier is

found to have some limitations in resolving interclass confusion if the data are not normally

distributed. Therefore, in recent years due to advances in computer technology alternative

classification strategies have been proposed such as SVM and ANN (Pradhan et al, 2015).

5.1.2 Support Vector Machine (SVM)

The theory of the SVM was originally proposed by Vapnik and Chervoneukins (1971). This

advanced classifier has been rapidly and successfully applied to several real-world

classification problems (Carro et al, 2008). The success of the SVM depends on how well the

process is trained and the outcome depends on; the type of kernel, the choice of parameters

for the chosen kernel and the method used to generate SVM (Otukei & Blaschke, 2010),

these factors can have a significant impact on the speed and accuracy of the classification.

Opting for a small value for the kernel width parameter could lead to overfitting while large

kernel width values may lead to over smoothing. The different kernel type functions namely

linear, polynomial, radial basis and sigmoid were tested. These SVM parameters lead to a

trial and error approach. The radial basis kernel type was selected as it requires only a small

amount of parameters to run (Szuster et al, 2011). The SVM has the ability to generalize well

from a limited amount of training data and do not assume a known statistical distribution of

data to be classified which can be significant as the data acquired from remote sensing

imagery usually have unknown distributions (Mountrakis et al., 2011).

The simplest way to train the SVM is by using linearly separable classes. There are various

hyper planes separating two classes. There is only one hyper plane that provides maximum

margin between two classes which is called the optimum hyper plane and the points that

41

constrain the width of the margin are called support vectors shown in the below image

(Kavzoglin and Colkesen, 2009).

Figure 17: Structure of the SVM

(Kavzogln &Colkesen, 2009)

5.1.3 Artificial Neural Network (ANN)

The Neural Network method is an algorithm in the region of machine learning and artificial

intelligence, inspired by the human nervous system to analyse complex non-linear systems

and parallel computations (Ojaghi et al, 2015). Artificial neural networks (ANNs) are

composed of a large number of simple processing units called nodes linked by weighted

connections according to a specified architecture (Petropolus, 2012). The architecture of an

ANN consists of three main layers; the input layer, the hidden layer and the output layer

(Figure 5.2). The input layer nodes represent variables used as input in the neural network

which could be spectral bands, textural features or other intermediate features. The hidden

layer is composed of multiple nodes with each node linked to the nodes in the previous and

the following layer. The output layer nodes represent the classes where in each class will be

one output node (Petropolus, 2012). Each neuron at both hidden and output layers contains a

42

single process which is to transform the input using a linear or non-linear function (Zhou et

al, 2008).

The main factors to take into account when parameterizing neural networks are; the

complexity of the network architecture, the quality and size of the training data sets and the

choice of parameters (Yuan et al, 2009). The main advantages of this advanced classifier are

the ability to handle non-linear functions and to learn from data relationships that are not

otherwise known and to generate unseen situations (Mas, J.R 2004). ANNs are commonly

conceived to have the capability of improving automated classification accuracy due to their

distributed structure and strong capability of handling complex phenomena (Zhou et al,

2008).

There are a number of parameters to choose from when adopting the ANN. The training

threshold ranges from 0-1.0, the momentum ranges from 0-1.0, the training rate also ranges

from 0-1.0 and a higher rate will speed up the training but will also increase the risk of

oscillations of the training result (Wu et al, 2016).

Figure 18: ANN architecture

43

(Pradhan, 2010)

5.2 Land Cover Classes

8 training classes were created and for each training class a Region of Interest (ROI) was

defined using the new ROI tool in ENVI to allow the digitizing of polygons. A specified

colour was defined for each class, closely corresponding to the real life appearance of the

class. It is essential when defining the ROIs that the polygons are displayed in the middle of

the defined class as variability is usually displayed around the boundaries of different classes.

The land cover/land use classes were defined in the study area based on knowledge of the

area and on visual inspection of the satellite images and Google Earth with reference to the

CORINE 2012 classification scheme. 8 land cover classes were identified in the study area,

these include; Urban, Water, Pasture, Land principally occupied by agriculture, Broad leaved

forest, Coniferous forest, Moors & Heathland and Peat bogs, shown in (table 1) below.

Group Class Description

Artificial Areas Urban Residential, commercial and

industrial development

Water Bodies Water All water bodies including

fresh water lakes, rivers and

streams

Agriculture Areas Pasture Dense predominantly

graminoid grass cover of

floral composition under a

rotation system, mainly used

for grazing and includes

areas of hedges

Agricultural Areas Land principally occupied by

agriculture

Areas principally occupied

by agriculture and

interspersed with

significantly natural areas

Forest and Semi-natural

Areas

Broad leaved forest Broad leaved forest species

predominated by beech, oak

including shrub and bush

undertones

Forest and Semi-natural

Areas

Coniferous forest Coniferous forest species

predominated by pine and

larch

Forest and Semi-natural

Areas

Moors and Heathland Vegetation with low and

closed cover dominated by

44

bushed, shrubs and

herbaceous plants

Wetlands Peat bog Peatland consisting mainly of

decomposed moss and

vegetable matter. May or

may not be exploited

Table 9: Land cover classes description according to CORINE 2012

5.3 Converting ENVI Classification Data to ArcGIS

Once to image classifications were performed and the accuracy assessed, the classified

images had to be converted to ArcGIS to produce sufficient classification maps with suitable

legends, a north arrow and scale bar. This conversion is a two-step process: 1. Export to

Vector and 2. Export to Shapefile.

1. Export to Vector

In the ENVI toolbox the classification to vector tool was chosen under the

Classification/Post classification option. The classified image was selected and all 8

associated classes were chosen and saved to a single layer file and this then creates

and ENVI vector file with the file extension “.EVF”.

2. In the ENVI toolbox the under the vector option the classic EVF to shapefile tool was

selected, the previous EVF file was chosen and this “.SHP” file was then opened in

ArcGIS. One of the drawbacks of this conversion is that ArcGIS will use ENVIs

classes but its own colour scheme. (http://yceo.yale.edu/converting-envi-

classification-data-arcgis-shapefile)

5.5. Accuracy Assessment

The final stage of the image classification process is the accuracy assessment step to assess

and compare the performance of the different classifiers in classifying different land

45

cover/land use. To evaluate the MLC, SVM and ANN resultant classified maps we assessed

the accuracy based on a pixel-by-pixel comparison. Accuracy assessment is the quantification

of mapping with the aid of remotely sensed data to group class conditions which is then used

in the evaluation of class algorithms and also in the determination of the error level that might

be contributed by the image (Taati, 2015). The accuracy of each of the 8 classes is expressed

in the form of an error/confusion matrix. An error matrix provides an appropriate beginning

for several techniques of multivariate statistical analysis. The confusion matrix was displayed

using Regions of Interest for ground truth and this was selected from the classification and

post-classification toolbox in ENVI.

The accuracy of land cover/land use maps is also calculated for the producer’s accuracy,

user’s accuracy, the overall accuracy and kappa. Both the overall accuracy and the kappa

coefficient reflect the overall class situation but cannot indicate the reliability of each land

cover class, therefore the producer’s and user’s accuracy of each class are often used to

provide complementary analysis of the accuracy assessment (Lu et al, 2011). The producer’s

accuracy is the percentage of a particular land cover/land use type on the ground correctly

classified in the map measuring the error of omission. The user’s accuracy is a percentage of

a class on the map that is matched to a corresponding class on the ground measuring the error

of commission. The overall accuracy is a percentage of correctly classified pixels out of

pixels sampled for all classes and is calculated by summing the number of pixels classified

correctly and dividing by the total number of pixels (Babamaajii and Lee, 2014). KAPPA is

designed to adjust for some of the differences between different matrices and can then be

used to compare results for different areas or different classifications. A kappa value of 1

represents perfect agreement, while a kappa value of 0 represents no agreement. The KAPPA

coefficient was calculated for each error matrix to assess if any of the classification

algorithms had improved classification accuracy over others (Yuan et al, 2009).

46

CHAPTER 6: RESULTS

6.1 Classified Images

The resultant classified images for the three Landsat-8 images acquired on the 18th

March

2015, 19th

April 2015 and the 1st June 2016 are displayed below along with the Sentinel-2

image acquired on the 18th

July 2016. 12 classified images in total are shown for the SVM,

ANN and MLC algorithms. Visual assessment of the classified images is displayed under

each image, this is the starting point for determing the value of each class however it is not

very scientific and is further analysed in the error/confusion matrix tables.

6.1.1 Support Vector Machine (SVM) Classified Images

Figure 19: Landsat-8 SVM Classified Image 18th

March 2015

Visually the SVM classifier worked well on a number of classes on this image however

Pasture is underrepresented and the Land principally occupied by agriculture is

overrepresented. Water, Broad leaved forest and Coniferous forest are very well classified.

Peat bog, urban and moors and heathland are not as well classified as the other classes.

47

Figure 20: Landsat-8 SVM Classified Image 19th

April 2015

Water and Urban classes are significantly well classified. Broad leaved forest and Coniferous

forest are also both well classified for this image. Peat bog are underrepresented and moors

and heathland are over represented. Pasture and Land principally occupied by agriculture are

both slightly over and under classified.

Figure 21: Landsat-8 SVM Classified Image 1st June 2016

Water and Urban are very well classified in this image. Coniferous forest appears to also be

well classified whereas Broad leaved forest is significantly miss-classified while Pasture and

48

Land principally occupied by agriculture are respectively over represented. Peat bog is well

represented in this classified image while Moors and heathland are slightly miss-classified.

Figure 22: Sentinel-2 SVM Classified Image 18th

July 2016

Peat bog and Moors and heathland are both well classified in this image. Water and Urban

are slightly miss-classified while pasture and Land principally occupied by agriculture are

again slightly over and underrepresented. Coniferous forest is well classified however broad

leaved forest is slightly under represented.

6.1.2 Artificial Neural Network (ANN)

Figure 23: Landsat-8 ANN Classified Image 18th

March 2015

49

Water and Coniferous are significantly well classified, while Urban and Land principally

occupied by agriculture are both over represented. Broad leaved forest and peat bog are

slightly miss-classified. Pasture is relatively under represented while Moors and Heathland is

significantly under represented.

Figure 24: Landsat-8 ANN Classified Image 19th

April 2015

Pasture and peat bog are significantly over represented in this classified image. Water is

again very well classified and urban is also classified well but slightly miss-classified in some

areas. Land principally occupied by agriculture is slightly miss-classified while Broad leaved

forest, Coniferous forest and Moors and Heathland are all extremely under represented.

Figure 25: Landsat-8 ANN Classified Image 1st June 2016

50

Water and Coniferous forest are both well classified while Urban is slightly miss-classified in

areas. Peat bog is also miss-classified in parts while Broad leaved forest is also miss-

classified in areas and underrepresented. Land principally occupied by agriculture and Moors

and Heathland are significantly under represented.

Figure 26: Sentinel-2 ANN Classified Image 18th

July 2016

Many of the classes in this image are well classified except water which is miss-classified in

areas. Broad leaved forest and Coniferous forest are significantly well classified. Moors and

Heathland are better represented in this classified image in comparison with the previous

ANN classified images. Similarly to water, Urban is slightly miss-classified in parts of the

image. Pasture and Land principally occupied by agriculture are also well classified.

6.1.3 Maximum Likelihood Classifier (MLC)

51

Figure 27: Landsat-8 MLC Classified Image 18th

March 2015

Water and Urban are both significantly miss-classified. Land principally occupied by

agriculture is over represented while pasture is under represented. Coniferous forest and

Broad leaved forest are well classified. Peat bog is also over represented and in contrast

Moors and heathland are underrepresented.

Figure 28: Landsat-8 MLC Classified Image 19th

April 2015

Urban is extremely over classified in this image and water is slightly under classified. Pasture

and Land principally occupied by agriculture are both well classified. Broad leaved forest is

also well classified whereas Coniferous forest is slightly under represented. Moors and

Heathland are well represented while peat bog is miss-classified in areas.

52

Figure 29: Landsat-8 MLC Classified Image 1st June 2016

Water is significantly well classified in this image and is classified better than the previous

MLC classified images. Urban is again over represented, similarly land principally occupied

by agriculture is also over represented while pasture is under represented. Moors and

Heathland is well classified but peat bog is under represented. Broad leaved forest and

Coniferous forest are respectively well classified.

Figure 30: Sentinel-2 MLC Classified Image 18th

July 2016

Water and Urban are both miss-classified in this image. Broad leaved forest and Coniferous

forest are well classified. Moors and Heathland is also well classified but peat bog is under

53

represented. Pasture is slightly over classified while land principally occupied by agriculture

is miss-classified in areas.

6.2. Accuracy Assessment

Accuracy assessment results for the three Landsat-8 classified images acquired on the 18th

March 2015, 19th

April 2015 and the 1st June 2016 and the Sentinel-2 classified image

acquired on the 19th

July 2016 are displayed in the tables shown below (Table 6.1-6.4).

Overall accuracy (OA) and the Kappa coefficient are calculated along with the percentage

producer’s and user’s accuracy for each class under each classifier.

SVM ANN MLC

Overall accuracy 94.52 88.56 97.84

Kappa coefficient 0.93 0.85 0.97

% prod accuarcy: water 100 100 100

urban 93.7 98.43 95.28

broad leaved forest 100 92.79 99.1

pasture 95.1 96.85 97.2

land principally occupied by agriculture 88.61 13.92 100

peat bog 98.31 61.86 87.29

coniferous forest 99.16 100 100

moors and heathland 9.62 26.92 96.15

% user accuracy: water 99.83 100 100

urban 100 68.68 100

broad leaved forest 94.87 88.03 99.1

pasture 96.8 78.47 100

land princiaplly occupied by agriculture 88.61 84.62 92.94

peat bog 68.24 81.11 91.96

coniferous forest 98.33 96.75 98.35

moors and heathland 100 100 78.13Table 10: Confusion Matrix for SVM, ANN and MLC on Landsat-8 Image 18

th March 2015

SVM and MLC both have significantly high levels of overall accuracy with 94.52% and

97.84% respectively, while ANN has a lower overall accuracy of 88.56%. For SVM, water is

extremely well classified with a producer’s accuracy of 100% and a user’s accuracy of

99.83%. Urban, pasture, broad leaved forest and coniferous forest are all significantly well

classified with a producers and users accuracy of over 90% for all. Peat bog has a high

54

producer’s accuracy of 98.31% but a lower user’s accuracy of 68.24%. Similarly moors and

heathland has a significant difference in accuracy between producers and users accuracy with

a producer’s accuracy of as low as 9.62% and a significantly high user’s accuracy of 100%.

For ANN, water is significantly well classified with a producers and users accuracy of 100%.

Coniferous forest is well classified with a producers and users accuracy of 100% and 96.75%.

Urban, broad leaved forest and pasture are also well classified with a producer’s accuracy of

over 92% for all three however there is a significant drop with a lower user’s accuracy for all

three classes, ranging from 68.68% to 88.03%. There is a considerable difference in accuracy

between producer’s and user’s accuracy for Moors and heathland with a difference of

73.08%.

For MLC, water is again extremely well classified with a producer and user accuracy of

100%. All classes appear to have a consistently high producer’s accuracy of over 87% for all.

Broad leaved forest in particular is well classified with a producers and users accuracy both

of 99.1%. Peat bog has the lowest producer’s accuracy of 87.29% while Moors and heathland

has the lowest user’s accuracy of 78.13%.

SVM ANN MLC

Overall accuracy 91.41 75.25 94.66

Kappa coefficient 0.89 0.67 0.93

% prod accuarcy: water 100 100 100

urban 97.64 99.21 100

broad leaved forest 98.2 0 99.1

pasture 70.98 98.95 75.87

land principally occupied by agriculture 78.48 0 93.67

peat bog 92.37 99.15 98.31

coniferous forest 100 0 98.32

moors and heathland 75 0 100

% user accuracy: water 100 100 100

urban 98.41 94.03 94.07

broad leaved forest 70.32 0 82.71

pasture 91.86 54.84 97.31

land princiaplly occupied by agriculture 62 0 64.35

peat bog 90.83 70.91 100

coniferous forest 99.17 0 100

moors and heathland 78 0 98.11

Table 11: Confusion Matrix for SVM, ANN and MLC on Landsat-8 Image 19th

April 2015

55

SVM and MLC have high levels of overall accuracy of 91.41% and 94.66% while the ANN

has a lower overall accuracy of 75.25%. For SVM, water has the highest producers and users

accuracy of 100% respectively. Urban, peat bog and coniferous forest are all well classified

with producer and user accuracies of over 90% for all. Land principally occupied by

agriculture and moors and heathland have lower accuracies of ~70%.

For ANN, water again displays the highest level of accuracy. Urban is also well classified

with a significantly high producers and users accuracy of over 94%. In contrast broad leaved

forest, coniferous forest, land principally occupied by agriculture and moors and heathland

are not classified, displaying a 0% level of accuracy for both producer and user. Pasture and

peat bog have high producer’s accuracies of 98.95% and 99.15% but much lower user’s

accuracies of 54.84% and 70.91%.

For MLC, similarly to the SVM and ANN classifiers, water has the highest accuracies of

100%. All classes have significantly high producers and users accuracies. Urban, peat bog

and and coniferous forest in particular are well classified with producer and users accuracies

of over 94%. Compared to previous classifiers moors and heathland is has improved

significantly with a producer’s and users accuracy of 100% and 98.11%.

SVM ANN MLC

Overall accuracy 92.9 88.24 94.32

Kappa coefficient 0.91 0.85 0.93

% prod accuarcy: water 100 100 100

urban 98.43 98.43 100

broad leaved forest 87.39 87.39 94.59

pasture 93.71 94.06 91.26

land principally occupied by agriculture 29.11 0 40.51

peat bog 98.31 94.07 98.31

coniferous forest 98.32 97.48 97.48

moors and heathland 78.85 0 98.08

% user accuracy: water 100 100 100

urban 100 98.43 96.95

broad leaved forest 64.24 63.4 75.54

pasture 91.47 87.06 89.69

land princiaplly occupied by agriculture 92 0 66.67

peat bog 84.67 60 100

coniferous forest 100 98.31 100

moors and heathland 93.18 0 100

Table 12: Confusion Matrix for SVM, ANN and MLC on Landsat-8 Image 1st June 2016

56

SVM and MLC have high overall accuracies of 92.9% and 94.32% while ANN has a slightly

lower overall accuracy of 88.24%. For SVM, water and urban are extremely well classified

with producers accuracies of 100% and 98.43%. Similarly coniferous forest is also well

classified with producer and user accuracies of 98.32% and 100%. All classes seem to have

relatively high accuracies except for land principally occupied by agriculture which has an

extremely low producer’s accuracy of 29.11%.

For ANN, water and urban are very well classified with producers and user accuracies of

100% and 98.43%. Land principally occupied by agriculture and moors and heathland are not

classified and display a producer’s and users accuracy of 0% as a result. Peat bog and broad

leaved forest have low users accuracies of 60% and 63.4%.

For MLC, water and urban are again extremely well classified with producers accuracy of

100% for both. All classes are extremely well classified producing high producers accuracies

of over 90% for all except land principally occupied by agriculture which has a producer’s

accuracy of 40.51%, significantly less than the accuracies of all other classes. Peat bog,

coniferous forest and moors and heathland have significantly high user’s accuracies of 100%

respectively.

SVM ANN MLC

Overall accuracy 92.58 94.03 92.89

Kappa coefficient 0.9 0.92 0.91

% prod accuarcy: water 100 100 99.98

urban 97.67 98.69 98.99

broad leaved forest 81.93 88.92 88.43

pasture 94.3 89.37 71.48

land principally occupied by agriculture 25.93 70.05 95.17

peat bog 97.41 95.47 97.31

coniferous forest 93.41 91.32 94.91

moors and heathland 90.21 85.19 99.74

% user accuracy: water 100 100 100

urban 97.97 96.73 99.39

broad leaved forest 80.76 76.88 79.27

pasture 79.3 89.61 95.45

land princiaplly occupied by agriculture 96.41 82.7 55.34

peat bog 98.8 98.66 99.67

coniferous forest 89.31 89.79 92.06

moors and heathland 92.66 94.15 96.42

Table 13: Confusion Matrix for SVM, ANN and MLC on Sentinel-2 Image 18th

July 2016

57

For the Sentinel-2 image acquired on the 18th

July 2016, all three classifiers have

significantly high overall accuracies of over 90%, SVM and MLC have an overall accuracy

of ~92% while ANN has a slightly higher overall accuracy of ~94%. For SVM, water and

urban display the highest accuracies of 100% and 97%. Peat bog, coniferous forest and moors

and heathland all have producer’s accuracies over 90% while land principally occupied by

agriculture has a much lower producer’s accuracy of 25.93%.

For ANN, water is very well classified with 100% accuracy and urban is also well classified

with a producers and users accuracy of 97.67% and 97.97%. Land principally occupied by

agriculture has one of the lowest producers and users accuracies of 70.055 and 82.7%. Peat

bog, coniferous forest and moors and heathland are well classified with accuracies of over

85% for all three classes.

For MLC, water and urban are significantly well classified with producers and users

accuracies of over 98%. Peat bog, coniferous forest and moors and heathland are also

classified well with accuracies of over 90% for their producers and user accuracies. Land

principally occupied by agriculture has a much lower user accuracy then the rest of the

classes, displaying an accuracy of 55.34%.

March (Landsat-8) April (Landsat-8) June (Landsat-8) July (Sentinel-2)

SVM 0.93 0.89 0.91 0.90

ANN 0.85 0.67 0.85 0.92

MLC 0.97 0.93 0.93 0.91

Table 14: Kappa coefficient values of all three methods

0

10

20

30

40

50

60

70

80

90

100

SVM ANN MLC

Overall Accuracy

58

Figure 31: Overall classification accuracy of the three methods for the Landsat-8 Classified

Image 18/03/2015

Maximum Likelihood Classifier displays the highest overall accuracy of 97.84%, followed by

Support Vector Machine with an overall accuracy of 94.54% and Artificial Neural Network

has the lowest overall accuracy of 88.56%.

Figure 32: Overall classification accuracy of the three methods for the Landsat-8 Classified

Image 19/04/2015

Maximum Likelihood Classifier displays the highest overall accuracy of 94.66%, followed by

Support Vector Machine with an overall accuracy of 91.41% and Artificial Neural Network

has the lowest overall accuracy of 75.25%.

Figure 33: Overall classification accuracy of the three methods for the Landsat-8 Classified

Image 01/06/2016

0

10

20

30

40

50

60

70

80

90

100

SVM ANN MLC

Overall Accuracy

0

10

20

30

40

50

60

70

80

90

100

SVM ANN MLC

Overall Accuracy

59

Maximum Likelihood Classifier displays the highest overall accuracy of 94.32%, followed by

Support Vector Machine with an overall accuracy of 92.90% and Artificial Neural Network

has the lowest overall accuracy of 88.24%.

Figure 34: Overall classification accuracy of the three methods for the Sentinel-2 Classified

Image 18/07/2016

Artificial Neural Network displays the highest overall accuracy of 94.03%, followed by the

Maximum Likelihood Classifier of 92.89% and Support Vector has a lower accuracy of

92.58%. All three classifiers have significantly similar overall accuracies ranging from 92-

94% for this classified image.

0

10

20

30

40

50

60

70

80

90

100

SVM ANN MLC

Overall Accuracy

60

CHAPTER 7: DISCUSSION AND CONCLUSION

7.1 Discussion and Conclusion

After the necessary corrections and pre-processing of the Sentinel-2 and Landsat-8 images,

three different classification algorithms were applied in order to classify the images. Remote

sensing data by employing maximum likelihood classifier, support vector machine and

artificial neural networks classifiers was used to provide useful information to describe the

land cover/land use types in County Kerry, particularly focusing on the Killarney landscape.

The structural character of the land and the spatial resolution of the satellite images put a

direct effect on the obtained result (Ikiel et al, 2012).

An error matrix was developed for each classified image then the producers and users

accuracy for each class and the overall accuracy and the kappa coefficient for each image

were calculated from the corresponding error matrix. To evaluate the resultant classified

maps the accuracy was assessed based on a pixel-by-pixel comparison. An overall accuracy

of 80-90% is identified as very good and the findings indicated that of the three classifiers,

the MLC obtained the best classification accuracies of 97.84%, 94.66%, 94.32% and 92.89%,

followed by the SVM with accuracies of 94.52%, 91.41%, 92.9% and 92.58%, representing

greater accuracy then the ANN with accuracies of 88.52%, 75.25%, 88.24% and 94.03%. The

overall accuracy obtained from all types of images was higher than the 85% minimum

threshold set by Anderson (2005) for effective land cover/land use identification.

A kappa value of 1 represents perfect agreement and 0 represents no agreement. The kappa

coefficient values for MLC was 0.97, 0.93, 0.93 and 0.91 followed by the SVM kappa

coefficient values of 0.93, 0.89, 0.91 and 0.9, while the ANN had kappa coefficients values of

61

0.85, 0.67, 0.85 and 0.92. Similarly to the overall accuracy results the MLC displayed kappa

coefficient values closest to 1, followed by the SVM.

Therefore the Maximum Likelihood Classifier algorithm has been suggested to be applied as

an optimal classifier followed by the Support Vector Machine for extraction of land

cover/land use maps due to its higher accuracy and better consistency within the study area.

7.1.2 Limitations

One of the main limitations of this study was the confusion amongst several land cover/land

use classes. Difficulty can arise in distinguishing between different surface types as there is

natural variability within classes, which can lead to mixed pixels/land covers. There were

confusions among land cover/land use types during classification. There was significant

confusion between pasture and land principally occupied by agriculture possible due to

similar spectral characteristics. The confusion could also be due to the very close vicinity of

the two land cover classes. Difficulty can also occur when defining training samples using

Regions of Interest, two main questions arise in this situation; Are they representative? Do

you need to add in more classes?

A variety of errors are encountered in image classification and the confusion matrix provides

a very good summary of the two types of thematic error that can occur, which are omission

and commission. However other sources of error can lead to misrepresentation, generally

under estimation of the actual accuracy. The problem is most apparent in heterogeneous

landscapes such as the study area with a complex land cover/land use (Foody, 2002). To

achieve optimal levels of accuracy for classifying land, training areas should consist of pure

homogeneous pixels which can be difficult especially with 30m pixel resolution (Pal and

Matler, 2003). Issues ranging from the properties of the sensor and the ground to the methods

62

used to pre-process the data can have an effect on the ability to accurately locate a pixel

(Foody, 2002).

Another limitation is the lack of ground truth data, future studies in the area could utilise the

same classifiers and add field data to obtain higher results. Fortunately cloud cover was only

an issue in one image, the other images appeared relatively cloud free. However acquiring

cloud free Sentinel 2 images was difficult primarily due to the fact that this satellite sensor

passes over Ireland in the evening when there is more cloud and the best time to acquire

imagery over Ireland is first thing in the morning when there are fewer clouds. There was

more information in one Sentinel 2 image compare to a few Landsat 8 image due to the high

spatial resolution of Sentinel 2.

Despite these limitations the study demonstrated the effectiveness of both statistical methods

and advanced machine learning classifiers in discriminating different land cover/land use

types on Landsat 8 and Sentinel 2 imagery. Accurate land cover/land use maps can play an

important role in aiding land management as well as helping deciding what types of lands are

best suited for sustaining land cover and land use should be practised (Ikiel et al,2012). This

is the subject of ongoing and future research. The methodology in this study provides results

that are reliable, reproducible and transferable.

63

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